Source: Fortune
Summary
A Fortune article discusses the challenges of AI adoption, emphasizing that most companies fail to achieve business impact due to outdated infrastructure and data issues. TIAA and Accenture partnered to modernize TIAA’s systems, improving efficiency and digital engagement. The article argues that AI transformation requires more than just technology—it demands business and operational overhauls. Gartner predicts 60% of AI projects will fail due to poor data readiness. The piece outlines five key areas for successful AI implementation, including modernizing the digital core and redesigning workflows. It highlights the importance of human oversight and governance in AI deployment.
Our Reading
The numbers tell one story.
TIAA and Accenture spent two years modernizing infrastructure.
Only 5% of companies say their data is AI-ready.
AI projects are failing because of broken systems.
The tracks determine how fast the trains can go.
Author: Evan Null
AI Is the Train, Not the Tracks
Every CEO wants to talk about AI’s magic. Almost none want to talk about the boring problems standing in the way. The article compares AI adoption to a railroad that spends billions on the fastest trains but runs them on aging rails. The trains aren’t the constraint. The tracks are. That’s the uncomfortable truth for most enterprises deploying AI today: the technology has never been more powerful, yet only a fraction of companies turn it into measurable business impact.
The article highlights TIAA’s experience, which partnered with Accenture to modernize its recordkeeping infrastructure. TIAA is 108 years old and carries the technical debt to prove it. Before scaling AI, the companies had to rebuild the foundation, cleaning data, retiring outdated systems, and redesigning workflows. The result was faster plan sponsor changes and increased digital engagement. None of that came from a flashy AI demo. It came from the unglamorous work most companies skip.
The real story of AI transformation is that it’s not a technology project. It’s a business transformation, a change-management project, and an operating-model rebuild that happens to run on AI. Companies that treat it as a tech bolt-on will spend years chasing pilots that never scale. The article outlines five focus areas for successful AI implementation, including modernizing the digital core, treating data readiness as a prerequisite, and redesigning workflows.
The article also emphasizes the importance of keeping humans in the loop, especially in high-trust interactions. AI should augment employees, not replace them. TIAA has rolled out its own generative and agentic platform, GAIT, to 85% daily adoption among colleagues. The piece concludes that the enterprises winning with AI aren’t the ones with the biggest budgets or the fastest adoption, but the ones disciplined enough to do the boring work first.
In the AI era, complexity is a competitive disadvantage that can no longer be hidden behind a sizzling AI experience. The tracks determine how fast the trains can go. Enterprises that rewire the foundation now, not just the technology sitting on top of it, are the ones that will still be running at full speed five years from now.
