A real outcome
A presentation, analysis, workflow, prototype or decision pack grounded in the team’s work.
01 Plan a brighter team
See what your team could do with AI.
Practical workshops where your people build useful work, check the results and present what they have learned.
Example workbenchContent
Illustrative workflowWorking brief
Turn a campaign question into a source-backed briefing pack.
- 01Define the decision
- 02Research with sources
- 03Draft the pack
- 04Review the claims
- 05Present the work
What leaves the roomResearch note, draft assets and a review recordClaims link back to sources; final language stays with a human reviewer.
02 What this is
Not prompt engineering. Outcome engineering.
A working session measured by what the team can use after it.
A visible method
The brief, AI-assisted steps, human decisions and checks are all inspectable and reusable.
A prompt parade
No collection of clever commands without ownership, evidence, success criteria or a next decision.
AI theatre
No invented case studies, unsupported claims or hidden automation presented as business proof.
03 Choose a programme that fits
Start with the people and the decision.
Explore the reviewed outlines. Prices and delivery windows remain quote-led until the right faculty, toolchain and format are confirmed.
1.5 days · General AI
Native
Work beyond the chat box
For
Employees and managers expanding beyond occasional prompting
Make
One useful work product, reusable instructions or workflow, a verification checklist and stated limitations
Check
Four to six worked examples, one guided build and one selected application; no universal mastery or production deployment
A few possible applications
- Referenced briefing and presentation
- Checked data analysis and management summary
- Policy or knowledge assistant prototype
04 Use the tools your work needs
Tool-aware. Vendor-neutral. Human-owned.
The toolchain is chosen against the job, organisational policy and availability. Logos indicate tools a workshop may cover; they do not imply sponsorship or endorsement.
OpenAI
Claude
Gemini
xAI
Higgsfield
Seedance05 Work with a coach
Practical guidance, with evidence before assignment.
Faculty identities and portraits are shown. Specific biographies, credentials and programme assignments will be published only after approval.
06 Build the internal brief
Give colleagues something they can inspect and share.
01
Choose the audience and outcome.
02
Add the constraints that change the recommendation.
03
Download, save or create a read-only seven-day share.
04
Request staff review when the internal team is ready.
Custom plans are private to the browser owner until they deliberately create a share. Dates are preferences, not reservations.
07 Before you decide
Questions teams usually ask.
Do people need to know how to code?
No for the standard Native track. Engineering and advanced automation work need a separately approved toolchain and qualified faculty.
Can we use our organisation’s data?
Only when the organiser has approved the data, access and tool policy. Safe sample data is the default when that evidence is not ready.
Will we leave with something usable?
That is the design goal: one selected work product, its repeatable method, a verification record and stated limits—not a collection of disconnected exercises.
Can I send the plan to colleagues?
Yes. The planner can create an immutable, read-only seven-day link after showing exactly which structured fields will be shared.
Can I book a date on the website?
Not yet. You can record a non-binding date preference for staff review; no date is held or confirmed online.
08 Make the next conversation concrete