3 reasons why machine learning projects fail - and how to avoid them
There’s no denying the competitive edge and the value promise that AI has to offer: confident prediction of future demand, faster analysis and insight generation from vast amounts of data, surfacing inherent business process efficiencies, and more. But when push comes to shove, many AI projects either fail to scale, are put on hold or simply never materialize. How come?
Gaurav, our VP of Business Development, spends a lot of his time speaking to senior business leaders about their artificial intelligence dreams and how they can best achieve them. It’s his job to ensure the project runs smoothly and the foundations are defined around a value generating future.
"Typically a business leader will approach us with a complex decision challenge they want to overcome; maybe it’s forecasting demand for their products or raw material, maybe it’s engine tuning. We’ll then go over that problem with a fine-tooth comb, looking over their data and processes to define what the best AI solution could be.
“Finding a solution is just the start, developing one that scales is the long game.”
"The most important part of my job is to ensure that the right foundations are in place that help our customers generate long term value.
“It is a common expectation that you can buy an AI solution off the shelf, plug it in, and away you go. Anyone thinking that is in for a shock. There are a number of things businesses often fail to consider before starting their AI journey."
So what are the main reasons projects barely get out of the starting gate?
Reason 1: Lack of buy-in from your people
Fear can surround terms like machine learning and AI. People fear that their job may be at risk. People fear the change it will bring.
“Human and AI collaboration is key - it’s one of our core beliefs. We don’t hand over a machine or some code that you switch on and leave. Your teams are critical to the long-term success of the project. They still own the decisions, but now they have another source to strengthen their instincts or warn them.“
Solution: Bring people together with a clear vision and purpose
Successful AI works with people. You can never replace the experience that your people possess. It’s here to help companies grow and serve their customers better. Get that point across to alleviate any worry in your teams.
“Do not risk alienating the exact people who are going to be critical to the whole thing succeeding. Be sure that teams understand that they are the linchpin of the project.”
Reason 2: No common definition of success
“Most people I speak with don’t have goals and expected value for their AI projects. How can it be a success without those clearly defined?"
Solution: Clearly define goals and objectives.
“Think about how your AI strategy aligns with company’s business strategy. Ensure that these projects are part of the digital and business transformation landscape. It has to be business led, and value driven.”
Reason 3: Boxed-in thinking
Break free of the proof of concept merry-go-round. AI has long shown immense promise and potential however businesses are struggling with the pace of adoption. This is due to the scalability limitation of technology solutions.
Solution: get out of the box
“Set-up a central AI program and invest in deploying solutions that are forward looking and that scale for value. Through this programme identify multiple use cases across the organisation, then do an impact assessment to prioritise areas of adoption. Here are they key areas I get our customers to think about:
Implementation will take months, and there’s a chance people will lose their steam. Find the sweet spot between the impact and ease of implementation. Once that is done think about adoption. How quickly can it be scaled further?
Build the right team
Having a multidisciplinary team is super helpful. Individually, everybody thinks they understand what needs to be done, but you need input from all the parties to come together to find the right solution. These trade-offs cannot happen in silos.
Replace the myopic view of doing POCs (proof of concept) with a long term ‘scale for value’ view. Look at everything with a holistic lens.