What Cannes Revealed About GTM, and Why Your Strategy Isn't Built for What's Coming

Four days at Cannes. Two very different conversations happening in parallel.

On the main stages: efficiency, productivity, content acceleration. A more predictable topic circuit, dressed up in different keynotes, which is understandable. When you're speaking to thousands, you play more to the safe centre.

The more interesting conversation was happening outside of it. At the fringe events like the AI x Tech Sandbox and smaller panel rooms. Here the focus shifted from what AI can do, to the fundamental shifts it's forcing in how businesses compete, where value gets built and why most GTM strategies aren't designed for this new operating reality.

The real conversation wasn't about tools. It was about competitive conditions.

One of the leading tech analysts gave what was the sharpest talk of the week. His point: most businesses are still doing the old stuff faster with the new technology. The harder question, the one most organisations aren't yet asking, is where AI breaks something, improves something, or unlocks something in how they operate and compete. Not better ads or faster campaigns, but what actually changes at a structural level.

The analogy he used has stayed with me. When the internet arrived, journalism wasn't fundamentally changed by it. But journalism had been built on local printing plants, distribution monopolies and captive advertising markets, and those got demolished. The product survived. The business model didn't.

The quieter risk in 2026 isn't that AI replaces your team. It's that it removes the thing your market position has been quietly resting on.

Products can be replicated faster, features copied overnight, and new competitors can appear, reach credibility, and start winning deals before slower incumbents have agreed on a response. If speed, scale and product advantage are becoming less defensible by default, what is enterprise value actually built on?

The day two discussion at Cannes circled this without quite landing on it. Whether it's proprietary data, distribution depth, brand strength, customer relationships, category ownership, or the ability to keep learning faster than the market moves, the honest answer is probably a combination, but only if deliberately built and continuously maintained, not assumed. Most businesses, if they're honest, couldn't confidently answer that question right now.

For B2B, there's a specific commercial implication most businesses are missing.

There was also a layer that felt specifically relevant for B2B, and it's one that doesn't get talked about enough.

AI may help buyers move faster in research. But in high-stakes decisions, the kind B2B operates on, confidence still depends on proof, provenance and critical thinking. WSJ research found that only 6% of senior business decision-makers rated AI summaries as highly trustworthy for significant decisions. Only 1% of people researching a brand through AI were taking the output at face value.

By the time buyers are in a conversation with your sales team, the picture they have of you was assembled largely without you.

Think about what that means in practice. Buyers are forming views before you see them, through AI-mediated discovery, synthesised from whatever is findable, citable and credible enough to surface.

The question isn't really about AI optimisation. It's about whether your strongest proof points, expertise and differentiation are visible, structured and credible enough to be found, cited and trusted in a world where that first impression is increasingly assembled by a machine. That's a GTM question, not a technical one.

Bolting AI onto an existing strategy doesn't answer it.

Most GTM strategies were built for a different set of conditions: relatively stable buyer behaviour, known categories, consistent channels, and competitive dynamics that move at a pace annual planning can handle.

AI is changing several of those assumptions at once. Buyer discovery patterns are shifting, category definitions are being contested, and competitive signals are accelerating. The intelligence needed to stay calibrated is moving at a speed annual planning cycles weren't designed for.

Adding AI tools into existing workflows doesn't solve this. It just makes the old approach faster.

What's needed is a different frame: GTM as an operating system, not a strategy you set and execute, but a connected system that continuously reads the market and activates in sync.

That's the problem Magnitude was built to solve. Magnitude connects three things that usually sit separately in B2B organisations: outside-in intelligence on markets, customers and competitors; inside-out intelligence on accounts, content and commercial positioning; and activation, turning that intelligence into content, campaigns and sales tools without the usual lag between insight and output.

The result isn't faster marketing. It's a more commercially coherent business, one that knows where it stands, keeps its value proposition calibrated to real buyer needs, and can move when conditions change rather than six months after they have.

The businesses that compete well over the next three years aren't necessarily the ones with the best AI tools. They're the ones that have rebuilt their commercial model around continuous intelligence, always working from an accurate read of where to play and how to win.

The conversations in Cannes kept circling that argument without quite saying it directly. We're saying it now.

Key takeaways

The businesses that will pull ahead aren't waiting for the AI landscape to settle. They're building commercial operating models designed to keep moving as it does, grounded in real intelligence, activated fast, and continuously calibrated to where buyers actually are. If you'd like to explore what a GTM operating system could look like for your business, speak with the Magnus team.