AI won’t fix a broken company. Rewiring it will

Every CEO loves to discuss the potential of AI, yet very few are willing to address the underlying challenges standing in the way of successful implementation. Imagine a railroad company investing heavily in the fastest trains, yet operating them on aging tracks. The tracks are the real obstacle, not the trains themselves. This is the harsh reality for many organizations that have adopted AI: the technology is more powerful than ever, but only a small percentage of companies are able to turn it into tangible business results.

The rest are simply amplifying their existing problems by layering AI on top of outdated IT systems, siloed data, and inefficient workflows. We have firsthand experience with this issue. Just over two years ago, our organizations collaborated to modernize TIAA's recordkeeping infrastructure, an organization that is 108 years old and carries significant technical debt.

Before we could scale AI, we had to rebuild the foundation beneath it, which involved cleaning data, retiring outdated systems, and redesigning how work actually flows. The outcome was remarkable: TIAA can now change investment options for employees' retirement plans in a matter of days, rather than weeks, and digital engagement across TIAA's millions of participants has increased by 13%.

Importantly, these improvements were not the result of flashy AI demonstrations, but rather the result of the less exciting, yet crucial, work most companies overlook. In today's AI-driven landscape, transformation is not merely a technology project; it is a comprehensive business transformation, a change-management initiative, and a complete overhaul of operating models.

Companies that approach AI as a bolt-on solution will find themselves trapped in a cycle of pilots that never scale. Here are five key focus areas we believe distinguish companies that are leading the way from those that remain stuck in perpetual pilot mode, along with the actions leaders should take now: Prioritize modernizing the digital core before scaling AI deployment.

Do not attempt to layer AI on top of legacy systems and expect positive results. Analyze which platforms serve as the backbone of your operations, retire the rest, and rebuild the infrastructure on which AI agents will operate. Treat data readiness as a prerequisite, not an afterthought. Only 5% of businesses claim their data is ready for AI, and Gartner predicts that 60% of AI projects will be abandoned by 2026 due to insufficient data readiness.

The solution lies not in acquiring more data, but in establishing a unified, governed platform with high-quality pipelines that structure the data you already possess before it reaches a model. Redesign the workflow, rather than simply automating individual tasks. Simply automating a broken process will only cause it to fail more quickly.

Begin by mapping out the end-to-end workflow, and then determine which aspects of the process AI should enhance. Apply an 80/20 lens to each role: some tasks may only require a 20% change, while others may necessitate an 80% transformation, and those directly involved in the work are best positioned to determine the appropriate approach.

Maintain human oversight in situations where trust is paramount. When a 73-year-old retiree calls to make critical decisions about their life savings, the level of care required surpasses what a chatbot can provide. Empower employees with AI-powered tools to enhance their efficiency and effectiveness. At TIAA, we have deployed a generative and agentic AI platform called GAIT to 85% of daily active employees, while reserving high-stakes, high-trust interactions for humans augmented by AI.

Build a resilient infrastructure with strong governance, security, and adaptability. Maintain a tech-agnostic approach so that you are not overly reliant on a single frontier lab's roadmap. Strengthen third-party and cyber defenses as the attack surface expands, and ensure that audit trails and human oversight are integrated into the orchestration layer itself, particularly in regulated industries where compliance cannot be an afterthought.

These points may not be particularly exciting or generate headlines about groundbreaking demos, but the organizations that excel with AI are not those with the largest budgets or the quickest adoption rates; they are the ones that demonstrate discipline in addressing the foundational issues first: cleaning data, modernizing the core systems, and redesigning workflows to align with how AI actually functions, not just how it is marketed.

In the era of AI, complexity has become a competitive disadvantage that can no longer be hidden behind an appealing AI experience. The true measure of an enterprise's success in the AI era will be its ability to rewire the foundation, not just the technology sitting atop it. The companies that invest in rewiring the foundation now, rather than merely enhancing the technology on top of it, will be the ones that continue to operate at full capacity five years from now.