Win With AI by Using It in Ways Only You Can

There is a worry creeping through boardrooms and strategy meetings that nobody quite wants to say out loud. It goes something like this: if we are all using the same AI tools, trained on the same data, producing similar outputs, where exactly does competitive advantage come from?

This is not paranoia. It is a legitimate strategic concern, and it deserves to be taken seriously.

Mehdi Paryavi, CEO of the International Data Center Authority, put it bluntly in a recent interview: “If you and your competitor are all using the same service, you have no edge over each other. Their AI and your AI against each other; I don’t know who’s going to win.”

Researchers at MIT Sloan Management Review reached a similar conclusion, arguing that AI will be “a source of homogenization,” not differentiation. The World Economic Forum has observed that “as more organizations access the ability to generate software and automate tasks with AI, traditional technology advantages are becoming less distinctive.”

The fear is real. But here is the thing: it is pointing at the wrong culprit.

The Sea of Sameness

Consider two mid-sized companies in the same industry. Both have embraced AI-powered content marketing. Both are using the same suite of tools for blog posts, social media, email sequences. Both are publishing more than they ever have before.

And both sound exactly the same.

Their blog posts hit identical keywords. Their LinkedIn posts follow the same structures. Their email sequences feel interchangeable. If you stripped away the logos, you might not be able to tell them apart.

This is not a hypothetical scenario. Marketing researchers have documented that AI-assisted content is becoming “virtually indistinguishable” across competitors. Studies show that companies with distinctive brand voices see roughly 20% higher customer retention, yet that distinctiveness is eroding as teams lean harder on AI tools that pull from the same patterns.

Even consumers have noticed. Research indicates that a significant majority of people (over 80% in some surveys) can detect AI-generated content. And when they detect it, engagement drops. Human-generated content, according to one analysis, receives more than five times the traffic of AI-generated content.

As one marketing researcher observed: “When your team leans too heavily on AI tools, your content starts blending in. It stops sounding like you and starts sounding like everyone else.”

Neither company is winning because neither company is different.

The Deeper Problem

The homogenization concern is backed by serious research and strategic thinking. When competitors use identical systems to think, write, and decide, the outputs converge.

MIT Sloan researchers have been direct about this: “The advantages AI confers will be conferred on all. By definition, if everyone has access to the same technology, even if it is new and valuable, it may move the market as a whole but will not uniquely advantage anyone.”

They also point out how rapidly the tools themselves are commoditizing. Algorithms and training data are widely available. Hardware competition is fierce. Talent is plentiful. Open-source models reliably erode the advantages of proprietary corporate offerings.

An IBM executive put it starkly in late 2025: “The workflow itself is where the money is. The model is a commodity, and we will keep getting better and better models.”

Read that again. The model is a commodity.

If the model is a commodity (and it is) then obsessing over which model to use is like obsessing over which brand of hammer to buy when you are building a house. The hammer matters, but it is not what determines whether the house stands.

The Reframe

Here is where the conversation needs to shift.

The problem is not that everyone has access to powerful AI tools. The problem is what most companies are applying those tools to.

Generic use cases yield generic results. Content generation, basic customer service automation, standard analytics dashboards: these are the same applications that every competitor is pursuing. The tool is identical. The application is identical. So the outcome is identical.

Researchers at UC Berkeley’s California Management Review predicted this would happen. They argued that early AI applications “will quickly become table stakes; investing in them will be the price of entry for doing business, rather than the ticket to success.”

In other words, using AI for the obvious things keeps you in the game. It does not help you win.

The companies pulling ahead are doing something different. They are applying AI to processes that are uniquely theirs, workflows that competitors could not replicate even if they tried.

The World Economic Forum captured this insight: “In this future of commoditized technology, an enterprise’s unique way of operating will be what truly sets it apart.”

Process Is the New Moat

McKinsey’s research on AI adoption has been tracking what separates high performers from the rest. One finding stands out: organizations achieving meaningful business impact are “nearly three times as likely as others to say their organizations have fundamentally redesigned individual workflows.” Workflow redesign, they found, has one of the strongest contributions to achieving meaningful business impact of all the factors tested.

