Jeremy Korst on the state of AI adoption, accountable acceleration, changing business models, and synthetic personas (AC Ep30)

2026-01-29 · Source: Humans + AI · Field: Business & Management — Artificial Intelligence & Machine Learning, Corporate Strategy & Leadership, Human Resources & Workforce Development · Depth: Intermediate, extended

Summary

The "Humans Plus AI" podcast, featuring Jeremy Khorst, CEO of Mindspan Labs and co-author of the Wharton GBK Annual Enterprise AI Adoption Study, explores the evolution of enterprise AI adoption from initial experimentation to "accountable acceleration." The discussion highlights that 72% of organizations formally measure GenAI ROI, with 74% reporting positive returns, indicating a shift towards tangible value. A key focus is the human element, with 89% of leaders believing AI enhances employee skills, while 70% acknowledge it may replace some, and 43% worry about skill atrophy. The conversation also delves into intentional experimentation, the emerging use of digital twins and synthetic personas in market research, and the increasing investment in domain-specific AI applications and internal R&D, which accounts for 30% of AI technology budgets.

Key takeaway

For CTOs and VPs of Engineering navigating AI integration, prioritize clear communication of an AI vision derived from your core business strategy. Focus on fostering a culture of intentional experimentation, empowering middle management, and aligning leadership to effectively manage skill augmentation versus replacement. This approach will enable your organization to move beyond basic productivity gains towards redesigning workflows and developing domain-specific AI solutions that deliver measurable ROI and competitive advantage.

Key insights

Successful enterprise AI adoption requires strategic vision, intentional experimentation, and strong leadership alignment across all organizational levels.

Principles

• AI is a tool to achieve strategy, not a strategy itself.
• Culture of intentional experimentation drives AI adoption.
• Leadership alignment is crucial for AI transformation.

Method

Translate overall strategy into an AI vision, empower early adopters for intentional experimentation, and foster top-down support with bottom-up action to identify quick wins and scale successful AI applications.

In practice

• Experiment with digital twins for market research.
• Invest in internal R&D for specialized AI systems.
• Run AI programs side-by-side with existing workflows.

Topics

• Enterprise AI Adoption
• AI ROI Measurement
• Workforce Augmentation
• Digital Twins
• Market Research AI

Best for: CTO, VP of Engineering/Data, AI Product Manager, Executive, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Humans + AI.