From AI Readiness to Rollout: How to Drive AI Adoption in the Workplace

Elizabeth McFarlan Scott
Jul 23
4 min read

In our recent article on What it really takes to be AI-ready, we explored what it means to prepare your organization (your people, systems, and culture) for the shift AI brings. That foundation is essential, but readiness, on its own, isn’t enough.

Once the strategy is set and the basics are in place, the next challenge is turning that groundwork into real action.

That’s where many companies hesitate: because it’s hard to know what doing AI well actually looks like day-to-day and how to drive AI adoption in the workplace.

So how do you get AI adoption off to a strong start? Here is a selection of real-world practices we see work inside organizations: simple, practical steps that build confidence, drive early wins, and help teams make meaningful progress.

1. Treat AI as a Cross-Functional Business Initiative

One of the biggest missteps we see is when AI is treated as a standalone IT project or a curiosity exercise for HR or L&D. Instead, AI must be treated as a business-wide priority, embedded in the organization’s growth, efficiency, and talent agenda.

It’s important to bring in voices from finance, operations, customer service, legal, product - any function that can benefit from AI. AI adoption needs to be a shared responsibility rather than something owned by a single department.

Why? AI isn’t just a tool but a new way of working.

2. Establish Clear, Accessible Use Guidelines

As excitement builds, so does confusion. Employees want to know what’s okay, what’s risky, and when to ask for help. From the beginning, it’s essential to set internal guidelines that are practical, transparent, and easy to follow.

That might include:

When generative AI can be used (and when it shouldn’t)
What types of data should never be entered into AI tools
When human judgment is required to review, override, or validate AI outputs
And more!

If anything, these guidelines should not form a 20-page policy document but rather a simple playbook with well-placed FAQs that employees can easily access, understand, and act upon.

3. Identify and Empower Your Early “Power Users”

In every company, there are people who instinctively tinker, test, and explore. Find them early and give them space to lead. These power users can pilot tools, uncover practical use cases, and share what works with their peers.

They don’t need to be in technical roles. Often, they’re in frontline or operational teams, and their insights are especially useful because they’re solving real problems in real time.

4. Run Hackathons and Targeted Pilots

You don’t need to launch enterprise-wide initiatives to get started. Low-stakes, high-impact experiments such as internal hackathons or focused pilot programs are a great way to test ideas, build internal excitement, and collect early wins.

Keep them simple and business-driven. Invite cross-functional teams to tackle real problems using AI tools, then share the outcomes widely.

This builds momentum and shows the broader organization that AI isn’t abstract. It’s already solving problems today.

5. Provide Training and Upskilling for Everyday Use

Comfort with using AI tools varies widely across teams and generations. It’s important to build training into onboarding and development programs and ensure that both existing employees and new employees are included.

As a part of the process, it might be helpful to focus less on the tech itself and more on practical application:

How to write a good prompt
How to evaluate AI-generated content
When to rely on AI and when to trust your own judgment
How to avoid compliance or privacy issues
Where to go and get help if you are not sure

You are not turning every employee into an AI expert but rather giving teams tools to make their work efficient and help people feel informed and capable.

6. Tap Into Internal Communities Like ERGs and Guilds

Employee groups and internal communities can be powerful accelerators of adoption, especially when it comes to equity, inclusion, and ethics.

Some companies are forming AI guilds or Slack channels to share prompts, tools, or lessons learned. Others are using ERGs to host honest conversations about how AI might affect different teams and career paths.

These groups can also surface concerns early, helping you address resistance before it slows things down.

7. Plan for the Enthusiasm Dip

Most AI rollouts start with a burst of excitement. But that early energy doesn’t last forever.

There’s often a point where the tools feel less shiny, and adoption plateaus or small roadblocks add up. This “trough” is normal—and it’s where your persistence matters.

You can prepare for it by communicating wins, revisiting power users, offering fresh training, or second-wave pilots. Progress doesn’t have to be dramatic, but it does need to continue, and a consistent approach from the business leaders helps.

Final Word

The best AI strategies don’t rely on one bold move. They build steadily, with small, intentional steps that meet people where they are.

If your organization is ready to move from strategy to action, start small. Focus on real problems, useful tools, and helping people feel confident using them.

And if you’d like help building your roadmap - from crafting guidelines to designing pilots to activating learning - we’re here for that. Let’s talk.