In boardrooms worldwide, the wrong question is being asked: “Which AI tool should we buy?” The right question is: “Why do we need AI?” Without clarity on purpose, organizations risk falling into vendor hype, wasted spend, and failed adoption.
The Wrong vs Right Questions
| Wrong Question (Tech-Centric) | Right Question (Business-Centric) |
| “Which vendor is best?” | “What business problem are we solving?” |
| “How accurate is the model?” | “How does this reduce cost, risk, or increase revenue?” |
| “How fast can we deploy?” | “How do we integrate AI into our workflows?” |
| “Do we have the budget?” | “What ROI will justify continued investment?” |
Data Behind the Shift
- 82% of failed AI projects cite “unclear business objectives” as the root cause (PwC, 2024).
- Companies that frame AI with business KPIs upfront are 2.7x more likely to report positive ROI (McKinsey, 2024).
- Global AI investment hit $330B in 2024, yet most of it was wasted due to misaligned goals (IDC, 2025).
A Better Framework for Leaders
- Start with Business Value – Define impact in dollars, risk, or customer outcomes.
- Identify the Use Case Fit – Match AI to the right problem (fraud detection, personalization, risk scoring).
- Define Success Metrics – Tie AI performance to KPIs: churn rate, fraud losses, customer retention.
- Evaluate ROI, Not Just Accuracy – A 5% accuracy boost is meaningless if it doesn’t save money.
- Plan Integration Early – AI only works when embedded into enterprise workflows.
Conclusion
The most powerful AI strategy doesn’t start with “what”—it starts with “why.” Leaders who shift the conversation unlock ROI, while those chasing tools remain stuck in cycles of hype and disappointment.
References
- https://www.pwc.com/us/en/services/consulting/analytics/ai-and-analytics.html
- https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-survey-the-state-of-ai-in-2024
