How Leaders Can Use AI to Solve Real Business Problems

2026-07-07 · Source: HBR IdeaCast · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Project & Product Management · Depth: Intermediate, extended

Summary

Journalist and author Josh Tyrangiel, in an interview for the HBR IdeaCast, argues that successful AI adoption hinges on identifying specific business problems rather than chasing hype or choosing models first. Drawing from his book "AI for Good," Tyrangiel emphasizes treating AI as a tool, not a strategy, and setting realistic expectations. He highlights the Cleveland Clinic's approach, where AI improved hospital operations by increasing transfer rates and cutting emergency room wait times by 90 minutes using a Palantir-built scheduling tool. Another example details how Bayesian Health's predictive software, integrated with Epic, reduced sepsis mortality by 41% (saving approximately 1000 lives) by adding explainability. Tyrangiel advises executives to foster transparent communication, develop strong CTO relationships, and approach AI deployment with an R&D mindset, setting clear budgets.

Key takeaway

For executives weighing AI investments, prioritize identifying concrete business problems where AI can deliver measurable value, rather than adopting technology for its own sake. You should foster deep collaboration between technical and domain experts, communicate transparently with your workforce about AI's role, and approach deployments with an R&D mindset, setting clear budgets. This strategy, exemplified by the Cleveland Clinic's success, ensures AI serves your organizational goals effectively.

Key insights

Successful AI adoption prioritizes solving specific business problems over technology-driven hype.

Principles

• AI is a tool, not a business strategy.
• Domain expertise must supersede technical expertise.
• AI implementation requires human talent and continuous tweaking.

Method

Identify specific business problems, assess data quality, integrate AI solutions with existing systems, and continuously refine based on real-world feedback and explainability.

In practice

• Use AI to optimize hospital bed management.
• Deploy predictive software for early disease detection.
• Translate policy manuals into AI-readable code.

Topics

• AI Adoption Strategy
• Business Problem Solving
• Healthcare AI Applications
• Organizational Change Management
• AI Implementation Challenges
• Predictive Analytics

Best for: Executive, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by HBR IdeaCast.