Why AI Projects Fail

Question 1

Why do AI projects need multidisciplinary teams?

Accepted Answer

The page explains that real AI solutions are built by teams of specialists—machine‑learning engineers, data scientists, data engineers, product managers and domain experts—rather than a single "AI person".  Without this blend of expertise, projects often fail:contentReference[oaicite:21]{index=21}.

Question 2

What happens if businesses underestimate AI complexity?

Accepted Answer

Business leaders sometimes overestimate AI’s capabilities or treat AI as a plug‑in.  The page notes that underestimating complexity leads to misaligned budgets and unrealistic expectations, causing initiatives to fail:contentReference[oaicite:22]{index=22}.

Question 3

Why is a data audit important before starting an AI project?

Accepted Answer

One of the missteps listed is starting AI work without auditing available data.  Without understanding data quality and availability, projects may stall or produce unreliable results:contentReference[oaicite:23]{index=23}.

Question 4

What other missteps cause AI initiatives to fail?

Accepted Answer

Additional reasons include not having the right team in place, mis‑understanding the problem’s complexity, misaligned budgets, and assuming AI can simply be added to existing processes:contentReference[oaicite:24]{index=24}:contentReference[oaicite:25]{index=25}.  These issues mean many AI projects never deliver business value.