福布斯：AI修不好坏公司，要重构它

中文

评论指出，单靠AI技术无法拯救陷入困境的企业，需要从业务流程与组织架构层面进行重构。

原文链接：https://fortune.com/2026/08/20/tiaa-accenture-rewire-broken-country-ai-adoption/

Every CEO wants to talk about AI’s magic. Almost none want to talk about the boring problems standing in the way. Picture a railroad that spends billions on the fastest trains in the world, then runs them on the same 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 rest are stacking agents on top of decades of legacy IT, siloed data, and broken workflows, which doesn’t accelerate the business. It just automates the dysfunction faster. We know this firsthand. Nearly two years ago, our organizations partnered to modernize TIAA’s recordkeeping infrastructure. TIAA is 108 years old and carries the technical debt to prove it. Before we could scale AI, we had to rebuild the foundation underneath it, cleaning data, retiring outdated systems, and redesigning how work actually flows. The payoff: plan sponsors can now change investment options for employees’ retirement plans in days instead of weeks, and digital engagement across TIAA’s millions of participants has risen 13%. None of that came from a flashy AI demo. It came from the unglamorous work most companies skip. That’s the real story of this moment: AI transformation isn’t 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. Here are five focus areas we believe separate the enterprises pulling ahead from those stuck in perpetual pilot mode, and the actions leaders should take now.

• Modernize the digital core before you scale agents. Don’t layer AI on top of legacy systems and hope for the best. Audit which platforms are actually load-bearing, retire the rest, and rebuild the infrastructure agents will run on. AI amplifies whatever foundation it’s given, good or bad.
• Treat data readiness as a prerequisite, not an afterthought. 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 fix isn’t more data, it’s a unified, governed platform with quality pipelines that structure what you already have before it ever reaches a model.
• Redesign the workflow, not just the task. Automating a broken process just makes it fail faster. Map the end-to-end workflow first, then decide what AI should touch. Apply an 80/20 lens to every role: some jobs will change 20%, others 80%, and the people doing the work are best positioned to say which is which.
• Keep humans in the loop where trust is the product. When a 73-year-old retiree calls to make a decision about their life savings, that moment requires a different level of care than a chatbot can offer. Put AI in employees’ hands first to make them faster and better, TIAA has rolled out its own generative and agentic platform, GAIT, to 85% daily adoption among colleagues, while reserving high-stakes, high-trust interactions for humans augmented by AI, not replaced by it.
• Build for resilience, governance, security, and optionality. Stay tech- and model-agnostic so you’re not betting the enterprise on a single frontier lab’s roadmap. Strengthen third-party and cyber defenses as attack surfaces grow, and build audit trails and human oversight into the orchestration layer itself, especially in regulated industries where compliance can’t be an afterthought.

None of this is glamorous. It won’t generate a headline about a breakthrough demo. But the enterprises winning with AI aren’t the ones with the biggest budgets or the fastest adoption, they’re the ones disciplined enough to do the boring work first: clean data, a modernized core, and workflows rebuilt for how AI actually works, not how it’s marketed. 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. The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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