Why clarity of strategy & focused execution are the story, not adoption

BCG’s 2026 AI transformation work found that only around 5% of organisations have generated substantial financial gains from AI — though the few that have show roughly four times the three-year shareholder return of everyone else. McKinsey’s parallel measurement work makes the diagnosis sharper still: most companies cannot draw an auditable line from model performance to user adoption to operating KPIs to financial impact. Activity is high. Evidence is thin.

So the fashionable board question — “What is our AI strategy?” — turns out to be the wrong one. It invites a slide deck: pilots, copilots, a vendor logo wall, a training completion rate. None of that tells a board whether the business is safer or more valuable. The question that actually earns its place at committee is the ‘Founder Question’:

With £100 million, frontier models, no legacy systems and no sacred cows, where would we attack ourselves first?

The gap between “we adopted AI” and “we understand what AI does to our economics” is exactly where a challenger gets in.

What the smartest firms actually agree on

The leading strategy firms and business schools are finally coming to a simple truth - AI value is not captured by access to models. It is captured by redesigning the business around workflows, proprietary advantage and measurable economics. Everyone can rent the same frontier model. Nobody can rent your customer data, your distribution, or your regulatory licence. That is where the real argument sits — not in who has the best chatbot.

Three AI disruptors. Three warnings.

AI-native start-ups aren’t simply “using AI more” than incumbents — they’re built on a different premise: intelligence is infrastructure, not headcount. AWS’s 2026 research found 68% of AI-native start-ups have a comprehensive AI strategy versus 45% of start-ups generally, 72% have built proprietary AI capability versus 30%, and 98% employ dedicated AI talent versus 70% of large enterprises. A Harvard/INSEAD study reported by Business Insider found these firms run about 25% smaller, with 13% more engineers and far fewer entry-level staff and managers than their non-AI-native peers.

Three examples bring this to life:

Cursor – reinvent the product. Instead of adding AI to traditional coding software, Cursor rebuilt the developer experience around AI agents that can understand, write and modify software. The result: reported recurring revenue reached $2bn by March 2026, while 64% of the Fortune 500 now use it. Lesson: don’t bolt AI onto yesterday’s product. Ask: if we started again today, would we build our core product the same way—or could an AI-native startup make it obsolete?

Harvey – reinvent the workflow. Harvey applies AI deeply to legal work—researching, drafting, reviewing contracts and executing complex workflows that once consumed hours of expensive professional time. It now serves 142,000+ lawyers across 1,500+ organisations, including 60%+ of the AmLaw 100, and was valued at $11bn in March 2026. Lesson: don’t scatter AI across 100 productivity pilots. Find the few high-value workflows where AI can collapse hours into minutes—and build a defensible advantage around your data, expertise and customers before someone else does.

Sierra – reinvent the economics. Sierra’s AI agents don’t just answer customer questions; they can take actions and resolve problems, with pricing increasingly tied to outcomes rather than software seats or human hours. It passed $100m ARR just seven quarters after launch and was valued at over $15bn by May 2026. Lesson: AI may not merely reduce your costs—it can destroy the basis on which you charge. Ask: what happens if a competitor delivers our customer’s outcome at a fraction of our price?

Corporates can reinvent themselves too

John Deere and Caterpillar show that century-old industrial companies can become technology businesses without abandoning their core. Deere has moved from selling tractors to combining machines with GPS, computer vision, AI, autonomy and a digital Operations Center—turning farming into a data-driven system and creating software and recurring-revenue opportunities around the machine. Caterpillar has built an ecosystem of 1.6m+ connected assets, using their data, analytics and AI for predictive maintenance, productivity and autonomous equipment. Lesson: incumbents can transform—but only when technology becomes core to the product, customer value and business model, not an IT initiative bolted onto the side.

The Six Ps of an AI strategy: a board framework, not a CIO one

How do you systematically make connected proactive choices from where you are to where you want to be - with clarity regarding competitive advantage (cost or differentiation), how to get there & how to measure the outcome. Welcome to the 6 P’s framework - a simple framework akin to what the leading strategy houses and business schools are selling:

Posture. By business line: are we defending the core, deploying AI inside the current model, reshaping the operating model around it, inventing something new — or deliberately waiting? Vague answers here mean nothing downstream will be sharp either.

Profit pools. AI doesn’t hit “the company” evenly. One way to look at this is from the frame of how an agentic start up might attack your business? As per the examples of Cursor, Harvey and Sierra discussed above.

