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Hidden Constraints and Unseen Opportunities
Artificial intelligence was among the most actively discussed topics at the European Business Summit. In nearly every session—whether focused on project launch, product promotion, cost optimization, or business scaling—the use of AI was presented either as a universal solution or a potential threat. We set out to identify the key lines of discussion and to examine AI without illusions: as a tool whose value depends not only on its capabilities, but also on a clear understanding of its inherent limitations.
The widespread adoption of AI is driving systemic changes in how the value of certain types of work is perceived. The most visible trends include:
At first, AI impresses: it is literate, logical, polished, and universal. But the more it’s used, the more its sameness becomes apparent. Content generated with AI almost always carries recognizable traits:
The result is content that’s hard to criticize—but impossible to remember. It doesn’t provoke irritation; it provokes indifference. It is “smooth” like corporate etiquette: safe, sanitized, and impersonal.
It’s becoming increasingly clear: when comparing handcrafted content with AI-generated material, the difference is like that between mass production and artisan work. Same phrases, same patterns, same reactions. It quickly becomes tiresome—and undermines trust.
The market’s response is predictable: demand is rising for individuality, original thinking, and content that reflects a real human—not a digital template. Paradoxically, the mass accessibility of AI has only increased the value of “manual labor” in intellectual work.
A neural network is neither a conversation partner nor an expert. It is a language model designed to generate plausible text that resembles a coherent answer. This is a fundamental distinction — and one that was rarely articulated directly at the summit. Most users, in fact, do not realize it.
The core design principle is resource optimization. The model is built to consume as little compute and energy as possible: this is an architectural priority. A faster response means a simpler one. Simpler means more generalized.
This leads to the first key limitation:
Depth is a cost the model avoids by default. It does not generate the best response — it generates the most statistically probable one. That is why even well-formulated, complex questions often receive templated, superficial, and predictable answers. Depth requires excessive computation, and the model is not designed for that.
The second limitation: even when you upload a file, document, or spreadsheet, the AI does not analyze it sequentially like a human would. It does not “read” the text — it extracts fragments and infers what is likely to be there.
Once it “believes” it has grasped the core, analysis stops. Even if contradictions, nuances, or exceptions are present, the model prefers to approximate their content rather than extract it precisely.
This is the critical distinction from an analyst or domain expert: AI does not establish truth — it reproduces a probabilistic template. It “guesses” what is typically said in response to similar inputs and generates an answer accordingly.
This results in factual inaccuracies, logical substitutions, and loss of nuance — especially in complex or professional contexts. In fields like finance, law, project management, or corporate structure, where precision is critical, neural networks produce systematic errors. Not because they are poorly trained — but because their architecture is designed to optimize effort, not accuracy.
A separate issue is adaptation to the user’s cognitive level. If the question is vague, casual, or poorly structured, the model picks up on this and mirrors the tone. The intelligence of the answer reflects the intelligence of the input.
This is not a hypothesis — it’s a direct consequence of the attention mechanism: the language of the prompt shapes the response structure.
There’s a fair observation: a well-formulated question contains half the answer. This is especially true with neural networks — the precision, structure, and logic of the query directly determine whether the model can go beyond generic output and move toward solving a specific task. Yet most users fail to construct proper prompts:
As a result, they receive an “average” answer — grammatically correct, logically sound, and safe, but practically useless. This creates the illusion of output: the text exists, but its real-world value often does not.
The outcome of these algorithms is global simplification. The model intentionally lowers complexity to ensure accessibility for the average user. It avoids assumptions, does not formulate hypotheses, does not go deep, and takes no risks — because it cannot identify its reader and must default to a generic level.
What appeals to a broad audience irritates professionals.
AI is suitable for drafts, prototypes, presentations, and early-stage discussions. It is acceptable when “slightly above average” is good enough. But when the result lands on a professional’s desk, the response is rejection — and increasingly, frustration.
Another fundamental issue is ideological filtering. Most models are architected with built-in mechanisms for political correctness, inclusivity, tolerance, and multicultural sensitivity.
While intended for positive outcomes, in practice this results in semantic sterilization of language.
As a result, the neural network refuses to call things by their names. It dilutes language, replacing clarity with vague generalities that hinder decision-making.
In its effort to offend no one, it removes the structural meaning from the text.
AI, as a tool, becomes a vehicle for broadcasting a particular ideology — where avoiding offense takes precedence over precise evaluation.
This is not about imposition, but about a subtle shift in meaning that leads to the erosion of value-based judgments and the inability to assess actions, phenomena, or decisions with clarity.
The conclusion is clear: neural networks can and should be used — but understanding their mechanics and limitations is critical.
AI is not knowledge or reasoning. It is a templating technology that must not be applied to analytical tasks without strict oversight.
Without expert intervention, it is not a tool — it is a source of distortion.
The summit featured practical use cases — not theoretical models or marketing brochures, but real, operational commercial solutions. They demonstrate that with proper task design, neural networks can become not just assistants, but integral infrastructure components of a business model.
Digitization of 70+ hours of lectures by leading professors, followed by avatar creation reflecting pace, intonation, and behavioral patterns. The resulting video content is perceived as a “live” instructor. Outcome: scalable EdTech delivery and reduced production costs.
AI-driven bots perform proactive selling: identifying needs, addressing objections, and adapting communication style. Operate 24/7, synchronized with catalogs, boosting conversion rates and eliminating the need for headcount growth.
AI delivered via paid subscriptions: legal, medical, therapeutic, and educational assistants integrated with specific protocols and databases. Outcome: domain-level expertise, increased user trust, and monetization potential.
AI is a powerful tool, but its effectiveness depends entirely on the expertise behind it. Without proper task formulation and architecture, even advanced models underperform. Successful implementation requires governance, experience, and awareness of limitations.
Finetic Consulting, in collaboration with technology partners, offers the following:
If you’re looking to embed AI as part of your business model — let’s talk. We don’t use templates. We work based on your goals.