The Inference

Before he advised Toyota and American Airlines on AI, Tim Doll was a creative director with a fine arts degree. Now CEO of Precocity, he tells host Michelle Hawley that boards asking for an "AI strategy" are asking the wrong question.

Companies don't need to compete with Anthropic or OpenAI, Doll says. Most will get more value from applying these companies' models to their own work.

In this episode, Doll explains why his team still puts humans in charge of checking AI outputs, why he thinks many of the apps companies are rushing out the door are just proofs of concept and why, exactly, chatbots use warm, paper-colored backgrounds. Doll also gets candid about a problem the industry still hasn't solved: how to charge for AI work based on results rather than hours.

In this episode of The Inference, host Michelle Hawley is joined by Tim Cook, who researches what happens when we outsource our thinking to machines and writes Psychology Today's Algorithmic Mind column, and Dr. Matt Stroud, Chief Scientific Officer at a stealth-mode AI startup and author of "Digital Liberty."

The conversation centers on a question most companies aren't asking: is AI making employees more productive or just making their work look better? The trio unpacks a new study on AI-assisted coders, the difference between driving AI as a tool vs rubber-stamping its output and why measuring productivity without measuring comprehension is a trap.

In this episode of The Inference, host Michelle Hawley sits down with Rakshit Ghura, VP and General Manager of Digital Workplace Solutions at Lenovo, to explore why so many enterprises are stuck in the AI experimentation phase and what it takes to move beyond it. Rakshit makes the case for what he calls "cognitive enterprise transformation" — embedding intelligence directly into workflows rather than treating AI as a standalone technology layer — and offers practical guidance on where leaders should start.

Rakshit breaks down the biggest barriers holding organizations back, from data silos and process complexity to rising cybersecurity concerns. He also shares Lenovo's AUE framework — Adoption, Usage and Effectiveness — for measuring the success of AI implementations.

In this episode of The Inference, Paul Curtis, CTO and Ecommerce Director at easyJet, joins host Michelle Hawley to discuss what it actually takes to move AI from a proof of concept into production inside one of Europe's largest airlines.

Drawing on easyJet's experience running over 150 POCs, Curtis shares how the company has narrowed its focus to AI use cases that deliver measurable value, from empathy-aware customer service bots to hyper-personalized holiday content tailored down to the individual. The conversation covers how composable architecture and MCP adoption have become essential prerequisites for agentic AI, why total cost of ownership is the question most executives are still avoiding and how the build-vs-buy calculus is shifting as SaaS products lose ground to custom AI solutions.

Shri Nandan, VP of AI Products and Experiences at Comcast, joins host Michelle Hawley to discuss what it actually takes to build and scale AI inside one of the largest enterprises in the country.

Nandan shares how her team uses agentic frameworks, customer data platforms and cross-functional alignment to drive AI strategy at Comcast. The conversation covers the real costs of scaling AI beyond the pilot stage, how to prioritize ideas using a revenue-cost-KPI-resource framework and why governance has become the most complex part of the job. Nandan also explores where AI delivers the clearest value in customer experience and who bears accountability when AI agents go wrong.

Daniel Wu spent years building AI infrastructure at JP Morgan Chase — including the firm's largest LLM production system and its first agentic AI solution — before moving to Stanford's AI Professional Program.

In this inaugural episode of The Inference, he names the measurement trap killing most enterprise AI strategies, breaks down why 88% of organizations experimenting with AI see almost no measurable business impact, and explains why the gap between demo and production isn't a gap at all — it's a canyon.