Becoming AI-first isn’t about deploying more tools, but about changing how work gets done.

In this episode, Laura Stevens breaks down why AI transformation is fundamentally a leadership and operating model challenge, not a technology one. The conversation explores how organizations must redesign workflows, confront technical debt, rethink roles and responsibilities, and build trust in AI systems to unlock real value. Drawing on patterns across industries, Laura explains why smaller organizations often move faster, how AI exposes hidden inefficiencies, and what leaders must do to shift from managing tasks to overseeing outcomes. 

This is a grounded, practical look at what AI-first actually means in practice - and why most organizations underestimate the change required.

Dive deeper: Download your AI-first playbook here

Takeaways

• AI transformation requires a redesign of organizational workflows.
• Technical debt can become a strategic bottleneck for AI adoption.
• AI should be integrated into business processes, not treated as a separate technology.
• Trust in AI is hindered by psychological barriers and fear of loss of control.
• Roles in organizations must shift from task execution to oversight and strategy.
• Smaller organizations often adapt to AI faster due to less legacy complexity.
• AI adoption should focus on outcomes rather than isolated use cases.
• Leadership must understand that AI changes how value is created in organizations.
• AI can expose existing complexities and inefficiencies in processes.
• The success of AI transformation depends on the willingness to rethink how work is done.