Lessons Enterprise CIOs Can Learn from Public Sector AI Spending
Facing pressure on AI investment returns, enterprise CIOs can draw on the public sector's lean innovation experience. New York State CIO Dru Rai emphasizes achieving sustainable AI applications through controlled experimentation, pragmatic technology choices, and prioritizing security and privacy within limited budgets.
As technology leaders face pressure to build successful AI projects and deliver return on investment, lean, cost-conscious innovation in the public sector offers valuable lessons for enterprise CIOs.
New York State Chief Information Officer Dru Rai underwent a mindset shift when he moved from the corporate world to the public sector in 2023. He previously worked at Quaker Houghton Company, Ball Corporation, DuPont Coatings, GE Advanced Material, and EY.
Similar to the private sector, his innovation team aims to use AI to save time and resources, but the focus is not on improving corporate profits, but on serving state residents, which highlights the importance of ensuring the security of the tools and data used.
"Our policy is to ensure we serve the people," Rai said. "We believe AI is a productivity tool—we can squeeze more taxpayer dollars to get work done faster and better—but we cannot sacrifice the health, safety, and privacy of the public."
Over the past few years, enterprise AI budgets have continued to rise, and despite economic headwinds, C-level executives face more scrutiny over the costs and returns of AI projects in 2026.
Earlier this year, many enterprises encouraged employees to use AI as much as possible to foster innovation and experimentation and demonstrate value to boards. But as AI providers shift to usage-based or outcome-based pricing models, enterprises feel the real cost of "pay-per-token," and these incentives are fading in many companies.
Rai said enterprises can start thinking about IT spending the way government workers have always had to. His team is forced to rely on less funding while delivering more results.
"When you have less money, you find more creative ways to use it," he said.
One strategy Rai brought from the private sector to state government is establishing a culture and process of "rapid trial and error in a highly controlled environment." He applies this approach to AI pilot projects, saying teams need to identify early on issues in pilots that could hinder scaling, so they can cut losses rather than continue investing.
"This makes us more resilient and ensures money goes to the right places, because I can't afford major mistakes," Rai said.
Timing innovation
Although it may seem contrary to many CIOs' instincts, Rai said that stopping the pursuit of the fastest results can be beneficial. Budget-sensitive decisions may mean choosing a lower-cost, slightly slower AI model, but ultimately it can help employees reach the same conclusions or outputs.
Rai said his team does not strive to be first, but focuses on finding sustainable solutions. "I'm happy to be second," he said.
Rai noted that the role of the CIO is changing in both the public and private sectors. Today, CIOs need to anticipate problems and find solutions, guiding teams through technology transformations. Learning how to best use AI is similar to how he mastered other tools over his 30-year career, which he calls "events in the evolution" of technology.
Rai believes future IT leaders will need to address the ongoing reliance on hardware such as GPUs and future quantum computing to continuously update their technology stacks.
Technology leaders may need a certain resilience to cope with changes in budgets and role expectations, but a public servant mindset and good technology partners help achieve this, Rai said.
"Our budget will never be enough," Rai said of his AI budget, "so you have to figure it out, adjust priorities, and find a way."