Bottom Line: 80% Of AI Projects Fail. But is That Bad?
Why the high failure rate isn't what you think.
This fabulous read strives to find the “root causes of failure” for AI projects and surveys 65 AI/ML experts to find out what’s going wrong!
The results show a staggering reality: AI projects have an estimated 80% failure rate, twice that of IT projects overall!
The problem with this comparison is that AI projects cannot be equated to typical IT projects. AI projects are better classified as innovation projects because they strive to use new technology to do things that have never been done before. You can’t compare this with updating servers!
With innovation, project failure rates of 80% are the norm! That’s why the 80% figure doesn’t bother me in the least!
My first book, “Innovation Lab Excellence,” focused on how to make innovation projects work, and many of the solutions for innovation problems are the same as with AI. Read how below.
👉TAKEAWAYS
Five leading root causes of the failure of AI projects:
Industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI.
The organization lacks the necessary data to adequately train an effective AI model.
The organization focuses more on using the latest and greatest technology than on solving real problems for their intended users.
Organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure.
AI projects fail because the technology is applied to problems that are too difficult for AI to solve.
👊STRAIGHT TALK👊
Now, to prove that AI projects are, in fact, Innovation projects in disguise, I want to use my book “Innovation Lab Excellence.”
The diagram below shows my “innovation best practices,” and I will show how these best practices map almost perfectly onto the report’s recommendations for successful AI projects!
The 12 Innovation Best Practices from my book Innovation Lab Excellence.
The report’s recommendations for successful AI projects start with ➤. The corresponding “best practice” from my book have 🔹.
➤Industry leaders should ensure that technical staff understand the project purpose and domain context.
🔹Best Practice: “No Carte Blanche.” meaning the project must be focused on the business.
➤Industry leaders should choose enduring problems: AI projects require time and patience to complete.
🔹Best Practice: Transform, not Disrupt. Innovators should focus on incremental betterment of the business, not a sudden disruption.
➤Industry leaders should focus on the problem, not the technology: Successful projects are laser-focused on the problem to be solved, not the technology used to solve it.
🔹Best Practice: Focus on People. Technology is secondary to how people use the technology!
➤Industry leaders should invest in infrastructure: Up-front investments in infrastructure to support data governance and model deployment can reduce the time required to complete AI projects.
🔹 Best Practice: Buy Don’t Build. Yes! To make this all work, companies need to buy the best tech they can from experts in the business
➤Industry leaders should understand AI's limitations: When considering a potential AI project, leaders need to include technical experts to assess the project's feasibility.
🔹Best Practice: Balance Staffing. True experts must be brought in; it is best to put them on staff.
I hope that convinced you that AI is an innovation project and high failure rates are to be expected!
Thoughts?
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