Todd James on Turning AI Strategy into Operating Reality

In this special edition of The Executive Outlook , we sat down with Todd James—Founder and CEO of Aurora Insights —whose career has traced the arc of AI from experiment to enterprise. Over more than two decades, he’s led AI and data strate gy on a global scale, including inside a Fortune 25 retailer, and helped leadership teams turn big promises into repeatable results. From the first few minutes, it was obvious: Todd doesn’t treat AI as a novelty; he treats it as a system for better decisions, organized, contextual, and designed to compound.

He smiles when he talks about how it began. “I was telling a group of students the other day—the role I do now didn’t exist when I was in their seats.” Technology pulled him in when opportunity felt scarce. If he could master computer science, he figured, he could pay the bills and build a life. The AI turn arrived later, a little over a decade ago, while he was in an operating role at Fidelity. Data stores were swelling, computers were getting cheap, and machine learning was maturing from lab demos to production. “You could see the writing on the wall,” he recalled. “So, I dug in, refined my skills, and started lobbying that this needed to be thought about at a strategic level.” It wasn’t one leap as much as a series of pivots—curiosity meeting necessity, timed to the moment.

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Scale shaped his instincts. At Kroger, Todd served as the Head of AI, as well as the Chief Data & Technology Officer at the company’s data science, insights, and retail media company 84.51°. In this broad role, he led AI and enterprise data initiatives that shaped large-scale data science and technology capabilities across the organization. People often assume these roles are mostly about math. He shakes his head. “It’s more like 30% technology and 70% business and organizational work,” he said. “Large enterprises are big ships with small rudders. It takes time to turn—but when you do, the wake is immense.” In practice, that means hours spent aligning stakeholders so technical teams can win. The tools matter; the choreography matters more.

Retail made the craft vivid. He talks about the hum of transactions and signals—high velocity, thin margins, and constant trade-offs. “Start with the customer,” he said. “Make the experience seamless and personal, online and in store, how they want to shop, when they want to shop.” Then pull the thread across the supply chain: origins, movements, routes, picks, packs, and handoffs. “Those are optimization problems. Those are AI problems.” Where AI Pays First in Retail: personalization, demand and inventory, routing, labor scheduling, fraud and shrink detection, and pricing optimization.

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The best results weren’t just operational—they were human. “We heard, ‘I’m making it to my kid’s soccer game more often,’ or ‘I finally have more time to do what I love, help customers.’ That’s how good data works. It gives time back.” He’s quick to add that high velocity doesn’t guarantee high quality; the craft is upgrading the data and the decisions it fuels. 

His guidance for small and midsize businesses begins far from hype. “Step one is education,” he said. “Even watching conversations like this podcast helps leaders build the instincts to spot AI opportunities.” Then look for judgment on a scale. “AI’s practical gift is turning judgment problems into machine prediction problems—anywhere lots of decisions meet lots of data.” It’s the next chapter in a familiar playbook: first process optimization (Lean, Six Sigma), then deterministic automation, and now prediction where judgement once lived. The question is simple and repeatable: Where are decisions frequent, consequential, and data-adjacent? 

That lens brings him to the boardroom, where pressure and gaps collide. “Roughly 53–55% of boards feel real urgency to accelerate AI—which is good,” he said. “But about 80% have minimal or no hands-on AI experience. You wouldn’t expect them to—it isn’t how their careers were built.”

The opportunity is obvious: diversify boards with operators who’ve shipped AI into production, invest in director education, and increasingly bring AI advisors to raise capability. Many CIOs, meanwhile, are pulled deep into the data plumbing, getting AI-ready systems which can steal the cycles they need for storytelling and value translation across the C-suite. If that narrative isn’t told, attention and funding fade. 

Todd james is seeing two advisor patterns work in practice. First, structured sessions that help boards and C-suites position AI for value and scale. Second, a genuine coaching relationship—peer-to-peer—with someone who’s lived in the organizational dynamics and sequencing of weaving AI into strategy and then into operations. “Think of it like golf—I’m a horrible golfer, by the way—but you need the right club for the right hole,” he laughed. “For many leaders, that ‘right club’ is a peer advisor who’s done this at scale.”

He’s blunt about a persistent misconception: the single magic use case. “Leaders expect one AI project to move the bottom line dramatically,” he said. “Value absolutely must show up on the bottom line—but you’re changing decision points. Impact multiplies as you change many of them.” His curve is non-linear: touch enough connected decisions and you change the model of the business. The path is pragmatic—ship one use case, build just enough foundation, so the second is easier, the third cheaper, and the fourth faster. “Think system, not stunt.” 

If you dropped him into a new data leadership role tomorrow, he wouldn’t start with a roadmap deck. He’d start by listening. “They already think you’re smart—that’s why you’re hired,” he said. “In the first months, the pressure isn’t to prove brilliance. It’s to learn the business deeply enough to make recommendations that work in that culture, for those customers, against those priorities.” He maps decision rights early—who actually decides what—because without that visibility, you can’t move the system. 

Change moves faster when a unit is in distress. The hardest case is a company doing well today that needs to evolve to keep doing well tomorrow. Comfort dulls urgency; warm water makes you forget that it cools. In those rooms, understanding who truly decides what—and how—becomes non-negotiable. If you can’t map decision rights, you can’t move the system.

Advisors, he believes, will reshape how enterprises absorb AI. The classic pyramid—one senior partner managing a bench of juniors—matters less as AI compresses execution. What matters more are leaders who’ve operated on a scale, missed a step, learnt, and shipped again. “There’s a difference between someone who leaves when the project ends and someone who has to live with the outcomes,” he said. “When you’ve had to live with your decisions, you collaborate differently. You think longer-term about other leaders’ priorities because you’re still there after launch.” 

He doesn’t sugarcoat the personal standard he expects of leaders. “In business, as in nature, you learn, you change, or you die,” he said plainly. “Be a lifelong learner.” It’s the behavior that underwrote every pivot in his career, from driving ships in the military to running consultancies to operating in finance and retail to standing up AI. “Careers feel long and then suddenly short,” he said. “What always saved me was a voracious appetite for learning. Take feedback seriously. Put in extra cycles at night and on weekends. Build a network where you give and get knowledge.” He grinned at a thought experiment. “Even if the world went back to farms—won’t happen—but even then, a lifelong learner would still be in the best position.” 

As we wrapped, Todd returned to the theme that threaded every example: AI is a team sport played across decisions. The tools will change; the compounding comes from culture—listening first, aligning second, scaling third—until better choices show up as better days for customers and employees. The boardroom will keep asking for speed. The operators will keep asking for clarity. Governance, translation, and education are bridges. When the ship finally turns, it doesn’t feel like magic. It feels like momentum.

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