833: The 10 Reasons AI Projects Fail — with Dr. Martin Goodson

Common Pitfalls

Martin Goodson highlights common pitfalls in AI projects, emphasizing the importance of having a clear business purpose. He notes that many projects fail due to a lack of proper business reasoning, often using AI as a solution in search of a problem 1. This misalignment leads to wasted resources and efforts, particularly with the rise of generative AI applications 2. Goodson states, "I know of many projects, real projects, where people have made the decision to use AI...and then as a fourth step somebody says, do we actually need this thing?" 1.

   

Organizational Challenges

Organizational challenges often stem from a disconnect between AI projects and business goals. Jon Krohn discusses the importance of data readiness and the misconception that data is inherently valuable 3. Goodson shares his experience, noting that projects often fail when designed without real data, likening it to "doing taxidermy without looking at live animals" 4. This highlights the need for organizations to align their AI initiatives with practical, data-driven insights.

   

Technical Challenges

Technical challenges in AI projects include issues of reproducibility and unnecessary complexity. Goodson points out that non-reproducible results are a major hurdle, despite the availability of tools like git and code review 5. He advocates for simplicity in model design, warning against the allure of complex methods that are hard to interpret 6. "I'm such an advocate for simplicity," he states, emphasizing the value of starting with straightforward methods to truly understand the data 6.