August 26, 2026
A crowded market, hundreds of solutions, the pressure to keep up. The most useful thing to do right now is stop and ask the right questions.
Today, a new product is being announced that promises to bring "AI agents" into companies. It looks like a ready-made solution to a real problem: labor shortages, operating costs, the need for efficiency.
An SME that comes across this information asks itself: "Is this the solution for me? Or just another product I need to understand before I can decide?"
A legitimate question, with an answer that isn't exactly simple.
The Market Offers, But Doesn't Explain
There are, depending on the source, somewhere between 16,000 and 90,000 active AI tools worldwide today. Every week brings dozens of new solutions. Every vendor says their product is the answer. Every one promises time savings, cost reduction, automation.
Adoption is growing. In the European Union, 20% of enterprises with 10 or more employees used AI in 2025. In Romania, the figure is 5.2% – below the European average, but rising.
But the data also show another side of the story. MIT NANDA reports that only 5% of GenAI pilots deliver measurable business value. S&P Global Market Intelligence shows the share of companies abandoning most of their AI initiatives jumped from 17% in 2024 to 42% in 2025. For SMEs, a Resultsense analysis estimates average implementation costs of £321,000, with 44% of projects delivering "only minor gains."
Experimentation with AI is growing faster than organizations' capacity to turn it into value.
What It Means to Choose an AI Product
Every AI product offers a predefined capability. A sales agent, an accounting assistant, a data-processing tool. It comes with authority limits, human oversight, certifications, a promise of compliance.
Choosing such a product assumes the user already has clear answers about the problem, the fit, the data, the success criteria, and what happens when something goes wrong — answers that rarely exist before a proper evaluation. A product can supply some of those answers, but not all of them, because the rest depend on the unique context of each business.
The Decision Before the Technology
A useful framework for any SME evaluating an AI solution includes ten questions:
- What business problem are we trying to solve?
- Could we solve it without AI?
- How will we measure the outcome?
- What happens if the system gets it wrong?
- What can the AI do on its own, and what can't it?
- Who is accountable for the result?
- What data does it use, and under what conditions?
- Who will use the system, and who will be affected by it?
- What legal and governance obligations arise?
- What do we do if the pilot doesn't deliver the expected value?
These questions aren't technical. They're decisional. They can be asked before any purchase, regardless of vendor.
An answer to these questions can be one of four things:
Each one is legitimate, as long as it's the result of an informed evaluation.
The Regulatory Context Adds Its Own Complexity
The AI Act's high-risk obligations were originally due to apply from August 2, 2026 — but the EU's "Digital Omnibus on AI" (Regulation (EU) 2026/1744, in force since July 27, 2026) pushed that deadline to December 2, 2027 for standalone high-risk systems, and to August 2, 2028 for AI embedded in regulated products (EUR-Lex, Regulation (EU) 2026/1744).
In Romania, only 36% of companies are familiar with the AI Act's provisions, below the regional average of 39%. Only 8% of SMEs say they're prepared for an AI audit.
This gap between legal obligations and actual readiness is a vulnerability. Buying an AI product doesn't remove this vulnerability — it can amplify it, if the product is used in a context that hasn't been properly assessed.
And last but not least, the conversation about AI is dominated by two narratives. One says AI will transform everything and that any delay is a loss. The other says the risks are too high and you need to know how to reduce them.
Between them lies a less visible space: the space of informed decision-making. A space where questions precede the purchase, where evaluation precedes implementation, where clarity precedes enthusiasm.
This is a neutral space in which any SME needs to be able to answer the question: "do I have a problem worth delegating to a machine?" Any answer is valid, as long as it's the result of reflection, not pressure.
Instead of a Conclusion
An article describing market confusion doesn't have to be alarmist. It can be descriptive and useful. It can offer a framework, not a verdict. It can invite reflection, not panic.
For the reader who recognizes themselves in these lines, the question remains open: what problem do you want to solve with AI, and what do you need to know before deciding?
The answer to that question matters more than any product.