How C2ER Members Are Using AI in Applied Research Today

Artificial intelligence is no longer a future consideration for applied economic and workforce development professionals. According to a recent C2ER and LMI Institute member survey, AI is already becoming part of how researchers, analysts, and practitioners conduct analysis, communicate findings, and support decision-making every day.

AI is already changing applied economic and workforce development research. As always, C2ER members are showing the industry what comes next.

More than 93% of respondents reported using AI tools either regularly or occasionally in their work, with nearly half using them daily. For C2ER members, that level of adoption signals something important: the conversation is no longer about whether AI will influence the field. It already is.

What is emerging instead is a more practical and consequential question: how can AI be integrated into applied research workflows in ways that improve efficiency while preserving trust, rigor, and analytical quality?

The survey results suggest that C2ER members are approaching this transition thoughtfully.

Members reported using tools such as ChatGPT, Claude, Microsoft Copilot, and Gemini primarily for tasks that accelerate research and communication workflows. The greatest value identified was in time savings, faster research, improved writing quality, and insight generation. AI is helping practitioners summarize documents, organize information, draft reports, and move more quickly from data to actionable insights.

At the same time, respondents drew a clear line between where AI is useful and where human expertise remains essential.

While members are increasingly comfortable using AI for drafting, synthesis, and exploratory work, many remain cautious about relying on it for high stakes analysis, forecasting, or policy-sensitive conclusions. Concerns about hallucinations, trust in outputs, data privacy, and bias were among the most commonly identified barriers to broader adoption.

That caution reflects the culture of applied research itself.

C2ER members are trained to validate findings, question assumptions, and ensure methodological integrity. The survey showed that most respondents manually review AI outputs, cross-check information against trusted data sources, and validate findings through traditional analytical methods or peer review. In other words, members are not outsourcing judgment to AI. They are using AI to strengthen productivity while maintaining professional responsibility for interpretation and accuracy.

Perhaps most interestingly, the survey suggests that AI may actually increase the importance of uniquely human skills within the profession.

When asked which skills are becoming more important, respondents emphasized interpreting and validating results, communicating insights, and applying domain expertise.

As AI becomes more capable of generating information, the ability to contextualize, explain, and critically evaluate that information may become even more valuable.

The survey also highlighted a significant opportunity for peer learning across the C2ER network.

Members expressed strong interest in practical training tied directly to real work, examples from peers, shared templates, and implementation guidance. Many organizations represented in the survey are still developing formal AI policies and training structures, even as staff increasingly experiment with these tools independently.

That creates an important opening for the C2ER community.

The organizations that benefit most from AI will likely not be those that adopt tools the fastest, but those that develop the most trusted, validated, and operationally mature approaches to using them effectively.

For C2ER members, the challenge ahead is not simply adopting AI. It is helping shape what responsible, evidence-driven AI use looks like in applied economic and workforce development research.

Please click here to view charts and detailed survey results.