Proprietary advantage. Most companies have no AI moat — they have a rental agreement with the same model everyone else uses. Durable advantage comes from what can’t be rented: proprietary data, embedded workflow, trust, brand, distribution, regulatory position. The board question is simple — what’s still ours once everyone has the same model?

Platform. Buy, boost, build, self-host or avoid — by use case, not by enterprise-wide edict. Buying is fast and undifferentiated. Building is powerful and expensive. Most companies will end up hybrid: frontier models for hard reasoning, cheap models for routine work, tight controls where a regulator is watching.

Process. This is where strategy either becomes real or quietly dies. AI bolted onto a bad process just produces faster bureaucracy. The board shouldn’t accept “we have 40 use cases” as evidence of anything. Ask instead which five workflows have been redesigned end-to-end, who owns each one, and what the before-and-after economics actually are.

Proof & permission. Proof means value is measured, not asserted. Permission means the risk appetite is explicit — where AI can act alone, where it must stay a copilot, where it’s banned outright. As agentic systems move from answering questions to taking actions, this stops being a technology question and becomes a board risk-appetite question.

The cost curve nobody’s watching

There’s a second reason “proof & permission” earns its place as a standalone P: AI is not simply getting cheaper. Commodity-tier models are in genuine price freefall — a routine task now costs a fraction of what it did eighteen months ago. But the frontier, agentic layer is moving the opposite way. Run the current published output-token prices in order and the ceiling has roughly quintupled since the cheapest commodity model, before you even count the volume: agentic coding tasks have been measured consuming up to 1,000 times more tokens than a simple reasoning query, with up to 30x variance between two runs of the same task and no guarantee the extra spend buys extra accuracy. “AI gets cheaper” is the headline. “AI is deflating at the commodity layer and inflating at the frontier layer” is the board-relevant sentence — and it’s why unit economics per workflow, not per licence, belongs on the dashboard.

What to ask at your next board meeting (& how to measure progress)?

Ten sharp questions for your next board meeting, below — and a worked example of what an actual AI strategy dashboard looks like, in this week’s Library piece. Between them, “how many copilots have we deployed?” stops being an acceptable answer to anything, and the only question that matters takes its place: where will AI change the economics of our business before the market does it for us?

FIVE FOR THE CHAIR · EXTENDED

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Disclaimer: These are my personal views, shaped in a fast-moving environment and open to revision. They should not be taken as representing the perspectives of any boards or advisory roles, past or present.

Sources & Further Reading

Diligent Institute, Board AI use rises: Director Confidence Index June 2026 — diligent.com/resources/blog/dci-board-ai-use-2026

McKinsey, The AI reckoning: How boards can evolve — mckinsey.com/capabilities/mckinsey-technology/our-insights/the-ai-reckoning-how-boards-can-evolve

BCG, CEOs and Boards Are Aligned on AI in Theory, but Divided in Practice — bcg.com/publications/2026/ceos-and-boards-are-aligned-on-ai-in-theory-but-divided-in-practice

McKinsey, From promise to impact: how companies can measure and realize the full value of AI — mckinsey.com/capabilities/quantumblack/our-insights/from-promise-to-impact-how-companies-can-measure-and-realize-the-full-value-of-ai

McKinsey, Cost versus value: Managing agentic AI system performance — mckinsey.com/capabilities/quantumblack/our-insights/cost-versus-value-managing-agentic-ai-system-performance

Harvard Business School / HBR, The Gen AI Playbook for Organizations, HBR Prize note — hbs.edu/news/releases/hbr-2025-prize

MIT Sloan, Buy, boost, or build? Choose your path to generative AI — mitsloan.mit.edu/ideas-made-to-matter/buy-boost-or-build-choose-your-path-to-generative-ai

London Business School, How can you use AI to redefine competitive advantage? — london.edu/think/think-ahead-ai-for-competitive-advantage

INSEAD, Why Strategy Matters in the Age of AI — knowledge.insead.edu/strategy/why-strategy-matters-age-ai

AWS, Global Startup Trends Report: AI-native startups — press.aboutamazon.com/2026/6/aws-global-startup-trends-report-reveals-ai-native-startups-are-reaching-billion-dollar-valuations-in-half-the-time

Stratechery, Agents Over Bubbles — stratechery.com/2026/agents-over-bubbles

Your board was built for risks that hold still. AI doesn’t.

