AI is already operating inside your organization. Your staff are using generative AI tools to draft emails, summarize policy documents, analyze data, and prep briefing notes. 
 

All of this is happening without a coherent, enterprise-level strategy. 

Which means decisions about AI are being made: 

• individually 

• inconsistently 

• invisibly 

That’s not innovation. That’s unmanaged risk. 

An AI strategy is not about “starting AI.” 
  

Without a strategy, AI amplifies the wrong things 

Government systems are very good at one thing--scaling whatever already exists. 

If your processes are slow, AI can make them faster—but still slow in the wrong places. 
  

If your data is biased, AI can make those biases more efficient. 
  

If your policies are unclear, AI will apply that ambiguity at machine speed. 

This is why an AI strategy has to start before technology. 

A real AI strategy answers questions like: 

• what problems are we trying to solve for citizens? 

• where is human judgment essential—and where is it not? 

• what decisions should never be automated? 

• what level of explainability do we require for public trust? 

• how do we ensure AI improves equity instead of undermining it? 

Without those answers, AI doesn’t transform government. 
It industrializes its flaws. 

AI strategy is a trust stragtegy 

In government, trust is the currency. 

And AI—used poorly—can burn through trust faster than almost any other technology we’ve seen. 

Citizens don’t care whether a decision was made by a: 

• legacy system 

• human caseworker 

• AI model 

They care whether it was: 

• fair 

• transparent 

• timely 

• accountable 

An AI strategy establishes: 

• clear accountability for AI-supported decisions 

• standards for explainability and auditability 

• guardrails around surveillance, consent, and data use. 

A strong AI strategy starts with mission outcomes: 

• reducing wait times 

• improving eligibility accuracy 

• increasing compliance through better guidance 

• supporting frontline staff under pressure 

• making services more accessible to vulnerable populations 

Your strategy should clearly articulate where: 

• AI creates material public value 

• it does not 

•  where simpler solutions are better 

This clarity is what prevents wasted investment—and public embarrassment. 

 
AI changes the operating model, not just the toolset 

This is the part most agencies underestimate. 

AI is not just another system you plug in. 

It changes how 

• work is done 

• decisions are made 

• roles evolve 

• accountability flows. 

An AI strategy must address operating model questions: 

• how do humans and AI collaborate in service delivery? 

• what new skills do managers and frontline staff need? 

• how do we redesign processes around AI, not bolt it on? 

• who owns model performance over time? 

If you don’t answer these questions deliberately, they get answered accidentally and accidental operating models are never good operating models. 

Strategy enables speed 

There’s a false choice often presented in government--move fast and be reckless or move slow and be safe. 

A well-designed AI strategy enables responsible speed. 

It allows agencies to: 

• move faster on low-risk, high-value use cases 

• apply stronger controls to high-impact decisions 

• reuse patterns, standards, and governance instead of reinventing them 

Strategy reduces friction because people know: 

• what’s allowed 

• what’s not 

• how to proceed 

That’s how you scale innovation without chaos. 

 
What a government AI strategy should include 

Let’s get concrete. 

A credible government AI strategy typically includes: 

• A clear vision tied to public value and mission outcomes 

• principles for responsible and ethical use 

• A prioritization framework for AI use cases 

• data readiness and quality standards 

• governance and accountability models 

• workforce and capability development 

• vendor and procurement considerations 

• metrics for success beyond cost savings