Executive Summary

Artificial intelligence is reshaping the practice of applied economic and labor market research by automating routine analytical tasks such as data cleaning, literature synthesis, descriptive statistics, and standardized reporting. As these activities become faster and easier to perform, the profession must reconsider where its greatest value lies. The role of applied researchers will increasingly shift away from technical problem-solving and toward helping leaders identify the right problems, interpreting evidence in context, and guiding collaborative efforts to address the economic challenges that matter most to communities.

This transition elevates the importance of curiosity, judgment, creativity, and leadership in applied research. It also highlights a critical role for the Council for Community and Economic Research (C2ER) in helping researchers adapt by ensuring our professional network members learn from one another, identifying critical emerging economic issues, and developing insight and leadership skills that members need to succeed.

Two historical comparisons help frame the transition underway. First, the spread of the internet in the 1990s and early 2000s transformed applied research by dramatically reducing the time required to acquire data, shifting researchers’ work from data collection toward analysis and interpretation. Artificial intelligence may extend this shift by automating routine analytical tasks and allowing researchers to focus more on synthesis, judgment, and strategic insight. Second, the restructuring of manufacturing during that period illustrates how technological change can reduce demand for routine work while increasing productivity. The transformation of applied economic research may follow a similar pattern, meaning that fewer opportunities are available for researchers or that certain research skills become integral capabilities for organizational and program leaders.

As a result, applied researchers must evolve from technical problem-solvers into problem-finders who identify the most important economic challenges facing their communities and guide leaders in finding the best evidence-based solutions to act on them. Researchers’ value will lie in combining multiple data sources, interpreting evidence within local context, and translating analysis into practical guidance for policymakers and practitioners.

In this environment, C2ER must be more than a provider of tools or training. It must be the place where applied researchers turn for inspiration, motivation, and professional growth and where they sharpen curiosity, creativity, leadership, and critical thinking while learning from peers. By strengthening this professional network, C2ER can help researchers lead efforts to make their communities better places to live, work, play, and do business.

Background

As AI advances, routine analytical tasks in applied economic development research are increasingly automated, elevating the importance of interpretation, integration, and strategic insight. Applied researchers must adapt both their methods and their professional roles. Younger researchers, in particular, will need to develop technical fluency more quickly so they can serve as “quality agents,” ensuring that AI outputs are valid and reflect the nuance of real-world economic conditions.

Research on the evolution of knowledge work points to a similar shift. Jones finds that as knowledge becomes more accessible, researchers spend less time gathering information and more time interpreting and applying it (Jones, 2009). The diffusion of the internet illustrates this transformation. Online data sources and digital archives did not eliminate applied research jobs, but they fundamentally changed them by shifting effort from data acquisition toward analysis and insight. AI represents the next stage of this transition. As organizations integrate automated systems into their workflows, demand for routine analytical tasks declines while demand for oversight, evaluation, and higher-level thinking increases (Microsoft, 2024).

Economists describe the internet as a “general purpose technology” (or GPT) that reshaped how work is organized across industries. Its most important effect was to reduce the cost of acquiring and transmitting information (Goldfarb, 2019). As access to data expanded, the value of professional work shifted toward interpreting larger volumes of information, integrating multiple sources, and applying insights into decision-making. Today, the scale and complexity of available data exceed what individuals can manage without assistance, making AI an essential tool for analysis. As a result, AI is likely to drive a similar shift across white-collar occupations, including applied economic development research. Early evidence suggests that entry-level roles (often centered on routine analytical tasks) may be the most affected (ADP Research Institute, 2024).

A parallel can be seen in the restructuring of manufacturing in the late twentieth century. Automation and globalization reduced demand for routine production work, and manufacturing employment fell from roughly 19 percent to 10 percent of the workforce during the 1980s and 1990s  (U.S. Bureau of Labor Statistics). Between 2000 and 2020, automation eliminated 1.7 million additional manufacturing jobs, even as new roles emerged in other sectors  (Hicks, 2015). Manufacturing did not disappear, but it became more productive and more technically demanding, requiring fewer workers with higher skill levels. Similar dynamics may emerge in applied research.

