Organisation training

For organisations whose people do knowledge work.

Teams whose week goes on reports, advice, analysis and decisions. Three kinds of work in particular.

Built on the tasks your people already have.

  • Judgement-heavy work

    Advisers, reviewers, assessors and decision-makers.

    Roles where the value sits in the call being made: advice, review, assessment, and decisions with consequences. Here AI is a thinking partner rather than a shortcut, and using it well is a skill in its own right.

  • Writing, analysis and reporting

    Teams that produce documents, briefs, reports and analysis.

    Week in, week out, their expertise goes on the mechanical parts of that work rather than the parts that need it.

  • The repetitive work that eats capable people’s weeks

    Skilled people kept from the work they were hired to do.

    Admin, drafting, summarising, formatting: the recurring tasks that fill a capable person’s week.

Brought in by

  • L&D and People and Culture teams building a durable program
  • Function and team leads growing fluency in their own people
  • Not-for-profit and peak-body leaders training the whole team at once

What your people learn.

Three skills for working with large language models, practised on their own tasks: spotting where a model genuinely helps, working it with their own expertise, and the experimental thinking that makes both possible. And above all, we train the habit of not settling for an output that only seems good enough.

The three skills, in full

Training that changes what people do.

Telling people about AI doesn’t build skill or shift mindset. Doing the work does. Three principles run through every engagement, each with a return you can name.

  1. 01

    Experience-based learning, in real working contexts

    Skill and mindset shift are produced together, by doing the work, live, on participants’ own real tasks.

    Participants work live, on tasks from their own desks, with the tools they actually use. What moves people is taking a real task, the kind they have been quietly dreading, and watching it get done, and done well. That firsthand moment is when a new way of working takes hold.

    What you get

    A new way of working that takes hold, because it was built on real tasks from real desks.

  2. 02

    Evidence-based learning design

    Spaced sessions, retrieval practice, and practice close to the real job: the science of training that lasts.

    The design rests on established learning science: spaced sessions for consolidation, retrieval practice for retention, and practice on tasks close to the real job (what learning science calls near transfer), so skills move from the room into daily work. Real work is attempted in the gaps between sessions, and the gaps are where the mindset shift takes root.

    What you get

    Skills that move from the room into daily work, and are still there weeks on.

  3. 03

    Psychologically informed delivery

    Resistance isn’t uniform, and each kind of it needs a different response.

    Some people feel identity threat: what AI means for their professional value. Some show status quo bias. Some face an imagination barrier, with no relatable example to take the first step. A research background in cognitive psychology and decision-making lets these be read in the room and worked with.

    What you get

    People who come round because their own concern was taken seriously.

Format, length and logistics bend to fit you. The learning science that makes it stick does not.

What participants at Thiess said.

01 · Built on real work

100% agreed or strongly agreed: “I found the content relevant and applicable”

That’s the survey answer that matters most for training like this: whether it matched people’s own work, which is what lets skills carry over into the job.

02 · Spaced across sessions

“Very engaging, and liked that it was split over 4 sessions to enable maintained focus.”

A participant, AI capability training at Thiess

These are participants’ own reports, not a measure of what changed in their work. That’s what the measure stage, and capability measurement, are for. The Thiess engagement, on Organisations and their teams

Part of every automation build.

When we build workflow automation for an organisation, this training comes with it, timed to when each part goes live. How a build runs, on Workflow automation

Built around you.

Every engagement runs in four stages: diagnose, develop, measure what changed, sustain (the training shape →). Everything else we shape with you, around your audience, your goals, and the work your people actually do.

Sessions

Three, spaced

Three sessions is the sweet spot, two the practical minimum, with real work attempted in the weeks between.

Format

In person, virtual or hybrid

Sized to your audience and rhythm, from small groups to large cohorts, for teams and whole functions.

Tools

Tool-agnostic

Claude, ChatGPT, Gemini, Copilot. Built around your existing tools, the current frontier tools, or both. The thinking transfers whatever platform your organisation runs on.

Content

Your people’s real work

Hands-on workshops built around the tasks your people already do, with the tools they actually use.

Optional, paid extension

Capability measurement

Every engagement measures what changed in the work. Capability measurement goes deeper: a rubric built for your team that maps how your people use AI against meaningful dimensions, so you see your team’s capability in detail, not just a satisfaction score. The measurement science behind it, on Research and evaluation

Start with a conversation.

Tell us which work fills your people’s week, and we’ll shape the training around it.

Book a conversation All training