future research trends

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The future of research revealed

April 20, 2022

By Adrian Mulligan

Researchers lay bare the challenges and opportunities they face in a post-COVID world

The research ecosystem has been undergoing rapid and profound change, accelerated by COVID-19. This transformation is being fueled by many factors, including advances in technology, funding challenges and opportunities, political uncertainty, and new pressures on women in research.

At Elsevier, we have been working with the global research community to better understand these changes and what the world of research might look like in the future. The results were published today in Elsevier’s new Research Futures Report 2.0

Commenting on the report, Elsevier Research Director Adrian Mulligan said:

It’s clear from the results of the Research Futures Report 2.0 that we’re at a point of change. There is uncertainty and added pressures on the research community because of the pandemic. Universities, governments, research information providers, and funders working collaboratively are best positioned to help researchers manage that pressure.

Despite this uncertainty, researchers also believe there are long-term opportunities, most notably new levels of collaboration and openness across the research community, plus new sources of funding and technologies, which can help create a bright future for research.

Adrian Mulligan talks about the previous  Research Futures  report with colleagues in New York.

The report builds on a previous Research Futures study in 2019, carried out with the global research agency Ipsos MORI to gather predictions from funders, publishers, technology experts and researchers on what research might look like in 10 years’ time. The aim of the Research Futures project is to gather the views and opinions of researchers across the world to help us better understand the challenges and opportunities they face. Elsevier will use these insights to look at steps we could take to better support the research community in the future.

One point is clear: we can best prepare for the future by working together.

Key findings

Publishing moves faster, with more open knowledge.

The  Research Futures Report 2.0  shows that the past two years have driven progress in both speed and openness in the communication of research. Around two-thirds (67%) of researchers globally now consider preprints a valued source of communication, up from 43% before the pandemic — a shift likely driven by the increased role of preprints in finding ways to tackle COVID-19. While preprints are becoming more popular, they have not benefited from the pivotal role of peer review or had any additional value added to them by publishers. For example, 94% of version-of-record articles published in Elsevier journals have content changes made during the editorial process, and 13% of submissions go through major changes, according to 2021 Elsevier data. Also, 54% of respondents said they planned to publish open access, 6% higher than in 2019.

Funding is harder, but new opportunities emerge

Despite COVID spotlighting the importance of research, funding continues to be a major challenge for researchers, with half (50%) stating there is insufficient funding available in their field. Just one in four (24%) researchers believe there is enough funding for their work; worryingly, this figure has declined from nearly one in three (30%) in 2020. Researchers cite fewer funding sources, increased competition, changing priorities and the diversion of funds to COVID-19 related fields.

Looking ahead, researchers expect more money for research to become available from businesses, with 41% believing that corporate funding for research will increase. Government funding has also increased as a proportion of research budgets since 2019, which has led to a growth of funding across various subjects. For example, Materials Science research has seen the biggest growth in funding satisfaction in 2021, with 35% saying available funding is sufficient — almost triple the percentage (12%) who were satisfied with funding levels in 2020.

Women in research face new pressures — and adapt

While women in research were faster to adapt during the pandemic, they still face unique challenges. Elsevier’s research shows that they are:

Expecting to collaborate more than they did before the pandemic: 64% expect to increase work with researchers across different scientific disciplines, up from 49% in 2020.

Embracing technology faster than their male counterparts: 53% of women scientists think the use of technology in research will accelerate over the next 2 to 5 years versus 46% for men.

More likely to have shared their research with the wider public than men: 60% of women versus 55% of men have shared their research publicly.

Women reported having less time to do research during lockdowns, which could slow or hamper their future career prospects. 62% reported they were finding it difficult to find a good work-life balance during the pandemic, compared to just 50% of male researchers — a trend which could have significant negative long-term effects on the careers of women in research.

Researchers are collaborating more

As teaching, publishing and funding accelerate and increase the pressure on researchers, how they work has changed — and not necessarily for the worse. Researchers are collaborating more. Just over half (52%) state that they are sharing more research data now than 2 to 3 years ago, and the number of researchers who say they are collaborating more than in the past has grown to 63% from 48% pre-pandemic. The gains are across geographies and disciplines. Researchers in Computer Science have seen the biggest rise, with 76% agreeing that there is more collaboration involved in their projects than previously — a substantial rise from the 41% who agreed pre-pandemic.

More researchers are embracing AI

AI has been embraced more than ever during the past two years, though some caution remains. 16% of researchers are extensive users of AI in their research, and while high take-up in Computer Sciences skews that number (64% of computer scientists are heavy users), attitudes across a number of specialties have grown more positive. In Materials Science, which covers the structure and properties of materials and the discovery of new materials and how they are made, 18% are now likely to be extensive users of AI in their research, up from zero a year ago; in Chemistry, the number has grown from 2% to 19% and, in Maths, from 4% to 13% since 2020. Attitudes towards the use of AI in peer review is perhaps where we have seen the greatest shift in attitude: 21% of researchers agree they would read papers peer reviewed by AI — a 5-percentage point increase from 2019. Those age 55 and under are the most willing to read AI-reviewed articles (21%), while those age 56 and over have increased their willingness compared to a year ago (19%, up from 14% last year). At the same time, most researchers surveyed continue to object to AI peer review, with almost two in three unwilling to read such articles (58 percent) — a similar proportion as in 2020.

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Project methodology

In total, over 2,000 researchers responded to two separate global surveys: 1,173 researchers responded in July-August 2021 and 1,066 in July 2020. Responses have been weighted to be representative of the global researcher population by country (UNESCO/OECD data). Base sizes shown in this report are unweighted unless otherwise stated. The full methodology is available in the report.

Contributor

Adrian Mulligan

Research Director for Customer Insights, Elsevier

14 emerging trends

Vol. 53 No. 1 Print version: page 42

In 2022, psychological science will play an increasingly outsize role in the debate about how to solve the world’s most intractable challenges. Human behavior is at the heart of many of the biggest issues with which we grapple: inequality, climate change, the future of work, health and well-being, vaccine hesitancy, and misinformation. Psychologists have been asked not only to have a seat at the table but to take the lead on these issues and more (See the full list of emerging trends ).

Psychologists are being called upon to promote equity, diversity, and inclusion (EDI): Amid a nationwide reckoning on race—and a 71% increase in EDI roles at organizations over the past 5 years—psychologists are increasingly being tapped to serve as chief diversity officers and act in other similar roles. But the field is also at an inflection point, being called upon to be more introspective about its own diversity in terms of the people who choose to become psychologists, the people who are the subjects of psychological research, and the people who have access to psychological services.

Psychologists are now the most requested experts by the mainstream media. As our culture increasingly sees mental health as an important piece of overall well-being, psychologists are being called to serve in a wider array of roles, including in entertainment, sports, advocacy, and technology.

On the technology front, the delivery and data collection of psychological services is gaining increased interest from venture capitalists. Private equity firms are expected to pour billions of dollars into mental health projects this year—psychologists working on these efforts say greater investments will help bring mental health care to millions of underserved patients.

That said, the urgent need for mental health services will be a trend for years to come. That is especially true among children: Mental health–related emergency department visits have increased 24% for children between ages 5 and 11 and 31% for those ages 12 to 17 during the COVID-19 pandemic.

That trend will be exacerbated by the climate crisis, the destructive effects of which will fall disproportionately on communities that are already disadvantaged by social, economic, and political oppression.

Reporters and editors for the Monitor spoke with more than 100 psychologists to compile our annual trends report, which you’ll find on the following pages. As always, we appreciate your feedback and insights— email us .

The rise of psychologists

Reworking work

Open science is surging

Prominent issues in health care

Mental health, meet venture capital

Kicking stigma to the curb

New frontiers in neuroscience

Millions of women have left the workforce. Psychology can help bring them back

Children’s mental health is in crisis

Burnout and stress are everywhere

Climate change intensifies

Big data ups its reach

Psychology’s influence on public health messaging is growing

Telehealth proves its worth

Trent Spiner is editor in chief of the Monitor . Follow him on Twitter: @TrentSpiner

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Top scientific breakthroughs and emerging trends for 2023

January 31, 2023

The pace of innovation never slows, and the impact of these scientific breakthroughs will redefine the way we live, work, and connect with the world around us. From space exploration at the largest scale to diagnostics at the single-cell level, these breakthroughs will inspire innovators to push the boundaries of what is possible. To stay ahead of emerging trends, new discoveries, and unique perspectives, we invite you to subscribe to CAS Insights.

A new era of space exploration

Need to be reminded of how incredibly vast our universe is? The first ever photos from the James Webb Space Telescope are awe-inspiring. While this is the most technically advanced and powerful telescope ever created, the learnings about our universe will lead to future missions and exploration for generations ahead. Recently, the newest mission to the moon was launched as NASA’s Artemis Program which will pave the way for a future mission to Mars. This new era of space exploration will drive technological advancements in fields beyond astronautics and stimulate progress in real-world applications like materials, food science , agriculture, and even cosmetics.

A milestone in AI predictions

For decades, the scientific community has chased a greater understanding of relationships between protein functions and 3D structures. In July 2022, Deep Mind revealed that the folded 3D structure of a protein molecule can be predicted from its linear amino-acid sequence using AlphaFold2 , RoseTTAFold , and trRosettaX-Single algorithms. The algorithms’ predictions reduced the number of human proteins with unknown structural data from 4,800 to just 29. While there will always be challenges with AI, the ability to predict protein structures has implications across all life sciences. Key challenges in the future include modeling proteins with intrinsic disordered properties and those that change structures by post-translational modifications or to environmental conditions. Beyond protein modeling, AI advancements continue to reshape workflows and expand discovery capabilities across many industries and disciplines .

Developing trends in synthetic biology

Synthetic biology has the potential to redefine synthetic pathways by using engineered biological systems (i.e., microorganisms, for which a large part of the genome or the entire genome has been designed or engineered) to manufacture a range of biomolecules and materials, such as therapeutics, flavors, fabrics, food, and fuels. For example, insulin could be produced without pig pancreas, leather without cows, and spider silk without spiders. The potential in life sciences alone is unbelievable, but when applied to manufacturing industries, synthetic biology could minimize future supply chain challenges, increase efficiency, and create new opportunities for biopolymers or alternative materials with more sustainable approaches. Today, teams use AI-based metabolic modeling, CRISPR tools, and synthetic genetic circuits to control metabolism, manipulate gene expression, and build pathways for bioproduction. As this discipline begins to cross over into multiple industries, the latest developments and emerging trends for metabolic control and engineering challenges are showcased in a 2022 Journal of Biotechnology article .

Single-cell metabolomics set to soar

While much progress has been made in genetic sequencing and mapping, genomics only tells us what a cell is capable of. To have a better understanding of cellular functions, proteomic and metabolomic approaches offer different angles for revealing molecular profiles and cellular pathways. Single-cell metabolomics gives a snapshot of the cellular metabolism within a biological system. The challenge is that metabolomes change rapidly, and sample preparation is critical to understand cell function. Collectively, a series of recent advancements in single-cell metabolomics (from open-sourced techniques, advanced AI algorithms, sample preparations, and new forms of mass spectrometry) demonstrates the ability to run detailed mass spectral analyses. This allows researchers to determine the metabolite population on a cell-by-cell basis, which would unlock enormous potential for diagnostics. In the future, this could lead to the ability to detect even a single cancerous cell in an organism. Combined with new biomarker detection methods , wearable medical devices and AI- assisted data analysis, this array of technologies will improve diagnosis and lives.

New catalysts enable greener fertilizer production

Every year, billions of people depend on fertilizers for the ongoing production of food, and reducing the carbon footprint and expenses in fertilizer production would reshape the impact agriculture has on emissions. The Haber-Bosch process for fertilizer production converts nitrogen and hydrogen to ammonia. To reduce energy requirements, researchers from Tokyo Tech have developed a noble-metal-free nitride catalyst containing a catalytically active transition metal (Ni) on a lanthanum nitride support that is stable in the presence of moisture. Since the catalyst doesn't contain ruthenium, it presents an inexpensive option for reducing the carbon footprint of ammonia production. The La-Al-N support, along with the active metals, such as nickel and cobalt (Ni, Co), produced NH3 at rates similar to conventional metal nitride catalysts. Learn more about sustainable fertilizer production in our latest article .

Advancements in RNA medicine

While the application of mRNA in COVID-19 vaccines garnered lots of attention, the real revolution of RNA technology is just beginning. Recently, a new multivalent nucleoside-modified mRNA flu vaccine was developed. This vaccine has the potential to build immune protection against any of the 20 known subtypes of influenza virus and protect against future outbreaks. Many rare genetic diseases are the next target for mRNA therapies, as they are often missing a vital protein and could be cured by replacing a healthy protein through mRNA therapy. In addition to mRNA therapies, the clinical pipeline has many RNA therapeutic candidates for multiple forms of cancers, and blood and lung diseases. RNA is highly targeted, versatile, and easily customized, which makes it applicable to a wide range of diseases. Learn more about the crowded clinical pipeline and the emerging trends in RNA technologies in our latest CAS Insight Report.

Rapid skeletal transformation

Within synthetic chemistry, the challenge of safely exchanging a single atom in a molecular framework or inserting and deleting single atoms from a molecular skeleton has been formidable. While many methods have been developed to functionalize molecules with peripheral substituents (such as C-H activation), one of the first methods to perform single-atom modifications on the skeletons of organic compounds was developed by Mark Levin’s group at the University of Chicago . This enables selective cleaving of the N–N bond of pyrazole and indazole cores to afford pyrimidines and quinazolines. Further development of skeletal editing methods would enable rapid diversification of commercially available molecules, which could lead to much faster discoveries of functional molecules and ideal drug candidates.

Advancing limb regeneration

Limb loss is projected to affect over 3.6 million individuals per year by 2050. For the longest time, scientists believed the single biggest key to limb regeneration is the presence of nerves. However, work done by Dr. Muneoka and his team demonstrated the importance of mechanical load to digit regeneration in mammals and that the absence of a nerve does not inhibit regeneration. The advancement of limb regeneration was also achieved by researchers at Tufts University who have used acute multidrug delivery , via a wearable bioreactor, to successfully enable long-term limb regeneration in frogs. This early success could potentially lead to larger, more complex tissue re-engineering advances for humans, eventually benefiting military veterans, diabetics, and others impacted by amputation and trauma.

Nuclear fusion generates more net energy with ignition

Nuclear fusion is the process that powers the sun and stars. For decades, the idea of replicating nuclear fusion on earth as a source of energy, in theory, could fulfill all the planet's future energy needs. The goal is to force light atoms to collide so forcefully that they fuse and release more energy than consumed. However, overcoming the electrical repulsion between the positive nuclei requires high temperatures and pressures. Once overcome, fusion releases large amounts of energy, which should also drive the fusion of nearby nuclei. Previous attempts to initiate fusion used strong magnetic fields and powerful lasers but had been unable to generate more energy than they consumed.

Researchers at Lawrence Livermore National Laboratory’s ignition facility reported that the team was able to initiate nuclear fusion, which created 3.15 megajoules of energy from the 2.05 megajoule laser used. While this is a monumental breakthrough, the reality of a functioning nuclear fusion plant powering our grid may still be decades in the making. There are significant implementation hurdles (scalability, plant safety, energy required to generate the laser, wasted by-products, etc.) that must be addressed before this comes to fruition. However, the breakthrough of igniting nuclear fusion is a major milestone that will pave the way for future progress to be built upon this achievement.

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What's next? Forecasting scientific research trends

Associated data.

Data and implementation details are provided with summary statistics in Supplementary Table S2 . The variables used for the model are listed in Supplementary Table S3 . The SciTrends application is available at: https://huggingface.co/spaces/hadasak/SciTrends with a search and browsing capabilities. Users can also use the attached API with the associated files. The website code for acquiring data, extracting features, training models and loading the provided pretrained models, as a webserver is provided in https://github.com/HadasaK1/SciTrends/tree/main/SciTrends . The code repository is available in https://github.com/ddofer/Trends .

