Difference between being AI-ready, or just AI-curious, to define 2026
British businesses are being urged to rethink their AI readiness before making unnecessary adoptions by a technology consultancy CEO. According to Leading Resolutions boss Pete Smyth, with the initial novelty of generative AI having faded, the time has come to consider enterprise-scale transformation in 2026.
Despite significant investment, a widening “readiness gap” threatens to derail AI adoption. According to F5’s State of AI Application Strategy Report, only 2% of enterprises qualify as “highly AI-ready,” while 77% sit in “moderate readiness,” lacking the governance or security controls needed for safe scaling. The recent EY Responsible AI Pulse survey supports this, with 99% of polled organisations reporting financial losses from AI-related risks, and 64% of those losses exceeding $1 million.
According to UK technology consultancy Leading Resolutions, the true barrier to harnessing AI for commercial success in 2026 is a widespread lack of operational maturity and ‘enterprise-grade’ foundations needed for basic AI onboarding.
“British businesses are at a critical crossroads, where the rush to stay competitive is inadvertently creating a culture of ‘blind adoption’,” warns Pete Smyth, the firm’s CEO. “Without a fundamental shift in how boardrooms approach governance and data hygiene, the very tools meant to drive growth will instead become significant liabilities. Too many organisations are rushing headfirst into AI adoption without the operational, cultural and governance maturity required to do it safely or successfully. AI is an enterprise capability that demands enterprise-grade readiness, rather than just being a side project.”
For boardrooms, AI readiness is a key factor, and yet the distinction between just curiosity and actual AI capability marks whether the technology presents strategic growth or systemic risk. And while many companies are committed to exploring AI’s potential within their business, curiosity alone cannot protect their bottom line. In fact, curiosity without due diligence can be costly, as poor AI governance is increasingly leading to financial losses.
Smyth added, “Businesses want AI to solve everything from their cost challenges to customer experience. However, few are laying the groundwork needed for meaningful impact. Where hype is outpacing reality, most initiatives stall or even backfire if they lack strong governance and clear data structures. Fragmented responsibilities and siloed initiatives are frequently undermining trust and stunting business growth.”
Key hurdles
He continued by identifying some of the major pitfalls of rapid AI adoption without the necessary safeguards put in place. One of the more pervasive of these is “Shadow AI”, where employees turn to unapproved AI tools to solve immediate problems, inadvertently creating massive data-sovereignty and security risks.
Organisations that “invest early in data hygiene and controlled access” see a much faster time-to-value than those that prioritise speed, Smyth contends. Rushed AI deployments and a “speed-to-market” primary driver ultimately create long-term debt, and this is what is behind the fact that “more than half of CEOs haven’t realised any financial benefits from their AI integrations.”
Looking ahead, the key pillars to building true AI readiness for organisations in 2026 will be crucial to sort the ready from the simply-curious. On top of data maturity and security, that also means clearly governed frameworks and accountabilities is an essential step in AI readiness. Additionally, “AI implementation shouldn’t be considered purely a technological hurdle”, but a shift in skills and company culture to meaningfully impact workflow.
“Educating your workforce on safe use and aligning your initiatives to directly measurable commercial outcomes is all essential for strategic onboarding,” Smyth concluded. “AI should not be a gamble. It needs to be a strategic capability built on resilience and responsibility. Organisations rarely fail at AI due to inadequate technology, but instead because their structure and workflow foundations are not aligned to support it. For businesses looking to bridge the gap between AI curiosity and capability, the first step is a structured, honest assessment of their own readiness.”
