Why is using ChatGPT so simple, yet deploying enterprise AI so hard?

Navigating the Generality-Accuracy-Simplicity Trade-Offs in Enterprise Generative AI

Hi All,

This week I have a slightly different type of post. My collaborators

Alex Oettl

(Professor of Innovation and Strategy at the Georgia Institute of Technology) and Sampsa Samila (Professor of Strategy at IESE, Barcelona) have a new working paper “From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI.”

If you are interested in catching up on the absolutely latest research (from CS, Strategy, Economics, and spanning academic studies and industry reports) on Generative AI and what it means for organizations. Check out the full paper here. We learned a lot writing it, and hope that you find both our framework useful and the literature review a nice upgrade to your knowledge about AI in the field.

Here is the punchline

Generative AI appears to offer a free lunch: incredible power (Generality & Accuracy) through a simple chat box (Simplicity). But this user-facing simplicity is an illusion.

The paper introduces the Generality-Accuracy-Simplicity (GAS) framework to argue that the fundamental trade-off between these three elements hasn't disappeared: it has been relocated.

Where?

The complexity is shifted from the user to the organization, re-emerging as hidden infrastructure costs, new compliance burdens, the need for specialized talent, and a persistent "accuracy ceiling."

Our key takeaways:

• Complexity is Relocated, Not Eliminated: AI's ease of use for the individual creates immense, often invisible, complexity for the firm. This is why Enterprise AI doesn’t seem like the easy “win” that ostensibly should come from just purchasing site licenses to your favorite chatbot.

• The "Accuracy Ceiling" is a Core Strategic Constraint: Competitive advantage comes not just from adopting AI, but from designing workflows and cultivating human expertise to manage its inherent limitations.

• The New Competitive Moat is Mastering This Complexity: Sustainable advantage will come from designing new “intelligent” workflows, building complementary human expertise, and making deliberate choices about where to operate on the Generality-Accuracy frontier. The latter is particularly crucial. If your firm conducts high-accuracy, low generality tasks, you have considerable distance to traverse to make AI useful for you. This distance—the hidden complexity you’ll need to develop and manage—isn’t costless.

• Human Judgment is the New Bottleneck: As the cost of generating content plummets for everyone (not just you!), the value of critically evaluating, refining, and contextualizing it skyrockets.
  

This is a shift from viewing AI as a simple cost-reducer to seeing it as a catalyst for deep organizational redesign.

We hope this framework helps leaders, researchers, and builders navigate this new landscape.

You can read the full paper on SSRN: From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI.