In this current climate, it’s even more important to ensure that team members are as optimized as possible in order to pivot to best support and deliver in the most efficient and nimble way possible. Although sometimes overlooked, playbooks have been a successful option to continue to foster collaboration and as we all know, the right people “in the room” are highly influential in the overall quality of the SDLC.

Introduction: Defining what it means to operationalize AI—not just building models, but integrating them successfully into business operations.

Roadmapping for AI: Strategies for aligning pilots, capacity, and team structure to business goals.

“Lab vs. Crowd” Methods: Balancing deep experimentation with broader organizational buy-in.

Building Support Systems: How to grow AI skills and capabilities company-wide, starting from a lean or solo team.

Deep Dives:

Successes and challenges from scaling AI at CampMinder.

Techniques for centralizing AI processes and moving from pilots to full operations.

Capacity building, resource allocation, and nurturing a culture of innovation.

Real-World Examples: Melissa’s lessons learned through hands-on operationalization with cross-functional teams.

• Operationalizing AI requires more than technical aptitude; it’s about centralizing, scaling, and integrating AI across the whole organization, starting with good roadmapping and support systems.
• The most successful teams blend focused R&D (“lab”) with broad enablement and engagement (“crowd”)—you need buy-in and participation beyond isolated pilots.
• Real momentum comes from intentionally building capacity, nurturing innovation, and sharing responsibility so that AI becomes part of everyday work, not just a side project—especially when starting with a small or single-person team.
• These practical insights equip leaders, engineers, and testers to systematically build and scale AI capabilities, avoiding common pitfalls and ensuring lasting organizational impact.