Good Article on Why AI Projects Fail
Today I came across this very good article focused on lessons learned, which could help anyone interested in these topics. It included a good mix of non-technical problems.
This is the link to the article, along with my commentary on the Top 3 items listed: https://www.cio.com/article/3429177/6-reasons-why-ai-projects-fail.html
Item #1:
The article discusses how the “problem” being evaluated was misstated using technical terms. At least some of these efforts are conducted “in a vacuum.” Given the cost and strategic importance of getting these early-adopter AI projects right, that surprised me.
In Sales and Marketing, you start the question, “What problem are we trying to solve?” and evolve that to, “How would customers or prospects describe this problem in their own words?” Without that understanding, you can neither vet the solution initially nor quickly qualify the need for it when speaking with customers or prospects. That leaves room for error when transitioning from strategy to execution.
More collaboration with Business likely would have helped. This was touched on at the end of the article under “Cultural challenges,” but the importance seemed to be downplayed. Lessons learned are valuable – especially when you are able to learn from the mistakes of others. This should have been called out early as a major lesson learned.
Item #2:
This second area had to do with the perspective of the data, whether that was the angle of the subject in photographs (overhead from a drone vs horizontal from the shoreline) or the type of customer data evaluated (such as from a single source) used to train the ML algorithm.
That was interesting because assumptions may have played a role in overlooking other aspects of the problem, or the teams may have been overly confident they could get the right results with the data available. In the examples cited, those teams identified the problems and took corrective action. A follow-up article describing the process used to determine the root cause in each case would be very interesting.
As an aside, from my perspective, this is why Explainable AI is so important. Sometimes, you just don’t know what you don’t know (the unknown unknowns). Understanding why and on what the AI is basing its decisions should help provide better-quality curated data up front, as well as identify potential drifts in the wrong direction while it is still early enough to make corrections without impacting deadlines or deliverables.
Item #3:
This didn’t surprise me, but it should be a cause for concern as advances are made at faster rates and organizations race to be first to market with an AI-based competitive advantage, potentially with less validation than ideal. The last paragraph under ‘Training data bias’ stated that based on a PWC survey, “only 25 percent of respondents said they would prioritize the ethical implications of an AI solution before implementing it.“
Bonus Item:
The discussion about the value of unstructured data was very interesting, especially when you consider:
- The potential for NLU (natural language understanding) products in conjunction with ML and AI.
- This is a great NLU-pipeline diagram from North Side Inc. in Canada, one of the pioneers in this space.
- The importance of semantic data analysis relative to any ML effort.
- The incredible value that products like MarkLogic’s database or Franz’s AllegroGraph provide over standard Analytics Database products.
- I personally believe the biggest exception to this assertion will be GPU databases (like OmniSci) that easily handle streaming data, can accomplish extreme computational feats well beyond traditional CPU-based products, and have geospatial capabilities that add an extra dimension of insight to the problem being solved.
Update: This is a link to a related article that discusses trends in areas of implementation, important considerations, and the potential ROI of AI projects: https://www.fastcompany.com/90387050/reduce-the-hype-and-find-a-plan-how-to-adopt-an-ai-strategy
This is an exciting space that will grow significantly over the next 3-5 years. The more information, experiences, and lessons learned are shared, the better it will be for everyone.
Where Ideas, Experiences, and Lessons Learned Intersect 