Blog Executive AI Adoption Starts at the Top

Learn why executive enablement is the step many AI rollouts skip, and how Insight’s Executive Leadership Copilot Enablement Training helps leaders put Microsoft 365 Copilot and Cowork to work in their day-to-day workflows.

A leadership team invests heavily in AI. Six months later, adoption has stalled. Employees are not changing how they work, and executives are asking why.

One common reason is simple: the people leading the initiative have never experienced AI solving a meaningful problem in their own work.

Most AI rollouts begin with pilot users, training sessions, adoption champions, and dashboards. Meanwhile, the people setting strategy, approving budgets, and shaping organizational behavior often receive little more than a high-level overview.

That gap matters. Leaders who have never used AI to prepare for a board meeting, summarize weeks of communication, or draft an initial analysis have limited context for evaluating investments and setting realistic expectations. Employees notice, too. When leaders do not use AI themselves, adoption can quickly feel optional rather than important.

Research reflects this challenge. McKinsey’s 2025 State of AI survey found that 88% of respondents report regular AI use in at least one business function, yet nearly two-thirds say their organizations have not begun scaling AI across the enterprise. Only 39% report enterprise-level EBIT impact.

AI adoption is partly a technology challenge. It is also a leadership challenge.

The gap most AI rollouts leave open

Leaders rarely become AI advocates because they attended a training session. They become advocates when AI helps them solve work they care about.

For an executive, that might mean reducing the time required to prepare for a board review, synthesizing updates across teams, or creating a useful first draft that would otherwise take hours to assemble. Once leaders see practical value in their own workflows, conversations about broader adoption become easier and more credible.

The problem is that executive enablement is often approached the same way as employee training. Executives have different responsibilities, workflows, and constraints. A general curriculum built for the average knowledge worker may explain the product, but it rarely addresses how leaders spend their time. Senior leaders need help identifying a small number of repeatable use cases that create immediate value.

Insight’s Executive Leadership Copilot Enablement Training is built around that approach: personalize the experience, include executive assistants from the start, and measure behavior change rather than attendance alone.

Leadership behavior also affects the broader workforce. BCG found that the share of employees who feel positive about generative AI rises from 15% to 55% when leaders provide strong support. Yet only about one-quarter of front-line employees say they receive that support. Employees pay attention to what leaders do, not only what they endorse.

What does effective enablement include?

What does Executive Leadership Copilot Enablement Training look like?

Not every leader starts in the same place. Some have never used Microsoft 365 Copilot. Others rely on it every day. Understanding that difference before training begins helps make the experience more relevant and useful.

Before training starts, participants are evaluated using several inputs, including a pre-training survey, Microsoft 365 Copilot usage data, and a qualitative assessment by the delivery team. Together, those inputs help identify current experience levels, common pain points, adoption barriers, and the workflows leaders want to improve.

The goal is not to label participants. It is to build the right experience for them. A leader who has used Copilot hundreds of times does not need the same session as someone who is just getting started. Their training track, use cases, and level of instruction should reflect where they are today.

Once participants are assessed, they move through four stages of Executive Leadership Copilot Enablement Training:

1. Discovery

The engagement begins by understanding how leaders work today. Discovery sessions identify common workflows, time-consuming activities, pain points, and opportunities where Copilot and Cowork can provide immediate value.

2. Build

Training materials, prompt libraries, use cases, and examples are customized based on what discovery reveals. The focus is on the work leaders already do, not generic demonstrations.

3. Deliver

Participants attend live, instructor-led sessions where they apply Copilot and Cowork to real business scenarios. Training is paired with practical reference materials and how-to guidance they can continue using after the engagement.

4. Reinforce

This is where many AI programs stop, but adoption often starts. Reinforcement includes resources such as session recordings, office hours, follow-up guidance, and post-training measurement to help leaders build lasting habits and identify additional opportunities to use Copilot and Cowork in their daily work.

The guiding principle throughout the program is simple: personal productivity first, broader adoption second. Leaders who experience value in their own work become far more effective champions for AI across the organization.

What participants take away

These trainings are designed to create lasting behavior change, not just awareness.

The engagement produces practical resources that support continued use after training, including:

• Customized training materials aligned to executive and assistant workflows
• Copilot use cases and prompt libraries tailored to leadership responsibilities
• Session recordings and reference guides for continued learning
• Baseline and post-training adoption metrics
• Executive readouts summarizing adoption progress and next steps

Leaders also identify two or three repeatable workflows they can begin using in areas such as meeting preparation, communications, decision-making, and information gathering.

