AI Adoption Is a People Problem Before It’s a Technology Problem

Organizations are investing in AI at an extraordinary pace. They're purchasing tools, providing access, developing policies, and training employees on how to use them. Yet even after all of that investment, many leaders are still asking the same question: Why aren't people using it?

Jackie Cook believes the answer may have less to do with the technology and more to do with the way organizations approach change. Giving employees access to AI doesn't automatically change the way they work. True adoption requires people to make different decisions, rethink familiar workflows, exercise judgment in new ways, and understand where AI actually belongs in their day-to-day responsibilities.

In this Influential Women masterclass, Jackie Cook, Founder & CEO of Momentum Group, LLC, explores why successful AI transformation begins with people and work rather than technology. Drawing on more than 20 years of experience spanning organizational transformation, operations, product strategy, talent development, and leadership enablement, Jackie brings a practical, human-centered perspective to helping organizations move beyond AI experimentation toward meaningful adoption.

At the center of the masterclass is an important distinction between deployment and adoption. Deployment means making the technology available. Adoption means the way people work has actually changed. Jackie introduces her four-stage Adoption Path: Access, Ability, Application, and Adoption, demonstrating why knowing how to use AI isn't the same as knowing where AI belongs in your work.

Jackie also challenges leaders to reconsider what they may interpret as employee resistance. Hesitation around AI can stem from unanswered questions about trust, permission, accountability, risk, and expectations. Through her Four Ps of Permission, Practice, Protection, and Purpose, she explains how leaders can create the conditions employees need to experiment responsibly and understand why changing the way they work matters.

Finally, Jackie moves beyond the question of what AI can do to explore what AI should do. Her Recommend, Initiate, Execute model provides a practical way to determine the appropriate level of AI agency within a workflow while considering risk, reversibility, human judgment, trust, and escalation.

In this masterclass, you'll learn:

• Why deploying AI technology is fundamentally different from achieving meaningful AI adoption
• How to recognize whether AI has actually changed the way work gets done
• How to move through the four stages of Access, Ability, Application, and Adoption
• Why AI training and literacy alone may not translate into meaningful workplace transformation
• How to identify where AI actually belongs within existing workflows
• Why employee hesitation isn't always resistance and may instead signal a need for greater clarity
• How trust, permission, confidence, and accountability influence employees' willingness to work with AI
• How leaders can create the conditions for adoption through Permission, Practice, Protection, and Purpose
• Why organizations should ask what AI should do rather than focusing only on what it can do
• How to use Recommend, Initiate, and Execute to determine the appropriate level of AI agency
• How risk, reversibility, judgment, trust, and escalation can inform decisions about automation
• Why maximizing automation shouldn't necessarily be the goal of an AI strategy
• How to identify friction within an existing workflow before introducing AI
• Why human judgment, empathy, creativity, and context become increasingly important as AI capabilities expand
• How to redesign work intentionally so AI creates better decisions, better work, and meaningful value

AI transformation doesn't begin with AI. It begins with understanding the work, the people doing it, and the problems worth solving. By starting with friction rather than technology, leaders can move beyond simply introducing AI and begin thoughtfully redesigning work around the places where it can create genuine value.