How Senior Leaders Should Approach AI in Government
Senior leaders don't need to become AI engineers, but they do need to become confident owners of AI-enabled change. That's the takeaway from a panel discussion this week at Think AI for Government, where officials from across the public sector outlined eight practical steps for responsible adoption.
Rather than asking how quickly government can deploy AI, these steps focus on creating the right conditions for implementation that actually delivers value.
1. Start with the problem, not the product
Many organisations begin with the tool and then search for a use case. That's backwards.
"Quite often people will come to us with a solutionised problem and say, put AI here," said Pippa Fernandes, deputy director of AI strategy at DSIT. "You do something for a purpose. You do something for an outcome. You don't do it just for the sake of it," agreed Fawad Qureshi, global field CTO at Snowflake.
Leaders should define the challenge and let expert teams explore the best response. This approach reduces the risk of automating poor processes and increases the chance of meaningful change.
2. Stay sceptical and challenge the hype
Jason Kitcat, director of digital, data and technology at the Department for Business and Trade, warned leaders to be "very mindful, thoughtful around the claims that we are being told by suppliers."
Vendors have strong incentives to oversell. Public sector leaders need to ask hard questions about capability, cost, scalability and evidence before committing public money.
3. Measure value from day one
One of the strongest themes in the discussion was evaluation. Leaders need to understand whether tools are actually improving productivity, quality or user outcomes.
"How do you know it's actually working and delivering value?" asked Kitcat, who argued for before-and-after measurement so organisations can compare outcomes properly. Fernandes added that evaluators must be empowered to be honest: "Look for the right answer, not the answer they expect… they know you want to hear."
4. Fix your foundations first
Excitement about AI should not distract from the basics: data quality, legacy systems and process design. Poor foundations will undermine even the most advanced tools.
"Fix the data," said Fernandes. She described the current moment as an opportunity to improve infrastructure that's often been neglected. In many organisations, the real value of AI may be that it finally creates momentum to modernise core systems.
5. Put trust and ethics at the centre
Public sector AI must command confidence from citizens and staff alike. People are often less tolerant of mistakes made by machines than mistakes made by humans.
"Trust becomes the foundation," said Qureshi. That means governance, transparency and clear accountability are essential. He also cautioned leaders about reusing citizen data for purposes people did not expect: "Whenever you start using data for secondary purpose, double and triple check."
For public bodies, legitimacy matters as much as efficiency.
6. Create a culture of curiosity
Leaders don't need to become technical experts, but they do need to engage personally with AI and create space for others to experiment.
Fernandes challenged organisations to think about how many senior leaders have actually used the tools available to them. Innovation should be encouraged rather than feared. Teams need permission to test ideas, share learning and improve services iteratively. That cultural signal often matters more than any strategy document.
7. Build diverse teams to reduce bias
AI systems are shaped by the data they are trained on, the assumptions built into them and the people designing them. For senior leaders, diversity is not a communications exercise - it is a delivery requirement.
Bias in AI often reflects bias that already exists in society, said Qureshi. If organisations automate decisions without recognising that reality, they risk scaling existing inequalities.
He illustrated this with an example from homelessness services. Raw data alone could not explain why some women declined access to showers. Only direct engagement revealed that remaining visibly unclean was, for some, a safety strategy to reduce risk of assault. "Data sometimes does not give you the entire context."
Involve people with a broad range of perspectives and lived experiences from the start of any programme.
8. Empower your experts and hold the risk
AI success depends on trusted, capable teams being given the authority to solve real problems.
Fernandes said leaders who are willing to support their specialists and accept some risk can unlock significant change. "If you're prepared to hold some of the risk… you can make huge change." In practice, that means backing digital, data and operational experts to work together, rather than expecting technology alone to deliver transformation.
For senior government leaders looking to develop capability in this area, resources on AI for Government and AI for Executives & Strategy offer structured guidance on responsible adoption and change management.
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