Scientific research trends and interests evolve over time. The ability to identify and forecast these trends is vital for educational institutions, practitioners, investors, and funding organizations. In this study, we predict future trends in scientific publications using heterogeneous sources, including historical publication time series from PubMed, research and review articles, pre-trained language models, and patents. We demonstrate that scientific topic popularity levels and changes (trends) can be predicted five years in advance across 40 years and 125 diverse topics, including life-science concepts, biomedical, anatomy, and other science, technology, and engineering topics. Preceding publications and future patents are leading indicators for emerging scientific topics. We find the ratio of reviews to original research articles informative for identifying increasing or declining topics, with declining topics having an excess of reviews. We find that language models provide improved insights and predictions into temporal dynamics. In temporal validation, our models substantially outperform the historical baseline. Our findings suggest that similar dynamics apply across other scientific and engineering research topics. We present SciTrends, a user-friendly webtool for predicting future publication trends for any topic covered in PubMed.

1. Introduction

The progress of science relies on the discovery and dissemination of new knowledge through scientific publications. In an era marked by the rapid evolution of scientific knowledge, the ability to forecast trends is paramount for strategic decision-making for researchers, policymakers, and industries. Researchers can proactively identify emerging fields, guiding their own studies and collaborations, while policymakers can allocate resources more effectively in response to evolving scientific demands. Furthermore, industries can harness this technology to stay ahead of the curve, adapting their products and services to align with the latest advancements in science. Groundbreaking technologies such as 'quantum computing' were started as niche fields, and the prediction of science trends can direct investigators and investors to such promising fields. On the other hand, researchers, investors, or countries, will be able to avoid investing in fields that are likely to decline in their public relevance. For example, investing in the field of 'fossil fuel consumption' over the years will be less relevant in the shadow of the growing global awareness and efforts to reduce fossil fuel usage. Predicting such trends by machine learning models can be a pioneering step towards enhancing our understanding of the ever-changing scientific landscape.

The number of scientific papers published has been accelerating for at least four decades, and citation and annotation behaviors have changed with it [ 1 ]. Changes might be attributed to the continuous increase in research institutes and researchers, the increasing impact of publications for funding agencies, and academic careers [ 2 , 3 ]. In addition, acceleration in publication may reflect better automated data indexing (e.g., Science Citation Index (SCI) [ 4 ]), and the establishment of a keyword annotation scheme (e.g., the medical subject headings system, MeSH [ 5 ]). Other changes within the last four decades include the expansion of open access policies, increased research originating in industry, and the ongoing increase in the total number of researchers and expected research productivity output in many fields. While presenting the current state can be based on a historical view of analytical methods, predicting future trends is far more challenging [ 6 , 7 ].

Some fields, such as methods in computer science and biotechnology, have fast dynamics due to impactful technological breakthroughs (e.g., machine learning, CRISPR gene editing) [ 8 ]. In other domains, such as medicine, topics are often less dynamic over time (e.g., cancer). A topic's popularity is also influenced by social factors bias [ 9 ]. Statistical machine learning models are commonly used in attempting to forecast future events and trends [ 10 , 11 ].

Traditionally, most research on scientific publication behavior focused on descriptive analyses of past trends [ 12 ], or citation networks [ 13 , 14 ]. Such studies aimed to detect existing trends, such as defining topics with increasing popularity. Alternatively, predictive or prescriptive analytics approaches aim to predict future behaviors in order to answer questions such as which topics will become more (or less) popular or what can make a topic popular [ [15] , [16] , [17] ]. In this study, we aim to predict the future behavior of scientific topics. Note that "popular" is not referring to the number of researchers involved or to an absolute measure of the size of the scientific active community. Here, popularity refers to the relative fraction of all publications focused on a specific topic. Furthermore, changes in popularity address the time-dependent relative popularity topic compared to its own past history, as directly comparing popularity between different domains is problematic without context due to widely varying base levels of popularity.

A limited number of studies have investigated time-series data for forecasting future topic citations. Noorden et al. analyzed the absolute number of citations for a specific paper [ 18 ]. Other studies focused mainly on narrow domains (e.g., predicting domain-specific conferences [ 19 , 20 ]). Tattershall et al. looked at binary trends within 5 years in computer science terms without continuous fine-grained prediction of the target or exogenous variables [ 21 ]. Studies analyzing the relationships between patents and research publications [ 22 , 23 ] suggested that these types of publications carry mutual information. In the field of virology and human health, forecasting virus outbreaks is of utmost importance, as practical measures in a limited time frame are needed (e.g., designed vaccination, animal eradication, population isolation measures) [ 24 ]. Based on 16 studies from literature (PubMed and Google Scholar) the potential of forecasting influenza outbreaks at different scales globally was assessed. It showed promise in capturing the outbreak measures, while raising the need for further evaluation in real-time predictions [ 25 ].

Our goal is to predict the future popularity of diverse scientific topics, with an emphasis on life science, experimental science, and biomedical domains. We developed a methodology that accounts for the overall increase in the number of publications over time. We discuss novel exogenous factors, such as patents and per-domain publication trends, that can indicate if and how a field's popularity is going to change. We show meaningful results when predicting topic popularity five years into the future and discuss the potential impact of such predictions and predictive insights. We further present a user-friendly webtool for predicting future publication trends for 1–6 years for any topic covered in PubMed.

2.1. Data compilation

We constructed a diverse list of topics focused mostly on life science. We included neuroanatomical regions, experimental methods, and emerging biomedical technology. We also included domain-expert selections for science, technology, and engineering research topics. For example, ultrasound was selected as a technology with emerging use in broad medical diagnostics. Topics such as opioids had clear past historical and funding trends. Other topics were derived from Wikipedia's biomedical "emerging technologies" page. All together, we present 125 topics featured in PubMed publications ( Supplementary Table S1 ). The counts of topical publications are based on PubMed. The PubMed database contains over 35 million published scientific works in biomedicine, life sciences, and related fields.

2.2. Normalized publication measurement – “popularity”

The total quantity of publications is increasing over time. For example, the NIH's Medline database recorded 274K citeable scientific works in 1979 but 774K in 2021 ( www.nlm.nih.gov/bsd/medline_cit_counts_yr_pub.html ). This escalation in total publications has the potential to skew the perception of a topic's popularity if measured solely by the raw number of publications, as otherwise most topics will have more publications over time due to the confounding background trend over time. This necessitates a methodology that considers and overcomes naïve quantitative comparisons.

We adapted a measure of "popularity" that normalizes per-topic publication counts relative to the total annual output. Specifically, per year, the popularity of a given topic is calculated by dividing the number of that topic's publications by the total number of citeable, indexed publications in PubMed in the corresponding year.

We multiplied the popularity score by 100,000 in all results and figures for visual ease. Historical PubMed topic popularity was extracted using PubMed by Year ( https://esperr.github.io/pubmed-by-year ). Search queries are parsed by NCBI's automatic term mapping algorithm, which supports for term and acronym expansion. This normalization helps control for the overall increase in publications over time and is similar to the approach used in other works on trend and popularity analysis, such as Google Trends, where popularity signifies the proportionate presence of a topic within a total volume rather than absolute quantity, thereby offering a more accurate measure of prominence over time.

2.3. Features

The most recent historical (“Lagged”) value was extracted for each topic, as well as for different transformations of its past values (including 1st, 2d order differenced and percent change values relative to the preceding year). Additional derived candidate features were explored using the SparkBeyond Automated Machine Learning framework (as used in Refs. [ 10 , 26 ]) and included different lags and lagged interaction features (e.g., the difference and ratio of review to research papers for a topic), aggregated time-window-based features (e.g., the historical average popularity between 5 and 10 years beforehand), as well as differencing and similar transformations of each the remaining exogenous time-series variable (see below).

In addition to the time-series features above, based on each topic popularity, we added exogenous static and dynamic features based on other variables. For each topic, we calculated the relative ratio and difference in popularity due to review and non-review (i.e., original research publication) articles. The absolute quantity of publications (rather than the per-year normalized frequency) per topic was estimated by multiplying by the number of Medline publications that year. The total fraction of US publications out of all Medline-indexed publications was added per year. The number of patents per topic, per year, was acquired by searching the U.S. Patent and Trademark Office's PatentsView database (patentsview.org/download) for each topic, using the USPTO search API ( https://github.com/ddofer/Trends/blob/main/patentsView_api_req.ipynb . The topic-relative fraction of all patents that year was also calculated. The first occurrence date of each topic (starting from 1946) was included, as was the first "valid" date (defined as an occurrence with at least 4 previous occurrences in the preceding 5 years) and the time elapsed between those dates, as well as from the prediction date. We experimented with adding the popularity of each topic's associated major MeSH term. We found that this did not improve the models, so it was removed from the final model.

2.4. Statistical modeling

Features were derived from the above inputs with a forecast horizon of 5 years (i.e., all time-dependent features were from at least 5 years before the target time of prediction), and the target is popularity 5 years in the future.

Machine learning regression models were trained using the CatBoost [ 27 ] and Scikit-learn libraries [ 28 ] with default hyperparameters. The pretrained deep language model used the sentence-transformers package [ 29 ]. The linear model is a Scikit-learn RidgeRegressionCV model. Boosting Tree is a CatBoost regression tree model with target encoding of the topic as a categorical feature. Tree and embedding augmented is the CatBoost model with the topic's name embedded as additional features, extracted using the deep learning all-MiniLM-L12-v2 model [ 30 ]. Other approaches, including additional scikit-learn models and pure deep learning or statistical forecasting models, were evaluated but had significantly worse results and inferior stability, so they were not used (not shown). Boosting tree models are interpretable, fast, relatively robust, and performant in most predictive tasks and were thus used as the representative model [ 31 ]. All models used do not explicitly account for non-stationary targets, which is an issue due to the increase in average popularity over time across targets, however they can learn it from corollary, dynamic features such as the year, or the lagging values.

As the last four decades better represent modern trends in scientific research, we limited the training data to the years 1979–2019. These years cover the historical USPTO PatentsView database. We do use prior historical data when generating features and conducting retrospective analyses. For modeling, we defined for each topic the relevant starting point as the first year in which it had citations in at least 4 of the past 5 years and at least 5 valid occurrences in the 1979–2019 period. This threshold was used to overcome observed errors in PubMed, with some topics appearing just a few times over decades, apparently due to erroneous annotations.

Model performance was evaluated using step-forward temporal cross validation, implemented in scikit-learn, over all topic time series simultaneously. The test set was defined using scikit-learn's temporal cross-validation protocol with 30 splits.

A humanized version summarizing the distribution and summary statistics of the topics is provided as Supplementary Table S2 . The table includes each topics popularity and descriptive features over time. An extended table with features per topic, per year, as used in training the models and evaluation is provided as Supplementary Table S3 (total 4176 rows, ∼400 columns).

2.5. Application and data implementation

A stand-alone implementation is provided in the code repository ( https://github.com/ddofer/Trends ). Additionally, we developed SciTrends, a GRADIO web application, for viewing predicted normalized PubMed occurrences of any topic, and it is automatically updated with new data. The application is available at: https://huggingface.co/spaces/hadasak/SciTrends . The website code for acquiring data, extracting features, training models and loading the provided pretrained models, as a webserver is provided in https://github.com/HadasaK1/SciTrends/tree/main/SciTrends .

Data and implementation details are provided in Supplementary Table S2 The table includes 4684 lines related to the 125 selected topics. Each line includes quantified information with rich, dynamic data. The code repository is available at https://github.com/ddofer/Trends . We have used USPTO search API. The code is provided in the repository. ( https://github.com/ddofer/Trends/blob/main/patentsView_api_req.ipynb ). We have developed a web application called SciTrends that allows the user to view the normalized PubMed occurrences by year for the topic of interest for the years 2023–2028. In addition, we provide an unlimited searching mode for any term in PubMed. For all these instances, we provide future prediction trend for 1–6-years. The application is available at: https://huggingface.co/spaces/hadasak/SciTrends .

3.1. The dynamics of scientific terms popularity levels

Fig. 1 presents an overview of 125 diverse topics and their breakdown into broad themes. Detailed information on the topics and their associated scientific domains is available in Supplementary Table S1 . Obviously, this list is not exhaustive, although the collection of topics represents a wide range of emerging and established scientific topics. In this study, we analyze these topics as a showcase.

Topics by domain. Overview of the 125 topics clustered by their association with high-level field domains. A full list is available in Supplementary Table S1 .

A sample of topics and their trends over time are illustrated in Fig. 2 A. We follow topics’ popularity (see Methods) over the past 45 years and show that topics gain, lose, and sometimes regain popularity over time. For example, while the popularity of opioids is quite stable, the differences over the years for stem cells and neuropeptides show very different levels of popularity and dynamics ( Fig. 2 A). There was a clear change in popularity in 1990 and 2010 for neuropeptides and stem cells , respectively.

Different levels of popularity in PubMed for the indicated topics. The popularity of each topic (y-axis) is normalized per 100,000 citations out of all citations that year. (A) Sample of topics in the years 1976–2022. (B) Changes in the levels of popularity as extracted from PubMed covering the years 1960–2022. Source: PubMed by Year (see Methods).

Fig. 2 B shows the dynamics for 62 years (1960–2022). Some topics display complex dynamics. For example, RT-PCR , which was only introduced in the early 90s, exhibits a sharp increase in popularity within 5 years (2005–2010) and a similar sharp decline that only stabilized in recent years. A similar trend was associated with restriction enzymes , which were the force behind molecular biology in the early days (1985–1995) and were then replaced by more simple and versatile technologies, including library preparation, CRISPR, and such. On the other hand, topics like species conservation and climate change monotonically increased in popularity, albeit only after two decades for the latter. The topic single cell shows a doubling in popularity that occurred in 1974, increasing popularity for two decades, and then remaining high but stable for the last 25 years.

We observed that the (normalized) mean popularity of our topics increased over time, despite our normalization. The overall change in popularity over time for all discussed topics is shown in Supplementary Fig. S1 .

3.2. Correlations with the popularity of scientific topics

Since topic popularity includes reviews, the popularity of just review articles on a topic is significantly correlated with its overall popularity. Specifically, the total popularity of all publications (combining review and non-review articles) in that same year shows a high Pearson correlation coefficient (r = 0.87) compared to 5 years before (r = 0.85), as is expected. We list the ratios of review articles to research works for each topic and year ( Supplementary Table S2 ). Note that it varies greatly by topic and the ongoing dynamics of a topic's popularity ( Supplementary Table S2 ). Overall, a popular topic will have a higher fraction of reviews, while a rapidly growing topic may have fewer review articles relative to original research works. We conclude that the relation between the feature and its trend (i.e., changes in its popularity in the future) is different from the static popularity at the same point in time. A popular topic may have many review articles, but a growing topic may have relatively fewer reviews.

We further anticipated that patents would reflect the popularity of the topic. Indeed, overall popularity is correlated with the number of patents in that year with a Pearson rank correlation (r = 0.37) and 5 years before (r = 0.40). This suggests that patents may be a leading indicator for scientific publication trends, with patents preceding “valuable” research. This observation is in line with the regulation of patent applications, where confidentiality is requested to prevent its public disclosure in scientific publications before its acceptance.

We observe that topic popularity over time is highly correlated with their own past values, e.g., from 5 years beforehand, as we might expect (r = 0.953, R 2  = 0.969).

