How Leaders Can Use AI to Solve Real Business Problems

July 7, 2026

AI Summary

5 min read

Josh Tyrangiel, a staff writer at The Atlantic and author of AI for Good, argues that most organizations are approaching artificial intelligence backward. They are “solving for AI”—pushing adoption wherever possible—instead of treating it as a tool to solve a specific business problem. Drawing on case studies from the Cleveland Clinic and other organizations, Tyrangiel explains that successful AI implementation depends less on the technology itself and more on clear problem definition, domain expertise, and a willingness to iterate. The conversation is a grounded, practical guide for leaders trying to navigate the hype without becoming either an evangelist or a doomsayer.

The Problem with the Hype Cycle

Tyrangiel describes the current AI landscape as a "classic spin cycle" driven by what he calls "drunkenness." The technology is genuinely impressive, but the marketing from AI labs—which he notes are for-profit companies using the language of science to project wisdom—creates a false sense of inevitability. Leaders are told they must not "miss the boat," but Tyrangiel points out that the boat in question belongs to the labs themselves, which are deeply in debt and need customers to pay off their investors. The result is a pressure to adopt AI without first asking what problem it solves.

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What you'll learn

1 (00:12) **Introduction & Guest Context** - Alison Beard and the host set up the conversation about AI hype and practical use, introducing Josh Tierngell, author of *AI for Good*.
2 (01:26) **Tierngell’s Arc: From Hype Cycle to Sober Assessment** - He describes his early reporting on AI, caught between "magic" and "doomer" narratives, and his eventual focus on the technology itself.
3 (03:14) **The Core Problem: Solving for AI vs. Solving a Problem** - The key to successful AI implementation is starting with a clear problem and realistic expectations, not believing vendor hype.
4 (04:56) **The "Don't Miss the Boat" Trap** - A biotech CEO’s meeting with Sam Altman illustrates the pressure from AI labs, who are for-profit companies with massive debts, to make AI seem indispensable.
5 (07:15) **Understanding Resistance to AI** - Resistance is not just fear; it’s a justified anger rooted in the failures of previous tech waves (social media, misinformation).
6 (10:36) **Case Study: Cleveland Clinic’s Problem-First Approach** - The CEO, Dr. Tomas Mihalyovich, explicitly rejects "magic beans" and insists that AI must serve medicine, not the other way around.
7 (17:40) **The Talent & Training Challenge** - The AI conversation shifts from technology to talent recognition and customer service.

+ Full timestamped outline available in the app

Guests on this episode

Josh Tyrangiel

Show Notes

Many organizations are investing heavily in AI, but too few are asking the most important question: What problem are we actually trying to solve? Journalist and author Josh Tyrangiel argues that successful AI adoption has far less to do with choosing the right model and far more to do with identifying the right business challenge—and following through. He shares why executives should resist the pressure to become "AI-native" overnight and instead focus on targeted, high-impact problems where AI can create measurable value. He also offers practical advice on improving operations, communicating change across an organization, and avoiding the costly mistake of treating AI as a strategy rather than a tool, with examples from the healthcare sector and beyond. Tyrangiel is author of the book AI for Good: How Real People Are Using Artificial Intelligence to Fix Things That Matter.