Two parallel developments are testing the UK’s approach to artificial intelligence deployment. A parliamentary investigation has found that government consultancy spending is spiralling with inconsistent reporting and limited oversight, while the Chancellor’s investment packages aim to make Britain the fastest AI adopter in the G7. Pete Smyth, CEO of consultancy Leading Resolutions, argues the combination should encourage caution rather than acceleration.
Speed alone is not a strategy. Against competitive pressure to move quickly on AI, the boardroom obsession with speed over everything is quietly undermining the very gains business leaders hope to achieve.
His concern is that organisations are scaling AI adoption faster than their internal structures can absorb. The consequences he cites are familiar to anyone who has watched previous waves of enterprise technology rollouts: technical debt, fragmented use cases, shadow AI proliferating outside governance frameworks, and a widening gap between investment and measurable return.
Smyth’s prescription is a readiness-first model — building governance structures, ensuring data quality, reducing shadow AI risks and aligning use cases to commercial objectives before scaling.
Businesses that take the time to align AI with strategy, invest in secure, clean data foundations, and pace their adoption will ultimately outperform those simply chasing short-term wins. Slowing down to ensure alignment is far better than watching a poorly governed initiative backfire.
He also pointed to capability gaps across both public and private sectors. “Transformation fails when organisations scale faster than they can adapt. Sustainable adoption must therefore lead with workforce upskilling and building a culture ready to absorb new tools.”
The argument is not new — “go slower to go faster” has been a consulting refrain through every technology cycle from ERP to cloud migration. Whether the current AI investment wave will reward the patient or simply pass them by is the bet every CTO is now making.
