The AI revolution is experiencing some growing pains. Across industries, business leaders are confronting the same challenge: AI pilots dazzle, but they don’t scale. An agent that reasons brilliantly in the sandbox turns unreliable the moment it touches a real, regulated business process.
The AI revolution is experiencing some growing pains. Across industries, business leaders are confronting the same challenge: AI pilots dazzle, but they don’t scale. An agent that reasons brilliantly in the sandbox turns unreliable the moment it touches a real, regulated business process.
Most leaders blame the model, hoping a bigger one will close the gap. Or they point to the need for more context because a model starved of the right enterprise data will never perform reliably in production. But even fully supplied context isn’t enough if nothing validates what the model produces. The real design issue is a balance between a system that generates and a system that checks.
Every AI system combines two kinds of intelligence. The neural side—the large language model (LLM)—is fluent,creative, and probabilistic. Think of it as the right brain. The second half, the symbolic side, includes logic, ontologies, rules, and policy, and is structured and deterministic. That is the left brain.
While both sides have their strengths, neither is capable of handling complex enterprise workflows on their own. The neural side is an improviser. When information is missing, it generates the most plausible response. That is useful when drafting a campaign or summarizing data. It is risky when deciding whether an insurance claim has been paid or a disputed charge has been refunded. By contrast, the symbolic side, with its rules-based limits and guardrails, is great at enforcing regulatory standards to the letter, but it cannot read a messy customer narrative or generalize past the cases it was explicitly coded to address.
This is where finding the right balance between neural and symbolic layers becomes so critical. Over-index on a generalist large language model and you run into problems with exceptions management, edge cases, and governance standards. Go too heavy on a symbolic model, and you lose the ability to interpret and extrapolate unstructured information. What’s needed is a governed context layer that captures meaning, rules, decisions, and institutional memory together.
In context
- Topic: Estrategia y Gobierno de TI — Decisiones de portafolio, costo total, gobierno y marcos de referencia.
- Source: CIO
- Published: 09/10/2026
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Why it matters
When an organisation announces a turn like this, the announcement is rarely the interesting part. What matters is everything it forces to be reordered underneath: committed budgets, live contracts, teams built for something else. That is where you find out whether the decision had real backing or was a slide in a deck.
When I look at a move like this, the question is not which tool they bought but which capability was left installed: an architecture function that holds the line on criteria, a prioritised portfolio with real business owners, and metrics somebody actually reviews every week. Without that, any announcement dissolves within twelve months.
What usually goes wrong
What usually goes wrong is the part never discussed with the areas that will carry the work. It gets decided at the top, communicated downward, and the resistance shows up not as opposition but as slowness. Nobody says no; everything simply takes twice as long and nobody can explain why.
What to watch
- How the relationship with current vendors is left, which is usually where the dependency nobody measured is sitting.
- Who ends up owning the decision, and with what budget of their own rather than borrowed.
- Whether business metrics appear or only delivery metrics — cost per transaction beats percentage complete.
How I read this entry
I would treat it less as a technology story and more as a market signal. If one player moves this way, in twelve to eighteen months the rest will have to answer something. Better to decide now, calmly, what that answer will be, rather than improvise it when a client asks.
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
