Aug 26 2026
Management

Four Tips to Navigate Gen AI Change Management

The four pillars of generative AI ROI ensure teams are given permission to fail, share lessons learned and keep valuable conversations going.

A whopping 95% of generative AI pilots ultimately fail, according to “The GenAI Divide: State of AI in Business 2025” report from the Massachusetts Institute of Technology’s Project NANDA. Why? “Brittle workflows, lack of contextual learning and misalignment with day-to-day operations,” the report states.

Even the best tools fall flat without enablement and operational readiness. When users don’t understand the what, why or how behind a technology, they simply won’t adopt it. Here are four nontechnical criteria that may determine whether an AI initiative will land or become shelfware.

Click the banner below to learn how to turn complexity into a business advantage.

 

1. AI Projects Need Executive Sponsorship

The fundamentals of change management generally follow the ADKAR model: awareness, desire, knowledge, ability and reinforcement. Executives serve as the engines for awareness and desire, answering “Why are we doing this?” and “What’s in it for me?”

AI requires a cultural shift, not just a software update. When frontline engineers see that their vice president continues doing things the old way, they’re less apt to trust the new way. They need to see leaders using generative AI publicly, through sharing summarized meeting notes or drafting strategy docs in real time. AI project sponsorship isn’t just a pep talk; it requires mandating AI-first thinking and holding line-of-business leaders accountable for driving use within their teams. Without that accountability, adoption is merely a suggestion.

GET THE DETAILS: Build a data infrastructure that supports AI initiatives.

2. Focus on Operationalizing AI For Success

If executive sponsorship provides the bridge from the boss’s office to the department, operationalization stands as the bridge between having a tool and actually wanting to use it. When AI is treated like a feature update rather than a process change, that’s the point at which most projects lose their momentum.

Showing users the “help me write” button doesn’t show them how to use AI. Showing teams real value, instead of a cool trick, is a business necessity. That’s also where teams shift from technical readiness (keeping the lights on) to operational readiness (getting work done). Success requires mapping AI tools to specific business drivers and objectives.

3. Find Internal AI Champions

To scale, businesses need a force multiplier: internal champions. Every organization has them — the early adopters and power users who played with large language models before they were assigned a license. Find them, and give them early access to new features and direct lines to enablement. Then recognize and reward them, with more than just a pat on the back, for leveraging the tools innovatively.

Last, give users a place to talk — a dedicated Google Chat space, a Teams channel or monthly office hours. Foster a community where these champions can share prompts, celebrate wins and help their peers. Peer coaching is the only way to turn knowledge into actual ability at scale.

95%

The share of generative AI pilots that ultimately fail

Source: Massachusetts Institute of Technology

4. Get Continuous User Feedback on AI Projects

When a user’s first prompt fails, and they have nowhere to report that friction, they aren’t going to try a second time. Instead, they will quit and go back to the old way of working. Ensure an active mechanism is in place to capture their feedback. If an agent isn’t performing, fix the prompt. If a specific use case is missing, don’t ignore it.

When a user finds an incredible prompt or app, broadcast their success. That encourages the silent majority of users to jump in and try it for themselves. This feedback loop moves projects from a one-time install into a full-fledged program, while acknowledging that technical readiness is just the first step on a journey toward a continuous conversation.

DISCOVER: Turn data into insights and accelerate artificial intelligence initiatives.

Without an official, integrated path of least resistance, employees are likely using the “back alley” to AI, copying and pasting sensitive corporate data into public chat windows as a means to an end. 

When we treat adoption as an intentional goal, AI stops being a novelty and starts being secure, core infrastructure as vital to the business as the network or email. Workflows become crowdsourced, and the best prompts and AI wins are shared and standardized across departments. The iceberg of cultural resistance finally melts and true AI adoption and value are achieved.

gremlin/Getty Images
Close

New Research from CDW Explores AI and Cybersecurity

Learn how AI is helping IT teams manage risk and improve resilience.