Ask any business what it actually does and the answer is almost always specific. A quarrying company extracts rock. A peatland conservation organization restores peatlands. A retailer sells items. The answer is not “we send emails” or “we have meetings.”
This distinction between what makes a business unique and the common operations surrounding it has always mattered. More than two centuries ago, Adam Smith recognized that productivity comes from specialization.
Workers focusing on narrow tasks consistently outperformed generalists, and economies grew by dividing labor into ever finer slices. Businesses succeeded not by doing everything, but by becoming exceptionally good at one thing.
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Founder and Managing Director of New Gradient.
Artificial intelligence (AI) doesn’t change this principle. If anything, it reinforces it. So why does much of today’s AI discussion assume the opposite?
The prevailing belief is that increasingly capable general-purpose models will eventually become the best solution for almost every task.
While these systems will undoubtedly transform how organizations operate, there are strong economic and technical reasons to argue that the greatest competitive advantage will come not from general AI, but from specialized systems built around the work that makes each organization unique.
Universal workplace infrastructure
General-purpose AI models are rapidly being adopted across every industry. These tools summarize documents, write code, answer questions, analyze data and automate routine knowledge work well enough that not choosing to use them will soon become a competitive disadvantage.
But their greatest strength also creates their greatest limitation. When every organization has access to the same capabilities, those capabilities stop being a differentiator. Email transformed business, but no company gains competitive advantage simply by having email. Cloud computing became essential infrastructure, but it does not distinguish one organization from another.
General-purpose AI is likely to follow the same path. As these models become ubiquitous, they will increasingly resemble infrastructure – in other words, essential for remaining competitive, but insufficient for pulling ahead.
The obvious question then becomes: where will competitive advantage come from?
Specialist work requires specialist AI
The answer lies in the work that businesses actually exist to do.
General-purpose models excel because they are trained on tasks performed by millions of people. Specialist work is different.
Going back to my opening analogy, a quarrying company does not just need help with emails. It needs to optimize blast patterns based on geological conditions, monitor crusher efficiency in real time and match production to market demand.
A peatland conservation organization does not just need help writing reports. It needs to map erosion across thousands of hectares of remote terrain from aerial imagery, then plan restoration interventions accordingly.
These are not simply harder versions of general tasks. They are fundamentally different problems requiring specialized data. And this is where general-purpose models begin to struggle.
The reasons for this are not primarily about intelligence, but economics. There is way more training data available for common business tasks than for specialized industrial or scientific ones.
More importantly, frontier AI labs have enormous commercial incentives to optimize their capabilities for features that millions of customers will use. By comparison, niche use cases are a harder sell from the perspective of return on investment.
But while the economics of model development may more immediately favor general capabilities, the economics of competitive advantage do not.
Specialization creates strategic value
Today, switching between AI providers is reasonably easy. Many organizations continue to move freely between ChatGPT, Claude, Gemini and other models as new capabilities emerge. Because these systems remain broadly interchangeable, changing providers carries relatively little cost.
But that will not always be true. In fact, that flexibility will survive only as long as the models remain largely generic. Once they acquire organizational memory, switching providers becomes far more difficult.
The next generation of AI is likely to move beyond simply responding to prompts. Instead, models will increasingly learn how organizations operate: how decisions are made, how workflows evolve and how years of accumulated expertise are applied in practice.
This goes far beyond larger context windows, searchable document repositories or vector databases. These technologies allow models to retrieve information. They do not fundamentally change the model itself. True organizational memory means the system adapts to the business over time.
In its strongest form, the model learns continuously, gradually embedding the organization’s knowledge into how it reasons and makes decisions.
That creates the potential for a lasting competitive advantage – but only if the organization retains ownership of that accumulated memory rather than effectively renting it from an AI provider. Otherwise, organizational memory becomes a source of vendor lock-in rather than a strategic asset.
Building for the specific
This is why specialized AI deserves a much larger piece of today’s conversation.
Across multiple industries we have found that the greatest competitive value comes not from deploying the largest available model, but from designing systems around specific operational problems.
For a major quarrying operation, we developed a computer vision system that monitors rock size distribution on crusher conveyors, detecting hidden downtime that amounted to five to ten percent of operational hours.
For peatland restoration, we built a tool that maps erosion from aerial imagery and 3D depth data, dramatically accelerating what was previously slow and expensive manual assessment.
The list goes on.
Each system delivers value because it was designed around a particular problem: trained on business-specific data, using a model architected to the exact task. In many cases, the models used were smaller, faster and less computationally demanding than general-purpose alternatives. In this sense, specialization is not about adding complexity. It is about removing everything that does not serve the specific goal.
The economics haven’t changed
As AI matures, the distinction between general and specialized systems will become increasingly clear. General-purpose AI tools will handle the operational tasks shared by every organization. These will become table stakes, like internet access or accounting software. Necessary, but not distinctive.
The activities that truly define a business – the specific niche it exists to fill – will come to rely on AI built for that purpose. This is not a prediction about technology. It is simply the economics of specialization applied to a new generation of tools. Just as businesses create value through specialization, so too will the AI that powers them.
For organizations looking to capture that value, the time to build is now. As generic AI becomes commoditized, providers themselves will increasingly turn their attention to specialized domains, narrowing the window for businesses to establish their own enduring advantage through specialization.
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