Most importantly, leaders leave with firsthand experience using Microsoft 365 Copilot to support their own work. That experience helps them speak more confidently about AI adoption and model its use across the organization.

What real executive sessions reveal

The most productive executive AI sessions are usually less about AI than they are about the work leaders already need to do.

Consider a typical Monday morning. An executive is preparing for a board review while an executive assistant gathers information from emails, meetings, documents, and project updates. The process can consume hours before the real review begins.

After enablement, the workflow might start with an AI-generated briefing that summarizes recent activity, highlights risks, identifies decisions, and produces draft talking points. The executive and assistant still review the material, check the sources, and apply judgment. They simply spend less time assembling the first version.

The early barrier is rarely a lack of interest. More often, it is skepticism about relevance. Executives want to know whether AI can support the work already on their calendars: customer meetings, operational updates, board materials, project reviews, and leadership communications.

A generic demonstration may earn polite attention. A live example tied to the participant’s own workflow creates credibility. It also gives the facilitator a natural opening to discuss where AI performs well, where human judgment remains essential, and why permissions, governance, and source quality matter.

A useful session should leave each leader with two or three repeatable workflows they can try that week. It should also give them enough direct experience to talk with their teams about AI without relying on a slide deck or secondhand examples.

Why executive assistants matter

Executive assistants are often the bridge between executive interest and daily AI use. They manage many of the workflows that shape a leader’s day, including briefing preparation, meeting coordination, follow-up, scheduling, and information gathering. If assistants are left out, adoption can struggle to become a habit.

Assistant enablement should therefore be treated as a core workstream. Valuable scenarios may include preparing meeting briefs, summarizing previous discussions, drafting follow-up notes, tracking actions and decisions, coordinating complex schedules, and building reusable prompts for recurring leadership rhythms.

Assistants also bring practical context that a training team may otherwise miss. They know where leaders lose time, which information is sensitive, when an output requires more context, and which workflows are likely to be repeated. Their feedback helps keep the program grounded in the realities of the executive office.

As leaders begin to self-serve some tasks, the assistant role may shift toward prioritization, content validation, relationship management, and workflow design. AI can reduce routine handoffs while creating more room for assistants to operate as strategic partners.

Trust is part of adoption

Usefulness matters, but trust matters just as much. Executives need to understand which information an AI tool can access, how permissions influence the answer, which content should not be included in a prompt, and when human review is required.

Those boundaries should be part of the enablement experience, not a disclaimer at the end. Leaders are more likely to use AI consistently when they understand both its practical value and its limits. Specific product capabilities should always be confirmed against the client’s licensing, tenant configuration, permissions, and current roadmap availability.

Measure more than prompt counts

Usage data is useful, but prompt counts alone can create a false sense of progress. One leader may submit hundreds of prompts without changing how work gets done. Another may use AI less often but improve a recurring decision, meeting, or communication workflow in a meaningful way.

A stronger measurement approach looks at outcomes such as time saved on recurring work, adoption of repeatable use cases, assistant participation, quality and speed of executive-ready drafts, reuse of proven prompts, and visible leadership modeling.

The most persuasive signal is often a specific moment when a leader sees hours of preparation reduced to a shorter review and refinement process. That is when AI moves from an abstract initiative to a practical way to create capacity.

A useful closeout should answer three questions: What changed in how leaders work? Which workflows are worth scaling? What support is needed to sustain the change? Those answers provide a clearer picture of progress than activity volume alone.

Executive adoption starts with experience

Organizations do not create value from AI simply by purchasing more licenses. Progress becomes visible when leaders can explain, from personal experience, how AI improved a real part of their work.

That confidence does not come from a company-wide announcement or a standard training deck. It comes from applying AI to the meetings, communications, decisions, and recurring workflows leaders already own.

When executives experience useful results firsthand, sponsorship carries more weight. Leaders can set clearer expectations, model responsible use, and help teams understand where AI fits into day-to-day work.

Executive adoption is not the only factor in a successful AI strategy, but it can strongly influence whether an initiative maintains momentum after the initial rollout.

Ready to get your leaders up to speed on what matters most?

Build practical AI fluency at the top. Insight’s Executive Leadership Copilot Enablement Training helps leaders and executive assistants turn daily work into repeatable, responsible Copilot and Cowork-supported workflows. To learn more about the program, connect with your Insight account team or contact us to discuss training options for your leadership team.