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What every project manager should know about managing data science and AI projects
Spoiler: If you are starting with requirements, think again
If you are a project manager, being assigned a data science or AI project may be a conflicting experience.
AI alone is slated to create up to $2.9 trillion (yes with a ‘t’) in business value by 2021, and despite the overall damper of the coronavirus, remains on the forefront of technology powering a recovery. But in the same breath, the odds of successful project delivery are not in your favour. Estimates of project failure in the field start at 80% and go downhill, with a July 2019 VentureBeat AI report estimating that 87% of data science projects never make it into production.
This is not for a lack of trying. On the topic of managing data science and AI projects, we have reputable university courses, published research, specialised tools and full blown professional certifications. But despite this effort, we seem to be no closer to success besides learning that deploying more people and money may — surprisingly — not be the answer.
And this is because resources are like computation. Throwing more resources at a problem does not get to the right answer — it just gets to the wrong answer more quickly.
