Most enterprises deploying AI are stacking it atop decades of legacy systems and siloed data, automating dysfunction rather than driving impact.
Only 5% of businesses say their data is AI-ready; Gartner predicts 60% of AI projects will fail by 2026 from lack of preparation.
The enterprises winning with AI are those that modernize their IT foundation, clean their data, and redesign workflows first—as TIAA did, cutting plan-option changes from weeks to days.
What happened
The authors—drawing on their work modernizing TIAA's recordkeeping infrastructure over nearly two years—argue that most companies are layering AI tools onto aging IT systems, siloed data, and broken workflows instead of rebuilding their foundations. TIAA itself had to clean data, retire outdated systems, and redesign workflows before scaling AI; the payoff includes enabling plan sponsors to change investment options in days instead of weeks and raising digital engagement across TIAA's participants by 13%.
Why it matters
Only 5% of businesses report their data is AI-ready, and Gartner predicts 60% of AI projects will be abandoned through 2026 for lack of AI-ready data. AI amplifies whatever foundation it is given—good or bad—so automating a broken process just makes it fail faster. Companies treating AI as a technology bolt-on will spend years chasing pilots that never scale, while those disciplined enough to modernize their core first stand to turn the technology into measurable business impact.
What to watch
The authors identify five focus areas: modernizing the digital core before scaling agents, treating data readiness as a prerequisite (not an afterthought), redesigning end-to-end workflows, keeping humans in the loop on high-stakes decisions, and building for resilience, governance, and security. TIAA has rolled out its own generative and agentic platform, GAIT, to 85% daily adoption among employees while reserving high-trust interactions for humans augmented by AI.
Ask the AI about this article →
The authors frame AI transformation not as a technology deployment but as a business and operating-model rebuild. They draw on a concrete case: TIAA, a 108-year-old organization burdened with technical debt, spent nearly two years modernizing its recordkeeping infrastructure before attempting to scale AI. That work—cleaning data, retiring obsolete systems, and redesigning workflows—was unglamorous but essential; only after that groundwork could TIAA's AI tools deliver measurable results. The article's central tension is that most enterprises skip this preparatory work, instead layering AI onto existing dysfunction. The authors cite Gartner's projection that 60% of AI projects will fail through 2026 due to lack of AI-ready data, suggesting the problem is systemic. The railroad metaphor—powerful trains on aging rails—captures the constraint: the technology is not the bottleneck; the foundation is. TIAA's experience with GAIT (85% daily adoption among employees) illustrates that AI succeeds when embedded into a modernized operating model, not when deployed as a standalone tool.
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