Over the past month, I had the opportunity to work on an in-depth target industry analysis for an economic development client. The goal was to assess the continued viability and potential of their existing and emerging industry targets considering economic shifts at both the local and sector level. To support me in this endeavor, I turned to Claude, an advanced generative AI system created by Anthropic.

Generative AI is rapidly emerging as a game-changing tool for economic and workforce development research. By leveraging natural language prompts, these AI models can quickly synthesize data, identify patterns and trends, and generate informative written content, charts, and tables.

For this industry analysis, I ended up using over 25 unique prompts with Claude to produce a comprehensive 20-page report. The AI generated detailed trend analysis and projections for each of the client’s target sectors. It also compiled comparative data on key site location criteria like utility infrastructure, real estate, and workforce requirements across six established and three emerging industry targets.

One fascinating application was having Claude assess the potential impacts of AI itself on the employment outlook in each sector. The results were eye-opening, with some industries poised to see job growth accelerated by AI adoption, while others face risks of technological displacement. This kind of forward-looking insight would be difficult to replicate through traditional research methods.

Of course, AI insights are only as good as the locally specific context and data behind them. So, when the AI surfaced potential gaps between industry requirements and current local assets, I knew I needed to dig deeper. Was the community already making moves to bridge those gaps?

Take the example of potential constraints in municipal water and electric power capacity relative to industry needs. A review of the city’s 2023-2027 Capital Improvement Plan showed they were, in fact, budgeting multi-million-dollar infrastructure upgrades to address those very pain points. Score one for the humans.

Workforce availability is another common concern for industry attraction, especially with the client community’s low 2.2% unemployment rate. Here again, a bit of additional research added essential nuance to the AI’s initial take. The presence of a major university churning out graduates with in-demand degrees, strong talent attraction initiatives, and the local quality of life and cost advantages all help counterbalance the tight labor market. The university’s innovation park also offers a unique asset for fostering industry R&D partnerships.

With the heavy analytical lifting done, I had Claude take a pass at editing the full report narrative. The AI quickly identified and corrected a variety of spelling and grammatical errors that my own eyes had glazed over after staring at the text for too long. A final AI-polished draft of the cover letter, and the report was ready for delivery.

The end product was an actionable, evidence-based assessment confirming the viability of the client’s target industries and sub-sectors. It provided a data-driven foundation for their industry recruitment strategies moving forward. And critically, the AI-assisted research process shaved an estimated 30% off the typical time required to produce a study of this depth and scope.

It’s important to recognize that generative AI tools are not a magic bullet. They don’t replace the need for human domain expertise, local knowledge, and strategic judgment. But when leveraged properly, AI can be a tremendously powerful force multiplier for economic development research and planning. Helping our clients and communities harness that potential is an exciting frontier for the field.