AI Is Changing Work. Is Leadership Changing Fast Enough?

AI adoption is accelerating across the workplace. But adoption alone does not tell us whether organizations are becoming more capable, more trusted, or more effective.

Blanchard’s recent research points to a more complicated reality. Employees are already using AI in meaningful parts of their work, often before organizations have established shared expectations for disclosure, review, accountability, and decision-making. Leaders are being asked to close that gap while protecting trust, strengthening judgment, and helping people adapt.

Ahead of Blanchard’s August 19 webinar, Leading Through AI: Practical Solutions That Put People First, Leah Clark spoke with AI experts Britney Cole, Jay Campbell, and Betty Dannewitz about the leadership practices organizations need now and the capabilities they will need next.

Leah Clark: Our research suggests that AI adoption is moving faster than shared norms, trust, and judgment. Where are organizations most likely to go wrong as they respond?

Britney Cole: One of the biggest mistakes is treating AI primarily as a technology rollout.

Organizations may introduce new tools, publish a policy, and encourage people to experiment. But employees are still left with practical questions. What are they allowed to do? When should they disclose AI use? What level of review is expected? Who is accountable when the output is wrong?

That uncertainty creates inconsistent behavior. Some employees move ahead quickly. Others hold back. Managers apply different standards across teams.

Leaders need to create clarity around responsible use, expected behavior, review, and decision ownership. They also need to model those expectations visibly. Without that leadership, adoption may increase while confidence and trust decline.

Leah Clark: We found that leaders and individual contributors often experience AI readiness differently. Leaders may believe expectations are clear while employees still feel uncertain. What distinguishes organizations that are managing that gap well?

Britney Cole: The stronger organizations look beyond adoption numbers. They ask whether AI is helping people make better decisions, solve problems faster, improve work quality, and create more value for customers and employees.

They also pay attention to the experience around adoption. Do people know what responsible use looks like? Do they feel safe raising concerns? Are managers reinforcing the same standards? Are employees involved in redesigning the work?

AI use by itself is not the outcome. The outcome is better work, stronger capability, and clearer decision-making.

Leah Clark: Managers are central to closing that gap, yet many do not feel qualified to lead conversations about AI. How can they build confidence without positioning themselves as technical experts?

Betty Dannewitz: Managers do not need to be AI experts. They need to be willing to learn alongside their teams and talk openly about what they are learning.

Confidence develops through practice. Managers can create space for responsible experimentation, ask curious and informed questions, and help the team reflect on what worked, what did not, and what needs closer review.

They also need to be clear about where human judgment and accountability still matter. A manager who can guide a thoughtful conversation, surface risks, and connect AI use to the team’s work may be more valuable than one who knows every feature of a particular tool.

Leah Clark: Trust came through clearly in our research. Employees are paying attention to whether leaders disclose their own AI use, explain decisions, and apply consistent standards. How can leaders embrace AI without weakening human connection?

Jay Campbell: Transparency matters. Leaders should be open about where they are using AI, what they are learning, and where the technology has limits. They should acknowledge mistakes and explain how they corrected them.

Leader behavior makes responsible use visible. It gives employees a standard they can follow and creates room for honest discussion.

Leaders also need to invite concerns rather than dismiss them. Employees may be worried about role changes, job security, quality expectations, or whether AI will influence decisions about their performance. Those concerns deserve a clear response.

Trust grows when people understand how decisions are being made, where AI is involved, and who remains accountable.

Leah Clark: Our findings point to judgment, critical thinking, and human connection as increasingly important capabilities. Which of these are organizations least prepared to develop?

Jay Campbell: Critical thinking is one of the biggest gaps. AI can produce confident, polished output even when the information is incomplete, inaccurate, or poorly suited to the situation. That creates a risk of false confidence.

Leaders need to ask better questions. What evidence supports this output? What assumptions are embedded in it? What context is missing? What bias could be present? What are the consequences if the recommendation is wrong?

Human skills matter just as much. Communication, empathy, adaptability, trust building, and sound judgment become more valuable as AI becomes more embedded in daily work.

AI can extend people’s thinking. People still own the judgment and the outcome.

Leah Clark: For organizations early in their AI journey, the temptation is often to begin with tools. What is a more disciplined starting point?

Britney Cole: Begin with a meaningful business problem. Look for areas where employees are losing time, repetitive work is slowing execution, customers are experiencing friction, or leaders are struggling to make consistent decisions. Then determine whether AI is the right solution.

In most organizational settings, the business problem should come before the tool choice. Starting with tools often leads to scattered experimentation and weak adoption. Starting with the work creates a clearer case for value, stronger criteria for success, and better decisions about where human oversight is required.

Leah Clark: Even with the right starting point, organizations can create overload by pursuing too many applications at once. How can they scale capability at a pace people can absorb?

