The default instinct, when budgets tighten and AI arrives in the same conversation, is to treat it as a cost-reduction tool. Trim the junior tasks, do the same work with fewer people, protect the margin. It feels rational. But it stops well short of what’s actually available.
We recently spoke to Ged Leigh, Fractional Marketing Director at The Marketing Centre, and he argues that productivity gains are only the start – and that every business will eventually get them anyway. The real opportunity is using AI to build competitive advantages that rivals cannot easily replicate: proprietary data, systems of intelligence that turn data into real insight, workflow integrations, and IP.
“AI can definitely improve productivity and efficiency, but everyone can and eventually will do that. The winning move for any business is to use AI in a way that creates new value, new competitive advantage – not just improving margin or saving cost on an existing thing in the business.” – Ged Leigh
The real opportunity, as Ged told us, is using AI to build competitive advantages that rivals cannot easily replicate: proprietary data, systems of intelligence that turn data into real insight, workflow integrations, and IP. Savings compound slowly. Competitive moats compound faster.
“Forward-thinking SME leaders are discovering something even more valuable than simply tools. They are finding that AI can help build what business strategists call ‘defensible moats’ – competitive advantages that are difficult for competitors to overcome.”
Those moats are built deliberately, by leaders who understand what they’re building and why.
Leon Calverley, Founder of Door4, arrives at a similar conclusion from a different direction. Every marketing team has a list of things it wants to do and a budget that covers only a fraction of them. Sound familiar?
This is the multiplication mindset: more surface area – more tests, more content, more prototypes, faster feedback – without asking for more budget to do it. Doing more for the same, rather than the same for less, is what changes the shape of what a marketing team can build. And for an agency, a growing multiplier justifies higher fees through compounded output, not cheaper delivery.
Two examples illustrate the difference between output that scales incrementally and output that scales structurally.
The first is case study production. Annabel Pearson, Marketing Lead at Door4, describes how the in-house built tool “Clarion” – an AI-assisted interview tool for building case studies – changed the team’s capacity entirely. The process works by setting up a project, inviting contributors to answer questions in their own time, then generating a finished piece built to exact parameters, ready to publish directly to the website template. Now, Annabel can have multiple case studies in progress at the same time, something that would have been unmanageable, if not impossible, before. The bottleneck of scheduling interviews and piecing together responses is gone. Contributors work async, and the freed-up time goes elsewhere.
The second is rapid prototyping. Leon describes how Door4 used AI to build a dashboard concept for a construction client exploring a bid management system. Rather than circulating Word documents with track changes, the team proposed a data-driven service and built a working alpha dashboard early in the process – giving the client something hands-on before a full commitment of time or budget. Faster delivery keeps client attention fresh across long technical projects, and the capability has opened doors for Door4 into new sectors, built on the same underlying skill set.
Speed is only valuable if quality holds. AI output sounds authoritative whether or not it is accurate – that was Ged’s core caution here – and credibility built over years can be undone quickly by content that hasn’t been checked. Proofreading matters more now, not less.
Annabel’s response to that risk is a specific habit:
The multiplication mindset and the quality discipline belong together. Producing more content carelessly is noise. The teams that benefit most are the ones that reinvest saved time into sharper thinking and more rigorous review, not just higher volume.
Leon has seen this pattern across enough businesses:
The businesses that treat AI as an efficiency lever for existing tasks will find they have done the same things slightly cheaper. The businesses that treat it as a capability multiplier will find they have built something their competitors will struggle to replicate.
Contributors:
Ged Leigh, Fractional Marketing Director (Regional Director), The Marketing Centre – LinkedIn
Leon Calverley, Founder, Door4 – LinkedIn
Annabel Pearson, Marketing Lead, Door4
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“AI can definitely improve productivity and efficiency, but everyone can and eventually will do that. The winning move for any business is to use AI in a way that creates new value, new competitive advantage – not just improving margin or saving cost on an existing thing in the business.” – Ged Leigh
“Fundamentally, every business has a backlog of ambition and tasks. If we use AI, we can achieve a greater proportion of that ambition. Most businesses are limited by budget – while they want to do a hundred things, they can only afford to do twenty. What automation and AI will help to do is achieve thirty of those things rather than twenty.”
“The biggest risk is the ease with which false information can slip through unchecked. The output sounds so confident that you can forget to question it. I always double check statistics in content and case studies that are produced with AI tools.”
“The primary barrier to AI adoption is leadership inertia. Even in organisations where more junior people have the skills and enthusiasm, there is little investment in or interest in technology from the top. The capability exists in the workforce – the will to act on it does not exist in leadership.”