By Kate Wade, Managing Partner of Wade Strategy, LLC and Founding Partner of The AI Strategies Group (AISG)

When electricity first came into factories, productivity didn’t skyrocket overnight. Managers simply swapped out steam engines for electric motors, retaining the same layouts and workflows. It took decades and a complete rethinking of how work was organized before the full benefits became apparent.

We’re at the same point with AI. Installing the technology is the easy part. Leading people through the fundamental shift in how work gets done is the hard part, and it requires a change in how leaders lead.

Why Old Playbooks Don’t Work

Traditional change leadership assumes:

• You know the end state.
• The tools won’t change mid-rollout.
• People need to adapt once, then carry on.

In AI adoption, none of those are true. Use cases evolve, models update overnight, and AI’s role in the business is constantly shifting. Leaders can’t just “manage resistance” anymore; they have to lead in motion.

Five Skills Leaders Need Now

1. Data Storytelling
   AI projects produce metrics, accuracy rates, processing times, and adoption counts, but numbers alone don’t inspire change. Translating them into human impact does.

For example, a logistics company introducing AI route optimization saw faster delivery times, but uptake was slow. Once managers stopped reporting “route efficiency improved by 7%” and started saying, “We’re delivering 500 more packages a week without adding trucks,” adoption spiked.

2. AI Literacy Without Jargon
   Teams need to understand what AI can do, what it can’t, and where people remain critical. Leaders don’t need to code, but they must explain AI in plain terms and give people enough context to contribute meaningfully.

When I was at a leading management consulting firm, they took this seriously. Everyone, from senior partners to brand-new associates, completed AI training that explained the possibilities and limitations of AI and generative AI at that moment in time. The intent wasn’t to turn us into programmers; it was to give us a shared understanding of the “art of the possible” so we could spot opportunities for AI without needing to write code.

The firm refreshed this training every six months as the technology evolved. That cadence kept people informed, sparked new ideas, and made AI an integral part of everyday strategic thinking, well before any single tool or use case was introduced.

3. Change Co-Creation
   Top-down rollouts often miss crucial context. Frontline employees spot edge cases that leaders and vendors overlook.

In a retail AI scheduling pilot, store managers were invited to run “what if” scenarios before launch. One caught that the system had scheduled a key associate on the day she volunteered at a local charity, a scheduling conflict no algorithm could predict without human input.

4. Trust Engineering
   Trust in AI isn’t just about accuracy; it’s about how the system is designed and deployed.

For example, an insurance company implementing AI for claims assessment built in a safeguard: any decision over a certain dollar threshold required human review. They also spot-checked a percentage of lower-value claims that were processed entirely by AI to verify accuracy, fairness, and consistency. This dual approach reassured staff and customers that AI was augmenting professional judgment, not replacing it blindly, and that its performance was being monitored continuously, not taken for granted.

5. Adaptive Decision-Making
   AI’s capabilities and contexts change quickly. Leaders must be willing to act with incomplete information and adjust fast, but they also need to ensure the system is working with enough quality data to make those adjustments meaningful.

At one manufacturing firm, leadership implemented AI-powered quality inspections despite a higher-than-ideal false positive rate. They recognized two things: first, the AI needed a strong base of accurate, representative data to improve; and second, the model required enough “at-bats” —real-world practice across a variety of scenarios—before the real benefits would emerge.

Rather than delaying deployment until conditions were perfect, they rolled it out to one product line, monitored it weekly, and retrained the model on new examples as they came in. That combination of data discipline and incremental rollout allowed them to refine both the AI and the workflow while keeping the business moving.

Building the Skills

These skills don’t appear overnight; they require deliberate practice:

• Pair leaders with “AI translators” who can bridge technical and business language in real time.
• Use leadership simulations where executives must make decisions based on partial AI outputs.
• Create regular “learning loops” where leaders, technical teams, and frontlines review outcomes together and decide on adjustments.

The Stakes for Leaders

AI is rewriting the playbook on change. Leaders who cling to old models risk becoming bottlenecks rather than catalysts. Those who learn to guide change in motion—communicating clearly, adapting quickly, and earning trust—will lead organizations that not only survive AI disruption but also thrive because of it.

In an AI-driven organization, adaptability isn’t a nice-to-have. It’s the job description.

Not sure where to start?

If you’re exploring AI use cases and want a practical way to align them with business value, I’m happy to share more. Contact me, Kate Wade, Managing Director of Wade Strategy, LLC and Founding Partner of The AI Strategies Group (AISG), to explore how your organization can take the first strategic step toward responsible, practical, and profitable AI adoption.