Presented by EY
Amy Jones, EY’s U.S. Public Sector AI Lead, recently explored a compelling framework for how government agencies might navigate the future of artificial intelligence. Known as the "Four Futures," this model presents a spectrum of potential AI trajectories shaped by policy choices, investment strategies, and operational realities.
The concept stems from the recognition that no single AI future is preordained. Government decisions today—about funding, regulation, and implementation—will determine the nature of the AI landscape in the public sector by 2030. According to Jones, these futures aren't abstract predictions but plausible outcomes rooted in real-world dynamics.
In contrast, the “Transform” future is more ambitious. It envisions a rethinking of how government services are designed and delivered. Rather than merely extending existing capabilities, transformation leverages AI to fundamentally reshape interactions between citizens and government. “We’re not just improving what we see; we’re rethinking lives and livelihoods,” Jones explained. In this future, AI becomes a strategic asset that enables government to meet people where they are, in ways previously unimaginable.
The third future, “Constrain,” reflects a less hopeful outlook. Here, government interest in AI remains high, but resources do not keep pace. Constraints in budget, policy, or workforce limit innovation and prevent the realization of AI’s full potential. Jones emphasized that while the technology may be ready, systemic limitations could block agencies from using it effectively.
The fourth future, “Control,” envisions a defensive posture in which AI is primarily used for compliance, security, and risk mitigation. In this scenario, innovation is sidelined by concerns about privacy, safety, and legality. According to Jones, “Control is the scenario where we’re really hesitant to use AI for innovation. We’re focused on keeping it from getting out too far, too fast.”
Jones acknowledged that growth and transformation are ideal but stressed that not every agency should aim for transformation. “Success does not always look like transform,” she noted. Different missions may require different levels of ambition, and for some, steady growth may be the optimal path.
Still, the key takeaway is the need for intentionality. Agencies must align policy, budget, governance, and operations to their desired outcome. Without that alignment, organizations risk drifting toward unintended futures. The decisions made today will shape what AI looks like across the public sector in five years.
Key Takeaways:
The “Four Futures” framework (Growth, Transform, Constrain, Control) offers agencies a roadmap for AI planning.
Growth and Transform are aspirational, while Constrain and Control highlight risks of underinvestment or overregulation.
Success requires coordinated effort across policy, budget, and governance to steer toward a desired AI future