Why Do AI Initiatives Fail? Cydni Tetro & Jacob Andra On Failure Modes for Digital Transformation

Описание: Enterprise AI projects fail at alarming rates. MIT research shows most organizations struggle to achieve meaningful ROI from their AI investments. In this episode of The Applied AI Podcast, host Jacob Andra sits down with Cydni Tetro to explore why enterprise AI transformation is fundamentally different from individual productivity gains, and what separates successful deployments from expensive failures.

Cydni brings rare depth to this conversation. Her career spans six years at Disney Imagineering commercializing innovation across business units, serving as CIO at one of the largest Coca-Cola bottlers managing 8,000 employees, and now leading digital transformation across a private equity portfolio. She also founded the Women's Tech Council, which has activated over 40,000 women in technology careers and generates $32 million in annual economic value to the state of Utah.

The conversation addresses a critical gap in how organizations think about AI. Most discussions focus on individual productivity. For example, using ChatGPT to draft emails faster or summarize documents. These gains are real but represent only the outer layers of what AI can accomplish. The deeper value requires tackling enterprise-wide challenges involving data integration, systems engineering, legacy infrastructure, and organizational change.

Cydni identifies three distinct categories of enterprise AI projects based on data complexity:

First, projects with centralized, structured data sources. She shares how her team deployed AI-powered cybersecurity tools in just 60 days because email and threat data already flowed into a single funnel. The data was accessible and structured, making implementation straightforward.

Second, legacy systems with legacy data. Manufacturing environments present particular challenges. Operational technology (OT) networks have historically been isolated from IT networks. These OT networks run plant equipment and were never designed to connect to the outside world. Adding AI requires new sensor arrays, network architecture changes, cybersecurity considerations, and workforce training. Some manufacturing lines are 20 to 30 years old, and organizations must maximize their lifetime value while somehow integrating modern AI capabilities.

Third, distributed datasets that must be organized before AI can deliver value. A procurement AI project Cydni evaluated would have required massive effort to create structured data from tens of thousands of contracts, serving a team of only two to three people. The ROI calculation did not justify the lift.

The manufacturing case study illustrates the full complexity of enterprise AI deployment. Manufacturing lines can have hundreds of decision control points. New sensors must interface with legacy equipment that may only output binary signals rather than the rich data AI models require. Teams must manage the transformation while maintaining production. The most successful approaches identify a single line, target one specific value driver (predictive maintenance, reduced unplanned downtime, or visual monitoring for faster problem resolution), and prove value before expanding scope.

Cydni and Jacob agree on the primary reasons enterprise AI projects fail. Organizations tackle too much at once. They allow scope creep that eliminates the ability to demonstrate value. They choose tools that do not match the problem. They underestimate data readiness requirements. And they neglect the human element: people who argue with AI outputs because they trusted their gut for decades, or teams overwhelmed by new responsibilities they were never trained to handle.

The conversation offers a corrective to the hype cycle. Enterprise AI delivers genuine value, but capturing it requires methodical scoping, minimum viable products that prove value quickly, careful sequencing of initiatives, and deep partnership with the people whose jobs will change. Organizations that approach AI as wholesale transformation typically fail. Those that identify the most important thing, build for that specific outcome, and earn the right to expand will win.

Learn more about Talbot West and enterprise AI transformation: https://talbotwest.com/

About the guest:

Cydni Tetro spent six years at Disney Imagineering commercializing innovation across business units, then served as CIO at one of the largest Coca-Cola bottlers. Today she leads digital transformation across a private equity portfolio spanning consumer products, technology, and real estate. She founded the Women Tech Council 17 years ago, building a community of over 20,000 technology professionals from high school to the C-suite.