AI is already automating many of the routine tasks that consume a large share of research time, including data collection and cleaning, literature reviews, descriptive analysis, and standardized reporting. The efficiency gains are significant. AI-assisted systematic reviews can dramatically reduce workload, and organizations report measurable time savings and faster productivity growth where AI is widely adopted (Zhihong Xu, 2025; IBM Institute for Business Value, 2023).

At the same time, many core elements of applied research remain difficult to automate. Human researchers retain clear advantages in stakeholder engagement, trust-building, and interpreting economic data within local context. They translate analysis into policy-relevant insights that reflect political, institutional, and community realities. Qualitative research, community engagement, and ethical oversight of AI-generated outputs all require human judgment.

In this environment, applied researchers must reposition themselves from data producers to insight interpreters. Their value will lie less in assembling datasets and more in identifying the right questions, synthesizing diverse information, and translating analysis into decisions that help communities respond to economic change. This shift places a premium on capabilities that machines cannot replicate, including problem identification, contextual interpretation, and stakeholder engagement.

From Problem-Solvers to Problem-Finders

Historically, applied economic researchers have been valued for their ability to solve analytical problems. Researchers received a question, assembled the necessary data, conducted analysis, and produced an answer. Artificial intelligence changes this equation. AI systems can increasingly perform many forms of technical analysis once the problem is defined. What AI cannot do effectively is identify which problems matter most to humans. The comparative advantage of applied researchers will increasingly lie in identifying the most important questions facing their communities, engaging with stakeholders, and framing issues in ways that lead to meaningful solutions. In this sense the profession is shifting from problem solving to problem finding.

That means the successful researchers must acquire a transforming set of skills that rely less on technical competence in key analytic tools and more on understanding context, nuance, and importance to the leaders who are asking questions. Core skills will include:

1. Integration of multiple data sources. Economic development decisions increasingly require combining traditional public datasets with real-time and administrative sources such as job postings, supply chain signals, workforce program outcomes, and firm-level data. AI tools can merge these sources, but they cannot accurately weight their value. Human researchers can better evaluate their reliability and build more credible analytical frameworks that connect them.
2. Forward-looking interpretations. Research must expand beyond backward-looking analysis. Traditional time-series analysis explains what happened in the past. AI enables forecasting, scenario testing, and short-term projections that practitioners increasingly require to guide real-world decisions about workforce demand, industry shifts, and regional growth.
3. Evidence validation. It is widely known and understood that AI systems often produce hallucinated sources, faulty correlations, or misleading interpretations. Applied researchers must serve as quality control actors who test assumptions, verify sources, and explain uncertainty.
4. Local knowledge and nuance. The value of place-based expertise is the contextual advantage it provides. AI systems rely primarily on electronic data and cannot replicate lived experience or deep local knowledge. Researchers who understand the institutional structures, industry networks, and workforce dynamics of particular regions remain indispensable.
5. Communication and policy translation. Decision makers need analysis that connects data to practical action. Researchers who clearly explain economic trends and their policy implications will remain highly valuable.

Beyond these core skills, adapting to this environment will require new technical and professional capabilities. Researchers should develop fluency with AI-assisted analytics tools, including large language models for exploratory analysis and prompt engineering. Data engineering skills will grow in importance as researchers work with APIs and multiple data pipelines. Programming literacy in languages such as Python or R will help researchers integrate AI tools into reproducible workflows. The key skill will be to understand what AI can do and what it fails to do effectively.

At the same time, complementary skills will become more valuable. These include qualitative research methods, participatory engagement with communities, narrative analysis, and expertise in data governance and algorithmic fairness. Economic decisions are shaped by human behavior, assumptions, and institutional context. AI models cannot fully capture these conditions. Applied researchers will increasingly advise clients on responsible data use, privacy, and ethical evaluation of AI-generated analysis.

How C2ER Can Help

The Council for Community and Economic Research (C2ER) must step forward to help the members of our field prepare for these changes and ensure that both new and mid-career researchers remain at the forefront of this transition.

