What Separates Scalable AI-Driven Innovation From Promising Experiments
Your best AI model won't scale. Here's what actually will — and it's not what most CTOs are betting on.
Why it matters
Forrester research reveals that AI scaling failures stem not from model limitations but from poor UX, rigid workflows, and perfectionist data strategies. Leaders need to redesign processes with end users and prioritize speed-to-value over capability.
The key facts
6 to knowScaling AI depends more on usability and workflow redesign than model capability
Minimum viable data approach outperforms waiting for full data readiness
Complex AI solutions fail to scale regardless of technical sophistication
Co-created workflow redesign with end users is critical to adoption
High-impact data prioritization proves value faster and sustains momentum
Sources: Google Cloud, APPLY, Aptar leadership perspectives
Go to the source
Forrester Blogforrester.com
Publisher excerpt: Forrester’s recent discussions with leaders from Google Cloud, APPLY, and Aptar highlight that scaling AI depends less on model capability alone and more on usability, co-created workflow redesign, and focusing on minimum viable data rather than waiting for full data readiness. Even the most…