UserTesting's Michael Domanic on Driving Enterprise-Wide AI Adoption through Creativity

How do you achieve 75% weekly active AI tool usage across an enterprise? At UserTesting, it started with a deliberate strategy to foster experimental adoption while maintaining governance guardrails. Michael J. Domanic, Head of Generative AI Business Strategy, tells Ravin their approach to transforming internal workflows through AI on this episode of The AI Adoption Playbook.

Rather than treating AI as purely a technical concern, UserTesting has built an impressive culture of AI adoption by empowering cross-functional teams, establishing clear usage guidelines, and meticulously tracking tangible business value. Their 800-person team has created 600 custom GPTs, many of which have transformed workflows across departments.

Michael also explores how UserTesting balanced centralized governance with democratized experimentation, their methods for measuring ROI, and why creativity — not just technical expertise — might be the critical ingredient for successful enterprise AI transformation.

Topics discussed:

• Building an enterprise AI strategy that achieved 75% weekly active users through balanced governance and democratized experimentation.
• Creating custom GPTs trained on organizational knowledge to streamline processes like OKR development across all company levels.
• Establishing cross-functional "AI ambassadors" and regular office hours to drive adoption and showcase successful use cases.
• Implementing clear AI usage guidelines that protect sensitive customer data while encouraging internal experimentation and innovation.
• Measuring AI ROI by focusing on business outcomes rather than time savings, with metrics tied to specific operational improvements.
• Balancing centralized AI governance through a dedicated council with decentralized, department-level experimentation and implementation.
• Exploring the differences between AI transformation and traditional digital transformation, including executive-level buy-in from day one.
• Discussing the challenges incumbent software companies face when integrating AI versus new AI-native applications built from scratch.
• Developing strategies for scaling AI applications from experimentation to production through appropriate security and governance protocols.
• Examining the philosophical aspects of AGI development and the importance of creativity rather than technical skills in leading AI transformation.