The One Method AI Implementers Use For Success

Who do you blame when AI projects fail? The technology? Your machine learning and data science team? Vendors? The data? Certainly you can put blame on solving the wrong problem with AI, or applying AI when you don’t need AI at all. But what happens when you have a very well-suited application for AI and the project still fails? Sometimes it comes down to a simple approach: don’t take so long.

At a recent Enterprise Data & AI event, a presenter shared that their AI projects take on average 18 to 24 months to go from concept to production. This is just way too long. There are many reasons why AI projects fail and one common reason is that your project is taking too long to go into production. AI projects shouldn’t be taking 18 or 24 months to go from pilot to production. Advocates of best-practices agile methodologies would tell you that’s the old-school “waterfall” way of doing things that’s ripe for all sorts of problems.

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