Authority Magazine just published an interview with our Global AI Leader, Guillermo Delgado, that lands where most AI coverage doesn’t: on the practical boundary between what sounds like AI and what actually works in enterprise settings. His central point is almost annoyingly useful: many “intelligent” outcomes come from analytics, optimization, and solid engineering discipline, not necessarily AI. The hype starts when we label everything AI and stop asking the harder question: what method solves this problem with the least risk and the most clarity?
Guillermo frames AI as one tool in a broader toolkit. That matters because the cost of choosing the wrong tool isn’t just wasted spend, it’s governance debt: biased outputs, fragile decisions, and teams that defer judgment to a system they can’t fully explain.
AI excels when the work is high-volume, repetitive, and language-heavy:
Used well, AI becomes a lever: it reduces cognitive busywork so humans can focus on decisions that actually change outcomes.
Humans outperform AI in the areas companies tend to underestimate until something goes wrong:
Delgado also points to a simple constraint: not everything that matters is available as training data, and not every “replacement” will be economically rational at scale.
The best systems don’t replace people, they re-balance work. AI proposes, summarizes, and detects patterns. Humans validate, decide, and own the impact. That’s not just a safety posture, it’s a performance strategy.
And if you want AI to hold up outside the demo, governance has to be part of the design: transparency, privacy protection, bias awareness, and human oversight from the start.
If you click through, there’s a lot more than the AI-vs-human framing. Delgado goes into his origin story (including an early “synthetic data” moment before the term was trendy), a funny-and-slightly-terrifying privacy lesson from customer profiling, and a real-world pricing case where a skeptical commercial leader becomes a believer after the model’s counterintuitive recommendation works. He also gets into responsible AI guardrails (bias, transparency, explainability) and shares examples of human + model collaboration that outperform either one alone, plus his “5 things to keep in mind” framework for deciding where AI belongs in the workflow. (Medium)