Fig. 3 displays a diverse set of topics that we analyzed within a narrower timeframe (1990–2020). We show that popularity can change sharply with 4–5 fold increases within 5 years (e.g., cannabidiol, Fig. 3 A). Many such cases represent breakouts for topics with relatively low (<50 per 100K normalized citations) previous popularity. Popular topics may also exhibit high growth and overall popularity for decades (e.g., 8000 for ultrasound , Fig. 3 B). Classical topics like vaccines and lipids display rather stable dynamics for decades ( Fig. 3 C). We emphasize that some topics are quite general (e.g., DNA , RNA ) and represent many subtopics that often exhibit distinctive popularity dynamics. We illustrate it by partitioning of the term RNA with respect to more specific related subtopics ( Fig. 3 D). Although RNA in general shows a stable trend ( Fig. 3 C), CRISPR (based on gRNA) and long noncoding RNA (lncRNA) have seen a 10-fold increase in popularity within 10 years, while transposons and ribozymes reached their maximum popularity in 2000. An interesting example refers to the 5-fold monotonic increase in rRNA ( Fig. 1 D). It is likely reflecting the emerging fields of microbiome and metagenomics, in which rRNA is used for sample characterization and microbial species identification. All these informative changes are deemed necessary by considering RNA as a general term. The normtable salized historical trends by years based on the PubMed database for each of the PubMed topics are available in the SciTrends application (see Methods).

Examples from the dataset showing different levels of popularity and dynamics over three decades (1990–2020) for selected representative topics. A-D cover a wide range in popularity, from 50 to 8000. (D) Subtopics in the molecular aspects of RNAs. Note that popularity is scaled by 100,000. Image source: PubMed by year (see Methods).

3.3. Natural language as a clue to topics’ dynamics

As shown in Fig. 3 , the topics we discuss cover not just different scientific domains but also vary in how general or specific they are, as well as representing different concepts. Specifically, there are topics that concern methods such as NGS (next generation sequencing) or PCR (polymerase chain reaction), while others cover biological fields that are precisely defined, such as anatomical regions. We expect different fields and entities to show different behaviors and temporal dynamics. Furthermore, topics greatly vary by the size and nature of their community, e.g., medical clinicians versus ethical researchers.

We would like to incorporate relevant information into our models to enhance the potential for learning latent dynamics. For example, we anticipate that technologies might become obsolete or be replaced by modern ones (e.g., the methodologies for transgenic mice or creating cell lines). Nevertheless, terms for well-defined anatomical entities such as the hippocampus are unlikely to be superseded, even if interest in them varies, and the topic will remain despite being subjected to new research technology. For example, fMRI (functional magnetic resonance imaging) is a leading technology associated with neuroscience and cognitive psychology that exhibits massive growth in popularity thanks to its ability to allow psychology experiments with live subjects in real time. But a topic such as genetic engineering will most likely not match the dynamics of the term eugenics. The latter has a negative historical context and thus is not expected to correlate well with genetic engineering, despite their shared roots and semantic similarity.

We used a pretrained deep learning language model, all-MiniLM-L12-v2 [ 30 ] to extract a quantitative representation (embeddings) for each topic, using the name of the topic as input, and used this embedding as an additional input feature (see Tree & embedding augmented Table 1 ). The language model was trained in advance on a general text corpus and was not fine-tuned. Information can be efficiently represented using such representations [ 32 , 33 ].

Model results for 5-year forecasting.

Table 1 shows the model's forecasting performance. The combined tree and embedding models have the best explained variance (R 2 ) scores and overall results. All results are reported for the test set (see Methods). Lag baseline is the last valid value of the target, or the transformed target, from 5 years beforehand in a regularized linear regression model.

3.4. Predictive model results

The scale of the analyzed data and its structure supported the use of tree-based boosting algorithms such as CatBoost [ 34 ]. Several machine learning models including linear regression and boosting tree (CatBoost) were trained on different targets: (i) the popularity of each topic in 5 years (ŷ), and (ii) the percent change in a topic's popularity in 5 years relative to the present (Δŷ). The latter target is a more challenging one that also implicitly neutralizes the naïve lagging baseline and reduces the bias due to differing mean levels of popularity between topics. We found that non-linear boosting tree models gave the best results ( Table 1 ). Our models outperform the historical lag baseline, which presents a proxy for human guesses. Similar performance for our models was achieved by other gradient boosting methods including XGBoost and lightGBM [ 34 ]. We further studied the features that contributed to the model. We further evaluated at the binary level: predicting if a topic would go up or down in popularity (i.e., binary prediction). At that level, we show 88 % accuracy compared to a 70 % baseline (most topics increased in popularity over time). These results were stable over time.

Supplementary Fig. S2 shows the SHAP analysis (based on Shapley values) for the model trained on the percent change target using the augmented deep learning embedding features. The top features of importance to the tree and text-augmented model are listed along with their values. We found that review articles strongly contribute to the model's performance. Numerous features associated with reviews are among the features that exhibit strong SHAP values.

3.5. Evaluation of unseen topics

Our results implicitly assume predicting changes in known topics. To reflect this, we added an evaluation of predicting completely novel topics over the 40 years covered in the data ( Table 2 ). In this setup, the train-test split is performed at the topic level rather than the time-level, using 30-fold groupwise splits while still predicting 5 years ahead. This is considerably more challenging, as it reflects the problem of “What will the popularity of a completely unknown scientific topic be like over many decades?”. This framework is unrealistically challenging, as we would expect a novel topic's behavior over decades to be predictable in advance, but it can be viewed as a proxy for lower-bound performance over completely unseen topics. We observe reduced performance and, surprisingly, no clear benefit from the text embeddings. We view the temporal evaluation setup as the more relevant one.

Model predictions for topic-level splits.

3.6. Predicting the rise and fall in topics’ popularity

We limited the training data up to 2019, partially due to the extreme societal changes and publication biases in the past 2 years during the COVID-19 epidemic [ 35 ]. Nevertheless, we examined the model predictions for the present time, i.e., the per-topic model predictions for 2022, using 2017 data.

Table 3 provides a sample of topics predicted to have the greatest relative (Δŷ) change, selected from the models’ top results. Model predictions for 2022 were sorted by the highest absolute predicted change relative to 2017 popularity, then selected. We list topics that had declining popularity before 2017 and are predicted to continue to decline until 2022. An example of an erroneous model prediction is influenza , likely due to the 2020 COVID-19 epidemic knock-on effects. We further show several topics with inverse directionality ( Table 3 ). These are other topics that were increasing in popularity in 2017 relative to their popularity level in 2016, which we predicted would decline in 2022, or that were predicted to show the opposite change trend. Specifically, their popularity decreased in 2017 relative to 2016, but nevertheless, we predict their trend to reverse. The list is sorted by the success order of the predictive model in each section.

Selected predictions for 2022 with information limited to the year 2017.

Manual analysis showed most predictions to be correct, at least at the binary trend level (increasing or decreasing), with examples such as cumin and graph neural networks ( Table 3 ). We included cases where the trend for a topic (increasing or decreasing popularity) is predicted to reverse, as was indeed the observed case (e.g., MRI, antibiotics , Table 3 ). Fig. 4 shows the actual changes in popularity for 2022 based on PubMed with a resolution of 1-year, 3-years, and 5-years for selected topics from Table 3 . Fig. 4 A shows representative topics with declined in popularity, while Fig. 4 B shows topics that do not agree with the previous year's trend.

Actual changes in popularity for 2022 based on PubMed. (A) 2022 predictions for a decline in popularity with the trend of 1-year, 3-years, and 5-years. (B) Predictions for 2022 that do not agree with the previous year's trend. The change in popularity for 3-years, 5-years, and 2017 to 2016 (stripped bar) is shown. The topics are representatives from Table 3 .

To improve the generality of our study, we developed an application for using PubMed's current information (as of 2022), allowing the user to activate the ML model of any topic or term of interest. Fig. 5 shows a screenshot of the results for two sets of terms that were new to the model ( Fig. 5 , top) and terms that were used for the training ( Fig. 5 , bottom). Normalized publication trends for viruses and vaccinations ( Fig. 5 , top) show a non-monotonic and quite complex historical trend for influenza , with a maximal popularity in 2010. This peak in popularity is a reflection of the outbreaks in North America (April 2009), where the new H1N1 influenza virus spread rapidly around the world within a few months [ 36 ]. The prediction for 2023–2028 supports a large variation for influenza, most likely following the suppression of publications and minimal occurrence of influenza during the COVID-19 pandemic, while the RNA vaccine is expected to keep gaining popularity relative to 2022 (by ∼3 folds).

A screenshot of the SciTrends application (Users can upload any topic or term that appears in PubMed (up to 10 topics can be loaded in a single run). The split between the normalized real data and the prediction trend is marked in 2022 (a vertical dashed line). The predicted trend is shown for six years from 2023 to 2028. User can search and browse in https://huggingface.co/spaces/hadasak/SciTrends , or use the attached API with the associated files.

Reanalysis of terms that were used for our model training ( Fig. 1 ) indicates a large increase in IL6 , the major interleukin that reflects the inflammatory response. The trend of other terms such as GABA and APOe seems to decline. Both terms have been extensively studied over the last 20 years, and while GABA is indicative of brain function and mental illness, APOe is a major genetic signature relevant for Alzheimer's disease. The SciTrends application provides a browsing option displaying the actual data from PubMed and the prediction trend by year for 1–6 years ahead.

4. Discussion

While bibliometric publication trend statistics are not a perfect approximation for scientific research, they can provide valuable insights into trends in a field and help researchers and investors make informed predictions about the future direction of research. It is possible to identify emerging trends in a field and predict, to some extent, how trends may evolve over time. With respect to the known quote from Niels Bohr (1970) "It is difficult to make predictions, especially about the future", we aim to predict scientific topics in 6 years in the dynamic life science and technology fields.

Often in experimental science, such as biology, new concepts and methods precede a vast increase in related research. A classic example is the use of a microscope, which established the germ theory and preceded a vast increase in research in human health, microbiology, vaccines, and more. A similar lag can be attributed to the discovery of DNA structure, which led to the evolution of molecular biology and its key technologies (RT-PCR, CRISPR, and NGS). We show that it is possible to outperform the naïve baselines by which what is popular today will be popular in the coming years. Long-term forecasting is non-trivial and has been thoroughly discussed in the electronic media [ 37 ].

We present a non-exhaustive list of 125 topics, representing a wide range of emerging and established scientific and biological topics. We then searched PubMed, using the data from PubMed by Year [ 38 ] and used it to identify trends in scientific research over decades. We observed that the mean popularity of all 125 topics increased over time, despite our year-based normalization ( Supplementary Fig. S1 ). We hypothesize this might be due to improved automated annotation methods resulting in more keywords being annotated across all studies. This pattern persists even with further attempts at naively detrending the popularity target (not shown) or the percent change target (Δŷ). Additionally, it might be explained as an outcome of survival bias. Specifically, we chose topics that are valid and active in the present time and most likely ignored the topics that became completely obscure and disappeared over time. Such bias may cause a shift toward more popular topics.

We used the data to train forecasting models to predict topics’ popularity six years in advance. We show that our models are capable of predicting future trends in existing topics and outperforming the historical (lag) baseline. Numerous studies also used quantitative approaches while analyzing publications and citations per topic with or without multi-year dynamics [ [39] , [40] , [41] , [42] ]. Additionally, studies used publications to identify hidden connections between topics [ 43 , 44 ]. A systematic identification of patterns in such large datasets can accelerate innovation and augment creativity [ 45 , 46 ]. In tracing the evolution of topics, abstracts were parsed by rhetorical framing [ 47 ]. They found that topics referred to in results sections tend to decline, while the opposite holds for topics appearing in methods sections. Such findings are in accordance with our study, where the abundance of reviews relative to original research articles often precedes stagnating or declining topic popularity.

An important concept in forecasting tasks are leading indicators, which are exogenous variables that provide predictive, potentially causal information about targets of interest. Patents are an intriguing candidate for predicting similar turning points in scientific research [ 23 ]. Commercially relevant works are advised to obtain defensive patents prior to publication. In this case, patents should most likely precede publications. We examine the CRISPR-based gene editing technology as a case study [ 48 ]. We observe from the data that the number of patents in earlier years is a strong, leading indicator and better predicts research papers than the number of patents in that same year (Supplementary Table S4 ). Recall that, typically, temporal distance reduces the correlation between variables. The Pearson correlation between the number of CRISPR publications and patents filed at the same year is r = 0.93, while the correlation with patents filed 1 year before is r = 0.98. Using patent values from 1 year into the future gives an even lower correlation with popularity (r = 0.88). This is in line with the hypothesis that patents might be a leading indicator for research publications. This observation is also in line with the regulation of patent applications where confidentiality is requested to prevent its public disclosure in scientific publications before its acceptance.

In this study, we found that a relatively high number of reviews correlate with reduced future topic popularity. This could be because review articles are often published when a field or a technology is mature. When a field is in its early stages, a lot of new research is published, especially in experimental sciences. In other instances, such as the recent COVID-19 global pandemic, the field is so dynamic that the relative number of reviews was suppressed in view of the flood of original publications. The 3-year period of COVID-19 also affected peer-reviewed protocols, the time lag in publications, and the abrupt change in science that was made in the fields that are related to public health, epidemiology, virology, and vaccination. It is likely that once a field matures, both conceptual and literature surveys will be presented, and it may stagnate. This is supported by different works on the relative locations and context of topics in articles. Examples include MRI and fMRI in cognitive and neuroscience research, or deep learning in computer vision [ 49 ], language, and biology [ 7 , 16 , 50 ].

This research is subject to several limitations. We have used PubMed as a ground-truth source for publication counts. However, PubMed does not include records from new channels for publishing in science, such as conference proceedings, open archives (e.g., BioRxiv), or online collections. Moreover, PubMed focuses mainly on medicine and biomedical sciences, and coverage of other research topics is limited. The generality of our prediction was not yet tested on other publication resources, such as scientific blogs, or domains that are not generally published on PubMed, such as social sciences or linguistics.

A cautionary aspect is cases of semantic transitions in terminology switching, where a topic may be referred to using different nomenclatures over time, e.g., NGS for deep sequencing. PubMed search algorithms help mitigate this by handling many acronyms automatically (e.g., "DNA" and "deoxyribonucleic acid"), but this is an aspect that our framework does not directly handle.

Defining something as popular is fuzzy and contextual. It can be viewed in relation to itself, i.e., a relative increase or decrease in popularity compared to the past (for example, neuropeptides, influenza, and mRNA vaccines), or in relation to other topics. For example, cancer research is an extremely active field, as it is a widely used term in immunology, cell biology, computational biology, medicine, and other scientific fields. In contrast, mentioning the genes that drive cancer (e.g., TP53, BRCA1, ATM) is far less represented in the cancer research literature.

The trends in scientific and technological topics are influenced by confounding effects ranging from human curiosity (e.g., space science), media coverage, and public awareness (e.g., gene therapy) or pressing challenges in public health (e.g., vaccines, climate change). These factors shape public and institutional interest in specific areas of science and technology. Thus, applications of this methodology to predict popularity as the basis for planning must also take human social context into account. For example, to help predict if something is a temporary "flash in the pan" or a meaningful, breakout topic that will remain relevant for years to come.

We found that unstructured text embeddings of topics using just their names provided additional information. The best predictive models out of those tested likely reflect the added capacity to learn latent dynamics between domains [ 7 , 26 ]. However, these results should be viewed with caution, as the underlying language model was trained on data from 2018. While we were not exposed to our tasks, the possibility of future information leakage cannot be discounted, barring a full retraining of the model for every year covered. Moreover, the limitations of the methods used should be acknowledged. Typical deep learning approaches do not support missing values and are not ideal for sparse time points per series or topic. The focus of this study is to share a useful tool with the broad scientific community, with the goal of improving forecasting accuracy in future work.

