3 Ways AI Projects Get Derailed, and How to Stop Them

The rate of companies implementing AI is continuing to skyrocket. Don’t fall victim to wasted time and a blown budget.
By Don Roedner – In the blink of an eye, AI has gone from novelty to urgency.

Tech leaders are telling companies they need to adopt AI now or be left behind. And a recent Gartner survey shows just that: AI adoption has skyrocketed over the last four years, with a 270 percent increase in the percentage of enterprises implementing AI during that period.

However, the same survey shows that 63 percent of organizations still haven’t implemented AI or machine learning (ML) in some form.

Why are there so many organizations falling behind the curve?

We meet with companies every week that are in some stage of their first ML project. And sadly, most of the conversations go more or less the same way. The project is strategic and highly visible within the organization. The internal proof of concept went off without a hitch. Now, the team is focused on getting the model’s level of confidence to a point where it can be put into production.

It’s at this point – the transition from proof of concept to production software development – that the project typically runs into big trouble. When we first meet with data science teams, their budget is often dwindling, their delivery deadline is imminent, and their model is still underperforming.

Sound familiar? The guidelines below might help your organization get its AI model to production on time without blowing your budget. more>

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