The TIAA AI transformation began not with a chatbot or a generative model, but with a demolition job: cleaning data, retiring outdated systems, and redesigning how work flows inside one of America’s oldest financial institutions.
TIAA, which Forbes reports was founded in 1918 by Andrew Carnegie and his Carnegie Foundation for the Advancement of Teaching, now manages $1.3 trillion in assets across more than 50 countries. It also, by its own account, carries more than a century of technical debt.
The firm partnered with an outside technology organisation nearly two years ago to modernise its recordkeeping infrastructure before attempting to scale artificial intelligence on top of it. The outcome: plan sponsors can now change investment options for employees’ retirement plans in days rather than weeks, and digital engagement across TIAA’s millions of participants has risen 13%.
The TIAA AI Transformation Blueprint: Fix the Foundation First
The lesson drawn from the project is blunt. Layering AI on top of legacy systems, siloed data, and broken workflows does not accelerate a business. It automates the dysfunction faster.
Five priorities are outlined for enterprises attempting the same shift.
The first is modernising the digital core before deploying agents. That means auditing which platforms are genuinely load-bearing, retiring the rest, and rebuilding the infrastructure that AI will run on.
The second is treating data readiness as a prerequisite. Only 5% of businesses say their data is AI-ready, and Gartner predicts 60% of AI projects will be abandoned through 2026 for lack of AI-ready data. The prescription is a unified, governed platform with quality pipelines, not simply more data.
Third: redesign the workflow, not just the task. Automating a broken process makes it fail faster. An 80/20 lens is recommended for every role, recognising that some jobs will change by a fifth, others by four-fifths, and that the people doing the work are best placed to identify which is which.
Humans Stay in the Loop Where Trust Is the Product
The fourth priority addresses high-stakes interactions. When a 73-year-old retiree calls to make a decision about their life savings, that moment requires a level of care beyond what a chatbot can offer, the authors argue.
TIAA has rolled out its own generative and agentic platform, GAIT, reaching 85% daily adoption among colleagues. High-trust, high-stakes interactions are reserved for humans augmented by AI rather than replaced by it.
The fifth priority is resilience: staying technology- and model-agnostic, strengthening third-party and cyber defences as attack surfaces grow, and building audit trails and human oversight into the orchestration layer. In regulated industries, compliance cannot be an afterthought.
The argument running through all five points is the same. Enterprises winning with AI are not those with the largest budgets or the fastest adoption rates. They are the ones disciplined enough to do what a Forbes analysis of TIAA’s journey describes as the unglamorous work most companies skip: clean data, a modernised core, and workflows rebuilt for how AI actually operates.
Companies that treat AI as a technology bolt-on, the authors warn, will spend years chasing pilots that never scale. Those that rewire the foundation underneath it are, in their assessment, the ones that will still be running at full speed in five years.
TIAA’s next stated step is extending AI tooling further across the enterprise while keeping human oversight embedded in regulated customer-facing interactions.
