5 minute read
AI Is quietly weakening your competitive advantage
Mark Simpson
Chief AI Officer
Everyone is “doing AI” right now. Copilot is switched on. ChatGPT is drafting emails. Teams are building internal automations. Low-code tools are stitching workflows together in days instead of months.
Productivity is up, but is your competitive advantage actually getting stronger?
In many organisations right now, AI isn’t building a moat.
It’s quietly eroding one.
The illusion of progress
In most business roadmaps currently, there feels a lot of activity.
Departments are automating manual tasks, internal tools are popping up everywhere, individuals are building prototypes in afternoons that would have taken engineering teams weeks only a few years ago
It all feels like progress but it’s also starting to look rather familiar.
Two decades ago, Excel spread through enterprises in exactly the same way. Every team built their own spreadsheet. Every function optimised locally. Data lived in silos. Productivity improved in pockets - but coherence suffered and we’re seeing the AI version of that now.
Tool sprawl. Shadow AI. Local optimisation. No shared North Star.
When everyone has access to the same foundation models, the same copilots and the same low-code tools, efficiency becomes table stakes. It does not become differentiation, rather, you behind to fade into obscurity.
The real moat isn’t the model
The strategic shift that many businesses are missing right now is that commodity is the intelligence model and differentiation is your context.
Startups can move fast, that’s nothing new and they’ll continue to do so. They can build impressive AI prototypes quickly and deploy MVPs in a weekend, but what they don’t have (and what established organisations do) is proprietary data, embedded workflows, deep customer history, sector credibility and integrated systems.
And that’s the moat. The strategic advantage that should be used as the foundation for innovation and progressive growth.
AI rewards proprietary data, clear structured workflows, deep customer understanding and a well-integrated technology stack.
If your AI initiatives aren’t tightly connected to those assets, you’re not strengthening your advantage, rather you’re simply becoming more efficient in the same way as everyone else. If all your competitors are becoming more efficient at roughly the same rate, nothing fundamentally changes.
Efficiency is a slippery slope
There’s another risk too.
AI projects often start with efficiency savings. Automate this. Reduce that. Save 15 minutes here. Save 30 minutes there. Across a 60,000-person organisation, that starts to adds up.
But what happens next?
If the conversation becomes, “AI made us 20% more efficient, so we cut 20% of cost,” you’ve entered a race to the bottom. A better, more powerful question is:
What are we going to do with the capacity AI creates?
Use it to improve CX? Accelerate product innovation? Enter new markets? Increase retention?
Peter Drucker’s warning feels painfully relevant here:
“There is nothing so useless as doing efficiently what should not be done at all.”
Automating a weak process faster doesn’t create advantage but redesigning the process around intelligence might. AI should be expanding organisational value, not simply compressing cost.
Treat AI like a product, not a tool
One of the clearest distinctions emerging is:
- Tools solve tasks.
- Products build advantage.
Many organisations are using AI as a collection of helpful tools. That’s fine as a starting point however tools are disposable. They don’t compound value like products do. A product is going to evolve over time, it has clear ownership, has measurable business impact, more than likely (hopefully) has a roadmap and is improved iteratively through clear feedback loops.
If your AI adoption isn’t being treated as a portfolio of products aligned to strategic outcomes, you’re likely optimising around the edges rather than redesigning the core.
The companies that are going to see the most value in the next 2–3 years won’t be the ones with the most pilots, they’ll be the ones with the clearest blueprint for how intelligence embeds into their operating model.
They’ll be moving fast but coherently, they’ll build systems that compound and they’ll define what their organisation looks like in an AI-native world and invest accordingly.
The strategic fork in the road
For most enterprises right now, they’re at something of a strategic fork in the road and a critical one.
One path leads to a landscape of disconnected tools and local optimisation. Teams experiment with AI to automate tasks, build small internal utilities and improve efficiency within their own areas of the business. These initiatives can produce genuine productivity gains, but without integration or strategic direction they remain isolated improvements rather than a source of lasting advantage.
The other path takes a more deliberate approach. AI is aligned with the organisation’s proprietary data, critical workflows and customer outcomes. Instead of being treated as a collection of tools, it is developed as a set of evolving products that improve over time, compound value and become embedded within the way the business operates.
Only one of these paths strengthens long-term competitive advantage.
There’s an irony in that, for the first time in decades, large organisations may actually hold the advantage. AI rewards context, deep data, established processes and customer trust - assets that most enterprises already possess, but those advantages only matter if they are used deliberately.
If AI is approached simply as a productivity tool, the result is incremental efficiency. However if it is approached strategically, it becomes a mechanism for differentiation.
Over the next few years, the organisations that succeed will not simply be those adopting AI the fastest. They will be the ones using it to reinforce what makes them uniquely valuable.
AI & Data
Product Management
Strategy