How many Boards or committees spend forty minutes on AI - where the CIO runs accuracy charts. The General Counsel updates on the EU AI Act & the AI policy document gets signed off without a question? Polished papers, all minuted.

No realtime metrics or discussion on model agency or risk classification? How many decisions in that room reflected that the system under discussion had shipped several vendor updates since the last cycle with updated token pricing & multiple APIs accessing your data — or that the model on the slide wasn’t the model in production.

The governance is theatre. The AI risk is live & different for 3 reasons:

It is probabilistic. Traditional software works or it doesn’t. AI is right most of the time — and wrong with total confidence some of the time. ‘Show me the test results’ gets you a confidence interval, not a certainty. Most directors have never governed one.

It doesn’t sit still. Models drift, vendors push updates, data shifts. A compliant system in January can be non-compliant in June without anyone touching the code. And it doesn’t stay where you put it: AI now sits inside spreadsheets, copilots, supplier portals and browser extensions, mostly uninventoried.

It moves at machine speed. Use cases deploy in weeks; board cycles run in quarters; assurance runs in years. A biased model can serve a million decisions before the next risk committee meets. A deepfake can move a share price in an afternoon.

So what: the annual audit cycle, the quarterly board pack and the policy attestation were designed for a different clock speed. Here are the ten ways that mismatch blows up — and the risk is rarely the technology. It is the governance around it.

1. Strategy — someone reprices your industry while you run pilots. The most dangerous slide is the one describing AI as ‘efficiency’. The real question is whether anyone — incumbent or new entrant — is using AI to reprice the industry around you. If management cannot say where your proprietary advantage now sits and what AI does to it, that is your first red flag.

2. Decisions — the model is wrong with confidence. Hallucination, bias, drift, opacity — and increasingly, decisions taken autonomously at machine speed before a human can intervene. The board does not need to validate models. It needs to know that someone does, and that the highest-stakes decisions carry a human check by design. Would you know if your AI got something wrong an hour ago — or only when a regulator, a customer, prospective hire or a tribunal told you?

3. Data — your crown jewels leak one prompt at a time. Staff are pasting confidential material into public tools today. Models are being trained on content the company may not own. Data governance is not hygiene; it is the foundation of defensible AI — and the first thing a regulator or claimant will pull on. Do you know which of your data is feeding which models? Are you sure you want foundation models accessing your data (ie not training your competitors), are ‘enterprise’ promises & guardrails valid when the underlying model retains data (ie Claude Fable). Is model control a competitive advantage or a moat in your category? The most recent high profile example is of Anthropic rebuilding elements of Figma, where a partner entered the category (Figma share price down circa 50%YTD). The recent debate is around in-house open source models running your own compute stack - given current frontier model token costs this is now becoming a viable (safer) alternative for many?

4. Cyber and fraud — the attacker now has AI too. Deepfake CEO impersonation is no longer hypothetical; finance teams have wired money to cloned voices. The launch and fanfare around Anthropic’s launch of Mythos has changed the Board conversation. However, one of the biggest risks lies within - ‘prompt injection’ can turn an AI assistant into a hijacked insider with real credentials. If cyber reporting to your board does not name AI-enabled threats & the new ‘surface’ they are attacking explicitly, the reporting is incomplete.

5. Regulation — ‘we didn’t know the tool used AI’ is not a defence. The EU AI Act is real, phased and binding for anyone touching the EU market - with the high risk regime landing this August. The UK floor is hardening; the SEC has already enforced against misleading AI claims. Regulators have stopped accepting an annual sign-off as evidence, they want model level records, continuously.

6. Operations — what happens if your AI vendor pushed a silent update tonight, would you find out from your own monitoring — or from your customers? One retailer only discovered its personalisation engine had quietly changed pricing logic after a routine vendor patch when margin fell two points, unexplained, in a single quarter. A yearly continuity test doesn’t catch a system that changes behaviour on a Tuesday night without telling you.

7. Reputation — one failure erases ten wins. AI failures are reputationally asymmetric. One unfair decision, one leaked dataset, one deepfake incident, and the value created by ten well-governed use cases evaporates. Trust is a board asset. Ethics has to be a control, not a communications line.

8. Talent — job enrichment vs displacement? Adoption without redesigning how the work gets done, lacking AI Literacy beyond the tech team. No incentives tied to value capture. Questions many boards are now asking - What is our AI workforce strategy? How are we measuring impact on jobs? What is our AI literacy baseline - for Colleagues, Executives & Directors? How can we deploy talent, rebundle ‘tasks’, build a new organisation model with different spans of control - with humans managing agents?

