Did you know that even 85% of AI projects fail to deliver? What a disappointment — especially when you think about how much time, effort, and money goes into AI adoption.
Research shows that there are various reasons why AI projects fail: including the lack of proper skills, limited understanding of the tech within the company, budget limitations, and so on. It seems like there are just so many ways to fail — even before you actually start your project. But before we get to THE reason why AI projects fail, let’s look at some examples of how you may fail.
Big is never big enough
Big data is a buzzword, but it’s also rather enigmatic. How big is “big”? How much data do you need? Data can be quite a problem. Not just because there’s not enough of it — though sometimes there is — but also due to issues with labeling, training data, etc. Because an AI system can only be as good as the data it’s fed with, it won’t bring you any tangible results if there’s no data behind it. So what’s the problem with data? Well, where do we start…
First, it really is a problem if there isn’t enough data. If the business you’re running is small and has a limited set of data, you have to carefully discuss your expectations and the current state of your data set with an experienced AI advisor or data scientist. How much data is enough? That’s a tricky question because that depends on the use case, the type of data, and the result you expect. However, we can often hear “the more, the better”. Seems like in data science projects, more is more, period.
The company is not on board
One of the challenges to AI implementation is the fact that senior management may not see value in emerging technologies or may not be willing to invest in such. Or the department you want to augment with AI is not all in. It’s understandable. AI is still seen as risky business — an expensive tool, difficult to measure, hard to maintain. However, with the right approach, which includes starting with a business problem that artificial intelligence can solve and designing a data strategy, you should track the appropriate metrics and ROI, prepare your team to work with the system, and establish the success and failure criteria.
As you can notice, I use the term “augment” when referring to the job AI is to perform — that’s because AI’s primary task is to augment human work and support data-driven decision-making, not to replace humans in the workplace. Of course, there are businesses aiming at automating as much as can be automated, but generally speaking, it’s really not AI’s cup of tea. It’s much more into teamwork. What’s more, it has been found that AI and humans joining forces gives better results. In a Harvard Business Review article, authors H. James Wilson and Paul R. Daugherty write:
In our research involving 1,500 companies, we found that firms achieve the most significant performance improvements when humans and machines work together.
However, as a leader, your job in an AI project is to help your staff understand why you’re introducing artificial intelligence and how they should use the insights provided by the model. Without that, you just have fancy, but useless, analytics.
To illustrate why this matters, let’s look at an example described by CIO magazine. A company called Mr. Cooper introduced a recommender system for its customer service to suggest solutions to customer problems. Once the system was up and running, it took the company 9 months to realize that the staff is not using it, and another 6 months to understand why. It turned out that the recommendations weren’t relevant because the training data included internal documents describing the problems in a technical way — so the model wasn’t able to understand the issues that customers described in their own words, not in technical jargon.
This example shows both the importance of the staff understanding why and how they should work with AI — and that they are allowed to question the system’s performance and report issues, and the significance of reliable training data.
Premature failure
You can even fail with AI before you start. Yeah, really. This happens when you jump in before having all the necessary resources — the data, the budget, the team, and the strategy. Without these elements, it’s only wishful thinking. That’s why we emphasize the importance of a strategic approach: making sure you are ready for artificial intelligence, identifying the appropriate business use case, outlining a decent data strategy, and establishing the goals. Starting without that strategy is difficult and risky. You want your AI project, especially the first one, to go towards a bigger objective but also achieve some quick wins along the way. This way, it proves its viability and mitigates the risk of you wasting your company’s money on a useless tool. The first AI project should not be a company-wide AI implementation but a proof of concept that gets the entire organization accustomed to the new normal. With time, both AI and your company will grow: your systems will be getting better and better, and your team will be more data-driven and efficient. It can be a win for all, if only you do it step by step and not lose sight of your objectives. AI is a tool that’s supposed to help you reach your goals, not a goal itself.
OK, cool, but what is THE reason why AI projects fail?
Yes. There is this one seemingly little thing that has nothing to do with AI, but has everything to do with your AI project’s success.
It’s how you manage your expectations.
The thing is that we tend to expect much, especially if something requires our time, money, and effort. AI projects do — it’s R&D, so it consumes both time and money, and quite often the end result remains a mystery. Organizations that are just getting on board with AI tend to expect a lot, and much of that can be simply not doable. While getting rid of all expectations is not possible, we can get real by strategizing. That’s why we need to understand the value of the right preparation for AI adoption and starting step by step, not going all in. It’s not about NOT expecting anything, it’s about expecting the right things. You can’t expect AI to perfectly reflect the operations and intricacies of the human brain, but you can expect it to accurately predict things for you — like, for example, which customers are about to churn. Before you start the actual model-building part, you need to think about your business, not the technology.
Want to know more about starting smart your AI adoption to make sure it follows through? Check out “A start-smart guide to successful AI adoption”!