5. Conclusions

The dynamics and value ranges of the topics vary greatly by topic and time and may span over 4 orders of magnitude, even after normalization. Despite these dynamic and scale-varying targets, our results suggest that scientific publication trends are predictable years in advance using historical data as well as patents and in-domain publication trends, such as the number of reviews relative to research articles. We suggest that such methods can be of great benefit for planning critical decisions regarding career development, technological implantation, training, and education, as well as for early-stage researchers investing in infrastructure and training. To empower the utility of our prediction models, we developed SciTrends as an interactive application that presents the profile of any topic of interest covered by PubMed and its trend prediction for the following 6 years.

Data availability

Credit authorship contribution statement.

Dan Ofer: Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Hadasah Kaufman: Writing – review & editing, Writing – original draft, Visualization, Software, Resources. Michal Linid: Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Investigation, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Michal Linial reports financial support was provided by Hebrew University of Jerusalem. Michal Linial reports a relationship with Hebrew University of Jerusalem that includes: employment. None.

Acknowledgments

We thank the members from D. Shahaf and M. Linial laboratories for sharing their ideas and valuable discussions. We thank N. Rappoport and R. Zucker for supporting the web application. This work was partially supported by the Center for Interdisciplinary Data Science Research (CIDR, #3035000440) at the Hebrew University, Jerusalem.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e23781 .

Appendix A. Supplementary data

The following are the Supplementary data to this article.

McKinsey technology trends outlook 2024

Despite challenging overall market conditions in 2023, continuing investments in frontier technologies promise substantial future growth in enterprise adoption. Generative AI (gen AI) has been a standout trend since 2022, with the extraordinary uptick in interest and investment in this technology unlocking innovative possibilities across interconnected trends such as robotics and immersive reality. While the macroeconomic environment with elevated interest rates has affected equity capital investment and hiring, underlying indicators—including optimism, innovation, and longer-term talent needs—reflect a positive long-term trajectory in the 15 technology trends we analyzed.

What’s new in this year’s analysis

This year, we reflected the shifts in the technology landscape with two changes on the list of trends: digital trust and cybersecurity (integrating what we had previously described as Web3 and trust architectures) and the future of robotics. Robotics technologies’ synergy with AI is paving the way for groundbreaking innovations and operational shifts across the economic and workforce landscapes. We also deployed a survey to measure adoption levels across trends.

These are among the findings in the latest McKinsey Technology Trends Outlook, in which the McKinsey Technology Council  identified the most significant technology trends unfolding today. This research is intended to help executives plan ahead by developing an understanding of potential use cases, sources of value, adoption drivers, and the critical skills needed to bring these opportunities to fruition.

Our analysis examines quantitative measures of interest, innovation, investment, and talent to gauge the momentum of each trend. Recognizing the long-term nature and interdependence of these trends, we also delve into the underlying technologies, uncertainties, and questions surrounding each trend. (For more about new developments in our research, please see the sidebar “What’s new in this year’s analysis”; for more about the research itself, please see the sidebar “Research methodology.”)

New and notable

The two trends that stood out in 2023 were gen AI and electrification and renewables. Gen AI has seen a spike of almost 700 percent in Google searches from 2022 to 2023, along with a notable jump in job postings and investments. The pace of technology innovation has been remarkable. Over the course of 2023 and 2024, the size of the prompts that large language models (LLMs) can process, known as “context windows,” spiked from 100,000 to two million tokens. This is roughly the difference between adding one research paper to a model prompt and adding about 20 novels to it. And the modalities that gen AI can process have continued to increase, from text summarization and image generation to advanced capabilities in video, images, audio, and text. This has catalyzed a surge in investments and innovation aimed at advancing more powerful and efficient computing systems. The large foundation models that power generative AI, such as LLMs, are being integrated into various enterprise software tools and are also being employed for diverse purposes such as powering customer-facing chatbots, generating ad campaigns, accelerating drug discovery, and more. We expect this expansion to continue, pushing the boundaries of AI capabilities. Senior leaders’ awareness of gen AI innovation has increased interest, investment, and innovation in AI technologies, such as robotics, which is a new addition to our trends analysis this year. Advancements in AI are ushering in a new era of more capable robots, spurring greater innovation and a wider range of deployments.

Research methodology

To assess the development of each technology trend, our team collected data on five tangible measures of activity: search engine queries, news publications, patents, research publications, and investment. For each measure, we used a defined set of data sources to find occurrences of keywords associated with each of the 15 trends, screened those occurrences for valid mentions of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative to the trends studied. The innovation score combines the patents and research scores; the interest score combines the news and search scores. (While we recognize that an interest score can be inflated by deliberate efforts to stimulate news and search activity, we believe that each score fairly reflects the extent of discussion and debate about a given trend.) Investment measures the flows of funding from the capital markets into companies linked with the trend.

Data sources for the scores include the following:

• Patents. Data on patent filings are sourced from Google Patents, where the data highlight the number of granted patents.
• Research. Data on research publications are sourced from Lens.
• News. Data on news publications are sourced from Factiva.
• Searches. Data on search engine queries are sourced from Google Trends.
• Investment. Data on private-market and public-market capital raises (venture capital and corporate and strategic M&A, including joint ventures), private equity (including buyouts and private investment in public equity), and public investments (including IPOs) are sourced from PitchBook.
• Talent demand. Number of job postings is sourced from McKinsey’s proprietary Organizational Data Platform, which stores licensed, de-identified data on professional profiles and job postings. Data are drawn primarily from English-speaking countries.

In addition, we updated the selection and definition of trends from last year’s report to reflect the evolution of technology trends:

• The future of robotics trend was added since last year’s publication.
• Data sources and keywords were updated. For data on the future of space technologies investments, we used research from McKinsey’s Aerospace & Defense Practice.

Finally, we used survey data to calculate the enterprise-wide adoption scores for each trend:

• Survey scope. The survey included approximately 1,000 respondents from 50 countries.
• Geographical coverage. Survey representation was balanced across Africa, Asia, Europe, Latin America, the Middle East, and North America.
• Company size. Size categories, based on annual revenue, included small companies ($10 million to $50 million), medium-size companies ($50 million to $1 billion), and large companies (greater than $1 billion).
• Respondent profile. The survey was targeted to senior-level professionals knowledgeable in technology, who reported their perception of the extent to which their organizations were using the technologies.
• Survey method. The survey was conducted online to enhance reach and accessibility.
• Question types. The survey employed multiple-choice and open-ended questions for comprehensive insights.
• 1: Frontier innovation. This technology is still nascent, with few organizations investing in or applying it. It is largely untested and unproven in a business context.
• 2: Experimentation. Organizations are testing the functionality and viability of the technology with a small-scale prototype, typically done without a strong focus on a near-term ROI. Few companies are scaling or have fully scaled the technology.
• 3: Piloting. Organizations are implementing the technology for the first few business use cases. It may be used in pilot projects or limited deployments to test its feasibility and effectiveness.
• 4: Scaling. Organizations are in the process of scaling the deployment and adoption of the technology across the enterprise. The technology is being scaled by a significant number of companies.
• 5: Fully scaled. Organizations have fully deployed and integrated the technology across the enterprise. It has become the standard and is being used at a large scale as companies have recognized the value and benefits of the technology.

Electrification and renewables was the other trend that bucked the economic headwinds, posting the highest investment and interest scores among all the trends we evaluated. Job postings for this sector also showed a modest increase.

Although many trends faced declines in investment and hiring in 2023, the long-term outlook remains positive. This optimism is supported by the continued longer-term growth in job postings for the analyzed trends (up 8 percent from 2021 to 2023) and enterprises’ continued innovation and heightened interest in harnessing these technologies, particularly for future growth.

In 2023, technology equity investments fell by 30 to 40 percent to approximately $570 billion due to rising financing costs and a cautious near-term growth outlook, prompting investors to favor technologies with strong revenue and margin potential. This approach aligns with the strategic perspective leading companies are adopting, in which they recognize that fully adopting and scaling cutting-edge technologies is a long-term endeavor. This recognition is evident when companies diversify their investments across a portfolio of several technologies, selectively intensifying their focus on areas most likely to push technological boundaries forward. While many technologies have maintained cautious investment profiles over the past year, gen AI saw a sevenfold increase in investments, driven by substantial advancements in text, image, and video generation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Despite an overall downturn in private equity investment, the pace of innovation has not slowed. Innovation has accelerated in the three trends that are part of the “AI revolution” group: gen AI, applied AI, and industrializing machine learning. Gen AI creates new content from unstructured data (such as text and images), applied AI leverages machine learning models for analytical and predictive tasks, and industrializing machine learning accelerates and derisks the development of machine learning solutions. Applied AI and industrializing machine learning, boosted by the widening interest in gen AI, have seen the most significant uptick in innovation, reflected in the surge in publications and patents from 2022 to 2023. Meanwhile, electrification and renewable-energy technologies continue to capture high interest, reflected in news mentions and web searches. Their popularity is fueled by a surge in global renewable capacity, their crucial roles in global decarbonization efforts, and heightened energy security needs amid geopolitical tensions and energy crises.

The talent environment largely echoed the investment picture in tech trends in 2023. The technology sector faced significant layoffs, particularly among large technology companies, with job postings related to the tech trends we studied declining by 26 percent—a steeper drop than the 17 percent decrease in global job postings overall. The greater decline in demand for tech-trends-related talent may have been fueled by technology companies’ cost reduction efforts amid decreasing revenue growth projections. Despite this reduction, the trends with robust investment and innovation, such as gen AI, not only maintained but also increased their job postings, reflecting a strong demand for new and advanced skills. Electrification and renewables was the other trend that saw positive job growth, partially due to public sector support for infrastructure spending.

Even with the short-term vicissitudes in talent demand, our analysis of 4.3 million job postings across our 15 tech trends underscored a wide skills gap. Compared with the global average, fewer than half the number of potential candidates have the high-demand tech skills specified in job postings. Despite the year-on-year decreases for job postings in many trends from 2022 to 2023, the number of tech-related job postings in 2023 still represented an 8 percent increase from 2021, suggesting the potential for longer-term growth (Exhibit 1).

Enterprise technology adoption momentum

The trajectory of enterprise technology adoption is often described as an S-curve that traces the following pattern: technical innovation and exploration, experimenting with the technology, initial pilots in the business, scaling the impact throughout the business, and eventual fully scaled adoption (Exhibit 2). This pattern is evident in this year’s survey analysis of enterprise adoption conducted across our 15 technologies. Adoption levels vary across different industries and company sizes, as does the perceived progress toward adoption.

Image description:

A graph depicts the adoption curve of technology trends, scored from 1 to 5, where 1 represents frontier innovation, located at the bottom left corner of the curve; 2 is experimenting, located slightly above frontier innovation; 3 is piloting, which follows the upward trajectory of the curve; 4 is scaling, marked by a vertical ascent as adoption increases; and 5 is fully scaled, positioned at the top of the curve, indicating near-complete adoption.

In 2023, the trends are positioned along the adoption curve as follows: future of space technologies and quantum technologies are at the frontier innovation stage; climate technologies beyond electrification and renewables, future of bioengineering, future of mobility, future of robotics, and immersive-reality technologies are at the experimenting stage; digital trust and cybersecurity, electrification and renewables, industrializing machine learning, and next-gen software development are at the piloting stage; and advanced connectivity, applied AI, cloud and edge computing, and generative AI are at the scaling stage.

Footnote: Trend is more relevant to certain industries, resulting in lower overall adoption across industries compared with adoption within relevant industries.

Source: McKinsey technology adoption survey data

End of image description.

We see that the technologies in the S-curve’s early stages of innovation and experimenting are either on the leading edge of progress, such as quantum technologies and robotics, or are more relevant to a specific set of industries, such as bioengineering and space. Factors that could affect the adoption of these technologies include high costs, specialized applications, and balancing the breadth of technology investments against focusing on a select few that may offer substantial first-mover advantages.

As technologies gain traction and move beyond experimenting, adoption rates start accelerating, and companies invest more in piloting and scaling. We see this shift in a number of trends, such as next-generation software development and electrification. Gen AI’s rapid advancement leads among trends analyzed, about a quarter of respondents self-reporting that they are scaling its use. More mature technologies, like cloud and edge computing and advanced connectivity, continued their rapid pace of adoption, serving as enablers for the adoption of other emerging technologies as well (Exhibit 3).

A segmented bar graph shows the adoption levels of tech trends in 2023 as a percentage of respondents. The trends are divided into 5 segments, comprising 100%: fully scaled, scaling, piloting, experimenting, and not investing. The trends are arranged based on the combined percentage sum of fully scaled and scaling shares. Listed from highest to lowest, these combined percentages are as follows:

• cloud and edge computing at 48%
• advanced connectivity at 37%
• generative AI at 36%
• applied AI at 35%
• next-generation software development at 31%
• digital trust and cybersecurity at 30%
• electrification and renewables at 28%
• industrializing machine learning at 27%
• future of mobility at 21%
• climate technologies beyond electrification and renewables at 20%
• immersive-reality technologies at 19%
• future of bioengineering at 18%
• future of robotics at 18%
• quantum technologies at 15%
• future of space technologies at 15%

The process of scaling technology adoption also requires a conducive external ecosystem where user trust and readiness, business model economics, regulatory environments, and talent availability play crucial roles. Since these ecosystem factors vary by geography and industry, we see different adoption scenarios playing out. For instance, while the leading banks in Latin America are on par with their North American counterparts in deploying gen AI use cases, the adoption of robotics in manufacturing sectors varies significantly due to differing labor costs affecting the business case for automation.

As executives navigate these complexities, they should align their long-term technology adoption strategies with both their internal capacities and the external ecosystem conditions to ensure the successful integration of new technologies into their business models. Executives should monitor ecosystem conditions that can affect their prioritized use cases to make decisions about the appropriate investment levels while navigating uncertainties and budgetary constraints on the way to full adoption (see the “Adoption developments across the globe” sections within each trend or particular use cases therein that executives should monitor). Across the board, leaders who take a long-term view—building up their talent, testing and learning where impact can be found, and reimagining the businesses for the future—can potentially break out ahead of the pack.

Lareina Yee is a senior partner in McKinsey’s Bay Area office, where Michael Chui  is a McKinsey Global Institute partner, Roger Roberts  is a partner, and Mena Issler is an associate partner.

The authors wish to thank the following McKinsey colleagues for their contributions to this research: Aakanksha Srinivasan, Ahsan Saeed, Alex Arutyunyants, Alex Singla, Alex Zhang, Alizee Acket-Goemaere, An Yan, Anass Bensrhir, Andrea Del Miglio, Andreas Breiter, Ani Kelkar, Anna Massey, Anna Orthofer, Arjit Mehta, Arjita Bhan, Asaf Somekh, Begum Ortaoglu, Benjamin Braverman, Bharat Bahl, Bharath Aiyer, Bhargs Srivathsan, Brian Constantine, Brooke Stokes, Bryan Richardson, Carlo Giovine, Celine Crenshaw, Daniel Herde, Daniel Wallance, David Harvey, Delphine Zurkiya, Diego Hernandez Diaz, Douglas Merrill, Elisa Becker-Foss, Emma Parry, Eric Hazan, Erika Stanzl, Everett Santana, Giacomo Gatto, Grace W Chen, Hamza Khan, Harshit Jain, Helen Wu, Henning Soller, Ian de Bode, Jackson Pentz, Jeffrey Caso, Jesse Klempner, Jim Boehm, Joshua Katz, Julia Perry, Julian Sevillano, Justin Greis, Kersten Heineke, Kitti Lakner, Kristen Jennings, Liz Grennan, Luke Thomas, Maria Pogosyan, Mark Patel, Martin Harrysson, Martin Wrulich, Martina Gschwendtner, Massimo Mazza, Matej Macak, Matt Higginson, Matt Linderman, Matteo Cutrera, Mellen Masea, Michiel Nivard, Mike Westover, Musa Bilal, Nicolas Bellemans, Noah Furlonge-Walker, Obi Ezekoye, Paolo Spranzi, Pepe Cafferata, Robin Riedel, Ryan Brukardt, Samuel Musmanno, Santiago Comella-Dorda, Sebastian Mayer, Shakeel Kalidas, Sharmila Bhide, Stephen Xu, Tanmay Bhatnagar, Thomas Hundertmark, Tinan Goli, Tom Brennan, Tom Levin-Reid, Tony Hansen, Vinayak HV, Yaron Haviv, Yvonne Ferrier, and Zina Cole.

