Four Moves that Turn AI Experiments into Business Reinvention

Most organizations don't have an AI adoption problem. They have an AI alignment problem. McKinsey's latest global survey found that 88 percent of organizations now use AI regularly in at least one business function, yet nearly two-thirds have not begun scaling it across the enterprise. The leadership challenge is no longer generating AI activity. It is ensuring that activity converts into meaningful enterprise value. Pilots are everywhere and curiosity is high, but the gap between individual experimentation and enterprise value stays wide. Closing it takes four deliberate steps, in sequence, that move AI from scattered activity to a real driver of growth. 

What 

Every leader needs a shared, working distinction between three levels of AI value: 

• Incremental Value/ROI: individual productivity gains. Faster drafting, faster summarizing, faster searching. Useful, but bounded by the person using it. 
• Transformative Value/ROI: a workflow or function operating differently. Roles shift, handoffs disappear, decisions get made faster and with better information. This is where most of the real near-term value sits. 
• Breakthrough Value/ROI: new business models. AI creates a capability, product, or revenue stream that didn't exist before. 

Confusing these three is the single biggest reason AI investment stalls at "interesting" instead of reaching "material." A team celebrating faster meeting notes is not the same conversation as a team redesigning how a function operates, and neither is the same as a team building a new offering. 

How 

Bring your cross-functional leaders into a facilitated Power of the Possible working session. This isn't an individual exercise. It's collective: as a group, leaders map the organization's actual use cases, in flight and proposed, against the maturity continuum of incremental, transformative, and breakthrough. This is where the mindset shift happens. Seeing the full portfolio laid out together, leaders stop defending their own pilot in isolation and start reckoning with where the organization actually sits, and most groups discover their portfolio is heavily weighted toward incremental. That collective realization, reached together rather than announced from the top, is what creates shared ownership. AND sets up every decision in Steps 2 through 4.

What 

Leaders can't credibly sponsor a transformation they haven't personally experienced. Every leader should be an active user of AI – this means hands on keyboards, not to save a few minutes on email, but to free up time for the work only they can do: setting strategy, aligning stakeholders, and rethinking how the organization operates as AI agents become part of the workforce alongside people. 

How 

Run leaders through an AI Masterclass built around their own real work: prepping for a board meeting, pressure-testing a decision, drafting a stakeholder message in their tone of voice, etc. Then, for leaders ready to go further, offer an Advanced AI Masterclass: building agents and custom GPTs that handle their recurring work directly, a chief-of-staff GPT that preps weekly briefings, an agent that monitors competitor and market signals, one that turns meeting notes into tracked actions and owners. The goal isn’t productivity, per se. It’s role-modeling. When a leader visibly uses AI and reinvests the time it returns into higher-value thinking, that becomes the permission structure for the rest of the organization to do the same. 

What 

Focus over breadth has an impact. Identify the 2–3 investments that will genuinely drive an AI-enabled organization, not the twenty pilots that feel active but don’t move the business. A ‘Big Bet’ targets a structural, cross-functional constraint, not a task, and it should live at the transformative or breakthrough level, not the incremental one. This can’t happen in a silo. A ‘Big Bet’ by definition cuts across functions, so it needs sponsorship and resourcing from more than one leader from day one. 

Example: a car manufacturer's Big Bet might be compressing the time between concept ideation and a vehicle hitting the market, funding the teams and agents that directly attack that cycle time rather than automating isolated tasks inside it.  

How 

In alignment with your leadership team and an activated AI governance team or AI Council, build an operating model that lets you prioritize and fund these bets quickly, using consistent organizational criteria (strategic fit, feasibility, time-to-value) rather than whoever pitches loudest. Pair that operating model with a governance and compliance framework, so the organization can move fast on Big Bets while operating in a responsible and safe environment: clear risk tiers, data and access controls, and defined approval paths for anything touching customers, regulated data, or external commitments. This is a standing capability, not a one-time exercise. The model should let you re-rank and reallocate as new opportunities and results come in, so funding decisions keep pace with what you’re learning.

What 

For the first time, efficiency and growth don’t have to trade off against each other, but only if savings are reinvested intentionally. Most organizations reach for the easy move: banking AI-driven savings straight into cost reduction and workforce reduction. That’s the short-term play. The smarter move is upskilling, reinvention, and reinvestment: retraining the workforce for higher-value work, redesigning roles around a human-plus-agentic model, and putting freed-up capacity back into the initiatives that grow the business. Left alone, efficiency gains quietly evaporate into lighter workloads or a smaller headcount. Captured deliberately, they fund your next transformative and breakthrough bets. 

How 

Model where a human-plus-agentic workforce actually creates capacity: which roles, which workflows, which decisions get faster or cheaper. Then make reinvestment explicit and trackable, routing that freed-up capacity into upskilling programs and the high-value initiatives that support your broader business objectives, rather than letting it default to headcount reduction or disappear into "business as usual." None of this happens without a modernized HR function. HR teams need to be equipped to redesign roles, build new skill pathways, and manage the workforce transition, or the reinvestment story stays siloed or theoretical. This is the step that converts AI from a cost story into a growth story. 

Don't wait for a perfect strategy. Run the Power of the Possible session this month, get leaders hands on keyboards this quarter, and fund your first Big Bets before the year is out. The urgency is real, but not because AI projects are scarce. They're accelerating everywhere. The leadership challenge is turning those projects into meaningful value—capitalizing on the existing momentum to deliver real business impact.