By Kat Rippy, M.S. & Ashley Seeley, Ph.D.
AI was not strategically planned over a two-year period, discussed in endless leadership meetings, or process mapped by tech change experts, it was downloaded overnight, showed up at every desk, and became part of the workflow before leadership had a chance to set ground rules. While the buzz around generative AI is loud, the rollout itself has been quiet, fragmented, and mostly grassroots. Employees are already using it. Some are thriving, others are stuck, and many managers are left trying to lead teams through a shift they don’t fully understand themselves.
Unlike past tech transitions (like the internet or video conferencing), this one didn’t wait for IT approval. There’s no barrier to entry, no formal training protocol, and no centralized strategy. Instead, early adopters are forging ahead with tools like ChatGPT, while others hesitate, overwhelmed or unsure what’s even possible. Everyone is experimenting, but few are aligned. As a result, the potential gains are being undercut by inefficiency, confusion, and a lack of clarity around what's working, when to use it, and how it works.
The Critical Role of Leadership
This is no longer about whether to adopt AI, it’s about how to lead people through a technology transformation that is already here. This includes:
Bridging the gap between early adopters and hesitant holdouts
Establishing norms and shared best practices
Supporting uneven skill levels across teams
Identifying where AI adds value and where it doesn’t
Shifting from passive permission to active enablement
AI has flipped the script. Access is everywhere, but direction is scarce. If leaders don’t step in with structure, expectations, and vision, the AI rollout will continue to grow… and maybe not in a way that benefits the business.
Leading the Shift: From Scattered Adoption to Strategic Integration
If the AI rollout has already happened without your direction, it’s not too late to lead. This moment calls for a reset, one that blends human ingenuity with AI-powered efficiency. Here’s how leaders can begin to turn fragmented AI use into focused value creation:
1. Acknowledge the Mess And Name It
Start by naming what’s true: AI is being used in pockets, with varying degrees of effectiveness and understanding. Open the conversation company-wide. Framing this as a shared experiment (not a failure of strategic foresight) builds trust and sets the stage for collaborative problem-solving.
2. Inventory What’s Already Happening
Before setting any policy, get curious. Where is AI being used? For what tasks? What’s working well and what’s not? Invite teams to share use cases, wins, concerns, and blind spots. You may discover emerging best practices hiding in plain sight.
3. Set Clear Expectations and Encourage Responsible AI Use
Without structure, AI use can create inconsistency, security risks, or even reputational harm. Establish simple norms:
When is AI use appropriate?
What types of work must be human-generated?
How should outputs be cited, checked, or reviewed?
Make these guidelines visible, adaptable, and easy to follow. Ensure employees understand ethical considerations, such as avoiding over-reliance on AI, verifying AI-generated outputs, and maintaining accountability.
4. Upskill and Reskill Continuously
Provide training that helps employees refine their ability to work alongside AI, ensuring they remain competitive in a shifting job landscape. Turn early adopters into educators. Host quick share-outs, internal demos, or AI “office hours” where team members can learn from each other. This reduces fear, increases transparency, and spreads capability more equitably across the organization.
5. Define Key Use Cases That Create Value
Don’t try to apply AI everywhere. Instead, help teams identify high-impact opportunities, like automating administrative work, summarizing content, or drafting first versions to generate ideas. Encourage employees to use AI as a tool for enhancing productivity and creativity rather than a substitute for critical thinking. Small, repeatable wins build confidence and momentum.
6. Co-Create Opportunities with Your Team
The most effective AI solutions often emerge closest to the work. Instead of prescribing where AI should be used, invite your team into the discovery process. Ask questions like:
Where are we spending too much time on repetitive tasks?
What’s blocking our productivity or decision-making?
Where do we see frequent rework, bottlenecks, or burnout?
Facilitate brainstorming sessions to identify where AI might alleviate pressure or open up new capacity. This approach empowers employees, surfaces high-impact applications, and turns AI integration into a collaborative effort, not a top-down mandate.
7. Assign Responsibility for Ongoing Exploration
AI is evolving fast. Create a cross-functional team or innovation lead to track new tools, test applications, and recommend updates to your approach. This allows the organization to stay agile without starting from scratch for each new AI tool.
8. Maintain Human-Centric Leadership
AI is valuable, but leaders must continue to focus on emotional intelligence, collaboration, and personal development.
9. Assess Performance Beyond AI Use
Talent evaluations shouldn’t solely focus on how well employees leverage AI, but on their ability to apply AI-generated insights effectively.
Leading with AI
AI is a present reality. While the rollout may have started without a plan, it’s not too late to lead with intention. Leading in an AI era isn’t about controlling AI usage; it’s about cultivating a workforce that thrives with AI as a strategic asset. Leaders don’t need to have all the answers, they have to be willing to ask better questions, bring people together, and shape how your organization learns, adapts, and grows. Partner with your teams to unlock AI’s potential, build shared confidence, and use AI as a tool for substantial, meaningful progress.
The shift has already begun. Now it’s time to lead it.