As we develop our 2026 work program, C2ER is committed to developing more training programs focused on AI in applied economic research. Workshops will address topics such as AI-assisted labor market analysis, integration of real-time data sources, and machine learning approaches to forecasting regional economic trends. These programs will emphasize practical applications relevant to economic development practitioners.

This role is important because access to AI training remains uneven. Surveys show that while roughly 44 percent of employers report offering AI upskilling programs, only about one-third of workers say they have access to them (Boston Consulting Group, 2025).

C2ER’s more important role may be to continue its efforts to strengthen the professional network as we adapt to these changes. In an AI-enabled research environment the most valuable capability may be identifying emerging problems early. C2ER’s network of state, regional, and local researchers provides a platform where members can compare experiences, identify emerging economic challenges, and share insights about which issues deserve attention in their communities.

C2ER also can support better problem identification and prioritization. Through conferences, working groups, and collaborative research, C2ER helps researchers surface the most important economic questions facing their regions. These conversations help researchers move beyond technical analysis toward deeper engagement in shaping economic solutions.

C2ER also seeks to work with leaders in our field to assess existing standards for responsible AI use in research and offer our own interpretation of these tools. Shared guidance on transparency, methodological disclosure, and validation of AI-generated results will help maintain credibility with policymakers and clients.

Another important role for C2ER is facilitating shared tools, methods, and infrastructure. AI tools and analytical infrastructure can accelerate research workflows. C2ER can help members experiment with these tools, evaluate their usefulness, and share best practices across the network. Many state and regional research offices lack the resources to develop advanced AI tools independently. C2ER will collaborate through its membership to coordinate shared data pipelines, analytic templates, benchmarking tools, and prompt libraries.

Peer learning will remain central to this effort. Research on workforce upskilling shows that employees adopt new technologies most effectively through experiential learning and peer exchange rather than formal instruction alone (McKinsey Global Institute, 2023).

Finally, C2ER has an invaluable role in helping to maintain standards and improve data infrastructure. C2ER plays a critical role in maintaining the integrity of applied research by promoting responsible AI use and advocating for strong federal statistical data systems. C2ER will continue advocating for strong public data infrastructure. AI systems rely on high-quality underlying data. Federal statistical agencies such as the BLS and BEA provide essential state and regional datasets that make applied research possible. Protecting and strengthening this infrastructure remains critical.

The history of industrial automation offers an important lesson. Technological change rarely eliminates entire fields of work. Instead, it reshapes them. Manufacturing did not disappear when automation arrived. It evolved into a more technologically sophisticated sector requiring different skills. Likewise, the internet transformed our access to data; AI now transforms our ability to manage and manipulate it in ways that are often well beyond our individual capabilities.

Applied economic development research is likely to undergo a similar transformation. Researchers who treat AI as a tool that expands their analytical capacity will become more productive and more valuable. Those who remain focused primarily on routine analytical tasks will face greater pressure.

The opportunity for the field is to combine AI-enabled analysis with contextual judgment, stakeholder relationships, and ethical oversight. Only human researchers can provide these, and C2ER can help ensure that applied researchers make the transition to these new roles successfully.

References

ADP Research Institute. (2024). Labor Market Trends in AI-Exposed Occupations.

Boston Consulting Group. (2025). AI at Work Global Survey.

Goldfarb, A. a. (2019). “Digital Economics”. Journal of Economic Literature, 57(1), 3-43.

Hicks, M. J. (2015). The Myth and Reality of Manufacturing in America. Ball State University Center for Business and Economic Research.

IBM Institute for Business Value. (2023). Global AI Adoption Index. 2023.

Jones, B. F. (2009). “The Burden of Knowledge and the ‘Death of the Renaissance Man’: Is Innovation Getting Harder?”. Review of Economic Studies, 76(1), pp. 283-317.

McKinsey Global Institute. (2023). Generative AI and the Future of Work.

Microsoft. (2024). The New Future of Work Report.

U.S. Bureau of Labor Statistics. (n.d.). Employment by major industry sector, 1980–2000.

Zhihong Xu, X. Z. (2025, November 4). Machine Learning-Assisted Systematic Review: A Case Study in Learning Analytics. Education Sciences, 15(11), 1488.