The Fluency Question
Virginia AI Futures
Virginia sits at an inflection point. If the Commonwealth's businesses grasp the AI opportunity early, Virginia becomes one of the most competitive, adaptive regional economies in the country. If they don't, a quiet erosion of competitiveness begins, slowly at first, and then all at once.
This research asks a straightforward question: how is AI going to impact Virginia's businesses? What are the forces that will shape that impact, and what could the resulting futures look like?
Using Shell's scenario planning methodology, we constructed four plausible futures, grounded in economic data and technology diffusion research, exploring how generative AI fluency, not mere adoption, will reshape 780,000 small and mid-size businesses over the next five years.
These scenarios are not predictions. They are provocations: conversation starters for business owners thinking past next quarter, executives navigating workforce and technology strategy, policymakers shaping the environment those businesses operate in, and anyone curious about what happens when a general purpose technology meets an economy this diverse.
If these scenarios spark new questions about Virginia's AI readiness and the economic stakes of getting it right, they've done their job.
Chapter 1: The General Purpose Technology Thesis →Shell's Scenario Planning
Shell's scenario planning methodology was developed at Royal Dutch Shell in the early 1970s by Pierre Wack and Ted Newland. Rather than forecasting a single future, the method identifies critical uncertainties and constructs multiple plausible futures. The goal is not prediction but preparation: making strategic decisions that perform well across different outcomes.