Role-by-role task audit
A short assessment of which tasks in each role are a good fit for AI assistance and which are not, before any training happens.
Enablement
AI training built for capability, not for licenses
Buying every team member a seat does not make them good at using it. We build training around your real workflows so the skill actually sticks.
Most companies solve AI adoption by buying licenses. Every employee gets access to a model, someone sends a short intro email, and the organization declares itself an AI-forward company. Then, months later, usage data shows most people never got past asking it to write emails, because nobody taught them how to apply it to the actual work in front of them.
A license is not a skill. Being capable of using these tools well means knowing how to write a prompt that gets a usable answer on the first or second try, knowing which of your team's tasks are actually a good fit for a model and which are not, and knowing how to check the output before it goes anywhere. None of that comes from a generic onboarding video.
We build training around the specific workflows your team already runs. A marketing coordinator, an operations manager, and a customer support lead need different things from the same underlying tools. Training that ignores that difference produces the same shallow adoption a license-only rollout does.
The measure of success is not attendance at a session. It is whether, a month later, people are using these tools inside their real work without needing to ask someone else how.
What it is
What the curriculum actually covers
Built around real tasks, not abstract capability tours.
We start by identifying the tasks in each role that are actually a good fit for AI assistance: drafting, summarizing, first-pass research, and pattern-finding in data are common candidates. We also identify what is a poor fit, because a curriculum that oversells capability produces people who trust output they should be checking.
The core skill we teach is not tool-specific button-clicking, it is how to direct a model well: giving it enough context, being specific about the format you need, and iterating on a weak first answer rather than accepting it. That skill transfers across tools and survives a vendor switch, which a tool-specific walkthrough does not.
We also teach verification habits directly: how to spot a confident-sounding wrong answer, how to check a model's output against a source you trust, and when a task needs a second person's review before it goes anywhere. This is the part most rollouts skip, and it is the part that prevents embarrassing mistakes later.
Formats vary by team size and need: live working sessions where people bring their actual current tasks, smaller role-specific workshops, and reference material people can return to after the session. We favor hands-on sessions over lecture, because the skill is built by doing the work, not by watching someone else do it.
Fit
Who this is for, and who it is not for
We would rather say no early than sell a program that cannot work.
Right fit
- A team that already has AI tool licenses but low or shallow actual usage.
- Leadership that wants a defined training program rather than an ad hoc, self-taught rollout.
- A team about to adopt AI tools and wants adoption to succeed the first time.
Not the right fit
- You want a one-hour, one-size-fits-all seminar to check a compliance box. That will not change behavior.
- You have no specific tools or workflows in mind yet and want a generic AI overview instead. Start with a scoping conversation first.
- You are looking for us to build the automation itself rather than train your team. That is covered under our automation and workflow services directly.
Deliverables
What the engagement includes
A program, not a single session, with material your team keeps.
Hands-on working sessions
Live sessions where people bring their real current work and practice directing a model against it, rather than a generic capability demo.
Reference material and prompt libraries
Written guides and role-specific prompt starting points people can return to after the training ends.
Verification and review training
Direct instruction on spotting incorrect output and deciding when a task needs a second reviewer before it ships.
Role-specific workshop tracks
Separate tracks for teams with meaningfully different workflows, so training reflects the actual work rather than a lowest-common-denominator overview.
Follow-up check-in
A session weeks after the initial training to see what is and is not sticking, and adjust the material based on real usage.
How we run it
How the program runs
Scoped around your team's actual roles before any session is built.
Step 1: Audit current usage and roles
We look at what tools your team already has, how they are actually being used, and which roles have the clearest opportunity for real gains.
Step 2: Design role-specific curriculum
We build sessions around real tasks pulled from each role, rather than a single generic deck used for every team.
Step 3: Run hands-on sessions
Sessions are working sessions, not lectures. People practice directing a model against their own current work with feedback in the room.
Step 4: Follow up and reinforce
A check-in after a few weeks confirms what is sticking and gives us a chance to correct habits before they calcify.
What is the difference between AI licenses and AI training?
A license gives someone access to a tool. Training gives them the skill to direct it well and verify its output on real work. Most low-adoption problems come from skipping the second part.
Where this connects
Where this connects
Training pairs directly with a few other programs.
Once a team can direct models well individually, the next step for repeatable processes is automation workflows where those individual skills get built into a system rather than repeated manually.
Marketing teams specifically often pair this training with marketing automation so people trained on directing models also understand the automated systems built around their work.
For teams doing research and message testing rather than production work, the relevant skill set is covered separately under AI marketing strategies which applies these same judgment habits to strategic decisions.
Teams already running custom bots benefit from training their staff on the tools directly, which connects to custom bots as the underlying systems the training prepares people to use and evaluate.
Questions
AI training and enablement questions we get asked
Get your team actually capable, not just licensed
Talk to us about what your team's real workflows look like right now.