Ep 85 | Enterprise AI Success: What Separates Results from Expensive Experiments

Cloudera, Inc.

Aug 12, 2026

Most enterprise AI use cases still aren't delivering measurable value. So what separates the projects that work from the ones that quietly disappear?

For Mark Ritcey, the answer comes down to disciplined execution. AI programs need a clear business problem and an organization prepared for how the technology changes the way work gets done.

In this episode of The AI Forecast, Paul Muller sits down with Mark Ritcey, Vice President of AI and Automation Delivery at Latentbridge and lecturer on AI and machine learning, to examine the decisions that shape enterprise AI success.

Mark has spent more than 25 years working across technology and automation, including leading enterprise transformation initiatives in highly regulated industries. He shares what he’s seeing inside AI programs today, where teams commonly go wrong, and why structured experimentation matters as organizations figure out where AI can create real value.

What separates AI success from failure, according to Mark and Paul:

Why AI projects need a clearly defined business problem
The risks of experimenting with AI for its own sake
How unrealistic expectations derail enterprise deployments
The role of governance as AI moves into production
How organizational change affects AI adoption
What CEOs and boards should consider before scaling AI

His advice for leaders is refreshingly straightforward: AI transformation requires diligent, structured work. There are no shortcuts around understanding the business and building the controls required to put AI into production responsibly.

Want to hear another perspective on enterprise AI adoption? Check out Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail.

Stay in touch with Mark:*
Mark on LinkedIn: https://www.linkedin.com/in/markritcey/

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