AI Team Upskilling

We build a clear plan for where AI pays off first, and hands-on training so your whole team can use it well. Your people learn on approved tools set up with your own documents.

How we run upskilling

The same senior team that builds AI tools for our clients runs the assessment and the training, so the advice comes from work it has shipped.

  1. We see where the time goesWe interview your team and look at the work itself, across one company or a whole portfolio, ranked by where AI would pay off first.
  2. Approved tools, on your documentsWe set up a short list of tools your team is allowed to use, connected to your own material, with written guidance on what's allowed.
  3. Training on real tasksWe run hands-on sessions by role. Leaders get the plan and the risks, and managers and staff work through their own tasks.
  4. We build the first projectWe build the top item on the plan, so the plan proves itself within weeks.

Your staff are already using AI

Staff paste company information, and sometimes patient information, into public AI tools because there's no clear guidance on what's allowed. Leadership usually finds out after the fact.

At the same time, the board wants an AI plan. In a healthcare PE portfolio, the operating partner has a budget and no one to test where AI pays off, and each company answers the question its own way. At pharma and medtech companies, the budget is approved and the AI roles stay open.

Chat licenses help each person work a little faster, but they don't show which work to change first or how to do it safely.

An engineer leaning in to help a nurse at her laptop during a team workshop
  • Leadership
  • Managers
  • Frontline teams

Trained on your real work.

  • Approved tools on your own documents
  • Written guidance on what's allowed

What we do

  • One assessment, ranked by payoff. We look at where your team's time goes and rank the places AI would pay off first, across one company or a whole portfolio, so the answers can be compared.
  • Conversations with the people doing the work. We run short interviews through our discovery work, so the plan reflects what people actually do all day.
  • Approved tools on your own documents. We set up a short list of tools your team is allowed to use, with your own material and accounts, so your material stays out of public chatbots.
  • Hands-on training. Sessions built around your team's real tasks, for leaders, managers and the people doing the work, with written guidance on what's allowed.

Then we build the first project the plan points to, so the plan proves itself. That's usually an AI agent or automation on the work that takes the most hours.

A doctor walking a nurse and a colleague through something on a laptop
92%
of physicians want more education and training on AI.
AMA, 2026 ↗
22%
of life sciences leaders say they've scaled AI, and 9% report significant returns.
Deloitte, 2025 ↗

Who we build it for

Specialty physician groups, pharma and medtech teams getting started, and PE firms that want every company trained the same way.

Questions we hear

  • We already pay for ChatGPT or Copilot licenses.

    Those help each person write faster. We build the tools that run your workflow: connected to your systems, using your own documents and data, and owned by you.

  • Can you do this across our portfolio?

    That's the model. One assessment across the portfolio, one project to prove it, then the next company. Each company's work stays with that company.

  • Who's the training for?

    Everyone who'll use the tools, in groups by role. Leaders get the plan and the risks, and managers and staff get hands-on sessions with their own work.

More capabilities

We'd love to chat.

Bring what's slow, rented or stuck. You'll leave with what we'd build first and how long it takes.

Book a call