Scottish Boardrooms Talk AI, But Clear Strategies and Ownership Still Lag

Scottish firms talk up AI but lack clear strategies

AI is on the agenda, but execution is lagging. New research from Livingston James and EY shows 71% of leaders discuss AI at board level, yet only 36% have a defined strategy. Another 63% say they still need to build one.

The study surveyed and interviewed 200+ C-suite leaders across Scotland's public, private and third sectors. Most respondents were from the business community (71%).

What's stalling progress

Ownership is murky. When asked who owns AI strategy, 46% pointed to technology, 16% to operations, and 12% to finance. 8% said no one owns it, while 6% said it's shared or fragmented across teams.

Many also flagged weak workforce planning and thin measurement. AI is often being bolted onto existing digital work without tight governance, risk controls, or clear investment rules.

Who has a strategy-and who doesn't

By sector: public (44%), private (38%), third (26%).

By size: micro orgs with fewer than 10 employees lead at 59%. Large organisations sit at 37%, medium at 33%, and small at 28%.

Small teams appear faster to formalise their approach. Larger enterprises aren't always converting board talk into documented plans.

Leadership signals that matter

Technology leaders see limited paths to the top job. Only 50% believe they could become CEO-the joint lowest alongside HR. Just 17% say they're involved in succession planning.

Only 3% of surveyed CEOs come from a technology background. Operations (35%) and commercial leaders (32%) dominate. That mix influences how AI gets prioritised, funded, and audited.

What keeps leaders up at night

People. Attracting, retaining, and developing talent remains the number one concern. Leaders also worry about falling behind competitors despite heavy tech spend, losing strong teams to more agile or purpose-led rivals, and re-skilling staff at scale.

Cybersecurity, ethics, and growing dependency on digital infrastructure are rising risks.

"Lots of people are talking about AI, but fewer are certain about how they should actually use it in the right way," said Ali Shaw, director at Livingston James. "CEOs will need to ensure they have the right people around them… That could mean involving senior tech executives more in areas like succession planning."

"Turning ambition into action now requires a shift from discussion to disciplined execution," said Cara Heaney, People Advisory Services and Managed Services Leader at EY Scotland. "The most successful organisations will be those that bring technology, people and governance together."

A 90-day AI execution playbook for boards

• Assign ownership: Name an executive owner (not just IT) and a cross-functional steering group. Write decision rights and escalation paths.
• Set guardrails: Approve a responsible AI policy covering use, privacy, security, model risk, and supplier standards. Nominate a model risk owner and data protection lead.
• Pick 3-5 use cases: Tie each to P&L or citizen outcomes. Define problem statements, constraints, and success metrics (savings, cycle time, quality).
• Fund and staff: Ring-fence budget. Assign a product owner and delivery squad per use case. Set a two-to-four-week sprint cadence.
• Measurement: Stand up an AI scorecard-value delivered, adoption, time-to-value, quality drift, risk incidents. Report monthly to the board.
• Workforce enablement: Launch role-based training, safe-use guidance, and internal sandboxes. Update job design and incentives.
• Data and platform: Map data access, security, and model choices (build, buy, partner). Limit tool sprawl and shadow spend.
• Compliance early: Run DPIAs, security reviews, and procurement checks before pilots scale. Log prompts/outputs for audit.
• Change and comms: Explain the why, the wins, and the limits. Share early results and course-correct in public.

Signals you're moving from talk to traction

• Named executive owner, clear decision rights, and a steering group that meets weekly.
• A board-approved AI strategy with a one-page scorecard and quarterly targets.
• Three use cases in delivery; at least one live in production with measurable outcomes.
• Adoption metrics trending up; measurable hours saved or revenue protected/created.
• Quarterly model risk and ethics review with documented actions.

Where to go next

• See how large enterprises frame AI governance with EY's perspectives: EY on Artificial Intelligence.
• Build executive and team fluency with structured learning: AI for Executives & Strategy, AI Learning Path for VPs of Strategy, and AI Learning Path for CIOs.