They also wish to thank the external members of the McKinsey Technology Council for their insights and perspectives, including Ajay Agrawal, Azeem Azhar, Ben Lorica, Benedict Evans, John Martinis, and Jordan Jacobs.

Special thanks to McKinsey Global Publishing colleagues Barr Seitz, Diane Rice, Kanika Punwani, Katie Shearer, LaShon Malone, Mary Gayen, Nayomi Chibana, Richard Johnson, Stephen Landau, and Victor Cuevas for making this interactive come alive.

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Top 11 Market Research Trends in 2024 to Keep an Eye Out for

Market research is a continually evolving sector that has helped brands, organizations, individual researchers, the market research industry, and academicians stay above the curve. Research functions only grew in a global economy that took a significant hit in 2020. 

If anything, focusing on conducting smarter, more efficient, and impactful research has been on the rise. No longer has research stayed primitive with long boring surveys sent to thousands of respondents hoping for a response. Market research has evolved because the importance of insightful data is now evident to everyone. 

From transactional data to customer data, position metrics to consumer research, and opinion research to academic research, differentiating between the right data and noise has become extremely important. 

Therefore, conducting smarter research is now the need of the hour. In a mostly open-ended ecosystem, it is tough to get a consensus. Still, surprisingly enough, most researchers, brands, and facilitators of research agree on emerging trends to keep an eye out for in 2023 and beyond.

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We have listed the research trends that we think will be the most defining factors for the global market research market in 2023 and beyond.

Top 11 Market Research Trends in 2023 to Keep an Eye Out for

Data can be construed differently by different people. What’s unchallengeable is that there is now a technological advancement in collecting and analyzing data. 

Social media plays a vital role in gathering sentiments; there is an advancement in psychological, economic, and scientific knowledge in data collection. These factors have led to market research evolution, and 2023 will see a marked shift in collecting transactional data. 

In no particular order you should keep an eye out for these research trends in 2023 and beyond to fortify your decision-making ability.

Smarter, shorter surveys

This trend gets talked about a lot, but the truth is that most researchers and research tools do not allow the ability to conduct smarter surveys. However, at the turn of 2020 and beyond, collecting the right data with good survey response rates and honest feedback is becoming increasingly important. 

Capturing transactional data at the point of experience with a smart, short, and efficient survey helps with better data collection and consumer insights. Negating demographic data collection when that information is already available and using smarter research questions at the point of experience will help reduce survey fatigue and collect data that matters.

For example, to collect choice-based data that is a cornerstone of most research projects, adding an anchored MaxDiff question in your choice-based scaling will allow you to get deeper insights without having to conduct multiple follow-up studies. 

Similarly, deploying omnichannel surveys at various touchpoints helps you collect the correct transactional data without compromising data quality.  

DIY in-house research 

For a long time, research has been considered a complex and labor-intensive process requiring extreme special qualifications to get right. In 2023, there will be a marked shift towards DIY in-house research with smarter research tools .

Simple to understand and deploy platforms without the need for complex scripting and powerful research questions is the way forward.

Conducting market research tools is the ability to collect quantitative and qualitative data with the bonus of having a good respondent base in one location. Gone are the days when each component required going to different platforms.

This will make the management of market research easier for all the stakeholders. DIY research and do-it-together (DIT) research, where the research tool provider chips with specialized research service

Longitudinal tracking

One of the biggest market research trends going into 2023 and beyond will be longitudinal studies and tracking. For far too long, research has relied on broken-down respondent sets that do not help gather a macro-level view of data and insights.

With the help of community management platforms , longitudinal research helps track behavior changes and derive market research industry trends in between external and internal factors on research and insights.

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Not just that, longitudinal tracking also helps with quicker and faster turnaround research from members who matter the most to you and insights you can trust and co-create with. Having a ready set of opt-in research-ready respondents can help with ongoing monitoring studies and expedite your experience transformation initiatives. 

Online qualitative research

One market research trend that has been a definite by-product impact of COVID-19 is online qualitative research. With limitations on in-person focus groups and other qualitative research methods, migrating this model online for continued tracking is the only option.

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But now that online focus groups are making an in-road in research models for research managers globally, there is a definite shift in understanding its value and importance. From using the right tool to manage data collection to gamification and digital rewards, moving your offline focus groups online has multiple benefits.

The ability to moderate video discussions while collecting data from a diverse target audience limited by geographical boundaries is a huge bonus.

Quality data collection

In a recent live video I did with the VP of Products at QuestionPro, Anup Surendran, we spoke about how hyper-personalization will help with better-quality data and insights. Going into 2023 and beyond, with the increase in the number of surveys a person is expected to respond to, standing out and collecting quality data will be challenging.

Personalizing data collection at the point of experience with intercept studies and smarter shorter surveys helps with data collection.

High-quality survey data reduces the time to market and aids insights management at costs that don’t cost an arm and a leg. Putting together studies that aid with high-frequency research and non-intrusive hyper-personalization will see higher levels of success going into 2023 and beyond. 

Instant responses 

Heading to a meeting and want some quick insights? Cannot choose between two names for a new product line and want quick responses?

Who wouldn’t like immediate insights from a group of respondents when you are stuck somewhere? In 2023, there will be a marked shift towards high-cadence, high-frequency studies with very high turnaround times. 

Small surveys or polls that are set up and deployed within minutes and answers and analysis within minutes are the future of market research. Using a smart product that allows you to tap into a pre-defined and mobile-ready respondent population, the scope for quick turnaround studies is very high.

Using your existing technology stack and internal communication tools, and CRM, the use cases would be limitless if you could create and push out studies that offer you responses representing a larger population. 

We at QuestionPro are building a mechanism that provides you with quick turnaround response times for short studies using our proprietary global panel of respondents to help you go from perceptions to decisions within minutes. 

Non-intrusive transactional studies

The biggest pet peeve of people not wanting to participate in research studies and respond to surveys is the many pointless questions and many such surveys.

But what if you could gather customer delight with a customer experience platform that leverages non-intrusive transactional studies that are highly engaging, short, and compelling to respond to? 

There will be a marked shift towards smarter customer engagement and monitoring studies that do not require respondents to fill in pointless surveys that include demographic data and repetitive information and can help manage customer satisfaction and legitimate NPS scores.  

With the help of smart intercept studies, scores from across various touchpoints help draw a better understanding of customer delight and help show vectors and factors that could lead to customer churn. 

Emotive surveys

One of the biggest trends in market research that I am most excited about is the added use of technology to capture respondent behavior and data with various factors like facial recognition and contactless surveys . 

Surveys that help you capture customer insights where respondents do not have to interact with an external device and sentiments directly are captured just by facial and visual cues will be extremely important going into 2023 and beyond.

A simple question, for example, at a point of experience where facial recognition captures respondent sentiments will go a long way in offering mature insights for brands.

Crowdsourced research 

Crowdsourcing in market research helps solve problems at scale with lower costs and makes it to our top 11 market research trends to keep an eye out for 2023. Since crowdsourcing uses the quali-quant method, the insights you can get from a diverse audience base are tremendous. 

Not only can it help solve problems, but it also helps to get an idea of a more extensive and varied population. The only drawback of this method is that for a brand to effectively leverage this model, there has to be an advantage of scale, which increases crowdsourcing’s success and feasibility.

Social listening 

Due to a large social media footprint, it is becoming increasingly important to listen to social media’s happening and derive sentiments from conversations. Since social media is highly emotive, there is a broader scope to get to the root of ideas, complaints, purchase behavior, macro, and micro trends, etc. 

Using social listening, you can isolate the data you care about and look for short-term indicators around consumer research. This is the future of ethnographic research. While social listening doesn’t make sense for all market researchers and brands, if done well, it can help to identify actionable vectors at a very early stage. 

AI-assisted data collection

Two common themes arose from market research in 2020 – personalization and convenience. Using AI-assisted data collection helps in advancing research for brands and researchers. The right data going to the right stakeholders in real time define the success metrics of market research. 

By limiting human interactions but increasing milestones and points of experience-based smart research, there is greater success in collecting the right data. This helps focus on deriving insights from the data and not on the data collection itself.

For long-form data and open-ended data, AI-based research and sentiment analysis help derive insights from text-based data without intervening and manually interpreting it.

 How QuestionPro helps in market research trends?

QuestionPro is a comprehensive online survey and market research platform that provides a variety of features and tools to help organizations remain ahead of market research trends. Here’s how QuestionPro can assist you:

Advanced survey creation

QuestionPro makes developing surveys with skip logic, several question kinds, and advanced customization easy. This flexibility lets researchers build surveys that follow market research trends and best practices.

Mobile-friendly surveys

QuestionPro lets researchers design mobile surveys. Given the rising trend of mobile survey participation, this assures a smooth and easy survey experience for respondents.

Automated survey distribution

QuestionPro distributes surveys via email, social media, and embedded surveys. These distribution choices help researchers to contact more respondents through digital media.

Advanced analytics and reporting

QuestionPro has comprehensive analytics and reporting tools that allow researchers to examine survey data and provide actionable insights. Researchers can use trend analysis, cross-tabulations, and statistical analysis tools to find patterns, trends, and correlations in data.

2023 will be a huge year for market research, and we are excited to play a pivotal role in making research easy and accessible to everyone. What market research trends are you looking forward to, and are you keeping an eye out for going into 2023 and beyond?

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By examining market trend data, investors can have a better understanding of how various assets have performed over time and make more informed decisions about where to put their money.

Artificial intelligence is the market research trend. It saves time and money and may increase data quality. In fact, 31% of researchers and brands believe an automated data quality solution can alleviate multiple business difficulties.

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Navigating The Future: 10 Global Trends That Will Define 2024

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We’re approaching the mid-point of a decade in which we’ve already seen significant global transformation. War, pandemic, economic turbulence, and shifts in political power both within nations and at the international level mean the world is quite different than it was at the start of 2020.

These major trends will continue to reshape society, and we can only expect the pace of change to increase. For many, environmental concerns are clearly at the top of the agenda, and the need to mitigate the impact of climate emergency will bring profound change.

At the same time, powerful and often frightening new technologies that we’re told have the potential to bring significant benefits to society, as well as cause unprecedented harm, will be a catalyst for further transformations.

With this in mind, here’s a rundown of what I believe will be the most significant global trends in 2024. These are issues that will impact the lives of everyone on the planet, and how they play out will have profound implications for the second half of the decade and beyond.

The Rise Of Intelligent Machines

In 2024, artificial intelligence (AI) is a part of everyday life and virtually no industry or aspect of our lives is untouched by it. While it’s undoubtedly driving innovation and creating efficiencies in fields as diverse as healthcare, space travel and ecological conservation, it’s also causing a fair amount of fear and uncertainty. The threat to jobs is real – although it will undoubtedly create new opportunities, just as it creates redundancies. There are also concerns that handing over control of our lives to algorithms can exacerbate divisions and inequality in society. In truth, no one knows where the AI revolution will take us as a society or as a species, but our actions in 2024 will be critical to setting us on a path that leads to a happy outcome.

Best High-Yield Savings Accounts Of 2024

Best 5% interest savings accounts of 2024, climate change increasingly a political issue.

If we follow the science, it’s clear that the urgency of averting the catastrophic effects of climate change is rapidly escalating as we enter 2024. Often, we are counting on technology to play a critical role, and innovations like clean energy and carbon capture will be part of the solution. However, the willingness of individuals and organizations to take responsibility, as well as the way that the political and economic trends mentioned here play out, will probably be even more critical. How much pain people will be willing to take on in order to reduce their environmental footprint will become an increasingly contentious issue in politics. 2024 represents a critical opportunity to find out whether the will exists to make changes and tough decisions needed to avoid some very nasty shocks in the near future.

Elections Will Determine The Course Of Democracy In The Second Half Of The Decade

Elections bring the opportunity for change, and 2024 will see leadership contests in a number of countries where a swing in the balance of power could have profound global implications. Citizens in the US, European Union, India, UK and Russia will be among those taking to the ballots (with varying degrees of opposition to incumbent powers.) In many of these nations, there is growing polarization between progressive and conservative, or nationalist and internationalist parties and voters. Victory is likely to embolden the winners – whatever side of the divide they occupy – to believe they have a remit to enact further social change. Whichever way the cookie crumbles, this is likely to impact the course of every other trend on this list in 2024 and throughout the second half of the decade.

Turbulent Times For Economies

A continuing slowdown in global economic growth is predicted for 2024 , threatening widespread knock-on effects on many aspects of society. Hard economic times typically result in governments choosing to reduce spending on public services and utilities, job cuts, a reduction in living standards and a growth in civil unrest. Slow growth also threatens national and international efforts to hit carbon net zero targets, which could have severe consequences. The possibility of a recession in the USA, a slowdown in China’s growth, and the ongoing conflicts in Ukraine and Israel are all factors. At the same time, growth in emerging countries, including Brazil, India, Mexico and Turkey, will lead us into an era where we will witness drastic changes in the overall balance of global economic power.

The Evolution Of Work

Changes to the way we work will continue to have an impact on many aspects of our lives and society. Although some companies are implementing back-to-office policies, remote and hybrid working remain at far higher levels than they were before the pandemic. This has the effect of improving global mobility, with workers no longer tied down to living in areas close to employment centers. However, it can also lead to increased social isolation and social cohesion. Managing this change will be an important challenge for organizations and individuals in 2024.

The Generation Gap

The gap between generations in terms of wealth and property ownership will continue to drive global and social change in 2024. According to research conducted in 2023 , the median wealth of millennials (born early eighties to late nineties) is less than half that of baby boomers (born mid-fifties to mid-sixties) at the same age. This could possibly lead to reduced social mobility as well as political polarization, bringing with it the danger of disenfranchised voters being drawn to populist or extremist political parties.

Ongoing Urbanization

By 2050, the UN projects that 66 percent of the world’s population will live in urban areas – up from 56 percent in 2022. While this has the potential to drive economic growth and prosperity, it also brings other challenges, such as overcrowding, pollution and increased cost of living. Tackling the impact of this huge change in many people’s way of life will be a priority for governments and industry in the coming years. Resources will also be needed to mitigate the effects of the brain drain on those who stay behind, many of whom are already underserved by essential services like power, healthcare and online connectivity.

Culture Wars

The term culture war refers to an ongoing polarization of society, often characterized by a left versus right or liberal versus conservative debate and largely conducted via social as well as what is increasingly called legacy media. The impact of this on society is clearly driven by the emergence of the internet as a tool that can be used to find information, including disinformation and propaganda. Much has been written in recent years about the echo chamber effect of online discourse in an ecosystem ruled by algorithms. Increasingly, we see audiences steered towards content that’s likely to confirm their biases while also inflaming feelings of injustice or inequality. Issues such as immigration, conspiracy theories and social justice stir heated feelings on both sides of the debate, but it isn’t just idle chit-chat. Divisive views spread via social media increasingly inform political policy, as can be seen from the rise of populist parties and policies around the globe, and even stoke extremist terrorism .