Not tool selection. Workflow redesign.

BCG has framed this with their 10-20-70 principle: AI success is roughly 10% algorithms, 20% data and technology, and 70% people, processes, and cultural transformation.

Let that ratio sink in. Seventy percent of the value comes from how you transform your processes and culture to leverage the technology. Not from the technology itself.

This creates a clear strategic implication. When the tool is a commodity, unique application becomes the differentiator. Proprietary data combined with proprietary processes equals results that competitors cannot copy.

As the California Management Review researchers put it: “Companies will not only need proprietary data, they’ll need a proprietary approach to drawing inferences from that data.”

The data is necessary but not sufficient. What you do with it, how you weave it into processes that only you operate, that is where defensible advantage emerges.

Differentiation in Action

Let me show you what this looks like in practice.

A global financial institution faced a labor-intensive challenge: reviewing thousands of commercial loan agreements annually. The process consumed a massive amount of legal work hours and was prone to human error. Every contract had to be analyzed for specific clauses, risk indicators, and compliance requirements.

Rather than deploying a generic AI tool for document processing, they built a system trained on their contracts: their specific clause patterns, their document types, their risk indicators. The AI learned to identify and categorize attributes unique to their agreements, patterns that existed only in their institutional data.

The result: a process that once consumed enormous amounts of time now completes in seconds. Compliance errors dropped dramatically. Operational costs fell significantly.

A competitor using the same underlying AI technology could not replicate this. They do not have the proprietary data. They do not have the institutional knowledge of what those patterns mean. They do not have decades of accumulated contracts that trained the system to recognize subtle variations that matter.

The AI technology was not proprietary. The application of it to their specific workflow, trained on their specific data, was.

Here is another example from healthcare. A major healthcare system wanted to improve early cancer detection and streamline oncology workflows. They did not just plug in an AI platform; they integrated it with their specific nurse navigator system, their patient data, their clinical registry processes.

The AI learned the patterns that existed only in their care environment. It centralized data that had been siloed across departments. It flagged early warning signs calibrated to their patient population. It automated registry workflows built around their specific compliance requirements.

Competitors using the same AI platform could not achieve the same results. The advantage came from the deep integration with processes that only that organization operated, workflows built over years that reflected their specific approach to patient care.

The value was not in the AI platform itself. It was in how deeply that platform was woven into workflows specific to that organization.

Finding What Only You Can Do

This leads to a practical question: how do you identify the workflows worth transforming?

Start by asking where you have unique data that competitors do not have. Every organization generates data through its operations, but some of that data is truly distinctive: patterns in customer behavior that only you see, institutional knowledge captured in documents that only you possess, relationships in your data that reflect your specific way of doing business.

Then ask which workflows are built on institutional knowledge accumulated over years. The processes that feel “just how we do things” often contain embedded expertise that is extremely difficult to replicate. That expertise, combined with AI, becomes a multiplier that competitors cannot access.

Consider which pain points exist because of your specific business model, not because of generic industry challenges. The frictions that arise from your particular combination of customers, products, geography, and history are often the most valuable targets for AI transformation, precisely because the solutions will be just as specific.

There is a simple test you can apply: if a competitor used this same AI tool, could they replicate what we are doing?

If the answer is yes, you are probably pursuing a table-stakes application. Necessary, perhaps, but not differentiating.

If the answer is no (because they lack your data, your processes, your institutional context) then you may be building something defensible.

The Strategic Shift

The AI tools will keep getting better. They will keep getting cheaper. They will keep getting more widely available. This is not a trend that is going to reverse.

That means the window for gaining advantage from tool selection is closing. It may already be closed for most applications.

What remains is how you apply those tools. What processes do you transform? What unique data do you leverage? What institutional knowledge do you encode into AI-augmented workflows?

Stop asking “Which AI tool should we buy?”

Start asking “What can we do with AI that no one else can?”

The companies that win will not have better tools. They will have transformed processes that are impossible to copy, because the advantage lives in their unique data, their institutional knowledge, and their proprietary way of operating.

AI is available to everyone. What you do with it that only you can do: that is the moat.