Over the past few years, headlines about artificial intelligence have almost always revolved around the same thing: who trains the biggest model, who breaks the latest record, who reaches the technological "frontier" first. It is a fascinating race, but one that is increasingly distant from the decisions a private company makes on any given Monday. For most SMEs, the competition that truly matters is not played out in the labs of the major providers, but on the field of business-applied AI: who manages to turn those models, now available to everyone, into faster processes, fewer errors and better decisions.
From the frontier to the playing field
The idea that "the real AI race is no longer at the frontier" has a very practical reading. The cutting-edge models from OpenAI, Anthropic, Google or Microsoft are today a resource within reach of almost any organization through an API or a subscription. In other words: the base technology has been democratized.
When a capability is available to everyone, it stops being a competitive advantage in itself. What sets one company apart from another is no longer "having AI", but how it uses it: what problems it solves, what data it feeds it, and how it integrates it into people’s daily work. That is where the game is decided, and that is where an SME can compete head-to-head with much larger companies.
Why the advantage is no longer in the biggest model
Chasing the most powerful model is like buying a Formula 1 engine without having a car, a track or a driver. Raw power is useless if it is not connected to a specific process that generates value.
For a company, the model is only one piece. The pieces that make the difference are others:
- Your own data, organized and accessible, which gives context to the model.
- Integration with the tools already in use (the ERP, email, the POS, e-commerce).
- The use case, that is, the real and measurable problem you want to solve.
- Adoption by the team, which is what turns the tool into results.
None of these elements depend on being at the technological frontier. They all depend on knowing the business well and on knowing how to ground the technology with judgment.
Where you win today: business-applied AI
Business-applied AI is not about "having AI for the sake of it", but about choosing specific use cases, with a benefit that can be measured, and executing them well. This is precisely the approach we advocate: start with the problem, not the trend.
A well-chosen use case has three characteristics. First, it tackles a task that today consumes time or generates errors. Second, it can be measured: hours saved, incidents reduced, shorter response times. And third, it is achievable with the data and systems the company already has, without pharaonic projects.
Concrete examples
These are scenarios recognizable to many private companies:
- Intelligent automation of administrative tasks: combining process automation (for example, with Power Automate) and AI to classify emails, extract data from invoices or prepare responses, leaving the final validation to a person.
- Assistants that query company information: through techniques such as RAG, an assistant can answer questions relying on internal documentation, with verifiable and traceable information, instead of "making up" answers.
- Analytics to decide with data: dashboards with Power BI that turn ERP data into decisions, rather than intuitions.
- Custom apps that put AI where the work happens: in the warehouse, at the point of sale or on the salesperson’s phone.
In all cases, the protagonist is not the model, but the business process that improves.
Organized data: the prerequisite
There is a phrase that sums up the real state of the market well: without organized and governed data, AI does not perform. An excellent model fed with scattered, duplicated or unreliable data produces unreliable results.
That is why, before "deploying AI", it is worth reviewing the foundations: where the data is, who can access it, with what quality and under what privacy and usage policies. This data governance is not bureaucracy; it is what allows a use case to work in production and not just in a demo. It is, moreover, the basis on which any serious and sustainable AI project is built over time.
How TISA grounds it
At TISA we have been helping private companies modernize their management since 1987, and that journey shapes our way of approaching AI: with our feet on the ground. As a Microsoft partner and custom software developer, we work on a solid foundation —Business Central as the ERP and Power Platform to extend and automate— and we add applied AI on top of that foundation.
Our custom development proposal fits naturally with this idea of a "race outside the frontier":
- Consulting to identify which data is relevant and which use cases make sense according to each company’s investment capacity.
- Development and execution of a use case adapted to the business, not a generic solution.
- Integration of AI with the systems the company already uses, so that the result reaches real work.
- Training for the team, because adoption is half of success.
We do not promise magic or the biggest model in the world. We promise judgment, closeness and sector experience to choose well and execute better.
Start with a use case, not with the technology
The conclusion is simple: while the major providers compete at the frontier, your company can win its own race on the field it truly controls, that of its processes, its data and its customers. The advantage lies in applying well what already exists.
If you want to explore which AI use case would have the most impact on your business, at TISA we help you assess it with no commitment. Write to us at info@grupotisa.com, call us at (+34) 971 305 885 or visit grupotisa.com and let’s start with what really moves the needle.