Rethinking Education

Gone are the days when education was only for the young. Work is changing, so the models of learning needed to prepare for work are changing, too. The speed of technological innovation means opportunities are opening up in industries that didn’t even exist when much of today’s workforce was at school. In advanced nations, there’s a shift towards lifelong learning, partly enabled by the emergence of online and remote learning technology. Employers will increasingly recognize the importance of reskilling and upskilling valuable workers, particularly as longer lifespans and later retirement lead to an older workforce. In emerging economies, we will see a growing demand for teachers as more of the population moves out of poverty. Again, new models of delivering education will be needed to serve the citizens of crowded megacities as well as children in remote rural areas.

Migration And Movement

Between 1970 and 2020, the number of people living in a country other than the one they were born in more than tripled . In 2024, some will be refugees fleeing war, some will be economic migrants in search of a better life, and some will be looking to escape to parts of the world where life is not yet overly disrupted by rising temperatures and sea levels. Economies will continue to benefit from an influx of mostly young, able-bodied and active workers. And fears of the strain that could be put on utilities and public services, or the impact of new arrivals on indigenous populations, will continue to fuel political division. In advanced economies, the offer of jobs, visas and education opportunities will increasingly be used to plug the skills gap and in trade negotiations with nations with emerging consumer markets.

You can read more about future tech and business trends in my books , The Future Internet: How the Metaverse, Web 3.0, and Blockchain Will Transform Business and Society , Future Skills: The 20 Skills And Competencies Everyone Needs To Succeed In A Digital World and Business Trends in Practice , which won the 2022 Business Book of the Year award. And don’t forget to subscribe to my newsletter and follow me on X (Twitter ), LinkedIn , and YouTube for more on the future trends in business and technology.

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38 Scientists Helping Chart the Future of Biomedical Research

Pew’s 2022 scholars and fellows selected to explore the intricacies of health and medicine.

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Biomedical researchers are at the forefront of scientific innovation, seeking answers to the world’s most pressing questions in human health. For 37 years, The Pew Charitable Trusts has encouraged these pursuits by supporting promising, early-career biomedical scientists tackling these challenges and driving medical breakthroughs.

This year, a total of 38 researchers join the Pew Scholars Program in the Biomedical Sciences ,  Pew Latin American Fellows Program in the Biomedical Sciences , and Pew-Stewart Scholars Program for Cancer Research . They will receive multiyear grants to pursue scientific interests in the United States and Latin America at a time when biomedical science has never been more critical.

Infectious disease prevention and treatment

The COVID-19 pandemic put a spotlight on the immune system in the fight against infectious diseases.

Several members of the 2022 class are studying the intricacies of this essential defense mechanism. One scientist will explore how bacterial exposure changes the body’s circadian rhythm—the cellular clock regulating activities such as sleeping and eating—and influences immune system function, particularly in how the body fights infections. Another will investigate how parents pass on disease-fighting knowledge to their children through what is known as “immune memory,” a process that can enhance their offsprings’ responses when exposed to the same pathogen. And another scientist will study how structural changes in cell surface molecules drive white blood cells to curb infections.

A separate group will investigate how to prevent and treat certain diseases. One scientist will study how immune cells respond to viral hemorrhagic fevers, while another will examine how Ebola enters host cells. Others will explore a new, promising class of antibiotics that targets the production of essential bacterial proteins, as well as a potential “universal vaccine” for rapidly mutating viruses such as HIV, SARS-CoV-2, and influenza.

Wonders of the brain

From walking and talking to sleeping and socializing, the brain is critical to the human body’s most essential tasks. But much is still unknown about how this complex organ coordinates with other bodily systems.

Scientists in this year’s class will explore how the brain and gut work together to ensure that the body has needed nutrition, and also which neurological processes are at play when individuals experience physical pain. One researcher will use schooling fish to explore how the brain processes visual information to inform movement, while another will seek to determine how neurons detect pheromones to decipher social engagement cues.

Because the brain controls functions from learning and memory to social behaviors, disturbances to the neural system can cause dire consequences. Some scientists will investigate mechanisms that contribute to neuronal dysfunction and potential strategies to keep this from occurring. For example, one researcher will explore how memory-encoding cells that are activated by negative experiences affect the development of Alzheimer’s disease. Another will examine how mutations in enzymes involved in protein clearance disrupt neuronal function and survival.

Decoding cancer development

Cancers develop when abnormal cells proliferate uncontrollably and spread throughout the body unchecked. The 2022 class is studying different elements of this complex disease—exploring mechanisms that drive cancer initiation and novel strategies to control its progression.

Cancer cells are highly reliant on the metabolic process for the energy they need to grow and spread. One researcher will examine the dependence of cancer cells on the lysosome—a specialized cellular structure—to fuel their demand for nutrients. Another will build a novel system to study how cancer cells adapt to new metabolic pathways to evade therapy. And one is looking at the compounds produced in the breakdown of nutrients used by acute lymphoblastic leukemia cells for clues about how treatment resistance mechanisms arise.

Researchers also are studying different aspects of the immune system response to cancer. One class member is examining how sensory neurons regulate an immune response to lung cancer, while another is investigating the effect a new immunotherapy has on T-cells and their ability to attack skin cancer.

Finally, a group will explore the role of genetic variations in cancer. Members of the 2022 class will study how mutations in a cell receptor that binds sugars contributes to the formation of aggressive cancers and also how accumulation of mutations promotes the risk for blood cancer development. Another researcher will look at how genetic variation can help protect us from cancer—using wolves in the Chernobyl Exclusion Zone, surrounding the failed nuclear power plant, as a novel case study.

Unraveling the human genome

The genome is a tightly packed set of instructions that determines everything from peoples’ physical traits to their behavioral tendencies. Careful regulation is needed to maintain the integrity of our genetic material and prevent a myriad of human diseases.

Members of the 2022 class are studying mechanisms that regulate proper gene expression. One will examine how DNA structural rearrangements are coordinated, while another will study how some novel and overlooked structures within the genome contribute to the maintenance of our genetic code. And one researcher will examine specialized genome sequences to understand how variations of gene products arise.

Researchers are also investigating the cellular machines that help accurately distribute and separate chromosomes during cell division, as well as how these specialized complexes ensure that DNA in the mitochondria—the powerhouses of the cell—is replicated with fidelity. Finally, some scientists are studying how disruptions or defects in proteins that help safeguard and maintain the genome contribute to the development of blood and bone cancers. 

Intestinal health and disease

The gut is home to trillions of bacteria, viruses, and fungi that coexist in a community, also known as the microbiome. Some components of the microbiome are present at birth, but exposure to environmental elements and diet as we age diversifies the makeup of this community.

One class member will explore how different lipids in human milk are processed by gut bacteria to promote health in babies. Another will examine how early antibiotic exposure can disrupt the composition of the microbiome and how these changes may contribute to weight gain later in life.

Gut health can be influenced by other factors as well. A member of the class will explore how brain signals may impact intestinal inflammation. Another scientist will investigate how factors such as age can alter nutrient transport by intestinal cells and lead to the development of metabolic and intestinal disturbances in the elderly.

Kara Coleman is the project director and Jennifer Villa is an officer with The Pew Charitable Trusts’ biomedical programs.

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9 Trends That Will Shape Work in 2024 and Beyond

• Emily Rose McRae,
• Peter Aykens,
• Kaelyn Lowmaster,
• Jonah Shepp

Looking ahead at a year of continued disruption, employers who successfully navigate these trends will be able to create a competitive advantage.

In 2023, organizations continued to face significant challenges, from inflation to geopolitical turmoil to controversy over DEI and return-to-work policies — and 2024 promises more disruption. Gartner researchers have identified nine key trends, from new and creative employee benefits to the collapse of traditional career paths, that will impact work this year. Employers who successfully navigate these will retain top talent and secure a competitive advantage for themselves.

In 2023, business leaders and organizations continued to contend with major shifts affecting the workplace, including the pressure of inflation on both employer and employee budgets, the emergence of generative AI (GenAI) , geopolitical turmoil, a series of high-profile labor strikes , increased tension over return-to-office (RTO) mandates , a shifting legal and societal landscape for DEI initiatives, the increased impact of climate change , and more.

• Emily Rose McRae is a senior director analyst covering the future of work and workforce transformation, and she leads the talent research initiative for executive leaders. Emily Rose works across all issues related to the future of work, including emerging technologies and their impact on work and the workforce, new employment models, and creating an enterprise-wide future of work strategy.
• Peter Aykens is a distinguished vice president and chief of research for the Gartner HR practice. He is responsible for setting the practice’s research agenda and strategy to address the mission critical priorities of HR leaders, including leadership, talent management, recruiting, diversity, equity and inclusion (DEI), total rewards, learning and development, and HR tech.
• Kaelyn Lowmaster is a director of research in the Gartner HR Practice. She focuses on the Future of Work including all areas of future strategy development, with a core emphasis on the impact of emerging technology on work and the workforce.
• Jonah Shepp is a senior principal, research in the Gartner HR practice. He edits the Gartner  HR Leaders Monthly  journal, covering HR best practices on topics ranging from talent acquisition and leadership to total rewards and the future of work. An accomplished writer and editor, his work has appeared in numerous publications, including  New York   Magazine ,  Politico   Magazine ,  GQ , and  Slate .

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• Published: 19 April 2022

Focus Issue: The Future Of Cancer Research

Nature Medicine volume  28 ,  page 601 ( 2022 ) Cite this article

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New treatments and technologies offer exciting prospects for cancer research and care, but their global impact rests on widespread implementation and accessibility.

Cancer care has advanced at an impressive pace in recent years. New insights into tumor immunology and biology, combined with advances in artificial intelligence, nano tools, genetic engineering and sequencing — to name but a few — promise ever-more-powerful capabilities in the prevention, diagnosis and personalized treatment of cancer. How do we harness and build on these advances? How do we make them work in different global settings? In this issue, we present a Focus dedicated to the future of cancer research, in which we take stock of progress and explore ways to deliver research and care that is innovative, sustainable and patient focused.

This year brought news that two of the first patients with leukemia to receive chimeric antigen receptor (CAR) T cell treatment remain in remission more than a decade later . Writing in this issue, Carl June — who helped to treat these first patients — and colleagues reflect on how early transplant medicine laid a solid foundation for CAR T cell development in blood cancers, and how this is now paving the way for the use of engineered cell therapies in solid cancers. In a noteworthy step toward this goal, Haas and colleagues present results of a phase 1 trial of CAR T cells in metastatic, castration-resistant prostate cancer — a disease that has seen relatively few new treatment options in recent years.

Up to now, CAR T cells have been used only in the context of relapsed or refractory hematological malignancies, but in this issue, Neelapu et al . present phase 2 study data that suggest CAR T cell therapy could be beneficial when used earlier in certain high-risk patients. In addition, prospective data from van den Brink et al . support a role for the gut microbiome composition in CAR T cell therapy outcomes, highlighting new avenues of research to help maximize therapeutic benefit.

Although the idea that the gut microbiome influences CAR T cell therapy outcomes may be relatively new, it has been known for some time that it has a role in the response to checkpoint-inhibitor immunotherapy. A plethora of microbe-targeting therapies are now under investigation for cancer treatment; in this issue, Pal and colleagues describe one such strategy — whereby the combination of a defined microbial supplement with checkpoint blockade led to improved responses in patients with advanced kidney cancer. In their Review, Jennifer Wargo and colleagues take stock of the latest research in this field, and predict that microbial targeting could become a pillar of personalized cancer care over the next decade.

The theme for this year’s World Cancer Day was ‘Close the care gap’ — a message that is woven through several pieces in this issue. Early detection strategies have enormous potential to make a difference in this area; reviewing the latest advances, Rebecca Fitzgerald and colleagues ask who should be tested, and how — and outline their vision for personalized, risk-based screening, keeping in mind practicality and clinical implementation. Journalist Carrie Arnold reports on an emerging strategy known as ‘theranostics’ that aims to both diagnose and treat cancers in a unified approach, highlighting the growing commercial interest in this field. Of course, commercial interest does not equate to widespread availability or equal access to new therapies, and increasingly sophisticated technologies — although beneficial for some — can serve to widen existing inequalities.

Pediatric cancers lag far behind adult cancers in terms of drug development and approval. Nancy Goodman, a patient advocate whose son died from a childhood cancer, argues that market failures are largely to blame for the gap — but that legislative changes can correct this. Although in some cases there is a strong mechanistic rationale for testing promising adult cancer therapies or combinations in children, translational research is also needed to identify new therapeutic targets — such as the approach taken by Behjati and colleagues , which sheds new light on the molecular characteristics of an aggressive form of infant leukemia.

Meanwhile, for adult cancers, countless new therapeutic modalities are on the horizon , and drug approvals based on genomic biomarkers have accelerated in recent years. Unfortunately, their implementation into routine clinical care is progressing at a much slower pace. In their Perspective, Emile Voest and colleagues point out that bridging this gap will require investment in health infrastructure, as well as in education and decision-support tools, among other things.

Perhaps the most striking gap is that between high-income countries and low- and middle-income countries, not only in terms of cancer survival outcomes but also in terms of resources and infrastructure for impactful research. In their Perspective, CS Pramesh and colleagues outline their top priorities for cancer research in low- and middle-income countries, arguing that cancer research must be regionally relevant and geared toward reducing the number of patients diagnosed with advanced disease. Practicality is key — a sentiment echoed by Bishal Gyawali and Christopher Booth, who call for a “ common sense revolution ” in oncology, and regulatory policies and trial designs that serve patients better.

To realize this goal, clinical trial endpoints and outcome measures should be designed to minimize the burden on patients and maximize the potential for improving on the standard of care. This should go beyond survival outcomes; systemic effects, including cachexia and pain, have a major impact on quality of life and mental health during and after treatment. Two articles in this issue highlight the enormous psychological burden associated with a cancer diagnosis; increased risks of depression, self-harm and suicide emphasize the need for psychosocial interventions and a holistic approach to treatment.

As noted by members of the Bloomberg New Economy International Cancer Coalition in their Comment , the widespread adoption of telemedicine and remote monitoring in response to the COVID-19 pandemic could, if retained, help to make cancer trials more patient centered. Therefore, as health systems and research infrastructures adapt to the ongoing pandemic, there exists an unprecedented opportunity to reshape the landscape of cancer research.

We at Nature Medicine are committed to helping shape this transformation. We are issuing a call for research papers that utilize innovative approaches to address current challenges in cancer prevention, detection, diagnosis and treatment — both clinical trials and population-based studies with global implications. Readers can find more information about publishing clinical research in Nature Medicine at https://www.nature.com/nm/clinicalresearch .

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Home / Blog

Trends and Skills for the Future of Research

May 7, 2019 

Technological advancements have made it easier than ever for organizations to access large amounts of data. However, with this information overload comes the challenge of managing, analyzing, and reporting on the data. Organizations are increasingly relying on professional researchers and research analysts to turn these large amounts of data into information they can use to make strategic decisions that will positively impact business operations.

Career Outlook for Researchers

Career opportunities in the research field are diverse and span a variety of industries. A social science researcher may focus on areas like healthcare and unemployment, conducting interviews and surveys to collect data for analysis. In a corporate environment, an operations research analyst can help his or her organization by reviewing business processes and identifying efficiencies, while a market research analyst may make production recommendations after examining consumer purchasing patterns.

The educational requirements for research jobs also vary by industry and the roles and responsibilities of the position. Most professional researchers have a bachelor’s degree in market research or a related field, such as a  Bachelor of Arts degree in Liberal Studies . Senior-level research positions typically require a graduate degree such as a master’s in business administration.

New Trends and Techniques for Researchers

Regardless of specific role or training, professional researchers need to understand the emerging trends and new techniques in this field to excel in their careers. Here’s what researchers should know about future research trends.

