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WHY FRONTLINE AND OPERATIONAL MANAGERS MAY BE THE MOST IMPORTANT LEADERSHIP COHORT IN ORGANISATIONS RIGHT NOW

Speaking with our clients and peers over the last few months about AI, we've heard a multitude of different stories about where organisations are with AI, from it being accepted and adopted to others who are still grappling with what it could mean for their people, the business, their jobs and the future of who their work gets done. What seems to be common though, is that AI is being used in organisations at the moment, sometimes in approved tools, but often in quiet workarounds. It raises the question as to whether that AI uptake is deliberate, or accidental - and if it's accidental, what could that mean? A lot of the organisations we've been speaking to seem to be getting stuck not because the tech is immature, but because the business hasn’t made room for it, and they are uncertain about the role AI could (or should) be playing, not just in their organisation, but in their industry. At Spring Point, we've decided to treat AI as an operating model and a behaviour change challenge, so we're now looking at how we design the decisions, roles, routines and leadership habits that make AI usable at scale, then defining the change work required for it not to evaporate after launch. There is a real opportunity in "Structured AI" adoption In most sectors, the pattern of AI adoption seems to be familiar: Small pockets of AI pop up; one team here, one champion there Tools land on desktops faster than governance, security and data controls can respond Leaders can’t tell which initiatives are worth backing, and which are noise Risk rises quietly: operational errors, reputational hits, workforce backlash These scenarios have played out over the past couple of years and will continue to do so. What it means is that AI spreads in ways that are fragmented, reactive and hard to see (and manage). So, after some internal discussion we decided to tackle the challenge of AI adoption by developing an adoption framework that we believe can be universally applied within an organisation to help plan and better manage AI in the workplace, with a view to not only establishing the playing field and rules, but also ensuring that the advantages and benefits of AI can be effectively leveraged to create value for the organisation. Our aim was to ensure that AI becomes intentional , prioritised and governable , without killing momentum. Defining the AI Adoption Framework (Beyond the Theory) We decided that to be useful, this framework needed to be more than a poster on a wall or a slide in PowerPoint deck. It needed to earn its keep in three ways: It needed to tie AI effort to real business outcomes (not “cool demos”) It needed to make the next decision obvious—and stops you funding the wrong things It needed to get the organisation ready (think processes, roles, skills and habits) not just the tech stack So, the Spring Point AI adoption framework (below) integrates six elements: Vision & Strategic alignment Value & ROI prioritisation Data & Technology foundations Capability, Culture & Change Risk & Governance

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