Conference Presentation, Keynote
AI Transformation Playbook: Practical Lessons from our AI-First Journey | Make | RAISE Summit 2026
- The industry is expected to undergo significant transformation driven by AI, with a strategic shift from automation to an AI-focused organization initiated 2.5 years ago and reaching 96% employee adoption by January of the current year.
- The transformation journey is projected to follow three distinct non-linear phases, including a second stage launched in the second year to prioritize AI through a formal program, and a third stage establishing an "AI bar" requiring every employee to integrate personal learning into performance management.
- Organizations are advised to anticipate that most initial experiments targeting low-hanging fruit will fail as they acquire new skills, and that current AI toolkits may become obsolete within one month, discouraging fixation on specific tools.
- Implementation plans involve starting from a current mandate to assemble a full leadership structure (leadership, middle management, and employees), with a dedicated "Samurai team" allocating 70% of time to hands-on implementation, 20% to coaching, and 10% to maintenance.
- Operational protocols include the ability to invoke "Code Purple" for a two-week undisturbed sprint to solve a single problem, while time savings are deemed irrelevant in favor of tying all work to core KPIs.
- Productivity metrics project a six-person team can execute 23 successful projects in a single year, contrasting with an industry benchmark of 5% to 10%, while a deep departmental implementation phase previously lasted between six and eight months.
- Historical project outcomes include a May initiative that has remained successful for two years, contrasted with a multi-language subtitle project that was impactful initially but is no longer live, highlighting the volatility of AI initiatives.
- The third phase requires a renewed focus on individual enablement, where employees are expected to select a quarterly milestone from options such as learning, building, or sharing knowledge, following an adoption increase from 16% to 96% with no reported employee attrition.
- Survey data indicates 87% positive response rates among employees after two quarters of the AI bar initiative, supporting the development of a "self-enforcing machine" that scales successful individual builds across the organization.
- Future priorities include establishing context management to centralize knowledge for easier project integration, introducing a four-phase framework for this management, and continuing to identify and scale successful use cases.