Three reasons why machine learning and artificial intelligence projects fail - and how to avoid them

There is no denying the competitive edge and value that machine learning (ML) and artificial intelligence (AI) has to offer: confident prediction of future demand, faster analysis, and insight generation from a vast amount of data, and more. However, research from IDC has found that on average 50% of AI projects fail for one in four companies. A clear indicator that when push comes to shove, many AI projects either can’t scale, are put on hold, or simply never materialise.

So, what are the main reasons such projects barely get off the ground?

Lack of buy-in from your people

Fear can surround terms like ‘machine learning’ and ‘artificial intelligence’. People worry their job may be at risk or that they won’t be able to keep up with the pace of change and skills needed. These fears are often coupled with a lack of understanding from leadership teams on what is needed on the ground to make data-driven decisions more effectively.

Education is needed to help those people using AI to view it as a partner, not a threat. AI works most effectively when augmented with human decision-making. In short, AI needs people just as much as people need AI. There will always be some things - such as unexpected events or disruption - that machines can’t predict, so people need to remain in control of decision-making at all times, collaborating with AI to strengthen their instincts and better assess risks.

To address these concerns, business leaders need to bring people together with a clear vision and purpose. At the same time, we need practical AI solutions that are designed for non-technical experts, rather than data scientists, to empower the people making decisions in business.

No clear definition of success According to McKinsey, just 8% of firms engage in practices that allow widespread adoption of AI. Before integrating any new AI or ML systems, the most important first step for businesses is to clearly define goals and objectives. AI strategies then need to be developed in alignment with the company’s business strategy. This ensures that AI projects are part of the overall business transformation, helping business leaders identify areas that need to be prioritised so there is a clear route to success.

Failure to reach agreement both internally and with technology partners on which strategies and objectives to pursue and key performance indicators, will put the success of a project in jeopardy before it’s really gotten underway.

Boxed-in thinking

When it comes to AI, business leaders can easily become trapped in the proof of concept (PoC) merry-go-round. AI has long shown promise and potential across industries and functions, however, due to the scalability limitations of many technology solutions, businesses are struggling with the pace of adoption.

So, get out the PoC box. Set up a central AI programme and invest in deploying solutions that are forward-looking and that scale for value. This will help identify multiple use cases across the organisation and enable you to conduct an impact assessment to prioritise the areas of adoption.

Implementation can take months, so, you need to find that sweet spot between business impact and ease of implementation. Choose AI platforms that can integrate seamlessly with other technology infrastructure you already have implemented across the business.

Next, build a multidisciplinary team and promote effective collaboration. Trade-offs cannot happen in silos - you need input from all the parties to come together to find the right solution. Lastly, replace the myopic, proof of concept approach with a long term ‘scale for value’ view, looking at everything with a holistic lens.

AI and ML technology continues to be viewed as a risk or as a double-edged sword, but addressing these common barriers to success from the start will put you in a better position to get your AI or ML journey out of the starting gates.