Your AI system just invented a customer fact. The cost? Trust, compliance, and potentially revenue.
When executives hear "Large Language Models just predict the next word," they dismiss the technology as simple. But this misses the critical insight: understanding why LLMs predict that word—and when they predict wrong—requires looking back at the 30-year history of Machine Translation (MT).
This is a subject close to my heart. My academic career, particularly my PhD research at the University of Malaga and my research work at the University of Coimbra, was deeply rooted in the processing and translation of human language by computers. Today, as I implement AI solutions for clients at First AI Movers, I see the exact same patterns—and the exact same risks.
The DNA of an LLM
In the early days of my research, we focused on statistical patterns. We tried to teach machines that "Hello" in English equates to "Hola" in Spanish, not because they understood the greeting, but because the probability of those words appearing in similar contexts was high.
Today, an LLM is, effectively, a massive translation engine. It is translating a user's intent (the prompt) into a response (the output). The mechanism is identical to what we built 20 years ago—just scaled to billions of parameters.
But here's what changed: the stakes.
Why This Matters for Business: The Translation Failure Risk
Why does a CTO or VP of Engineering care about my history in translation technology? Because it dictates how you build AI governance and operational AI implementation strategies.
Context is King: Just as a translator needs cultural context to avoid mistranslation, an AI Agent needs business context to avoid hallucination. This is why "Retrieval-Augmented Generation" (RAG) is crucial for enterprise AI readiness. Without it, your LLM is translating user intent into plausible-sounding fiction.
Hallucinations are "Mistranslations": When an AI lies, it's usually just making a bad statistical guess—a problem we dealt with in translation research for decades. The difference? In 2000, a mistranslation cost a document revision. In 2025, it costs regulatory exposure, customer churn, and board-level accountability.
The Governance Gap: Most organizations treat AI adoption as a technology problem. It's actually a linguistic engineering challenge. You need AI governance frameworks that account for the probabilistic nature of LLM outputs—not frameworks borrowed from deterministic software.
From Coimbra to the Boardroom: Building Robust AI Systems
Taking the rigorous principles I applied at CISUC and applying them to First AI Movers allows us to build systems that are robust, not just flashy. We treat AI adoption not as a magic trick, but as a complex linguistic engineering challenge requiring:
- AI Readiness Assessment: Understanding your organization's context maturity before deploying agents
- Workflow Automation Design: Mapping where translation (intent-to-output) creates business value vs. business risk
- AI Governance & Risk Advisory: Building guardrails that account for statistical uncertainty
- Operational AI Implementation: Deploying RAG, fine-tuning, and monitoring strategies that reduce hallucination risk
The executives who understand this—who see LLMs as translation engines, not magic—are the ones building defensible competitive advantage.
Written by Dr Hernani Costa | Powered by Core Ventures
Originally published at First AI Movers.
Technology is easy. Mapping it to P&L is hard. At First AI Movers, we don't just write code; we build the 'Executive Nervous System' for EU SMEs.
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