, having spent my career working across a wide range of industries, from small startups to global corporations, from AI-first tech companies to heavily regulated banks. Over the years, I’ve seen many AI and ML initiatives succeed, but I’ve also seen a surprising number fail. The reasons for failure often have little to do with algorithms. The root cause is almost always how organizations approach AI.

This is not a checklist, how-to manual, or list of hard and fast rules. It’s a review of the most common errors I have come across, and some speculation regarding why they happen, and how I think they can be avoided.

1. Lack of a Solid Data Foundation

In the absence of poor, or little data, all too often due to low technical maturity, AI/ML projects are destined for failure. This occurs all too often when organizations form DS/ML teams before they have established solid Data Engineering habits.

I had a manager say to me once, “Spreadsheets don’t make money.” In most companies, however, it’s the exact opposite: “spreadsheets” are the only tool that can push profits upward. Failing to do so means falling prey to the classic ML aphorism: “garbage in, garbage out.”

I used to work in a regional food delivery company. Dreams for the DS team were sky-high: deep learning recommender systems, Gen AI, etc. But the data was a shambles: too much old architecture so sessions and bookings could not be reliably linked because there wasn’t a single key ID; restaurant dish IDs rotated every two weeks, so it was impossible to safely assume what customers actually ordered. This and many other issues meant every project was 70% workarounds. No time or resources for elegant solutions. But for a handful of them, none of the projects had yielded any results within one year because they were conceived based on data that could not be trusted.

Takeaway: Invest in Data Engineering and data quality monitoring before ML. Keep it straightforward. Early wins and “low-hanging fruits” don’t necessarily require high-quality data, but AI definitely will.

2. No Clear Business Case

ML is typically done because it’s trendy rather than for solving a real problem, especially given the LLM and Agentic AI hype. Companies build use cases around the technology rather than the other way around, ending up building overly complicated or redundant solutions.

Think of an AI assistant in a utility bill payment application where customers only press three buttons, or an AI translator of dashboards when the solution should be making dashboards understandable. A quick Google search for examples of failed AI assistants will turn up numerous such instances.

One such instance in my working life was a project to build an assistant on a restaurant discovery and booking app (a dining aggregator, let’s say). LLMs were all the rage, and there was FOMO from the top. They decided to develop a low-priority safe service with a user-confronted chat assistant. The assistant would propose restaurants according to requests like “show me good places with discounts,” “I want a fancy dinner with my girlfriend,” or “find pet-friendly places.”

A year was spent developing it by the team: hundreds of scenarios were designed, guardrails were tuned, backend made bulletproof. But the essence of the matter was that this assistant did not solve any real user pain points. A very small percentage of users even tried to use it and among them only a statistically insignificant number of sessions resulted in bookings. The project was abandoned early and was not scaled to other services. If the team had started with the confirmation of the use case instead of assistant features, such a destiny could not have been attained.

Takeaway: Start with the problem always. Understand the pain point deeply, assign its value in numbers, and only then start the development journey.

3. Chasing Complexity Before Nailing the Basics

Most communities jump to the latest version without stopping to see if the simpler methods would suffice. One size does not fit all. An incremental approach, beginning simple and incrementing as required, almost always results in greater ROI. Why make it more complex than it needs to be when linear regression, pre-trained models, or plain heuristics will suffice? Beginning simple provides insights: you learn about the problem, find out why you did not succeed, and have a sound basis for iterating later.

I’ve implemented a project to design a shortcut widget on the home page of a multi-service app that includes ride-hailing involved. The idea was simple: predict if a user had launched the app to request a ride, and if so, predict where it would most probably go so the user could book it in one touch. Management decreed that the solution must be a neural network and could be nothing else. Four months of painful evolving afterwards, we found that the predictions performed amazingly well for maybe 10% of riders with deep ride-hailing histories. Even for them, the predictions were terrible. And the problem was finally fixed in one night by a set of business rules. Months of wasted effort could have been avoided if the company had started conservatively.

Takeaway: Walk before you run. Use complexity as a last resort, not a starting point.

4. Disconnect Between ML Teams and the Business

In most organizations, Data Science is an island. Teams build technically stunning solutions that never get to see the light of day because they don’t solve the right problems, or because business stakeholders don’t trust them. The reverse is no better: when business leaders attempt to dictate technical development in toto, set unachievable expectations, and push broken solutions no one can defend. Equilibrium is the answer. ML thrives best when it’s an exercise in collaboration between domain experts, engineers, and decision-makers.

I’ve seen this most often in large non-IT-native companies. They realize AI/ML has huge potential and set up “AI labs” or centers of excellence. The problem is these labs often work in complete isolation from the business, and their solutions are rarely adopted. I worked for a large bank that had just such a laboratory. There were highly seasoned experts there, but they never met with business stakeholders. Worse yet, the laboratory was set up as a stand-alone subsidiary, and exchanging data was impossible. The firm was not that interested in the lab’s work, which did end up going into research papers for academics but not into the actual processes of the company.

