Most AI investments fail—here’s what the winners get right |
Most AI investments fail—here’s what the winners get right
Generative AI stands apart from previous technological shifts: it’s fundamentally reinventing how businesses operate at breathtaking speed. What took farming mechanization decades—reducing agricultural workers from one-third of the U.S. workforce to 1%—AI is accomplishing in months.
Yet despite billions in investment, most organizations still struggle to move from pilot to production to adoption. In fact, according to Gartner® research, “in 2024, 60% of GenAI POCs were abandoned upon completion¹.”
The difference between AI experimentation and success isn’t about choosing the right large language model; it’s about much more.
Through our work with partners and customers at various stages of their AI journey, we’ve observed consistent patterns that separate successful implementations from those that stall. Organizations that successfully move from pilot to production focus on four interconnected pillars—and critically, they recognize that technology is only one of them.
Here’s what we at AWS see winners doing right.
1. Build Your Data Foundation Strategically
Simply having data isn’t enough—how you organize, govern, and activate it makes all the difference. Leading organizations implement three specific practices: connect all your data together, label and organize it so it’s easy to find, and set controls to ensure only the right people (or agents) have access to sensitive data sets.
Heavily regulated industries like financial services and healthcare often have an advantage here—their existing governance frameworks can accelerate AI initiatives. However, for organizations starting from scratch, rather than attempting to unify your entire data warehouse, start by working backwards from a specific use........