9. Concentration — it’s no longer just about ‘which model do we use”. It’s about whether a single government decision, vendor pivot or API or token price change could disable critical workflows overnight or make them uneconomic? Most third-party due diligence never asks about model provenance, change notification or sub-processor chains. Does the Board know where concentration risks lie, how quickly can we substitute and what’s the impact?

10. Economics — the money blows up quietly. Also new, and increasingly the one investors ask about. AI cost is now tied to physical infrastructure — compute, chips, power — and token pricing that can move under your business case. Meanwhile most enterprises are funding pilot portfolios with no stop/go criteria and no measured return. Capital committed to AI should face the same discipline as any strategic capex/opex: stage gates, downside cases, explicit return thresholds. Uber recently announced it burnt through its entire 2026 AI coding budget in four months and moved to cap token usage after the horse had bolted.

A board that checks in once a quarter cannot govern a system that changes every day. Today only 39% of Fortune 100 boards have any form of AI oversight — a committee, a director with the expertise, an ethics board. Only 6% have management reporting that goes beyond adjectives. That’s not a knowledge gap.

The uncomfortable close: none of the ten requires the AI to fail. Half of them detonate when the AI works exactly as designed — on the wrong data, at the wrong speed, under the wrong ownership, or at a price that no longer makes sense. How to fix it

A BCG survey of 625 CEOs and board members published last month (May 2026) found that 61% of CEOs believe their boards are rushing AI transformation. Not dragging their feet. Rushing it.

The explanation buried in the data is worse than the headline: the directors with the lowest confidence in their own AI knowledge are the most likely to believe their organisation is moving too slowly.¹

The directors who understand AI least are the ones pushing hardest for speed.

This inverts the conventional narrative — that boards are too cautious, too analogue, too late for the AI moment. The 2026 evidence says something more uncomfortable: boards are not behind AI. Some are ahead of what they understand. And that combination — urgency without literacy, momentum without oversight — is precisely how consequential decisions can get made badly, and at scale.

The Stat This Week
61% of CEOs say their boards are rushing AI transformation — and directors with the lowest AI literacy are the most likely to believe they are moving too slowly. Urgency is being generated by uncertainty, not by analysis. (BCG, May 2026, n=625.)

The silence in the room

The history of disruption rhymes. The pattern is common: creative destruction gets treated as business-as-usual until it isn’t. Have you sat in meetings approving AI spend with no clear value logic, approving the tech stack without challenging the design choices, treating AI governance as a checklist, measuring success by pilots or tokens, accepting ‘human oversight’ without testing if it’s for real? No need to put your hand up.

Are we asking the right strategic and governance questions of a technology now reshaping how organisations fundamentally compete and operate — price, recruit, source, credit-check, market — where agents, not humans, are increasingly making the decisions?

The question that matters most:

If a well-capitalised competitor rebuilt our core product around AI in the next eighteen months, what would remain of our competitive position?

That absence of that question that wasn’t on the agenda is the governance gap. Not speed. Not caution.

The numbers no longer allow a comfortable interpretation

Two findings now sit alongside each other in a way the boardroom cannot ignore.

Deployment is happening regardless of readiness. Grant Thornton’s 2026 AI Impact Survey finds nearly three in four organisations are giving agentic AI access to their systems and processes — piloting, scaling or running it in production. Just 20% have a tested AI incident response plan for when it fails.²

Capital is moving at the same speed. BCG’s AI Radar finds corporations expect to lift AI spending from 0.8% to 1.7% of revenues in 2026, with more than half directed at agentic systems.³ These are not experimental budgets. They are material capital commitments made into a technology most boards do not yet have the fluency to interrogate.

What changes when the actor isn’t a person

Today’s operating model rests on a hidden assumption: the actor executing a decision is a human who can be instructed, supervised, slowed down and held to account. Once decisions and actions are delegated to autonomous agents — pricing engines, customer-routing models, recruitment screens, refund bots, credit-check agents — that assumption breaks. And once it breaks, the substance of executive & board responsibility changes with it.

Governance moves from directing human actors making occasional, reviewable decisions to deciding how much agency to delegate to a non-human actor in the first place. Risk moves from slow, visible and auditable-after-the-fact to fast, opaque and emergent — errors propagating at machine speed before controls catch them. Controls move from detective to preventive, embedded (in the code) and real-time — hard caps, whitelists, kill switches built inside the agent. Compliance moves from periodic and sample-based to continuous and designed-in. Assurance moves from tracing the decision to validating the guardrails under stress, because the reasoning isn’t transparent. Accountability stays exactly where it has always sat — with a named human — because it can never transfer to the agent.

