Your AI Employee Enablement Roadmap
5 steps to better AI engagement plus how to avoid AI bias and opinions
The Missing Piece in Your AI Strategy: Employee Enablement
Most companies say they want to “embrace AI.”
Fewer can actually describe what that looks like for their people.
That’s where things break down. Not because leaders don’t believe in AI. It’s because they’re unsure how to empower employees to safely explore it.
There’s also a knowledge gap in “how” to enable them to explore it.
The truth is, AI transformation isn’t about having the perfect tool stack. It’s about creating the right environment for experimentation.
Here’s a simple roadmap I’ve seen work inside organizations that are actually making progress:
1️⃣ Normalize curiosity
Leaders need to make it clear: AI exploration is encouraged, not risky. Start small. Invite employees to share one AI win each week. It could be a task they made faster, a prompt that worked, or a creative new use case. When exploration becomes a conversation, curiosity scales naturally.
2️⃣ Provide psychological safety
Many employees hesitate because they fear “getting it wrong.” Create a sandbox mindset: mistakes aren’t missteps! They’re part of the learning curve. Model this from the top. If leaders show their own experiments (and failures), others will follow.
I’ve bombed a bunch of experiments that I shared broadly. Your fail could spark an idea in someone else.
3️⃣ Establish guardrails, not gates
A good AI policy should empower, not restrict. Provide a clear set of do’s and don’ts. Include what data is okay to use, which tools are approved, and how to handle sensitive information.
When people know the rules, they’re more likely to play the game.
4️⃣ Build a starter toolkit
Don’t make employees chase down tools. Centralize a handful of approved options. For example, ChatGPT for brainstorming, Claude for analysis, and a shared prompt library in Notion, Confluence, or a spreadsheet. The easier it is to start, the faster you’ll see value.
Get people comfortable with more in-depth prompting before you jump all the way to building agents and automations. That will come in time.
5️⃣ Celebrate small wins
Nothing builds momentum like recognition. Feature “AI Spotlight” stories in your internal comms. These can be quick highlights of how employees used AI to save time or spark ideas. These stories turn experimentation into a movement.
I’m looking to create an Creative AI Circle group that operates sort of like the old coding parties that would create a website in an evening. This group would pick an operational challenge and find a creative AI solution.
Bottom line:
AI enablement is a leadership skill. This is not something you can delegate to IT or HR. it has to be modeled, encouraged, and celebrated from the top down.
Start by giving your team permission to explore. The rest will follow.
Coming soon:
I’m working on a short guide called “The AI Enablement Roadmap: How to Build a Culture of Experimentation Without Losing Control.” It’ll include templates, policies, and real examples you can use inside your organization.
If you want early access, hit reply and I’ll put you on the list to get one of my first drafts.
Yes, I’m a big advocate of AI but I’m also learning about the pitfalls and how to work around them.
Recently, I was building a Human Resources AI Agent that would:
Parse data from job applicant submissions
Fill a spreadsheet with that data
Offer overviews of applicants’ experience compared with the job description.
(great resource for small HR teams, btw)
Seems pretty straightforward until you start reviewing the applicant overviews during testing.
I learned quickly how fast AI jumped to offering opinions on the applicants. It’s easy for AI to start “interpreting” rather than simply reporting. I wanted it to stick to the facts, not opinions.
Here are some steps I took to fix this problem:
I trained the AI Agent on neutral data. I avoided feeding examples that included subjective language like “strong communicator” or “great cultural fit.” I kept the AI agent training and examples factual and measurable.
I asked for evidence, not opinions. In my prompts, I used phrasing like “List the candidate’s relevant experience based on the resume text” instead of “Evaluate how qualified this candidate is.”
Educate it on how to interpret adjectives. The words “impressive,” “junior,” or “aggressive” carry hidden bias. Ask AI to summarize or extract data rather than judging based on adjectives.
Keep a human in the loop. Let AI do the heavy lifting, but have people review the outcomes. The goal here wasn’t to remove humans but rather to help them with some of the administrative work.
If you’re curious what the workflow looked like, an example of it is below.
Basically, when the HR team starts their day they have a spreadsheet with all applicant data. For teams without an ATS (Applicant Tracking System), something like this can save a ton of time.
That’s it for this week! I love talking AI at Work so reach out if there is use case you want to work through. Happy to share my experience!
This cultural layer is everything - it's what turns AI from a tool into a transformation. Getting this right is how teams move from experimenting to scaling efficiently, which is exactly what we explore in The Efficiency Playbook.