Last week, our UK Transformation community brought together senior industry leaders for an intimate discussion exploring AI readiness in practice, spanning leadership understanding, AI strategy, data maturity and people capability. The discussion offered a realistic view of how organisations can navigate the pace and complexity of technological change.
Here are our key takeaways from the discussion:
Change management is the real challenge: While leadership teams see the opportunity, a growing disconnect exists between strategic intent and operational reality. Middle managers and delivery teams often lack clarity on what AI means for day-to-day work. Bridging this gap is critical to unlocking progress
People-first adoption drives momentum: The most impactful examples shared were practical and incremental: short enablement sessions, ready-to-use prompts, and clear guidance. Bite-sized learning builds confidence. Large-scale programmes without hands-on application risk overwhelms teams and slows adoption
Governance must balance control and speed: Organisations are navigating the tension between innovation and risk. Blanket bans or restrictive access can push usage underground, while unclear guardrails create confusion. The consensus from the discussion was to provide safe experimentation space, clear boundaries and visible governance that enables rather than restricts
Data foundations matter, but perfection is not required: AI does not require perfect data, but it does require transparency. Senior leaders highlighted fragmented systems, duplicated data and unclear ownership as key barriers to scaling. Understanding data quality and governance is more important than waiting for flawless inputs
Capability extends beyond technical skills: AI readiness is as much behavioural as it is technical. Alongside digital literacy and applied AI skills, organisations need judgement, critical thinking and strong leadership communication. Psychological safety remains essential to encourage experimentation and remove stigma around tool usage
Value measurement must evolve: Many organisations struggle to define meaningful KPIs for AI. Early use cases often focus on productivity gains, making ROI difficult to quantify. Tracking behavioural and process metrics first provides a clearer path to sustainable value over time
We would like to thank all senior leaders who joined the discussion and shared their experience within AI readiness.
If you are interested in completing our new AI Readiness Assessment, please get in touch with our UK transformation team to learn more.
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