From AI curiosity to confident use.

Your people are already using AI. The question is whether they’re using it well. We run role-based workshops and reinforcement programmes that turn scattered experimentation into consistent, practical capability in the work your teams already do. Fully remote, customized to your workflows.

The problem

Access is not adoption.

Most organizations roll out AI tools and assume usage will follow. It doesn’t. People dabble, outputs are inconsistent, adoption is uneven across teams and roles, and quality quietly degrades because nobody was taught how to think with AI.

Training that works meets people in the work they already do: specific prompts for specific workflows, clear rules for when AI is appropriate and when it isn’t, and judgement habits that catch AI mistakes before they reach customers.

What we do

Five stages from access to durable capability.

01
Discover
Baseline where your teams actually stand with AI. What tools are in use, what workflows could benefit, where usage is strong, and where it is stalled or risky. Informed by a short internal survey and a leadership conversation.
02
Design
Curriculum mapped to your stack, industry, and roles. Executives get a strategic briefing. Function teams get scenario-based workshops tied to the work they already do. Power users and builders get deeper operator labs.
03
Enable
Live workshops and hands-on labs, delivered remotely. Role-specific scenarios using the customer’s real tasks, not abstract prompting demos. Safe-use rules and verification habits built into every session.
04
Reinforce
Champions network, office hours, job aids, and on-demand microlearning. One-and-done training rarely sticks. Reinforcement is where behaviour change becomes habit.
05
Measure
Adoption analytics, workflow-level outcomes, and quality signals. What tasks are taking less time, what outputs are improving, where risk is still showing up. Reported to leadership, not buried in an LMS.
What the numbers look like

People are using AI.
Most employers aren’t training them.

39%
of currently employed adults used AI tools at work in the past 12 months.
NY Fed, April 2026
15.9%
of employed adults say their employer currently offers any AI training.
NY Fed, April 2026
2×
more likely to produce top-quality work when employees receive cognitive AI-mindset training (Gap Inc. field experiment, n=388).
Microsoft Research + Gap Inc., April 2026
How we engage

Four packages, matched to who needs what.

Executive
Executive AI Briefing

A focused session for leaders and department heads. What AI actually changes in the business, where value plausibly emerges, what decisions leadership needs to make, and the safe-use expectations worth setting early.

  • Strategic overview
  • Decision framework
  • Adoption and risk primer
  • Q&A for your leadership team
Team
Team Workflow Workshops

Live remote workshops built around real tasks your team does every week. Scenario-based, role-specific, hands-on. For sales, marketing, support, HR, operations, finance, or anywhere AI could change daily work.

  • Role-specific scenarios
  • Hands-on with your actual tools
  • Job aids and prompt patterns
  • Safe-use guidance per function
Operator
Operator Labs

For power users, builders, and workflow owners who will go deeper. Evaluation habits, fallback and review patterns, human-in-the-loop design, and judgement about when AI is the right tool versus when automation alone is enough.

  • Structured labs
  • Evaluation discipline
  • Workflow playbooks
  • AI vs. automation decision patterns
Retainer
Enablement Retainer

Training alone rarely sticks. Ongoing reinforcement closes the gap: office hours, champion coaching, new use-case clinics, adoption reporting, and playbook refreshes as AI and your business both change.

  • Office hours + coaching
  • Champion development
  • New use-case clinics
  • Adoption dashboards

Common questions

Questions we hear before training starts.

Is this just AI literacy training?

No. AI literacy stops at awareness. We build capability tied to specific workflows, with measurable adoption and quality outcomes. The difference shows up in whether people actually change how they work, not whether they can define what a large language model is.

What if our team is at very different levels of AI experience?

That is the norm, not the exception. We baseline first, then segment. Executives get decision-level fluency. Teams get role-specific workshops. Power users get deeper labs. Everyone lands on the same rules for safe use and the same standards for quality.

How is this different from what Microsoft or Google already provides?

Vendor materials teach you what the product does. We teach your people how to use it well in your work, under your policies, against your measurable outcomes. Vendor onboarding fills in features. We fill in behaviour.

Do you train our trainers, or just run workshops?

Both, when it makes sense. Train-the-trainer is often the right path: we build capability alongside your internal enablement leads or champions, so your organization owns the operating discipline rather than renting it from us.

How do you measure whether training worked?

Three signals: adoption (usage analytics and repeat use across trained cohorts), quality (workflow-level outcomes your team already tracks), and safe-use behaviour (verification habits, policy adherence). We report to leadership on all three, not just seat-time.

Build AI capability your team owns.

Start with a scoping call. We’ll map where your people are, which roles need what kind of support, and which workflows should get enablement first. You leave with a 90-day programme outline, no obligation.