Teams are using AI without a shared approach
Set clear guidance for approved tools, confidential information, review, and accountability so adoption does not depend on everyone making their own rules.
AI & automation / AI enablement & training
AI your team knows how to use.
Turn scattered AI experiments into a practical way of working. We help your team choose the right tools, use them responsibly, and build repeatable habits around real tasks.
Based in Bloomington, Illinois. Working with teams across the United States.
Where it fits
Start with the work. Not a tool demo.
A subscription is not an adoption plan. People need to know which work belongs in AI, what information they can share, and how to check the answer. We design enablement around the roles, systems, and constraints already in your business.
Training has not changed day-to-day work
Go beyond a one-off answer. Build a practice coach from team expertise, investigate patterns across months of feedback, or create a research workflow your team can reuse and verify.
You know AI could help, but not where to begin
Compare opportunities by frequency, effort, risk, and business value. Choose a useful first workflow and decide how you will measure it.
What we deliver
A working playbook, not just a workshop.
Training can stand alone. An agent or custom build is only a next step when the work calls for it. We scope participants, sessions, materials, and follow-up before you commit.
- A role-by-role opportunity shortlist and practical rollout plan
- Hands-on sessions using fictional material or examples you approve
- Reusable prompts, task guides, and output-review checklists
- Tool and data-handling guidance, with named owners for adoption
Inside a training session
Turn your experience into an AI practice coach.
Your experienced people carry judgment that never makes it into a manual. We teach your team to have AI draw it out, turn it into guidance you approve, and build a coach colleagues can practice with.
From expertise to practice
01Let AI interview you
Your instruction “Help me turn my experience handling difficult client requests into a practice coach for new project leads. Interview me one question at a time. Ask for contrasting examples, exceptions, and why I made each decision. Reflect the rules back to me for correction before drafting guidance.”
AI interviewer “Tell me about two urgent requests that sounded similar but needed different responses. What changed your decision?”
Project lead “In one, an existing approval process stopped working. In the other, the client wanted an additional approval step before launch. One needed incident triage; the other needed a scope discussion. Urgency alone didn’t tell us which.”
What we teach: Ask for a second example and an exception. Correct the AI’s interpretation. This draws out the decision behind the answer, rather than collecting generic tips.
02Turn the answers into a coach
Ask AI to draft a decision guide: “Separate the questions we ask, the rules we follow, the exceptions, and what requires approval. Mark anything I have not explained. Do not invent company policy. Wait for my corrections.”
Sample guidance after expert review: Establish what worked before and what changed. Ask who is blocked. Distinguish a fault from a new request. Never promise additional work or a completion date before the delivery lead confirms it.
Then configure the practice: “Use this approved guide to role-play a client, one message at a time. Give me an ambiguous request. Reveal details only when I ask relevant questions. After three exchanges, pause and compare my exact words with the guide. Suggest one better question, then let me try again. If the guide does not cover something, flag it.”
What we teach: Give AI a defined role, approved reference material, and a clear interaction pattern. The expert checks the coach’s scenarios and feedback before the team uses it.
03Practice, get feedback, try again
Excerpt from a practice review
AI as the client: “We need you to fix approvals before tomorrow’s launch. This is holding everyone up.”
The learner: “I’ll get the team to have it ready tomorrow.”
Example coaching feedback: “You committed to a date before finding out whether this was a fault or new work. The guide requires delivery-lead confirmation. First ask: ‘Which approval step worked previously, and what happens now?’”
On the next attempt: the learner asks that question and discovers the client is requesting a new approval step. The conversation can now address scope and options instead of promising a fix.
What we teach: Inspect the feedback against the approved guide, challenge unsupported advice, and practice again with a harder variation. Save the reviewed guide and coach instructions in your approved AI workspace for the next team member.
04What your team leaves with
A reviewed decision guide, reusable interview and coaching instructions, and practice cases covering ordinary requests, exceptions, and missing information. Your team also learns how to update the guide when the work changes.
In a pilot, compare the coach’s feedback with an experienced reviewer and track where learners still need help. Use it for practice and coaching, with human oversight—not automated employee assessment.
How we work
Clear steps. Working progress.
Start with a free fit conversation. We will discuss the requirement, decide what needs a closer look, and quote the next useful scope privately.
Choose a useful starting point
Review the team, tools, and a small set of repeated tasks. Agree on what better work would look like.
Practice on a defined task
Run guided sessions with approved or fictional material. Check the output against agreed expectations, including when to stop using AI.
Make it repeatable
Leave usable playbooks, identify internal owners, and review what people are actually adopting.
Before we start
A few useful answers.
Do we need to replace our existing tools?
No. We first review the tools you already use and their available AI features. New software is only proposed when it solves a specific gap.
Can we start without sharing confidential information?
Yes. We can scope the work from a description and use fictional training material. Before any real information is used, we agree what may be shared, which tools may receive it, and who reviews the output.
Do we need an assessment before training?
No. A focused training engagement can be scoped directly. If the business priorities are unclear, our optional AI Opportunity Assessment can help decide where to start.
Tell us what you want to make better.
Tell us which roles you want to support and one task they repeat. A description is enough for the first conversation; no client records or app access are needed.