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Coach Adam with his son's soccer team
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Most marketing AI is all coach, no players

Claude or ChatGPT can tell you what to do with your marketing, but an AI model that is integrated into your tech stack will help you take your game to another level. 

Adam Griffith
Adam Griffith

02 July 2026

5 minute read

I coach my kids' football teams, and with the World Cup on I've had the game on the brain more than usual. It has also, a little oddly, sharpened how I think about AI in marketing.

Rolling out ChatGPT, Claude or Copilot to your marketing team has likely not transformed your marketing, any more than a great coach wins the World Cup on their own. It made everyone quicker at the first draft, the brief, the awkward stakeholder email. Useful, certainly. But it didn't put an extra goal on the scoreboard.

If that stings a little, you're in good company. Salesforce's State of Marketing report puts the share of marketers using generative AI at around 87 percent, up from about half two years ago. Adoption is all but universal, which is exactly why it has stopped being an advantage. When everyone has the same tool and the same head start, the floor rises for the whole industry and nobody pulls ahead. You didn't buy an edge. You bought a ticket to keep up.

Here's the uncomfortable part, and any coach knows it: a game plan has never scored a goal. Deloitte's latest State of AI in the Enterprise survey of Australian organisations found only 30 percent are using AI to deeply change how they work, against 34 percent globally, and that for most, AI is still automating the existing process rather than rethinking it. The plumbing tells the same story: Gartner reports that marketers actively use less than half the marketing technology they already own, and only about 15 percent qualify as 'high performers', the ones hitting their goals and showing a return. So the average team now sits on an underused stack and a pile of chatbot seats. Faster at tasks. No closer to transformed. All possession, but no goal.

The coach and the players

So what would 'different' actually look like? It starts with seeing that there are two kinds of AI in a marketing team now, and like a coach and the players, they are not doing the same job.

The first is the broad model, the one you've already rolled out - Claude, ChatGPT, Gemini, [insert other model of choice]. Think of this as the coach. It reads the whole game, knows every player, and can plan for whatever the opposition turns up with. The old knock, that these models sit in a separate tab knowing nothing about you, so you spend your life pasting in brand guidelines and last quarter's numbers, was true for about a fortnight. Set one up properly, with your standards and your context loaded in once, and it holds all of it and reasons over it beautifully. It's the sharpest thinking partner your team has ever had.

But the coach never touches the ball.

The second kind is the narrow tool, built for one job and plumbed straight into it. These are the players. Take Optimizely's Opal. It lives inside the marketing stack rather than beside it, working from your real content, your asset library, your customer data and your experimentation platform, and it can draft, test, personalise, publish and measure inside the system, with the brand rules and the audit trail already in place. The difference that matters isn't whether it knows your brand. The coach knows your brand too. It's that the players are out on the pitch, connected to the machinery and built to act on it at volume.

And Opal has plenty of company. Kentico has AIRA, Sitecore has SitecoreAI, and Salesforce has Agentforce, now buying Contentful to give it a content layer. Different names, one idea: put the AI where your content and data already live, and let it act there.

There's a catch, though. The thing that wins matches isn't your best players, it's whether they play as a team. The same is true of your technology: in MarTech's 2025 State of Your Stack survey, marketers named connecting their data as their biggest headache, well ahead of skills, cost or complexity. The tools were rarely the problem. The wiring was.

So which one do you back – players or the coach?

Both, on purpose.

Before that lands too neatly, the fair objection, and it's the one I hear most: the line between the two is blurring. The big models are growing connectors and agents of their own, so a capable team can wire a general model into its stack and have it act there. So why pay for the purpose-built tool at all? Because that wiring is the actual work, and someone has to build it and keep it running: the integrations, the guardrails, the marketing-specific behaviour, the part that keeps things safe and on-brand while they touch live systems. A narrow tool, like Opal, is that work, productised and maintained. Build your own and you've quietly taken on a software product to run. Sometimes that's the right call. More often, it isn't.

So the broad model is the coach, where the thinking gets done. The players are where the work actually runs, at speed. And the real edge, the part you can't buy off a shelf, is in how well the whole side plays together, across a stack that links up.

Which brings me to the question worth putting on the table in your next planning session. Not which AI you should be using, you're already using it, the same as everyone else. Ask instead: how much of your marketing is your AI actually out on the pitch, and how much is it just a clever voice from the sideline? Answer that one honestly, and you'll know whether you've got players winning games, or just a coach who never touches the ball.

Adam Griffith
Adam Griffith

02 July 2026

5 minute read

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