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AI and the CEO

What CEOs actually need to know about AI. Not the technology - the strategy, the investment, the organisational readiness.
20 August 2023·6 min read
Isaac Rolfe
Isaac Rolfe
Managing Director
enterprise-aileadershipstrategy
You're a CEO. Your board is asking about AI strategy. Your CTO is requesting budget. Your competitors claim they're "AI-first." And every conference you attend has an AI keynote that's either terrifyingly utopian or terrifyingly dystopian. Here's what you actually need to know.

What You Don't Need to Know

You don't need to understand how transformer architectures work. You don't need to know the difference between GPT-4 and Claude at a technical level. You don't need to have an opinion on AGI timelines.
These are interesting topics. They're not relevant to your job.
Your job is strategy, investment, and organisational readiness. That's what this piece is about.

The Strategic Question

The strategic question isn't "should we use AI?" That ship has sailed. Your team is already using ChatGPT. Your competitors are investing. The technology is real and improving fast.
The strategic question is: where does AI create genuine, measurable value in our specific business, and how do we capture that value responsibly?
This is a business strategy question, not a technology question. And it requires the same rigour you'd apply to any strategic investment: clear problem definition, honest assessment of capability, realistic timeline, measurable outcomes.

Three Things That Matter

1. Your Data Is Your Competitive Advantage

The AI models are commoditising. GPT-4, Claude, open-source alternatives - they're all getting better and cheaper. In 18 months, the model you use will matter less than it does today.
What won't commoditise is your data. Your institutional knowledge, your process documentation, your customer insights, your domain expertise. The enterprise that wins the AI era isn't the one with the best model. It's the one with the best data, organised in a way that AI can use.
If I could give you one piece of advice: start getting your data in order. Not a multi-year data transformation programme. Start with the knowledge that matters most - the expertise that creates competitive advantage - and make it accessible, structured, and governed.

2. AI Is an Organisational Change, Not a Technology Deployment

Every AI implementation I've seen succeed had strong change management. Every one I've seen fail treated it as a technology project.
AI changes how people work. It changes what skills matter. It changes who makes decisions and how. These are organisational changes that require communication, training, support, and leadership.
Your role as CEO is to set the tone. If you're excited but thoughtful, your organisation will be excited but thoughtful. If you're anxious, they'll be anxious. If you're dismissive, they'll ignore it until it's too late.
The right tone: "This is important. We're taking it seriously. We're investing deliberately. We're supporting our people through the change."

3. Governance Enables Speed

This is counterintuitive. Most CEOs hear "AI governance" and think "slow down." The opposite is true.
Without governance, every AI decision requires ad-hoc risk assessment. Who approves this use case? What data can the AI access? What happens when it's wrong? Each question triggers a new conversation and a new delay.
With governance, these questions have pre-answered frameworks. The team can move within safe boundaries without waiting for permission on every decision. Governance is the infrastructure that enables autonomous AI deployment across your organisation.
3.5x
revenue growth premium for companies rated 'AI-mature' versus 'AI-exploring' in McKinsey's 2023 survey
Source: McKinsey, The State of AI in 2023, August 2023

The Investment Question

How much should you invest in AI? The honest answer: it depends on your situation. But here's a framework.
Phase 1: Exploration (3-6 months, low investment). Let your team experiment. Run pilots on low-risk use cases. Build institutional knowledge about what AI can do for your business. This phase is about learning, not returns.
Phase 2: Foundation (6-12 months, moderate investment). Based on what you learned, invest in data infrastructure, governance frameworks, and the first production AI capability. This phase is about building the platform that future AI initiatives will run on.
Phase 3: Scale (12+ months, significant investment). Deploy AI across multiple use cases, building on the foundation you've established. This is where returns materialise.
The mistake most enterprises make is jumping to Phase 3 without doing Phases 1 and 2. They buy an expensive AI platform, skip the data work and governance, and wonder why adoption is low and results are disappointing.

What to Tell Your Board

  1. AI is a genuine platform shift comparable to cloud and mobile.
  2. Our competitive advantage will come from our data and our people, not from the AI model we choose.
  3. We're investing in data infrastructure and governance as the foundation for AI capability.
  4. We have specific, measurable use cases identified and a phased plan to deliver them.
  5. This is a multi-year investment with compounding returns, not a one-time project.
That's a credible, defensible AI strategy. It's also honest about what's known and unknown, which is what boards actually want to hear.