How to Stop Losing Money on AI pilots: Three Mistakes and What to Do Instead

Over the past few years, the financial industry has been racing to implement artificial intelligence. Companies are increasingly buying models and signing contracts with vendors, but that yields little or a terrifying result. According to an MIT study published last year, 95% of corporate AI pilots have no measurable effect on сompanies profits.

At the same time, the spending is skyrocketing, as it is estimated that last year the world spent 1.76 trillion dollars on AI, and in 2026 this figure is expected to grow to 2.59 trillion. So the money poured in and the money returned seem to differ greatly, and costs are rising basically for nothing.

However, the issue is not that the technology is weak. The AI models are becoming smarter each year, so much so that OpenAI decided to put its developments on hold so as not to go too far. So, in this case, the real downside is the lack of an operating model that can effectively use these technologies.

The Illusion of Having a Pilot

The scale of AI integration is so high that almost every fintech or traditional finance company has recently launched at least one AI pilot, for example, a chatbot for clients. But no matter what they launch, these stories altogether have quite an unpleasant ending.

It all starts with a particular team building a proof of concept that performs well under controlled test conditions. Then, after the demo, management is impressed and lets them deploy it, but at some point the project breaks down or proves ineffective. But why?

Of course, not every internal AI project fails, because in that case there would be no ML engineers left, and the technology would prove useless. But I’ve watched this failure cycle so many times that I already know its root very well. Simply put, it is the pilot itself, which, although it could be well done, was not designed to fit into how the business really works and functions from day to day.

As a result, a model that works brilliantly in isolation hits the combat circuit and meets legacy infrastructure or employees who don’t even know how to work with the technology. But none of these barriers is solved by a smarter algorithm. All of them can be fixed only by management decisions, and this is not the area of responsibility for data scientists.

Some Mistakes It’s Better to Avoid

Before we see how to integrate AI more effectively in a company, let’s consider other mistakes that can happen along the way.

The first and most important slip, as we have already mentioned indirectly, is treating AI as an add-on without rebuilding the processes. In fact, many companies take an existing workflow and simply embed an AI tool into one step, without even considering whether AI makes sense in that process.

So, as a manager, I would ask myself first: was this process even necessary in your company? If the workflow was inefficient before automation, it will remain just as inefficient after it. It may even get worse, because AI can remember negative patterns and repeat them.

The next mistake happens when managers underestimate the burden of compliance and governance. Financial services face some of the toughest regulatory pressures of any industry, and, for example, you cannot automate the whole compliance department with AI. The same goes for direct service provision and portfolio management. It seems like a universal truth, but you will be surprised how many founders slip up.

And last but not least, many companies forget to train their people for AI implementation. When technology takes over employees’ routines, the first instinct is often to reduce staff, since many tasks can be done faster with fewer people involved. Although it will cut your expenses in the near term, it is very short-sighted.

If you want real value, ask yourself a different question. What higher-level work can our people do now that they are freed from the routine? Are they ready for it with their current skills and motivation?

What to Start on Monday for More Efficiency

As a first step, audit your suspended AI pilots. That is, don’t introduce new ideas until you have properly dealt with the old ones. In 90% of cases, the problem is the lack of responsibility, the fact that no one is clearly accountable for scaling it. So assign ownership.

If someone already owns AI control, the next step is to review the process and rebuild it entirely. Don’t simply add AI to the existing workflow. My advice is to ask instead what this process would look like if we were designing it today from scratch, knowing AI can do primary analysis. Often the answer is much simpler than it seems.

Also, don’t forget to calculate real numbers that the AI model brings. If, six months after the launch, you cannot specify figures on the effectiveness of your model (for example, saved hours or increased profits), then it doesn’t work the way it should, so it’s better to reconsider its worth.

And, perhaps most importantly, don’t fire people, especially those responsible and those who have always done their job honestly. If people bring more money to the company than they earn in a year, the dismissal will become an issue for you very soon. And companies that don’t understand this lose their institutional knowledge and the trust of the team, which then costs much more.

Taken together, 95% of pilots do not reach the result, not because the technology has failed. They don’t make it because the organization around them has remained the same and outdated. This is the only variable that should be changed first.