AI adoption moved fast. Strategy didn’t always keep up. Now leaders have to decide where AI should create value, how work needs to change, and how they’ll know whether the investment is paying off.
AI adoption can move without an enterprise strategy. Individual teams can adopt tools, test use cases, and build new workflows before leadership has agreed on a common direction.
Grant Thornton’s 2026 AI Impact Survey of 950 senior business leaders found that 51% identify strategy as the biggest driver of AI ROI, yet only 22% of operations leaders have a fully developed and implemented AI strategy. The same research describes many organizations as scaling AI without the measurement and governance needed to show what is working.
Bottom-up adoption can make that gap harder to see. CIO.com reported that different teams at Docusign initially developed their own AI strategies and tooling before business leaders came together around shared pain points and technology decisions. That kind of experimentation can be useful, but it’s not a substitute for an enterprise strategy.
Strategy gets shaky when leadership is making decisions based on license counts, anecdotes, and vendor dashboards. Before deciding where to scale AI, leaders need to know what tools are being used, where AI is entering workflows, how effectively people are using it, and whether outcomes are changing.
McKinsey’s July 2026 research reinforces the point. In its enablement horizon, leaders were 5.3 times more likely to report enterprise value capture when workflows were redesigned than when they remained unchanged. That’s an association, not proof that redesign alone causes ROI, but it shows why strategy has to reach the work itself.
A list of approved tools, usage policies, and budgets is useful governance. It’s not the strategy.
Strategy should answer a more concrete set of questions: What is AI supposed to improve? Which workflows or business outcomes matter most? What will success look like? When should leadership scale, change, or stop an initiative?
Larridin’s Utilization × Proficiency × Value framework gives leaders a way to structure that measurement: who is using AI, how effectively they are using it, and what value the activity is producing. The point is not to reduce strategy to three metrics. It is to connect AI activity to the outcomes the strategy was supposed to create.
McKinsey found that in the automation horizon, leaders were 3.9 times more likely to report enterprise value capture when leadership teams demonstrated high AI fluency than when they demonstrated low AI fluency.
That doesn’t make fluency a magic ROI multiplier. It does make leadership capability part of execution. Leaders need enough AI fluency to evaluate use cases, question results, understand workflow implications, and make better investment decisions as the technology changes.
Larridin’s AI Impact platform gives leadership a shared measurement layer across AI adoption, proficiency, spend, workflows, and business impact. Instead of relying on separate views from procurement, individual AI vendors, and functional teams, leaders can see where AI is being used, where capability is developing, what it costs, and where outcomes are changing.
That turns measurement into a strategy tool. Leadership can compare the plan with what is actually happening and decide where to invest more, where to redesign work, and where an initiative is not producing enough value to justify continued spend.
It defines the outcomes AI is expected to improve, how those outcomes will be measured, who owns the decisions, what governance applies, and how leaders will decide whether to scale, change, or stop an initiative.
Anchor the strategy to business outcomes rather than specific tools. Models will change. A goal such as reducing engineering lead time or improving customer resolution time can survive those changes because leadership can evaluate new tools against the same outcome.
McKinsey describes three horizons: enablement, automation, and reinvention. Only 11% of leaders surveyed said their organizations were in reinvention. The practical point is not to race toward the third horizon. It is to make the organizational changes required at the horizon you are actually in, including workflow redesign, leadership fluency, and operating-model changes.
Put spend next to the outcomes the investment is supposed to produce. Show where adoption, proficiency, workflow performance, or business results are moving and where they are not. That gives the board a clearer basis for deciding whether an AI initiative should scale, change, or stop.
Larridin helps leadership connect AI usage, proficiency, spend, workflows, and outcomes so the strategy can be tested against what is actually happening across the business.
Book a discovery call to see where your AI strategy has visibility and where it still has gaps.