Betty Dannewitz: Start small and focus on a few high-value use cases connected to real work. Give people time to experiment, share what they are learning, and build capability in manageable steps. Expecting everyone to become an AI expert overnight creates unnecessary pressure and often produces shallow adoption.

Scaling should follow demonstrated value. When a team identifies a useful practice, understands the risks, and can repeat it consistently, the organization has something worth expanding. That approach builds confidence through evidence rather than urgency.

Leah Clark: We know that one-time training will not create lasting behavior change. What does an effective AI learning ecosystem need to include?

Britney Cole: An effective ecosystem connects people, content, technology, practice, and reinforcement.

Employees need clear expectations before learning begins. They need opportunities to apply new skills during the learning experience. And they need support afterward so new behaviors become part of the work. That may include manager conversations, coaching, peer learning, practice environments, job aids, feedback, and ongoing measurement.

The key question is where AI can improve the learning journey while preserving human judgment, reflection, and accountability.

Leah Clark: Betty, that raises an important implementation issue for HR and L&D. How do they translate an AI strategy into leadership behaviors people can see and repeat?

Betty Dannewitz: An AI strategy becomes real when it shows up in observable behavior.

That includes experimenting intentionally and responsibly, checking AI-generated work, and being transparent about how AI contributed to an output or decision.

Leaders need opportunities to practice those behaviors out loud and in the context of their actual work. A written principle such as “use AI responsibly” is too abstract on its own. People need to see what responsible use looks like in a meeting, a coaching conversation, a hiring process, or a customer decision.

The more specific the behavior, the easier it is to reinforce.

Leah Clark: How should organizations determine where AI can act with greater autonomy and where human oversight needs to increase?

Jay Campbell: The level of human involvement should rise with the level of consequence.

AI can be useful for research, summarization, idea generation, drafting, and pattern recognition. But decisions involving employees, customers, ethics, safety, legal exposure, or business strategy require stronger review.

Organizations need clear risk tiers. A low-risk internal draft may require light review. A recommendation affecting someone’s career, compensation, or access to an opportunity requires much more scrutiny.

The important point is to define those thresholds before a problem occurs.

Leah Clark: Accountability is another recurring issue. How can leaders use AI without allowing responsibility to become blurred?

Britney Cole: Leaders remain accountable for the decisions they make and the communications they approve.

Review should go beyond basic accuracy. Leaders should ask whether the output is relevant, whether important context is missing, whether bias may be present, and whether the recommendation fits the organization. They should also be clear about where AI contributed to the work.

Responsibility cannot be delegated to the tool. AI may inform a decision, but the leader still owns the consequences.

Leah Clark: Awareness is only the beginning. What helps people maintain these behaviors when the work becomes pressured, ambiguous, or complex?

Betty Dannewitz: A single learning experience can create awareness, but coaching and reinforcement help people apply what they learned when the work gets complicated.

Reflection prompts, manager conversations, peer learning, and opportunities to practice keep AI learning active. They help people examine how they reached a decision, where they relied on AI, and whether they applied the right level of review.

Over time, that reinforcement turns new ideas into repeatable habits. Without it, people often return to familiar ways of working or use AI inconsistently.

Leah Clark: What role does leadership development play in helping people adapt to continued technological change?

Jay Campbell: Well-designed leadership development can strengthen adaptability, judgment, communication, and psychological safety.

Those capabilities help leaders guide people through uncertainty without creating confusion or unnecessary fear. They help teams learn faster, discuss mistakes openly, and improve how work gets done.

Technology will continue to change. The need for leaders who can set direction, build trust, coach others, and make sound decisions will remain.

Leah Clark: Looking beyond today’s use cases, what shift should HR and L&D leaders begin preparing for now?

Betty Dannewitz: HR and L&D professionals should prepare for a shift from using AI mainly as a personal assistant to working alongside AI agents that can complete more complex, multi-step work.

That change will make AI fluency important. It will make distinctly human capabilities such as judgment, critical thinking, communication, and accountability even more essential.

Organizations will need to help people understand not only how to use AI, but how to supervise its work, question its recommendations, intervene when needed, and remain accountable for the result.

Leah Clark: One final question. What is the most important action leaders can take now?

Jay Campbell: Create a culture of responsible experimentation. Encourage people to test useful applications, share what they learn, and discuss failures without hiding them. Pair that experimentation with clear expectations for privacy, disclosure, review, quality, and accountability.

Organizations learn faster when people can exchange ideas openly. They perform better when that learning happens within clear boundaries.

That balance is what responsible AI leadership requires.

Would you like to learn more about the latest developments in AI applications? Join us for a complimentary webinar!

Leading Through AI: Practical Solutions That Put People First

Wednesday, August 19, 2026

Blanchard experts will be leading a conversation specifically designed for HR and L&D professionals. Explore ways to leverage the latest AI capabilities into your strategic plans and initiatives. Learn more here!