The Real Reason AI Projects Fail, According to Prezi’s CEO

Why It Matters

Aligning AI with a clearly defined business problem unlocks ROI and prevents costly missteps, a priority for any enterprise seeking competitive advantage.

Key Takeaways

• AI success depends on problem definition, not technology
• Gartner reports 50% of AI projects fail to deliver
• Firms ask “where AI?” instead of “what to fix?”
• Reframed steel‑mill problem improved delivery efficiency
• Align AI initiatives with clear business outcomes for ROI

Pulse Analysis

AI has become a buzzword in boardrooms, but the technology itself is rarely the obstacle. Gartner estimates that half of all AI initiatives fall short because they are not tied to a specific business need. Executives often start with the question “where can we use AI?” instead of defining the problem they need to solve. This misstep leads to wasted budgets, talent turnover, and missed competitive advantage, turning what could be a strategic lever into a costly experiment. Consequently, boards are increasingly demanding proof of impact before green‑lighting large AI spend.

Prezi’s chief executive Jim Szafranski illustrates the point with a steel‑mill pilot that initially targeted scheduling automation. The team soon realized the true bottleneck was the logistics of moving finished steel to customers, not the timing of individual shifts. By reframing the objective to optimize delivery routes and inventory flow, the AI model generated measurable gains—shorter lead times and higher on‑time delivery rates. The case underscores that a clear problem statement can turn a struggling proof‑of‑concept into a revenue‑positive solution. The revised model also reduced manual oversight, freeing staff to focus on higher‑value activities.

Leaders who want AI to deliver value should begin with a disciplined discovery phase. This means mapping current processes, quantifying pain points, and setting concrete success metrics before any model is built. Cross‑functional teams—combining data scientists, domain experts, and frontline staff—ensure the chosen use case reflects real‑world constraints. Ongoing monitoring and rapid iteration keep the project aligned with evolving business goals, while transparent ROI reporting justifies continued investment. Companies that embed this question‑first mindset report faster time‑to‑value and lower total cost of ownership. In short, asking the right question is the first step toward turning AI from hype into a sustainable competitive edge.