There’s a paradox few companies have the courage to confront in this early phase of Artificial Intelligence: if everyone uses the same tools, no one has a real competitive advantage.
On LinkedIn I put forward a provocation: if a company builds its automation on tools available to anyone, it isn’t creating value – it’s levelling its own performance down to a mediocre standard it shares with its competitors.
Here, on the blog, we want to go beyond the slogan and look at the facts. Why is the AI you use with a basic subscription not the engine of your future business, but merely a very powerful equaliser? And how can Italian companies escape this trap?
The “commoditisation” trap of AI
In economics, a commodity is a good available in large quantities and essentially undifferentiated. Copper, wheat, oil. Nobody buys copper from your company because it’s “your copper”: they buy it because it costs a cent less.
Today, basic access to language models is becoming a commodity. If you and your competitor use the same interface of the same model to write the same emails, summarise the same documents, translate the same manuals, your margin of advantage is exactly zero. You’ve simply become faster at doing the very same things.
When technology levels out, the advantage shifts. You no longer win by having the tool, but by knowing how to orchestrate the tool differently from everyone else.
Why public AI gives you the average, by design
Many entrepreneurs notice that public AIs answer in a generic, cautious way, with a slightly flat corporate tone. It’s easy to read this as a flaw.
It isn’t a bug. It’s the inevitable trade-off of a product designed for millions of different users.
A public model is trained to work acceptably for everyone – which means being general, cautious and neutral. That’s the right choice for a mass-market product, and it also protects you from reckless answers on legal or medical matters. But “general and cautious” is the exact opposite of “distinctive”.
The raw output you get from a public interface is, by design, the average. The same average your competitor gets, querying the same model with the same general knowledge.
It isn’t weakened by some conspiracy: it’s calibrated for the general public, not for your advantage. Value doesn’t come from “unlocking” the model, but from what you build around it: your data, your context, your processes. That’s where AI stops being the average and becomes yours.
The false debate: local vs. cloud
Faced with this, the IT market sells the cure-all of local (on-premise) AI: “Bring the model onto your own servers, so your data never leaves.”
It’s an important practice – one we adopt at GCompass for projects with sensitive or regulated data – but if you think moving the servers solves the strategic problem, you’re mistaken.
You can have a local AI just as generic as a sophisticated cloud AI. The problem isn’t where the computation runs, but how it’s applied to your specific business reality.
How to create real value: competence vs. ignorance
If basic AI is a commodity, how does an Italian company turn it into a real competitive advantage? There’s only one answer: building proprietary architectures around AI. At GCompass we guide clients through three pillars.
Data as the real wealth
The value isn’t in the model (which is public), but in your data (which is private). An AI that doesn’t know your customer records, your sales history, your margins is just a generic chatterbox.
First step: structuring company data so machines can read it.
Proprietary RAG
Instead of general knowledge (the same your competitor has), RAG techniques are used to “close” the AI within a perimeter: it queries only your manuals, your policies, your contracts.
This is where you dig the competitive moat.
Automating the invisible micro-processes
The mistake is trying to automate “the company”. We automate the boring, repetitive step that steals hours from the operations team every day.
We don’t replace the salesperson: we take away the manual data entry into the CRM after the call.
Conclusion: AI isn’t a product, it’s a competence
The companies that will dominate their markets five years from now won’t be the ones that “downloaded the best AI”. They’ll be the ones that developed the infrastructure, the data flows and the culture to make AI work exclusively in their interest.
Settling for the public, standardised version of Artificial Intelligence isn’t innovation: it’s just paying for a ticket to stay average, along with everyone else.
Is your company using AI as a standardised toy, or building a real competitive advantage? If you’re not sure where you stand, the fastest way to find out is a conversation with someone who does this every day.
Frequently asked questions
Why does public AI create no competitive advantage?
Because it is the same for everyone: if a business and its competitors use the same model with the same general knowledge, they get the same average output and the margin of advantage is zero. Shared technology makes you faster, not different: the advantage comes from what you build around the model, not from the model itself.
What does it mean that artificial intelligence has become a commodity?
It means that basic access to language models is now an undifferentiated good, like copper or wheat: available to anyone, on the same terms. When technology levels out, the advantage shifts from having the tool to orchestrating it differently from everyone else.
Does running AI on your own servers (on-premise) solve the problem?
Not by itself: a local AI can be just as generic as a sophisticated cloud one. On-premise is the right choice when data sovereignty and control over sensitive or regulated information are needed, but the strategic advantage comes from how the AI is applied to the specific business – data, context, processes – not from where the computation runs.
What is proprietary RAG and what is it for?
It is the technique that “closes” artificial intelligence within the perimeter of the company’s data: the system answers by querying only internal manuals, policies and contracts, instead of the general knowledge available to everyone. It is the concrete way to turn a public model into a tool that works exclusively for the company’s interests.
Where should an SME start to create value with AI?
With data and micro-processes: first structure company information (customer records, sales history, margins) so that machines can read it, then automate the repetitive steps that steal hours from the operations team every day. You do not automate “the company”: you take the manual CRM entry away from the salesperson, not the phone call.
How can you tell whether your company is building advantage with AI or just following the average?
With a structured check of the architecture: if the AI in use does not know the company’s data, is not closed on a proprietary perimeter and is not hooked into internal processes, it is producing the same output as the competitors’. GCompass offers a free 12-question test (AI Moat Check) to find out in five minutes.
Free test
Is your AI giving you an advantage, or just the average?
The AI Moat Check tells you, in 12 questions and 5 minutes, how close you are to a real competitive moat and where to start building it. Download it free.
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