The new first-order board question is no longer “how much risk are we willing to take?” It is “how much agency are we willing to delegate?” — where agents may act autonomously, recommend-only, or are banned outright; each with a named human owner; each with a tested kill switch.

AI doesn’t replace the board’s duties. It raises the metabolic rate at which they must be discharged.

We have been here before — and we didn’t learn the right lesson

When the internet arrived, large incumbent organisations appointed Chief Digital Officers. The CDO became the governance pressure valve — the person who ran the digital programme, attended the right conferences, brought in an army of vendors, seeded experiments and then asked to launch a venture fund. Boards called it digital strategy. Meanwhile insurgent entrepreneurs backed by smart VC money went directly after their core profit pools — reinventing the economic model, new distribution, ‘product’ as marketing, disciplined quarterly sprints, real-time customer metrics, not waterfall two-year IT projects out of date at the point of committing funds. Reinvention using technology, not technology applied to what they already did.

The companies that navigated that transition had something different: a willingness to self-disrupt — challenging their own legacy economics before someone else did — and boards and management teams who took a long view of what technology might do to their market, treated governance as part of product quality, and were clear about the problem they were solving and why their solution was 50%+ better. The best live example today is Google, managing an AI transition where Search still accounts for over $225 billion — more than 55% of total revenues.

Stripe and Square went after incumbents’ payment economics; Spotify rebuilt music distribution around access, not ownership; Airbnb did the same to hotels’ fixed-cost economics. On the other side of those bets sat the boards that watched it happen — Kodak, Blockbuster, Nokia, BlackBerry, Sears — others will follow. The governance failure was not a failure to understand technology. It was a failure to understand what technology did — to competitive dynamics, to cost structures, to who controlled the customer relationship. Crucially, digital was the CEO’s job, treated as a cross-business imperative with a board-level strategy. Not delegated to one function. Not run as an IT project.

The same structural deficit is repeating itself. We are, in 2026, roughly where we were in 2000 — the difference is that the cycle is compressed, the capital commitments are larger, and the competitive and regulatory environment is hardening.

The Four-Domain Frame — the navigation grammar

Stripped back to first principles, the WEF Oversight Toolkit, KPMG/INSEAD’s Global Principles, McKinsey’s AI Trust framework and BCG’s board guidance converge on ‘four domains’ a board must now own at the same time:

The Four-Domain Frame - Most boards govern one of these well. A few govern two. Not many govern all four simultaneously — this is not a surprise in a fast moving world of ambiguity, hype and uncertain outcomes. Nevertheless, the discipline is important.

The ten questions every board should be asking sit across the four domains. Each carries a consequence: the cost of it going unasked. That cost is the point.

Strategy & Innovation

1. If a well-capitalised competitor rebuilt our core product around AI in the next eighteen months, what would remain of our competitive position?

Most boards govern AI risk inside the existing business. Not many ask the disruption question — not whether AI creates risk in our operations, but whether it lets a rival make our operations irrelevant. Management is paid to defend the model that exists. The board owns the question of whether that model survives.

2. Is our AI investment buying competitive advantage — or operational parity that every rival will also reach?

Boards are approving AI spending that doubles in a single year. The right governance question is not how much. It is what for. Efficiency gains from AI are real and accessible to every competitor. The organisations building durable advantage are redefining the product, the customer relationship or the cost architecture in ways that are hard to replicate. Does the board know which category its spend falls into?

3. Is AI changing how this organisation makes decisions — or simply automating the decisions it already makes?

This is the line that separates real transformation from expensive process improvement. Most organisations use AI to do existing things faster. Fewer use it to do things differently — to make decisions with information they could not previously access, at speeds the operating model precluded, at a level of personalisation the old margin structure could not support. The board should know which category describes its programme.

Risk & Resilience

4. What is our AI risk framework built to catch — and what is it structurally incapable of seeing?

Risk frameworks identify the risks they were designed to identify. AI introduces categories no pre-AI framework was built to surface: emergent model behaviour, training-data bias at scale, adversarial manipulation, hallucination in high-stakes outputs, and — new in 2026 — agentic systems taking sequences of autonomous decisions across connected processes. An audit committee receiving a RAG status update on AI risk is not governing AI risk. It is receiving a summary of classified risks — which is a different thing entirely.

