AI in Strategy: A Peer Exchange on Scaling, Governance and Strategic Accountability

Broad AI experimentation is near-universal, yet measurable earnings impact remains rare. When we polled participants on the tangibility of AI impact in their organizations, the majority placed themselves in early or emerging stages. Those running more than five parallel initiatives were no more likely to report bottom-line results than those running two or three.

Understanding the gap starts with recognizing that not all AI work carries the same value potential.

Participants clustered around two patterns: broad experimentation without a strategic filter, or isolated pockets of progress disconnected from enterprise value. To understand why, it helps to distinguish between three categories of AI ambition:

• AI for daily productivity: assistants, automation, individual-level tools.
• AI for business transformation: process redesign, data and systems, organizational setup.
• AI for strategic differentiation: new offerings, monetization models, value chain repositioning.

Most organizations concentrate in the first category. Only very few move into true business transformation, while strategic differentiation remains largely absent – and the roundtable discussions made clear why this gap persists.

The root causes of the pilot-to-scale gap are consistent across industries and not primarily technical:

• Activity mistaken for strategy: no clear target picture, qualitatively or financially.
• Too many initiatives, not enough focus: portfolios that looked active but could not scale anything.
• The foundations were not ready: fragmented data, processes not designed for AI, governance absent.
• People were not brought along: fear, unclear expectations and missing competencies stalled adoption.

Each of these patterns points to the same underlying need: sharper choices, made earlier, and held to a higher standard.

The remedy is less breadth, more conviction. Our whitepaper introduces the concept of the AI Big Bet: an initiative the organization chooses to fully resource, large enough to visibly move the P&L and strategic enough to justify the disruption it causes. A Big Bet must be anchored in strategy, deliver material bottom-line impact, and drive business transformation or strategic differentiation.

That is the line between pilots and Big Bets: one makes individuals faster, the other moves the P&L. The organizations on the right side of that line tend to have one thing in common: they concentrate on 3 to 5 Big Bets and deprioritize everything else. Getting there is a question of focus and discipline – both of which come down to four impact moves.

Four impact moves define the path from experimentation to P&L impact:

• Set clear objectives and financial targets – owned by the CEO, tied to the P&L.
• Select your Big Bets – narrow 20 to 30 candidates down to 3 to 5 Big Bets.
• Ensure foundations are in place – governance, data, process landscape, AI-ready systems.
• Build execution fit for AI – learning cycles, stage gates, business ownership, top-down leadership.

Getting these moves right is ultimately not a technology challenge. It is an organizational one – and that puts the strategy function at the center of the conversation.

Perhaps the most forward-looking discussion across all cities concerned the role of the strategy function itself in an AI-enabled organization. The consensus was that the strategy office’s value-add is shifting, not diminishing. As AI takes over market scanning, benchmarking, scenario modeling and report drafting, the function moves toward what AI cannot do: judgment on ambiguous trade-offs, stakeholder alignment and execution leadership.

The hypothesis that generated the sharpest discussion: future strategy offices will spend 70% of their time on program management and strategy delivery, and only 30% on strategic planning and the execution of the strategy process itself. The inversion of today’s model.

The pace of AI will not slow down. What can change is how organizations respond to it – by choosing focus over breadth, direction over activity, and delivery over ambition. That is the work the roundtables surfaced again and again, and the work the next phase will reward.

The roundtables also reminded us why this kind of exchange matters. The hardest questions are being worked out in real time – and the fastest way through them is alongside others asking the same ones.

Our roundtables bring senior decision-makers together for a peer exchange built around a TTE impulse, a live demo and a structured discussion in an intimate group where real knowledge transfer happens.