Takeaway: Keep ML initiatives tightly aligned with business needs. Collaborate early, communicate often, and iterate with stakeholders, even if it slows development.

5. Ignoring MLOps

Cron jobs and clunky scripts will work at a small scale. That said, as the firm scales, this is a recipe for disaster. Without MLOps, small tweaks require engaging original developers every step of the way, and systems are fully rewritten over and over again.

Early investment in MLOps pays exponentially. It is not purely about technology, but having a stable, scalable, and sustainable ML culture.

Investing in MLOps early pays off exponentially. It’s not just about technology; it’s about creating a culture of reliable, scalable, and maintainable ML.  Do not let chaos befall you. Establish good processes, platforms, and training prior to ML projects running wild.

I worked at a telecom subsidiary firm that did AdTech. The platform was serving internet advertising and was the company’s largest revenue-generate. Because it was new (only a year old) the ML solution was desperately brittle. Models were merely wrapped in C++ and plopped into product code by a single engineer. Integrations were only performed if that engineer was present, models were never kept track of, and once the original author left, no one had a clue about how they were working. If the shift engineer had also left, the whole platform would have been down permanently. Such exposure could have been prevented with good MLOps.

6. Lack of A/B Testing

Some businesses avoid A/B testing due to complexity and opt for backtests or intuition instead. That allows bad models to reach production. Without a testing platform, one can’t know which models actually perform. Proper experimentation frameworks are required for iterative improvement, especially at scale.

What tends to hold back adoption is the feeling of complexity. But a straightforward, streamlined A/B testing process can function well in the early days and doesn’t require huge up-front investment. Alignment and training are really the largest keys.

In my case, without any sound way to measure user impact, it is up to how well a manager can sell it. Good pitches get funded, get fervently defended, and sometimes last even when numbers reduce. Metrics are manipulated by simply comparing pre/post launch numbers. If they did increase, then the project is a success, although it just so happened to be a general up trend. In growing firms, there are millions of subpar projects hidden behind overall growth because there is no A/B testing to continually separate successes from failures.

Takeaway: Create experimentation capacity early. Test large deployments required and let teams interpret results properly.

7. Undertrained Management

Undertrained ML management can misread metrics, misread experiment results, and make strategic mistakes. It is equally crucial to educate decision-makers as it is to educate engineering teams.

I was once working with a team that had all the tech they needed, plus robust MLOps and A/B testing But managers didn’t know how to use them. They used the wrong statistical tests, killed experiments after one day when “statistical significance” had been achieved (usually with far too few observations), and launched features with no measurable impact. The result: many launches had a negative impact. The managers themselves were not bad people, they simply did not understand how to use their tools.

8. Misaligned Metrics

While ML/DS organizations need to be business-aligned, that does not imply that they need to have business instincts. ML practitioners will also align to whatever metrics are provided to them if they feel they are correct. If ML objectives are misaligned with firm goals, then the result will be perverse. For example, if profitability is what the company wants but maximizing new-user conversion is a goal of the ML organization, they’ll maximize unprofitable growth through adding bad unit economics users who never return.

This is a pain point for many companies. A food delivery company wished to grow. Management observed low conversion of new users as the problem restraining the business from growing revenue. The DS team was requested to solve it with personalization and customer experience upliftment. The real problem was retention, the converted users didn’t come back. Instead of retention, the team focused on conversion, effectively filling water into a leaking bucket. Even though the rate of conversion picked up, it was not translated into sustainable growth. These mistakes are no business or industry size specific—these are universal errors.

They can be prevented nonetheless. AI and ML do work when crafted on sound principles, designed to solve real issues, and carefully implemented in business. When all the conditions are right, AI and ML turn into disruptive technologies with the potential to upend entire businesses.

Takeaway: Make ML metrics align with true business objectives. Fight causes, not symptoms. Value long-term performance, not short-term metrics.

Conclusion

The path to AI/ML success is less about bleeding-edge algorithms and more about organizational maturity. The patterns are apparent: failures arise from rushing into complexity, misaligning incentives, and ignoring foundational infrastructure. Success demands patience, discipline, and an openness to starting small.

The positive news is that all of these errors are completely avoidable. Firms that put data infrastructure in place first, maintain close coordination between technical and business teams, and are not distracted by fads will discover that AI/ML does precisely what it promises on the can. The technology does function, but it has to be on firm foundations.

If there is one tenet that binds all of this together, it is this: AI/ML is a tool, not a destination. Begin with the problem, confirm the need, develop iteratively, and measure always. Those businesses that approach it with this mindset not only don’t fail. Instead, they create long-term competitive differentiators that compound over time.

The future does not belong to firms with the newest models, but to firms that have the discipline of applying them sensibly.