5. How much agency have we delegated to non-human actors — and did we decide that, or has it happened to us?

Three in four organisations now give agentic AI access to their systems; only one in five has tested what happens when it fails.² The board’s instinct is to ask how much risk. The question that survives the next two years is how much agency. For every material AI use case — pricing, credit, recruitment, customer routing, supply chain, refund handling — there are three possible answers: autonomous, recommend-only, prohibited. Each with a named human owner. Each with a tested kill switch.

Governance & Accountability

6. Where does accountability sit when an AI-driven decision causes harm — and has the board formally assigned it to a named human?

This is not hypothetical. The EU AI Act’s requirements are now effective across European markets. The FCA’s AI governance expectations are hardening. D&O exposure on AI-related harm is live and being tested. Most boards have not formally assigned AI accountability — not in committee terms of reference, not in management responsibilities, not in the schedule of matters reserved. Accountability never transfers to the agent. The answer, when it matters, will be found in what was documented. Not in what was assumed.

7. Is AI a standing item on the board agenda — or does it appear only when something goes wrong or management asks for a budget?

Reactive governance is incident response with a board letterhead. The boards building real oversight treat AI as a live strategic conversation — at the frequency and seriousness of financial performance — not as a technology update in the CTO’s slot once a quarter.

Capability & Culture

8. Are we asking management the questions that matter — or the questions management has prepared us to ask?

This is the meta-question, and the one I find most difficult to answer honestly about my own contribution. When I have been on the presenting side of the table, I knew which questions were coming. I had prepared answers. The questions I had not prepared for were the ones that changed the dynamic — and they were almost never on the agenda. The BCG finding cuts both ways: if boards are pushing faster than management, the question is whether that pressure is informed or anxious. Governance is not about speed. It is about the quality of the decision.

9. Who in this boardroom has used an AI tool to do something consequential in the last thirty days?

I include myself in this question. If your understanding of AI is entirely briefing-derived, your pressure for speed cannot be informed. The board cannot meaningfully challenge management’s AI strategy if its understanding of AI is entirely second-hand. This is not about becoming data scientists. It is the same standard of practical engagement expected of any director overseeing a material business transformation. The board that governed digital without digital experience was the first wave’s structural error. Repeating it knowingly is harder to excuse.

10. Do we have the board composition to govern the company we are building — not just the one we have?

The NomCo’s job has always been to ensure the board has the skills the strategy requires. If the strategy now includes material AI transformation, agentic systems deployment and regulatory navigation under the EU AI Act, the skills required have changed. Not more technologists — more directors with the economic intuition to read what AI does to competitive dynamics, cost structures and the value of human judgment. A board effectiveness review that does not address this is answering the wrong question.

Effective AI governance is not a framework. It is a quality of attention — and the willingness to ask the question that was not prepared for.

Before your next board meeting

Of the ten questions above, which three would most change your board’s current AI conversation?

This week in The Library

Your AI governance isn’t an IT policy. That’s why it’s failing. The companion to this Manifesto — the framework, the board’s four jobs and the six diagnostic questions that test whether your governance is real. Read after this.

Next week in The Frame

Next week we discuss 10 ways AI can blow up your business. In a world where AI is probabilistic, it doesn’t sit still & it moves at machine speed - what are the risks and what is the emerging augmentation to the traditional governance model.

Disclaimer: These are my personal views, shaped in a fast-moving environment and open to revision. They should not be taken as representing the perspectives of any boards or advisory roles, past or present.

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Sources

1. BCG, AI in the Boardroom, May 2026 — survey of 625 CEOs and board members.

2. Grant Thornton, 2026 AI Impact Survey — agentic AI deployment and incident response readiness.

3. BCG, AI Radar 2026 — corporate AI spend as percentage of revenue.

Performance reference: MIT CISR, Board Digital Fluency and Performance, March 2025 — AI-fluent boards outperform peers by 10.9 percentage points in ROE; non-fluent boards trail by 3.8%. Carried in The Library’s board-effectiveness primer.

Frameworks referenced: WEF AI Governance Toolkit (2024–25); KPMG/INSEAD Global AI Governance Principles (April 2026); McKinsey, State of AI Trust 2026 — Shifting to the Agentic Era; BCG AI Radar 2026. Agentic governance logic drawn from working paper Governing AI Agents in the Enterprise (Library, May 2026).