When Marcelo Tribuj started Truelogic twenty-one years ago in Argentina, he was a young engineer with a simple belief: technology should make businesses faster, smarter, and more human.
Two decades later, Marcelo leads a team of over 600 engineers across Latin America, partnering with clients ranging from fast-growing startups to global enterprises. Their mission remains unchanged, helping innovation take root and scale through trust, excellence, and long-term partnership.
The Startling Truth: 95% of AI Projects Fail
During a recent Truelogic webinar, Marcelo opened with a simple but urgent observation:
“Despite all the enthusiasm and investment in AI, 95% of projects fail.”
The statistic, drawn from MIT research, stopped the audience cold. What happens between the promise of transformation and the reality of results?
Joining Marcelo were Gaby Kotliar, Truelogic’s Chief Technology Officer, and Ricardo Manhães, AI Architect. Together, they’ve led global AI initiatives for organizations such as The Associated Press, Johnson & Johnson, Experian, and iFood.
Their shared insight: most AI failures are not technical, they’re structural.
Where AI Projects Go Wrong
Ricardo began with a blunt truth:
Too often, companies fall in love with the sophistication of their algorithms and forget the fundamentals: clarity of purpose, data quality, and adoption.
MIT’s research shows a 40% drop between AI projects explored and those that reach production, a phenomenon Harvard researchers call “the experimentation trap.”
Success in AI Is About Context, Not Just Code
Gaby expanded on this:
In other words, success in AI depends less on code than on context.
So how do you integrate AI into your business without breaking it?
Marcelo put it simply:
The Common Pitfalls
Another common trap, Gaby explained, is thinking of AI as traditional software.
“Software delivers fixed outputs; AI delivers probabilities. AI is about forecasts, not guarantees. That’s why leaders have to manage it with evidence gates, not deadlines.”
Ricardo agreed:
“Even in production, AI is still in research mode. The work changes after the MVP is delivered, but it’s never done.”
They spoke of systems that learn and unlearn, of models that must be retrained and audited, and of trust that must be earned repeatedly.
How the Best AI Leaders Succeed
The leaders who succeed are those who treat AI as a living system — something that demands care, observation, and feedback.
Ricardo summed it up:
“If you can’t measure ROI from the data up, you’re managing hype, not performance.”
Real-world cautionary tales illustrate this:
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Zillow’s half-billion-dollar loss after over-relying on predictive models.
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A major bank where innovation stalled when data scientists were pulled into system maintenance.
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An e-commerce giant whose untagged data containers triggered an outage that took dozens of AI systems offline.
Different industries, same lesson: without governance, observability, and resilience, even strong models collapse.
The Three Pillars of AI Success
By the end of the session, Marcelo summarized the formula for being among the successful 5%:
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Focus on metrics tied to real outcomes.
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Define processes with checkpoints and feedback loops.
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Build a culture that learns and adapts.
Ricardo added:
“Treat AI like a living system. Measure, learn, adapt, iterate... that’s how the five percent succeed.”
From Potential to Performance
For Truelogic, this isn’t theory — it’s the core of their work.
With 21 years of experience, 600+ experts, and a 6% attrition rate that reflects a lasting culture, Truelogic helps companies move beyond pilots and proofs of concept to real, measurable transformation.
Learn from the Five Percent
If you’re curious about what separates the 95% from the 5%, Truelogic has distilled these insights into a new whitepaper: How to Make AI Projects Succeed: Lessons from the Five Percent That Deliver. It’s an honest look at what makes AI work, and what to avoid.
