When Uber set out to accelerate its use of artificial intelligence, it didn’t begin by asking which AI platform to buy. It began by asking a much harder question. How should work change if AI becomes part of every business process?
Rather than handing employees another chatbot, Uber created what it calls “Agentic Pods,” small teams of AI engineers embedded within business functions such as finance, legal, marketing, human resources, and procurement. Their mission wasn’t to automate individual tasks. It was to observe how work actually happened, redesign entire workflows around AI, and build intelligent agents that could execute large portions of those workflows. The results were remarkable. Financial planning fell from 15 hours to 30 minutes. Financial reporting dropped from two days to 10 minutes. Marketing quality assurance shrank from two weeks to less than an hour.
For the past twenty years, digital transformation has focused on helping people work better through technology. Organizations replaced paper with software, moved applications to the cloud, digitized customer interactions, and automated repetitive processes.
AI transformation changes the equation.
Instead of asking how technology can help people perform work, AI transformation asks which work should be performed by intelligent systems and which work should remain human. That distinction changes everything. It reshapes jobs, workflows, decision-making, and even the structure of the organization itself.
I’ve come to believe that successful AI transformation depends on three pillars. Ignore one of them, and the entire effort becomes unstable.
Pillar #1 … People
Companies don’t transform. People do. The biggest mistake I see is leaders treating AI as a technology project instead of a human one. They purchase enterprise licenses, deploy copilots, and expect productivity to improve automatically. It rarely does.
Employees need new skills. They need to understand not only how to use AI but how to supervise it, question it, validate it, and collaborate with it. Critical thinking becomes more valuable, not less. Judgment becomes a competitive advantage.
The luxury retailer Tapestry, the parent company of Coach and Kate Spade, offers an excellent example. Rather than limiting AI to the technology department, the company focused on changing how employees worked. It organized a companywide AI summit, created AI personas representing Gen Z shoppers to help teams understand customer preferences, introduced AI coaching for retail associates, and used AI-assisted design with 3D printing to reduce prototype development time by 75 percent. Yet executives repeatedly emphasized that the real transformation wasn’t the software. It was the culture. Human designers still made the final decisions. AI expanded creativity instead of replacing it.
That’s exactly what successful AI transformation looks like. People don’t become less important. They become more important because they’re responsible for directing increasingly capable intelligent systems.
Pillar #2 … Process
This is where AI transformation separates itself from digital transformation. Digital transformation improved existing processes. AI transformation redesigns them. Too many organizations ask how AI can automate today’s workflow. I think the better question is whether today’s workflow should exist at all.
Every business process should begin with three simple questions.
Does this activity create value?
Can AI perform part of the work?
Should the process itself be redesigned before AI is introduced?
The organizations generating the greatest returns aren’t automating yesterday’s processes. They’re inventing entirely new ones.
McKinsey reinforced this conclusion in its 2026 research on enterprise AI adoption. The firm found that organizations create the greatest business value not by giving more employees access to AI, but by redesigning workflows, operating models, and organizational roles around what AI makes possible. Companies that merely automate existing processes tend to realize incremental gains. Companies that rethink how work flows through the enterprise create transformational results.
Pillar #3 … Trust
Every AI success story eventually reaches the same point. Can we trust the output? The faster AI becomes, the more important validation becomes.
Organizations need governance that establishes accountability, security, privacy, quality, and explainability. Every critical AI decision should have a human owner. Every model should be monitored. Every important output should be validated before it influences customers, finances, or operations.
I don’t view governance as bureaucracy. I view it as the foundation that allows organizations to scale AI with confidence. The software industry illustrates this perfectly.
AI can now generate thousands of lines of code in minutes. That doesn’t mean the software is secure, maintainable, compliant, or aligned with business requirements. In many development organizations, the bottleneck has already shifted. Writing code is no longer the slowest part of software development. Validating AI-generated code has become the scarce resource.
The same principle applies far beyond software engineering. The more work AI performs, the more valuable human judgment becomes.
AI Transformation Is Business Transformation
Looking back, I think we’ve been using the wrong comparison. AI transformation isn’t the next chapter of digital transformation. It’s the beginning of a new operating model.
Digital transformation digitized work. AI transformation redistributes work between people and intelligent systems.
Digital transformation focused on efficiency. AI transformation focuses on redesign.
Digital transformation changed the tools employees used. AI transformation changes the role employees play.
That’s why I believe every successful AI transformation rests on three pillars. Develop people who know how to work alongside intelligent systems. Redesign processes instead of automating outdated ones. Build the trust that allows AI to operate at enterprise scale. Get those three pillars right, and AI stops being another technology investment.
It becomes the way the business creates value.
