Most leadership teams talk about AI adoption like it’s a future event, but we keep seeing a pattern show up in the mid-market companies we work with:
Even before there’s a formal AI policy or tooling, ~15–20% of the organization is already using AI daily.
Your starting problem isn’t “How do we get people to adopt AI?"
It’s:
“How do we harness what’s already happening - and scale it safely and consistently?”
Thanks for reading,
Robbie Allen
Founder & Managing Director
Automated Consulting Group
PS: If you want a practical playbook for turning the 20% into repeatable workflows for the other 80%, hit reply. Tell me your function (Ops, Finance, Support, Engineering) and what “better” means (speed, quality, cost, throughput).
Key Takeaways:
• Adoption is already underway. A meaningful slice of your org uses AI daily before leadership formalizes anything.
• The win is structure and lift. Turn scattered individual usage into shared patterns, workflows, and training.
• You need both motions. Bottom-up discovery finds the use cases; top-down rails make them scalable.
• Scale workflows, not tools. Standardize the process, evaluation, and data rules so improvements compound.
Why This Wave Looks Different
With past tech shifts, adoption was gated by the organization.
Cloud is the classic example: an innovative employee couldn’t materially “do cloud” alone. The company had to move infrastructure, permissions, and systems.
AI isn’t like that.
AI is accessible per-employee, right now. You can see meaningful daily usage before the company has done anything official.
It’s simply the nature of the technology, and it creates a new opportunity. You already have an internal group that’s experimenting, learning, and building instincts.
Call them what they are:
Your pilot team.
The Operating Model: Harness Bottom-Up, Then Productize It
The 20% aren't the end state - they're just the beginning.
The next level is taking what the 20% are doing and turning it into organizational capability.
Prepare for both top-down and bottom-up deployment.
Bottom-up is where discovery happens. Leadership cannot pre-plan every valuable use case because we still don’t know all the “killer” internal workflows AI will unlock.
So you let the curious people explore.
But you don’t stop there.
You capture what works, standardize it, and distribute it.
Here’s what that looks like in practice:
1. Inventory what the 20% are already doing
Ask:
What tasks are you using AI for?
What tools are you using?
What inputs do you paste in?
What outputs do you trust?
Where has it failed?
You’re not policing. You’re learning.
2. Identify “workflow-shaped” wins
Look for candidates with three traits:
Frequent (daily/weekly)
Measurable (cycle time, accuracy, throughput)
Reviewable (a human can validate output)
These are the workflows that can scale.
3. Create lightweight rails
Rails will help you make AI usable by more than power users.
As a baseline, establish:
Approved tools (or tool categories)
Clear data rules (what’s allowed, what’s not)
A simple review pattern (who checks what)
Basic logging expectations (so you can improve and explain decisions later)
This is how you turn informal usage into organizational process.
4. Productize the pattern for the 80%
Most employees aren’t “AI people.” They’re not chasing the newest model. They’ll use AI when:
The company tells them what’s allowed
It’s integrated into how they already work
It’s packaged as a repeatable workflow
Don’t ask them to become prompt engineers. Instead, give them a workflow, examples of good inputs/outputs, and a quality checklist.
5. Measure one outcome and iterate
Pick one metric per workflow, ship, learn, and then tighten. This is the best way to avoid the “We rolled out AI, and nothing changed” story.
The Takeaway for Leaders
Treating your existing AI users as your internal pilot team, then build the structure to scale what already works for the 20% across the rest 80% of the organization.
You don’t need a grand rollout - only to turn scattered usage into repeatable workflows with clear rails, measurable outcomes, and shared training.
And with that in mind, I’m curious:
If 20% of your team is already using AI daily, what would change if you made it official: captured their best workflows, standardized the pattern, and rolled it out to the other 80%?
Hit "Reply" and let me know.
– Robbie