Think Smaller: The Counterintuitive Path to AI Adoption

The following article originally appeared on Gradient Flow and is reposted here with the author’s permission.

By Ben Lorica | November 5, 2025 | 6 minute read

A Peculiar Moment in AI Development

We are experiencing a unique phase in AI progress. On one side, AI demos are impressive: intelligent agents reason and plan effortlessly, models generate original songs from text prompts, and tools create detailed reports within minutes.

Challenges in Real-World AI Adoption

Despite these advancements, many AI teams face what’s called “prototype purgatory,” where promising proofs-of-concept do not evolve into dependable, production-ready systems. Data confirms this: most enterprise generative AI projects fail to produce measurable business outcomes.

Identifying the Core Issue

The main problem is not the AI models themselves, but a "learning gap" where generic tools cannot adjust to the complexities of enterprise workflows. This aligns with observations from enterprise search systems, where the biggest hurdle isn’t the AI algorithms but the intricate and foundational challenges of the environment in which they operate.

Complexity in Agentic AI Systems

This issue becomes even more pronounced when developing agentic AI—often “black box” systems that are difficult to debug and show unpredictable performance drops when interacting with custom tools.

“The data backs this up: A vast majority of enterprise GenAI initiatives fail to deliver measurable business impact.”

“The core issue isn’t the power of the models but a ‘learning gap’ where generic tools fail to adapt to messy enterprise workflows.”

Author’s summary: Successful AI adoption requires overcoming a learning gap by tailoring solutions to complex enterprise workflows rather than relying solely on powerful models.

O'Reilly Media — 2025-11-06