Predictive Analytics

Predictive analytics refers to a sophisticated form of analysis using current and historical data to forecast future outcomes. Although using analytics to draw predictions about the future is not a new practice, predictive analytics is at the forefront of data analysis because of the advanced techniques involved. Some of the tools used in this practice include machine learning, artificial intelligence, data mining, and statistical and mathematical algorithms. These advanced tools and models allow for the creation of more accurate and dependable future predictions of trends, behaviors, and actions.

Because accurate future studies and forecasting are essential to most business models, researchers with predictive analytics experience are in demand. The valuable information generated by predictive analytics can be used by organizations to make strategic decisions about operations and identify opportunities and risks. For example, the financial services sector could use this practice to forecast market trends or create credit risk reports. Or government and law enforcement agencies may look to gather data about community crime and use that information to develop proactive safety measures.

Researchers need to keep abreast of this cutting-edge form of analytics because of its increasing usage. According to a report by Zion Market Research, the predictive analytics market in 2016 was valued at approximately $3.49 billion and is expected to continue to grow.

Digital Tools

Advancements in digital tools continue to change the way researchers work. In fact, it can be a challenge for researchers to stay up-to-speed with the new resources available to them. Here are just a few digital tools and trends that support and simplify the work of researchers:

• Search faster and easier. Researchers can spend less time searching for the right information by using search engines and curator sites such as CiteULike, Google Scholar, and LazyScholar.
• Manage and share data. Code and data sharing are becoming more common among researchers, with sites like Code Ocean and Datahub providing data management, storage, and sharing.
• Manage references. Sites such as EndNote and CitationStyles help researchers electronically manage their bibliographies, citation styles, and references.
• Connect with fellow researchers. Sites such as Academia and Addgene help researchers get expert advice and identify opportunities to collaborate or share findings.

Data Visualization

From the widespread use of infographics in educational materials to storytelling on social media platforms through video and pictures, there is a clear trend toward more frequent visual communication in society. When applied to data analytics, visualization is the term often used to describe the practice of taking standard data and statistics and displaying them in a visually creative way.

Researchers who want their analysis effectively communicated should take note of this trend. For example, a simple research report that presents the findings in a large numerical spreadsheet may be hard to understand and confusing to the average person. If that same information was displayed in a graphic chart or by telling a story with images, readers would more likely have a clearer picture and understanding of the report’s main points.

Researchers who want to implement this trend in their practice should:

• Consider the visual options available — whether it’s an infographic, chart, or slideshow
• Focus on their audience and the key messages they need to convey
• Remember to ensure the visual will highlight the actual data instead of serving as a distraction

Are you interested in learning more about the research profession and the techniques involved in predictive analytics and data visualization? Explore the Marville University Bachelor of Arts degree in Liberal Studies , and learn how this online degree could be your first step to a new career as a research analyst.

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15 Future Market Research Trends You Should Know

by Daniela Maria Zabrautanu

July 24 2024.

The global market research industry generates over $118 billion in annual revenue and the United States holds a 53% share of this market. Reflecting on the beginning of the year and the various predictions about crucial trends in market research for 2024, several companies, including Resonio, ReconMR, and TechCrunch, anticipated that artificial intelligence would be a major game-changer. Let’s now examine how these forecasts have developed.

1. Shifts in Research Methods

Shifts in research methods have become paramount.

Innovative techniques have emerged over the past few years. They allow companies to gather and analyze unprecedented amounts of data, making it crucial for market researchers to adapt swiftly. Additionally, advances in AI and machine learning have exponentially enhanced the accuracy and efficiency of data collection.

Traditional surveys are far from obsolete.

Now, researchers can combine them with techniques such as sentiment analysis and predictive analytics for more holistic insights. These approaches provide multifaceted understanding, blending quantitative data with nuanced qualitative insights, and utilizing key metrics for comprehensive analysis.

Industry leaders in 2024 have confidently embraced hybrid methodologies. Their effectiveness in yielding a comprehensive view of consumer behavior, alongside leveraging real-time data, has made them indispensable. By adopting these innovative research methods, they are staying ahead of the competition and driving exceptional, data-driven decision-making.

2. Impact of Digitalization

Digitalization has revolutionized market research, transforming how data is collected, analyzed, and utilized to inform strategic decisions. This transformation, driven by advancements in artificial intelligence (AI) , machine learning, and big data analytics, allows for more sophisticated and accurate insights than ever before.

Moreover, digital tools enable real-time data collection and analysis, ensuring that companies can rapidly adapt to market changes and consumer preferences. This agility is critical in today’s fast-paced business environment, where staying ahead of market trends can significantly impact success.

The shift from traditional to digital methods exemplifies the ongoing evolution in market research.

Role of Big Data

Big data has become a cornerstone in the field of market research due to its ability to provide deep insights that were previously unattainable.

Companies using big data analytics see an average 15% increase in ROI within the first year.

With big data, researchers can now analyze vast amounts of information quickly, uncovering patterns and trends that inform strategic business decisions. This capability enhances the accuracy and reliability of their insights, ultimately leading to more effective market strategies.

Embracing big data enables businesses to stay competitive and innovative. By leveraging sophisticated analytical tools, they can predict market dynamics and customer behavior with unprecedented precision, fostering sustained growth and success.

3. Advancements in AI

The artificial intelligence (AI) market is experiencing rapid growth, with its global value projected to soar from $208 billion in 2023 to $2 trillion by 2030 ( Source : Statista ). This surge is transforming various industries, including market research.

AI’s strides have captivated the market research landscape, transforming it with unprecedented insights. Intelligent algorithms can now analyze vast datasets swiftly, revealing patterns and trends with remarkable accuracy . This evolution empowers researchers to predict consumer behavior with unparalleled confidence and precision, enhancing strategic decision-making. The future of market research is undeniably intertwined with AI, heralding an era where data-driven intelligence shapes every business strategy.

Sentiment Analysis and Text Mining

Sentiment analysis and text mining revolutionize market research. They transform vast amounts of unstructured data into actionable insights.

In 2016, sentiment analysis—an advanced text-mining technique—gained prominence, enabling brands to gauge consumer emotions and preferences more meticulously. This crucial development allowed businesses to tailor their strategies effectively.

Today, it’s not merely about understanding emotions; it’s about creating a nuanced narrative of consumer interactions. Advanced algorithms enable the extraction of precise sentiments, providing deeper insights into consumer behavior and improving engagement.

The evolution of these technologies this year has been astounding. Integrating AI advancements with text mining, they’ve set the stage for real-time analytics, predictive modeling, and proactive consumer engagement strategies.

Organizations leveraging these tools are poised to lead, harnessing rich, data-driven insights on consumer sentiment.

What Challenges and Opportunities Lie Ahead?

• Navigating the dynamic landscape of market research trends.
• Sifting through immense volumes of big data to extract meaningful insights.
• Staying updated with technological advancements, such as AI and machine learning, requiring continuous learning and adaptation.
• Addressing rising consumer data privacy concerns, necessitating stricter data handling protocols.

Opportunities

• Transformative technology enhances predictive accuracy.
• AI-driven tools empower researchers to forecast trends with unprecedented precision.
• Advanced metrics allow for more effective measurement and analysis of data.
• Proactive strategizing enables businesses to anticipate consumer needs and preferences.
• Real-time data analytics fosters a deeper, more immediate understanding of market shifts.

Send yourself a research incentive

4. ai and machine learning in consumer insights.

Artificial intelligence (AI) and machine learning (ML) are transforming the landscape of consumer insights, offering unprecedented capabilities to analyze and interpret vast amounts of data. These technologies enable businesses to gain a deeper understanding of consumer behavior, preferences, and trends, leading to more informed decision-making and strategic planning.

Benefits of AI and ML in Consumer Insights

• Enhanced Data Analysis : AI and ML algorithms can process large datasets quickly and accurately, identifying patterns and trends that might be missed by traditional methods.
• Predictive Analytics : These technologies can forecast future consumer behaviors based on historical data, allowing businesses to anticipate market changes and adjust strategies accordingly.
• Personalization : AI and ML enable highly personalized marketing efforts by analyzing individual consumer data to tailor recommendations and communications.
• Efficiency : Automating data analysis reduces the time and resources required, allowing researchers to focus on strategic tasks.

Applications of AI and ML in Market Research

Case Study: AI-Driven Consumer Insights with Answer Rocket

AnswerRocket’s AI-driven analytics platform addressed the challenges faced by Food Service Rewards™, a prominent loyalty program in the wholesale rewards industry. By replacing their outdated data analytics system with AnswerRocket’s advanced solution, Food Service Rewards™ was able to overcome significant bottlenecks in data access and gain immediate insights into customer behaviors. This transformation enabled the company to enhance its promotional strategies and better manage customer churn, illustrating the impactful role of AI in modernizing data-driven decision-making.

• Outdated traditional data analytics platform that required technical expertise to query data.
• Bottlenecks in data retrieval causing delays in receiving insights.
• Difficulty in understanding customer churn and purchasing behavior.
• Need for faster insights to create effective promotions and minimize churn.
• Implementation of AnswerRocket’s augmented analytics platform.
• Enabled natural language queries for all users, regardless of technical skill.
• Provided instant insights into customer purchasing behavior.
• Allowed the team to show critical data insights directly on Zoom calls.
• Significant time savings, with hours saved each week due to faster data insights.
• Enhanced ability to create targeted promotions based on customer buying patterns.
• Improved visibility into customer purchasing behavior, allowing for better management of promotions and reduced churn.
• Competitive advantage gained through better understanding of customer purchase trends.

Identified Key Issues:

• Inaccessibility of data insights due to reliance on technical specialists.
• Delay in insights delivery impacting timely decision-making.
• Lack of visibility into customer churn and purchasing behavior affecting promotion effectiveness.

Related : Market Research Incentives : Motivate and Engage

Future Trends in AI and ML for Consumer Insights

• Real-Time Analytics : Increasing use of real-time data processing to provide immediate insights.
• Voice and Image Recognition : Leveraging AI to analyze voice and image data for deeper consumer understanding.
• Ethical AI : Developing AI systems that prioritize consumer privacy and ethical data use.

AI and machine learning are not just enhancing the capabilities of market researchers but are also paving the way for more dynamic and responsive business strategies. As these technologies continue to evolve, their impact on consumer insights will only grow, offering even more sophisticated tools for understanding and engaging with consumers.

4. Automation and AI

Automation and AI are revolutionizing market research, ushering in unparalleled efficiencies and accuracies. These technologies enable researchers to process massive data sets rapidly, detecting intricate patterns and trends with ease. As these advancements continue to evolve, their integration into market research promises even greater precision and insightful forecasting, driving informed, strategic decisions that elevate business success.

Efficiency and Cost Savings

In 2024, efficiency and cost savings remain pivotal in shaping successful market research strategies.

• Automation : Reduces manual labor and accelerates data processing.
• AI-powered tools : Enhance data analysis accuracy and insight generation.
• Cloud-based solutions : Enable scalable and cost-effective storage and computing.
• Self-service platforms : Allow businesses to conduct research without extensive external resources.

These innovations collectively minimize expenses while maximizing productivity.

Adopting these trends ensures businesses maximize their ROI in market research endeavors.

Ethical Concerns and Privacy

Ethical concerns and privacy issues have become paramount in market research.

• Data Collection Practices : Ensuring transparency and informed consent.
• Data Storage Security : Protecting personal information from breaches.
• Third-Party Data Sharing : Regulating and monitoring the exchange of data with partners.
• Anonymization : Implementing methods to anonymize data to protect user identity.
• Compliance with Regulations : Adhering to GDPR, CCPA, and other regional privacy laws.

Upholding these principles fosters trust and protects consumer rights.

Businesses must prioritize ethical guidelines to maintain integrity in their research practices.

Navigating these concerns confidently ensures sustainable and responsible market research.

5. Rise of Mobile Market Research

Mobile market research has rapidly transformed, revolutionizing data collection methodologies, making them more accessible and efficient. Businesses understand the value of real-time insights, harnessing mobile platforms to reach more diverse and geographically spread populations.

By leveraging advanced mobile technologies, research has become both instantaneous and ubiquitous. Participants are no longer confined to static locations, leading to a dramatic increase in participation rates. As a result, the insights garnered pass through a more authentic lens, reflecting genuine immediate consumer emotions and behaviors.

Benefits of Mobile-First Approaches

Adopting a mobile-first approach significantly enhances user engagement, offering unparalleled access to real-time data. Mobile platforms drive consistent interaction, providing a seamless experience for users worldwide.

This immediate access to data translates into quicker decision-making .

Furthermore, mobile-first strategies ensure that businesses remain agile.

Such flexibility is invaluable in rapidly changing markets and consumer landscapes.

Another notable benefit is the capacity to capture data across diverse demographics , ensuring inclusivity. This holistic approach enables researchers to develop well-rounded, actionable insights.

Ultimately, the shift to mobile-first isn’t merely an adaptation; it’s a transformation. Forward-thinking enterprises leverage this evolution to maintain a competitive edge and foster deeper connections with their audience.

6. Increased Use of Video in Qualitative Research

A study by Take Note revealed that 93% of market researchers are now utilizing online and video focus groups more frequently compared to three years ago.

The year 2024 has seen a remarkable rise in the adoption of video interviews or focus groups, revolutionizing the landscape of market research.

Researchers have found that video interviews offer richer, more nuanced insights than traditional methods.

With this innovative approach, participants can convey their responses candidly, capturing subtleties such as tone, facial expressions, and body language, which greatly enhance the depth of data collected.

Businesses leveraging video interviews stand to gain a competitive edge as they can tap into a more comprehensive understanding of their consumers. By embracing authenticity, they can build stronger, more genuine connections.

Related : Market Research Participant : Everything You Need To Know

7. Harnessing Social Media

In 2024, social media continues to be a cornerstone for market research. Projections indicate that by 2027, 5.85 billion people globally will be using social media ( Source : Statista ).

With billions of active users, platforms like Facebook, Instagram, and Twitter provide invaluable data, real-time insights, and user-generated content for analysts.

Harnessing social media allows researchers to tap into trends, sentiments, and behaviors, enhancing their strategies and decision-making.

Related : Market Research vs Marketing Research : What sets them apart?

Social Listening

Harnessing the power of social listening has become indispensable in 2024 market research.

• Real-time insights into consumer opinions.
• Enhanced predictive analytics for future trends.
• Immediate response to brand crises or positive engagement.
• Personalized marketing strategies based on consumer feedback.

These techniques help companies understand how their brand is perceived.

They can adapt swiftly to changes in consumer sentiment and preferences.

By 2024, these tools have evolved into critical components for shaping brand strategies.

8. Necessity for Agility in Market Research

In 2024, agility has become paramount.

Agile market research enables firms to respond more rapidly to changes. The need for speed and accuracy is driven by the fast-paced evolution of consumer behaviors, making it essential for companies to stay ahead. Consequently, businesses must adopt methodologies that allow quick pivots and immediate assimilation of new data to maintain competitive edges.

The benefits of agility in market research.

Shortened feedback loops provide timely insights into – not just what is happening – but why it is happening. This nimbleness fosters data-driven decision-making and enhances cross-functional collaboration.

The elevated landscape of market research necessitates innovative approaches and dynamic frameworks, reflecting the lessons learned throughout 2023 as agility proves indispensable. When companies embrace agility, they leverage the ability to swiftly incorporate insights, thus fostering more adaptive, responsive, and ultimately, successful strategies.

9. Addressing Data Privacy and Ethics

In today’s data-driven world, a commitment to upholding stringent ethical standards has never been more critical. Ensuring data privacy isn’t just a legal requisition but a central pillar of consumer trust. Forward-thinking organizations understand the intrinsic value of maintaining transparent and ethical data practices, fostering an environment where integrity in market research can thrive. This evolution marks a significant shift from merely complying with regulations to proactively championing data ethics, thereby fortifying long-term brand loyalty and societal trust.

GDPR Compliance

Maintaining GDPR compliance is essential for organizations to protect data integrity and consumer trust.

