No one agrees on the exact number, but estimates say anywhere from 50% to 80% of AI projects end in failure.
Photo by giancarlor_photo @Unsplash.comAI projects are notoriously difficult to design and implement. Despite the hype and the flood of new frameworks, especially in the generative AI space, turning these projects into real, tangible value remains a serious challenge in enterpriss.
Everyone’s excited about AI: boards want it, execs pitch it, and devs love the technology. But here’s the very hard truth: AI projects don’t just fail like traditional IT projects, they fail worse. Why? Because they inherit all the messiness of regular software projects plus a layer of probabilistic uncertainty that most orgs aren’t ready to handle.
When you run an AI process, there’s a certain level of randomness involved, which means it may not produce the same results each time. This adds an extra layer of complexity that some organizations aren’t ready for.
If you’ve worked in any IT project, you will remember the most common issues: unclear requirements, scope creep, silos or misaligned incentives.
For AI projects, you can add to the list: “We’re not even sure this thing works the same way every time” and you’ve got a perfect storm for failure.
In this blog post, I’ll share some of the most common failures we’ve encountered over the past five years at DareData, and how you can avoid these frequent pitfalls in AI projects.
If you ask, “ What does success look like for this project?” and get ten different answers, or worse, a shrug, that’s a problem.
A machine learning project without a sharp success metric is just expensive endeavor. And no, “ make a process smarter” is not a metric.
One of the most common mistakes I see in AI projects is trying to optimize for accuracy (or other technical metric) while trying to optimize for cost (lower cost possible, for example in infrastructure). At some point in the project, you may need to increase costs, whether by acquiring more data, using more powerful machines, or for other reasons — and this must be done to improve model performance. This is clearly not an example of cost optimization.
In fact, you usually need one (maybe two) key metrics that map tightly to business impact. And if you have more than one success metric, make sure you have a priority between them.
How to avoid it:
Set a clear hierarchy of success metrics before the project starts, agreed by all stakeholders involved
If stakeholders can’t agree on the aforementioned hierarchy, don’t start the project.
Too many success metrics are normally tied with the “ too many cooks” problem.
AI projects attract stakeholders, and that’s cool! It just shows that people are interested in working with these technologies.
But, marketing wants one thing, product wants another, engineering wants something else entirely, and leadership just wants a demo to show investors or show-off to competitors.
Ideally, you should identify and map the key stakeholders early in the project. Most successful projects have one or two champion stakeholders, individuals who are deeply invested in the outcome and can drive the initiative forward.
Having more than that can lead to:
conflicting priorities or
diluted accountability
and none of those scenarios are positive.
Without a strong single owner or decision-maker, the project turns into a Frankenstein’s monster, stitched together on last minute requests or features that aren’t relevant for the big goal.
How to avoid it:
Map the relevant decision stakeholders and users.
Nominate a project champion that has the ability to have a last call on project decisions.
Map the internal politics of the organization and their potential impact on decision-making authority in the project.
A Python notebook is not a product. It’s a research / education tool.
A Jupyter proof-of-concept running on someone’s computer is not a production level architecture. You can build a beautiful model in isolation, but if no one knows how to deploy it, then you’ve built shelfware.
Real value comes when models are part of a larger system: tested, deployed, monitored, updated.
Models that are built under MLops frameworks and that are integrated with the current companies systems are mandatory for achieving successful results. This is specially important in enterprises, that have tons of legacy systems with different capabilities and features.
How to avoid it:
Make sure you have engineering capabilities for proper deployment in the organization.
Involve the IT department from the start (but don’t let them be a blocker).
Most AI models will be “wrong” part of the time. That’s why these models are probabilistic. But if stakeholders are expecting magic (for example, 100% accuracy, real-time performance, instant ROI) every decent model will feel like a letdown.
Although the current “conversational” aspect of most AI models seemed to have improved users confidence in AI (if wrong information is passed via text, people seem ok with it 😊), the overexpectation of models performance is a significant cause of failure of AI projects.
Companies developing these systems share responsibility. It’s critical to communicate clearly that all AI models have inherent limitations and a margin of error. It’s specially important to communicate what AI can do, what it can’t, and what success actually means. Without that, the perception will always be failure, even if technically it’s a win.
How to avoid it:
Don’t oversell AI’s capabilities
Set realistic expectations early.
Define success collaboratively. Agree with stakeholders on what “good enough” looks like for the specific context.
Use benchmarks carefully. Highlight comparative improvements (e.g., “20% better than current process”) rather than absolute metrics.
Educate non-technical teams. Help decision-makers understand the nature of AI—its strengths, limitations, and where it adds value.
Just because you can slap AI on something doesn’t mean you should. Some teams try to force machine learning into every product feature, even when a rule-based system or a simple heuristic would be faster, cheaper, better. And it would probably inspire more confidence from users.
If you overcomplicate things by layering AI where it’s not needed, you’ll likely contribute to a bloated, fragile system that’s harder to maintain, harder to explain, and ultimately underdelivers. Worse, you might erode trust in your product when users don’t understand or trust the AI-driven decisions.
How to avoid it:
Start with the simplest solution. If a rule-based system works, use it. AI should be an hypothesis, not the default.
Prioritize explainability. Simpler systems are often more transparent, and that can be a feature.
Validate the value of AI. Ask: Does adding AI significantly improve the outcome for users?
Design for maintainability. Every new model adds complexity. Make sure you have the resources needed to maintain the solution.
AI projects are not just another flavor of IT, they’re a different beast entirely. They blend software engineering with statistics, human behavior, and organizational dynamics. That’s why they tend to fail more spectacularly than traditional tech projects.
If there’s one takeaway, it’s this: success in AI is rarely about the algorithms. It’s about clarity, alignment, and execution. You need to know what you’re aiming for, who’s responsible, what success looks like, and how to move from a cool demo to something that actually runs in the wild and delivers value.
So before you start building, take a breath. Ask the tough questions. Do we really need AI here? What does success look like? Who’s making the final call? How will we measure impact?
Getting these answers early won’t guarantee success, but it will make failure a lot less likely.
Written By
Ivo Bernardo
Business, governance, and adoption-focused material. Real-world implementations, case studies, and industry impact.
