👋 Hey friends,

A few years back, I worked on an AI prototype that felt unstoppable.

In our demo, it nailed predictions with 95% accuracy. The execs clapped. Someone even joked, “This is going to change everything.”

I walked out of that room buzzing. For a brief moment, it felt like all the late nights of feature engineering, model tuning, and last-minute bug fixes had paid off.

Fast-forward three months: the system was quietly shelved.

Why?

That was my wake-up call.

It taught me a lesson I’ve seen play out over and over since: AI prototypes don’t fail in the lab — they fail in the wild.

And the graveyard is crowded:

So in today’s edition, I want to unpack three big questions:

Let’s dive in.

That clever prototype built on one engineer’s laptop? It usually dies the first time it meets enterprise-grade requirements.

In hackathons and pilots, everything looks rosy. Models run on clean CSVs. The inputs are tidy. Latency isn’t an issue.

But in the wild?

A project that looked 90% done suddenly feels only 20% ready.

McKinsey estimates 90% of pilots never scale, mostly because companies underestimate how much invisible engineering work is needed. Deloitte adds that compliance and governance can add 50% more effort after prototyping.

Here’s the hidden trap: prototypes optimize for proof of concept, while production systems demand proof of resilience. What makes a demo sparkle — fast iteration, hardcoded shortcuts, one-off integrations — is often the very thing that kills it later.

Deeper insight: Scalability isn’t just about technical architecture. It’s about organizational readiness to invest in all the unglamorous work: compliance reviews, integration pipelines, monitoring dashboards, and redundant failovers. Without that investment, prototypes are castles built on sand.

Even technically sound models fail if the people meant to use them don’t trust them.

I’ve seen:

The underlying model was fine. The trust wasn’t.

PwC found that 65% of employees won’t trust AI unless it’s explainable. Forrester calls this the “explainability gap.”

But trust is more complex than just explainability. It’s about alignment with human judgment. If an AI tool constantly contradicts people’s instincts without justification, adoption collapses — even if the model is statistically right.

Hidden dynamic: adoption isn’t just about convincing end-users. It’s also about middle managers. If managers feel the AI threatens their control, they quietly sabotage adoption. I’ve seen tools banned in teams because a manager was embarrassed when the AI made them look wrong in front of leadership.

Deeper insight: Trust is not a byproduct of accuracy. It has to be designed. That means clear feedback loops, override mechanisms, transparent reasoning, and — perhaps most importantly — respecting human dignity in workflows.

Models aren’t static. Customer behavior, fraud patterns, even language itself shifts.

Without monitoring and retraining, models silently decay until they stop being useful.

Gartner predicts that through 2026, 60% of AI projects will be abandoned if they are not supported by AI-ready data. A 2024 survey of 1,203 data management leaders revealed that 63% of organizations either lack or are unsure whether they have the right data management practices in place—putting their AI initiatives at serious risk.

Think about it: your prototype trained on 2023 purchase patterns may be completely outdated by 2025 when a recession changes consumer behavior. Or a fraud model trained on yesterday’s scam tactics becomes useless once attackers evolve.

Real-world example: JPMorgan revealed it spends millions annually retraining decayed models. The cost of maintenance is now bigger than the cost of initial development.

Deeper insight: Drift isn’t a “bug.” It’s the natural entropy of reality. Treating AI models like static software guarantees failure. They’re more like living organisms — they need monitoring, retraining, and adaptation cycles to stay healthy.

When I first dug into RAND’s Anti-Patterns of AI (2024) and Root Causes of AI Failure (2025), one thing became clear: AI projects rarely fail because the math is wrong. They fail because the humans around the math don’t align.

These reports were based on interviews with 65+ seasoned engineers, scientists, and practitioners across industries. RAND’s conclusion is blunt: AI projects fail for organizational reasons far more often than technical ones.

Here’s a breakdown of their five most common failure modes — and what they really mean in practice.

This was the shocker: 84% of failures traced back to leadership decisions, not engineering flaws.

Typical patterns RAND saw:

One RAND interviewee described it like this: “We weren’t solving a business problem. We were solving for a PowerPoint slide.”

 Why this matters: AI requires long-term commitment. A forecasting model, for instance, often needs a year of historical data before its predictions stabilize. If leaders aren’t ready to commit for that long, the project is doomed before the first line of code.

RAND’s engineers often described themselves as “data janitors in disguise.”

Failures weren’t just about dirty or incomplete data — they were about missing context.

In other words: the absence of key context quietly cripples models.

