1 Why we need a new way to test AI opportunities
This chapter covers
- Why AI has become a competitive necessity
- The main reasons AI projects fail to deliver impact
- How testing ideas early prevents wasted investment
- What’s missing in current AI project evaluation methods
Artificial Intelligence (AI) offers extraordinary opportunities across every sector and business function. Yet, while organizations are racing to invest in AI, most are failing to translate its promise into performance. If you haven’t begun your AI journey, you’re likely losing competitiveness and leaving value on the table. But if you have started, chances are high you’ve already encountered frustration: more than 80% of AI projects fail to deliver lasting impact.
These failures come at a heavy cost: wasted time, sunk investments, user fatigue, and declining trust in data and AI. The problem isn’t a lack of data, ambition or technology. It’s a failure to bridge opportunity and execution. Too many AI initiatives stumble because they don’t solve real business problems, overlook user and stakeholder needs, are too complex for the organization’s maturity level or existing infrastructure, or rely on poor-quality data.
What’s most discouraging is that these pitfalls are known and largely preventable. We already have proven methods to address them: