By Joe Leslie, VP, Global AI Learning & Development
It’s September in Vegas, temperatures still hitting 95 degrees as fall arrived. L&D leaders from cutting-edge AI and tech companies filled conference rooms and the same conversation kept surfacing: their training programs were launching just as the tools they were teaching became obsolete.
That was the scene at Adobe Learning Summit where I had the opportunity to present two sessions on organizational AI agility. The candid discussions revealed how many brilliant professionals were openly grappling with using traditional approaches for rapidly evolving technology.
Here’s what we shared about Golin’s approach, and why it’s working when traditional methods fall short.
The Challenge Across Industries
Many organizations struggle with traditional training approaches that can’t keep pace with AI’s rapid evolution. Teams often need immediate answers to specific problems – like preventing AI hallucinations in client work – rather than comprehensive courses that may be outdated by launch.
Golin took a fundamentally different approach. Rather than starting with traditional training questions like “What should people know about AI?” we flipped the conversation entirely: “What specific problems can AI solve for our teams right now?”
This problem-first mindset changed everything. Instead of building unwieldy, hours-long courses, we focused on immediate pain points our people were experiencing in their daily work. The results validated this approach driving our adoption rate to 90% of employees in client-facing roles actively using AI, up from 15% in less than a year.
Creating Psychological Safety for Innovation
Our approach centers on building environments where teams feel confident experimenting with AI tools. This means creating psychological safety for employees to learn, iterate, and ask questions about new technologies. We recognize that successful AI adoption requires addressing how AI transforms roles and creates new opportunities for strategic work.
We’ve developed Matt West’s “Working in Beta” training course, which includes his AI Decision Matrix and Beta Mode Checklist – resources that help people develop essential human skills to thrive during times of immense change. These in-house tools complement our broader approach of making AI accessible and less intimidating.
The goal is finding people who have the answers and giving them a platform to share what they know with everyone else. Lindsay Peterson’s approach exemplifies this perfectly – though we provided extensive training on fact-checking and ensuring quality outputs, this eager MD and one of our biggest AI champions thought we could create simpler, more accessible processes for everyone. Lindsay developed a six-minute verification video for her team, and I was able to quickly cascade it to our entire global system. The video includes a verification checklist; a resource people can easily refer to “in the flow of work.” This shows how our best practitioners continuously improve and make our processes more accessible organization-wide.
Intelligence Augmentation Over Automation
Rather than viewing AI as a replacement technology, we champion Intelligence Augmentation – where human expertise and AI capabilities combine to deliver superior outcomes. This philosophy emphasizes how AI amplifies uniquely human capabilities like strategic thinking, emotional intelligence, and creative problem-solving, rather than replacing them.
Our methodology combines multiple approaches: training on our AI ambition, core tools, and policies, then role-specific pathways – strategists accessing research tools, creators learning content generation, account teams mastering client workflows. Our “CoLearn” AI mentoring program systematically pairs AI-mature mentors with colleagues seeking advancement, accelerating adoption through structured peer-to-peer knowledge transfer.
Progress as Part of a Broader Strategy
Our L&D approach operates within a comprehensive AI transformation strategy led by Chief AI Officer Jeff Beringer, encompassing AI integration, technology implementation and workforce development. During our most recent organization-wide survey, the learning and business components were already showing promising results between Fall 2024 and May 2025:
- Knowledge barriers dropped 48%
- 41% of employees advanced an entire AI maturity level
- Daily AI tool usage nearly doubled
- Questions shifted from “how do I use this?” to “how do I optimize this?”
- AI integration in client work rose 25%
- Quality improvements doubled
These metrics reflect the interconnected nature of our approach – L&D working alongside technology rollout and organizational change to create sustainable AI adoption across the agency.
Building Systems That Evolve
The summit sessions highlighted an industry shift: moving from scheduled training to just-in-time support accessible when needed. This requires teams willing to share knowledge authentically, leadership that embraces experimentation and psychological safety, and systems designed for continuous learning rather than static content.
Our experience shows that when you focus on effectiveness over efficiency and build learning systems that adapt alongside technology, you create sustainable competitive advantage. As our founder Al Golin once said, “Stay curious to stay relevant” – and in the age of AI, that curiosity must be paired with systems that help our people learn and evolve as quickly as the technology itself.