They plan their next steps
Map how AI could change the work they do or want to do, and decide what to learn, try and pursue next.
For universities, companies and foundations
Bring our four-week talent accelerator to students in your university or professionals in your company. In a world nobody can map, they build the direction, the skills and the judgement they need to thrive with AI.
Eight live sessions. No coding experience needed.
Organisations we have worked with
Why we do this
From climbing the ladder to riding the exponential curve.
With AI, people should gain more say over their working lives and the ability to build things they once needed a whole team to attempt. We want AI to expand what people can do and help them find new opportunities as work changes.
That means learning to direct AI, judge its work and decide what is worth doing. In the Accelerator, participants practise on real challenges, discovering what they can accomplish and choosing where to take it next.
Participants work in teams of three to five on a real problem from a Challenge Partner, an organisation that brings the challenge.
Map how AI could change the work they do or want to do, and decide what to learn, try and pursue next.
Set up AI with their materials and instructions. Practise giving it work, checking results and deciding what to use.
Work on a real problem, test with the person it is for, and hand over the work with a short guide and its known limits.
Discuss difficult choices and write a stance they can explain, including which decisions they would keep for themselves.
We assess each person on their own work. Each participant demonstrates what they did and explains their decisions. The shared team result does not determine their assessment.
At the final, each person presents their AI workspace and contribution to the challenge, a career plan, and a brief connecting changes in AI to their own practice and stance.
You can bring participants, contribute a challenge or pay for someone to attend. Your institution can do more than one.
Host
Bring people you teach, employ or support, and help connect the programme with suitable Challenge Partners. A cohort can include up to 40 people.
Discuss hostingChallenge Partner
Bring a problem a small team can work on over four weeks. Introduce it, choose an approach and review the final work.
Discuss a challengeSponsor
Cover the cost for students or professionals who could not otherwise join. A host brings the participants and a Partner brings the challenge. You make sure people who need it can take part.
Discuss sponsoringEach Challenge Partner works with at least two teams. Alongside the Partner's three meetings, the person who would use the solution takes part in testing during sessions 6 and 7.
Ugo and Madison teach all eight sessions and guide participants through workspace setup, the project and the final presentation.
Every session combines work on your challenge, personal AI operating system, career roadmap and stance on AI. What you learn in one shapes the others.
Meet your team and read the Challenge Partner’s brief.
Set up your AI workspace with your files and instructions.
Map how AI could change your current or future work.
Discuss how AI could change your job prospects and your say at work.
Ask the Partner questions and agree what your team will work on.
Write instructions that explain how you work and what matters to you.
Explain why this challenge matters to your next steps.
Examine when AI helps you learn. Write your first stance on using it.
Map the intended user’s work and identify where AI could help.
Save useful context so you can build on previous conversations.
Choose who you want to help and the difference you want to make.
Consider who else is affected when you change how someone works.
Prepare three approaches for the Partner, including what each would test and cost.
Question your AI’s answers and save instructions for a recurring task.
Choose what to learn next and find people learning it.
Examine the energy and resources your use of AI requires.
Build the approach the Partner chooses and test it on a real case.
Set limits on what AI can do independently as you build the workflow.
Choose a measurable result that shows your contribution.
Discuss how the location of your AI provider affects control over your data.
Watch the user try your solution. Measure what changes as you improve it.
Add an AI agent to check work against examples you graded yourself.
Map the people you can turn to and start one collaboration.
Discuss who owns AI-generated work and how its sources should be recognised.
Stop adding features. Watch the user follow your guide alone and improve unclear instructions.
Schedule checks and review which files and actions your AI can access.
Share what you learned where the people you want to reach gather.
Decide what needs human supervision and what a scheduled checker should record.
Demonstrate your solution to the Partner and hand over the files.
Adapt your personal operating system to a task from your week.
Finish your roadmap, including work boundaries, and discuss it with your team.
Examine how AI influences your choices and finish your stance.
CO-FOUNDER, FUTURE OF WORK LAB
I run the Accelerator so that students and professionals leave with the mindsets, the skills and the AI tools they need to face today’s disruption and keep learning as work keeps changing.
The program draws on years of hands-on work: with the United Nations on systemic problems, in San Francisco setting up and running an innovation accelerator for social change, then running talent accelerators for professionals and university students across Europe, Latin America, Africa, the Middle East and the Gulf. Most recently I have dedicated my time to building personal AI operating systems with individuals and small businesses who see AI as a way to widen the scope of what they can imagine and build.

CO-FOUNDER, FUTURE OF WORK LAB
I help professionals, new graduates and small businesses thrive in an economy where AI is part of every job. The people who do best will manage AI the way a good manager handles people: brief it well, delegate real work, review the output, raise the standard. That is what I teach.
I ran my own company for more than eight years and worked at the United Nations on complex global challenges. Today I build personal AI operating systems with professionals and students, and help universities and leadership programs bring AI and new ways of working into their curriculum. The question I keep coming back to: what is meaningful work when intelligence becomes abundant?
Dates and price are still being confirmed. Tell us who you would like to involve and the timing you have in mind.
There are two live sessions a week. Weekly hours, preparation time and any software costs are still being confirmed. Participants need a laptop they can install software on.
Hosts bring access to prospective Partners; who approaches and confirms them needs to be agreed. A Partner supplies the brief before session 1 and commits to sessions 2, 5 and 8 before the cohort starts. The intended user also joins testing in sessions 6 and 7.
Credit arrangements need to be agreed with the university. The programme runs over four weeks; how it fits your teaching calendar is part of the conversation.
Ownership, permitted data and confidentiality terms are not settled. These need to be agreed before anyone shares material or a cohort begins.
Participants demonstrate their own work and explain their decisions. Teams record what they tested and learned. Any sponsor reporting and permission to share participants' work need to be agreed; no reporting package is confirmed.
We build each accelerator around you: your organisation's focus, and what the students and professionals you work with need. Tell us who they are and whether you would like to host, bring a challenge or pay for someone to attend.
ugo@futureofworklab.co