RAND found that in many organizations, data engineering is undervalued. Executives see “data cleaning” as grunt work — but in reality, it’s the invisible scaffolding that makes AI possible.

Why this matters: without continuous investment in pipelines, governance, and monitoring, prototypes collapse under production’s messy, shifting data streams.

RAND called this an “anti-pattern”: teams chasing the latest framework, architecture, or trend instead of solving enduring business problems.

A story that came up often: engineers spending months migrating a stable prototype to a new deep learning library — not because it solved the problem better, but because it was trendy. By the time they finished, the business priorities had shifted, and the model never shipped.

Why this matters: innovation is only progress if it moves the business forward. Otherwise, it’s procrastination with better branding.

RAND and Schellman both hammer this point: AI is more than a model. It’s everything around the model.

Failures often happened after a “successful” demo, when the prototype couldn’t be deployed because:

Half of the failed projects RAND studied technically “worked” — but they died at the handoff to IT.

Why this matters: accuracy wins headlines, but infrastructure wins survival. The difference between a cool demo and a living product is often the unglamorous plumbing.

RAND calls it the “expectation gap.” Overselling AI was just as lethal as underfunding it.

Example: IBM’s Watson for Oncology. Sold as a revolution in cancer care, it struggled with real-world data and integration. Trust collapsed — and once trust is gone, especially in high-stakes fields, recovery is nearly impossible.

RAND’s takeaway: sometimes the problem is not solvable with current AI. Leaders who treat AI as a “magic wand” set themselves up for reputational damage as well as financial loss.

Why this matters: AI isn’t a universal hammer. Knowing when not to use it is as important as knowing when to double down.

What makes RAND’s work chilling is the consistency: across industries, companies are making the same mistakes over and over.

Deeper insight: AI failures are not random. They’re predictable. And they’ll keep repeating until leadership matures — until companies learn to scope problems carefully, invest in data infrastructure, and align AI with clear business value.

Until then? Prototypes will keep dying young.

Optimus AI Labs coined the term production gap — the chasm between lab success and production failure.

Why it happens:

This gap explains why only 35% of AI projects ever make it to production, and fewer survive a year.

Another hidden factor: talent misallocation. Data scientists spend 60–70% of their time cleaning and patching data pipelines instead of modeling. Optimus calls this “wasting Ferrari drivers on pothole repair.”

Deeper insight: The production gap isn’t just technical. It’s cultural. Many organizations see data engineering as grunt work — so they underinvest in it. But without data plumbing, no prototype survives.

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Fraud detection isn’t new. But Stripe succeeded where others stumbled. Why?

Result: less than 0.1% false positives, billions saved in fraud prevention, and high user trust.

Stripe didn’t treat AI as “magic.” They treated it as infrastructure. Radar’s success came from boring reliability, not flashy algorithms.

Air India faced a backlog crisis. Call centers were overwhelmed. Enter AI.g, a multilingual assistant.

What made it work?

Result: Backlog dropped by 20% in six months, staff adopted it because it reduced stress, passengers trusted it because it worked.

Success wasn’t about accuracy percentages. It was about relieving pain. AI.g succeeded because it earned both customer and employee trust.

IBM promised a $4B revolution in cancer care. The idea: read every paper, analyze records, recommend treatments.

Reality:

Result: hospitals abandoned it, MD Anderson canceled a $62M pilot, and Watson became a cautionary tale.

Watson shows that in high-stakes domains, overpromising kills faster than technical flaws. Once trust is lost with doctors — or patients — there’s no recovery.

Survivors like Stripe and Air India succeeded because they built for trust, infrastructure, and adoption.

Failures like Watson collapsed because they chased hype and ignored the ecosystem.

AI prototypes don’t fail because the math is wrong. They fail because the world around them — the systems, people, and leadership — wasn’t ready.

The longer I work in this space, the more I realize: models are disposable. Ecosystems are not.

When I think back to that 95% demo that never made it, I see what we missed:

MIT’s GenAI Divide said it perfectly: “The future of AI won’t be decided by those who experiment the most, but by those who bridge the gap between prototype and production.”

AI doesn’t fail because it can’t work. It fails because we treat it like a short-term project, instead of a long-term product.

If history teaches us anything, it’s this: hype cycles don’t kill technologies — they kill trust.

The real test isn’t who builds the flashiest demo. It’s who builds the most boringly reliable system — one that survives drift, wins trust, and quietly delivers value.

Over to you:
Have you been part of an AI project that failed — or one that survived? Looking back, what was the real difference-maker? 

Thanks for reading, friends
See you next time,
— Naseema

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