• Data Minimization : Only collect data necessary for specific purposes.
• Consent Management : Obtain clear and explicit consent from individuals.
• Privacy by Design : Integrate data protection from the outset of project development.
• Regular Audits : Conduct frequent audits to ensure compliance with regulations.
• Breach Notification : Promptly inform authorities and affected individuals in case of a data breach.

These practices not only ensure legal compliance but also enhance consumer confidence.

Adhering to GDPR helps organizations build a robust framework for handling personal data.

By prioritizing GDPR compliance, companies can establish themselves as trustworthy and responsible data stewards .

10. Managing Information Overload

Market research professionals face an escalating challenge in managing information overload.

In 2024, the exponential increase in data from myriad sources demands sophisticated strategies to filter and utilize relevant information.

Embracing advanced analytics and AI-powered tools can significantly streamline data processing, ensuring critical insights are not overlooked.

By investing in technology that automates data organization and highlights key trends , companies can maintain a competitive edge without being buried under excessive information.

Effective management of information overload is paramount for making timely, informed decisions in an ever-evolving market landscape.

Related : Market Research Tools : 8 Companies You Should Know

Tackling New Industry Competition

Source : Statista

The landscape of market research in 2024 presents a dynamic challenge with rising industry competition, but this also brings unique opportunities for growth.

Better tools and strategies have emerged to outperform competitors.

New entrants in the field should focus on leveraging cutting-edge technologies to analyze data, but also accentuate unique value propositions, differentiating themselves with expertise.

Experienced firms can further solidify their market position by nurturing creativity and fostering partnerships to adapt rapidly to changes. By prioritizing innovation and strategic alliances, these firms not only stay ahead but set new benchmarks in market research excellence.

11. Embracing Predictive Analytics

In 2024, embracing predictive analytics has become pivotal to shaping market research strategies .

Predictive analytics empower organizations with a forward-looking perspective, enabling them to anticipate market shifts and consumer behaviors. This proactive approach transforms raw data into actionable insights, fostering environments where strategic decisions are driven by precision and long-term vision.

Furthermore, the adoption of predictive analytics is not a singular event. It involves ongoing refinement and adaptation, as more sophisticated models and algorithms continuously evolve. Thus, organizations remain at the vanguard of industry developments, ensuring their strategies are always informed by the latest predictive capabilities.

By incorporating predictive analytics into their market research repertoire, companies can confidently navigate uncharted market territories and seize emergent opportunities . This strategic foresight not only safeguards their market position but also propels them towards unprecedented growth and innovation in an increasingly competitive landscape.

12. Adopting a Customer-Centric Approach

In 2024, prioritizing the customer has become a fundamental strategy within market research trends.

Customer-Centric Methods:

• Modern enterprises are embracing customer-centric methods to craft personalized experiences.
• This approach fosters deeper connections, retains loyalty, and enhances satisfaction.
• Centering operations around customer needs and preferences provides a competitive edge in saturated markets.
• Continuous engagement with real-time data and feedback mechanisms is essential.

13. Globalization in Market Research

The intricate fabric of the global market continues to interweave with evolving market research trends, presenting boundless opportunities and challenges for enterprises worldwide.

Globalization has amplified the importance of understanding diverse consumer behaviors across different markets.

With companies expanding their reach, the necessity for localized market insights becomes paramount. Shedding the one-size-fits-all approach , they embrace a portfolio of tactics tailored to regional nuances and preferences, enhancing their international footprint.

Moreover, technological advancements have bolstered real-time data collection and analysis, empowering firms to make informed decisions across borders rapidly. In this dynamic landscape, those who strategically harness this knowledge stand to gain unparalleled market advantages, transforming challenges into prospects for growth and innovation.

Related : Market Research Panels : Everything You Need To Know And More

14. Integration of VR and AR

The augmented and virtual reality (AR & VR) market is anticipated to achieve a revenue of $40.4 billion in 2024 . With an expected annual growth rate of 8.97% from 2024 to 2029 , the market is forecasted to reach a volume of $62.0 billion by 2029. The integration of virtual reality (VR) and augmented reality (AR) into market research is not just a futuristic concept anymore; it’s a dynamic and powerful reality in 2024. ( Source : Statista )

VR and AR technologies have revolutionized the way researchers gather and interpret data.

Augmented reality (AR) overlays digital information onto the real world, enhancing consumer experiences with additional product, service, or brand details. In market research, AR is a powerful tool for gathering real-time data on consumer behavior and preferences.

For instance, companies can use AR to create interactive displays in stores, providing extra product information and recommendations. Researchers can then collect data on consumer interactions, including engagement time and responses to specific messages. Notably, 71% of customers say they would shop more often if AR allowed them to try products before purchasing. ( Source : DataProt )

By creating immersive environments, they enable participants to interact with products and services in ways unprecedented before. These interactions (whether virtually or augmentedly experienced) provide invaluable, nuanced insights into consumer preferences.

15. Blockchain for Data Security

Blockchain revolutionizes data security in market research.

Integral to blockchain’s appeal is its immutable nature. Once data is recorded on a blockchain, it cannot be altered without consensus, providing a secure and transparent method for managing research data. Researchers, therefore, can rely on the integrity of the data, enhancing the credibility of their findings.

Trust is paramount in market research.

This technology ensures that data remains tamper-proof – an essential feature when dealing with sensitive information from diverse respondents. As such, companies leveraging blockchain can pledge the highest standards of data security.

Blockchain’s application in securing market research data illustrates the seamless intersection of technology and trust. As adoption grows in 2024, the benefits of blockchain’s robust security measures will become even more pronounced, paving the way for innovative, reliable data practices in market research.

Final thoughts on market research trends

The landscape of market research is undergoing a profound transformation, driven by emerging trends and technological advancements. From the integration of artificial intelligence and machine learning to the adoption of real-time analytics and ethical AI practices, the future of market research promises to be more insightful, efficient, and responsive than ever before. By embracing these trends, businesses can unlock deeper consumer insights, anticipate market shifts, and make more informed strategic decisions. As the field continues to evolve, staying abreast of these developments will be crucial for any organization aiming to maintain a competitive edge and foster sustainable growth. The future of market research is not just about collecting data but about harnessing the power of innovation to turn that data into actionable intelligence.

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What are the 3 main types of market research.

The three main types of market research are:

• Exploratory Research : This type of research is conducted to gather preliminary information that will help define problems and suggest hypotheses. It is often qualitative, involving methods such as interviews, focus groups, and open-ended surveys.
• Descriptive Research : This research aims to describe characteristics of a population or phenomenon. It is more structured than exploratory research and often involves quantitative methods such as surveys, observational studies, and data analysis to provide a clear picture of the market.
• Causal Research : Also known as explanatory research, this type investigates the cause-and-effect relationships between variables. It typically involves experiments and controlled studies to determine how changes in one variable impact another, helping to predict future trends and outcomes.

What are the 3 types of market trends?

The three types of market trends are:

• Short-term trends : These are temporary movements in the market that can last from a few days to several months. They are often influenced by current events, seasonal changes, or short-lived consumer behaviors.
• Intermediate trends : These trends span several months to a few years and are typically driven by broader economic cycles, technological advancements, or shifts in consumer preferences.
• Long-term trends : These are enduring changes in the market that can last for several years or even decades. They are usually shaped by significant societal shifts, demographic changes, or major technological innovations.

Which is a recent trend in market research?

A recent trend in market research is the increasing use of artificial intelligence and machine learning. These technologies enable researchers to analyze vast amounts of data more efficiently and accurately, uncovering deeper insights and patterns that were previously difficult to detect. This trend is revolutionizing the field by providing more precise predictions and enhancing decision-making processes.

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The World in 2030: Nine Megatrends to Watch

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Where will we be in 2030?

I don’t usually play the futurist game — I’m more of a “presentist,” looking at the data we have right now on fast-moving megatrends that shape the world today . But a client asked me to paint a picture of what the big trends tell us about 2030. And I’d say we do have some strong indications of where we could be in 11 years.

The directions we go and choices we make will have enormous impacts on our lives, careers, businesses, and the world. Here are my predictions of how nine important trends will evolve by 2030 — listed in order roughly from nearly certain to very likely to hard to say .

Nine Global Trends on the Horizon

Demographics: There will be about 1 billion more of us, and we will live longer. The world should reach 8.5 billion people by 2030 , up from 7.3 billion in 2015. The fastest growing demographic will be the elderly, with the population of people over 65 years old at 1 billion by 2030. Most of those new billion will be in the middle class economically, as the percentage of citizens in dire poverty continues to drop (a rare sustainability win). Even as the middle swells, however, the percentage of all new wealth accruing to the very top of the pyramid will continue to be a major, and destabilizing, issue. That said, the other megatrends, especially climate change, could slow or change the outcomes here.

Urbanization: Two-thirds of us will live in cities. The urbanization of our populations will increase, creating more megacities as well as small- and medium-size metropolises. Countervailing forces will include a rising cost of living in the most desirable cities. The effects will include the need for more big buildings with better management technologies (big data and AI that makes buildings much more efficient), and we will need more food moved in from where we grow it to where we eat it — or rapidly expand urban agriculture .

Transparency: Our world will become even more open — and less private. It’s hard to imagine that the trend to track everything will be going anywhere but in one direction: a radically more open world. The amount of information collected on every person, product, and organization will grow exponentially, and the pressure to share that information — with customers and consumers in particular — will expand. The tools to analyze information will be well-developed and will make some decision-making easier; for instance, it will be easier to choose products with the lowest carbon footprints, highest wages for employees, and fewest toxic ingredients. But all these tools will shatter privacy in the process.

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Climate Crisis: The climate will continue to change quickly and feature regular, extreme weather everywhere. Yes, there’s still uncertainty about how everything will play out exactly , but not about whether the climate is changing dramatically and dangerously. Significant inertia in both atmospheric and economic/human systems allows for a more confident prediction of what will happen in just 11 years. The Intergovernmental Panel on Climate Change (IPCC) has made clear how critical it is to radically alter the path of carbon emissions to hold the world to 1.5 degrees Celsius of warming. But that’s not likely to happen with current levels of commitment in global governments: The important Paris climate accord of 2015, in theory, agrees to hold warming to 2 degrees Celsius. But in practice, what countries have committed to so far will only hold us to no more than 3 degrees of warming . By 2030, we are very likely to already be at or approaching the 1.5 mark.

The results of climate change will be unrelenting. Many highly populated coastal areas will be in consistent trouble, as sea levels rise . The natural world will be much less rich, with drastic to catastrophic declines in populations of many species and major to total losses of ecosystems like coral . Droughts and floods will stress global breadbasket regions and shift where we grow major crops. The Arctic will be ice-free in the summer (this will allow ships to move freely in this region, which is technically good for shorter supply chains but a Pyrrhic victory at best). Between seas, heat, and shifts in water availability, mass migrations will likely have begun. By 2030, we will have much better clarity on how bad the coming decades after that point will be. We will know whether the melting of the major ice sheets will be literally inundating most coastal cities, and if we’re truly approaching an “ Uninhabitable Earth ” in our lifetimes.

Resource Pressures: We will be forced to more aggressively confront resource constraints. To keep volumes of major commodities (such as metals) in line with economic growth, we will need to more quickly embrace circular models: sourcing much less from virgin materials, using recycled content and remanufactured products, and generally rethinking the material economy. Water will be a stressed resource, and it seems likely that many cities will be constantly in a state of water shortage. We will need more investment in water tech and desalination to help.

Clean Tech: The transformation of our grid, our roadways, and our buildings to zero-carbon technology will be surprisingly far along. Here’s some good news: Due to continuing drops in the cost of clean technologies, renewable energy is dramatically on the rise , making up more than half the global new power capacity every year since 2015. By 2030, effectively no new additions of generating capacity will come from fossil-fuel-based technologies. Electric vehicles will be a large part of the transportation equation : While estimates about the share of EVs on the road by 2030 range from the teens to nearly 100% (assuming early retirement of internal combustion engines), nearly all sales of new vehicles will be EVs. This will be driven by dramatic reductions in the cost of batteries and strict legislation banning fossil-fuel engines . We will also see an explosion of data-driven technologies that make buildings, the grid, roadways, and water systems substantially more efficient.

Technology Shifts: The internet of things will have won the day, and every new device will be connected. Proponents of the “ singularity ” have long projected that by around 2030, affordable AI will achieve human levels of intelligence. AI and machine learning will plan much of our lives and make us more efficient, well beyond choosing driving routes to optimize traffic. Technology will manipulate us even more than it does today — Russian interference in U.S. elections may look quaint. AI will create some new kinds of jobs but will also nearly eliminate entire segments of work, from truck and taxi drivers to some high-skill jobs such as paralegals and engineers.

Global Policy: There’s an open question about how we’ll get important things done. I’m thinking specifically about whether global governments and institutions will be working in sync to aggressively fight climate change and resource pressures, and tackle vast inequality and poverty — or whether it will be every region and ethnic group for itself. Predicting politics is nearly impossible, and it’s hard to imagine how global policy action on climate and other megatrends will play out. The Paris Agreement was a monumental start, but countries, most notably the U.S., have lately retreated from global cooperation in general. Trade wars and tariffs are all the rage in 2019. It seems likely that, even more than today, it will be up to business to play a major role in driving sustainability .

Populism: The rise of nationalism and radicalism may increase … or it won’t. Even less certain than policy is the support, or lack thereof, of the mass of people for different philosophies of governing. In recent years, populists have been elected or consolidated power in countries as varied as the U.S., Brazil, and Hungary. And yet, in recent weeks, citizens in countries like Turkey, Algeria, and Sudan have pushed back on autocracy. Will that trend continue?

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How should business prepare.

Laying out strategies for companies to navigate this likely future world is a book-length conversation. But let me suggest a few themes of action to consider:

• Engage everyone in the sphere of the business world on climate. A dangerously changing climate is the biggest threat humanity has ever faced. But it’s not all set in stone … yet. Companies have an economic incentive and moral responsibility to work hard to reduce the damage as much as possible. Engage employees (stamp out climate denial), talk to consumers and customers about climate issues through your products, and change internal rules on corporate finance to make investment decisions with flexible hurdle rates that favor pro-climate spending. Most importantly, use influence and lobbying power to demand, at all levels of government, an escalating public price on carbon — and publicly admonish industry lobbying groups that don’t.
• Consider the human aspect of business more. As new technologies sweep through society and business, the change will be jarring. Those changes and pressures are part of why people are turning to populist leaders who promise solutions. Business leaders should think through what these big shifts mean for the people that make up our companies, value chains, and communities.
• Embrace transparency. To be blunt, you don’t have a choice. Each successive generation will expect more openness from the companies they buy from and work for.
• Listen to the next generation. By 2030, the leading edge of millennials will be nearing 50, and they and Gen Z will make up the vast majority of the workforce. Listen to them now about their priorities and values.

Predicting the future means projecting forward from what’s already happening, while throwing in expected inertia in human and natural systems. It can give us an impressionistic picture of the world of the future. Our choices matter a great deal, as individuals and through our organizations and institutions. Business, in particular, will play a large role in where the world goes. Employees, customers, and even investors increasingly demand that the role of business be a positive one.

Look, we could all just wait and see where these historic waves take us. But I prefer that we all work proactively to ensure that a better, thriving future is the one we choose.

About the Author

Andrew Winston is founder of Winston Eco-Strategies and an adviser to multinationals on how they can navigate humanity’s biggest challenges and profit from solving them. He is the coauthor of the international best seller Green to Gold and the author of the popular book The Big Pivot: Radically Practical Strategies for a Hotter, Scarcer, and More Open World . He tweets @andrewwinston .

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Terry white, shivani upadhyay, bucky fairfax, phil dahlin, annie thomas.