--- title: The Fractals System — a full-day workshop for teams | Fractals Collective description: A full-day workshop that gets a team through the AI transition: the mindset shift and the brief, practised on real work in the room. Built on the Fractals System — the goal is the clarity of your judgment. source: https://www.fractals.sg/system/ --- [fractals](https://www.fractals.sg/) [fractals.sg](/) / system # The Fractals System Alan Soon · Fractals Collective · September 2026 **The goal of this work is the clarity of your judgment.** Everything downstream — the tools, the automation — depends on it. The Fractals System is a full-day workshop I run with one team, on its real work. It exists because adoption turns on two things: a change in how people see their own role, and the ability to say what good looks like, clearly enough that someone else can produce it and you can check it. This page is the belief, the method, and the day itself. One full day, your team, your real work. Ask me for the outline and available dates. [alan@fractals.sg](mailto:alan@fractals.sg?subject=Fractals%20System%20workshop) 01 ## Where adoption stalls A stalled AI rollout starts as a decision about tools and ends as a licence nobody opens. The pilot produces something plausible, the operator spends an afternoon fixing it by hand, and the habit of doing the work yourself wins. The technology worked; the work didn't change, and a faster tool got bolted onto a role that stayed the same. From the outside Why the training doesn't stick — SUE Behavioural Design Tom de Bruyne draws the distinction that explains most of it, between awareness (knowing what AI can do), capability (using it when you try) and habit (reaching for it without deciding to). Most programs deliver the first, some the second, and almost none the third — which is why the returns fade three weeks after the workshop. His line: you cannot close a behavioural gap with more information. [AI training for employees: what actually works](https://www.suebehaviouraldesign.com/en/blog/ai-training-for-employees/), Tom de Bruyne, SUE Behavioural Design, 24 May 2026 The pressure behind that is measurable. In June 2026, Harvard Business Review published the case from Marco Argenti, the chief information officer at Goldman Sachs, on what the last year of agentic AI has actually changed about work. 80% of 1,320 tasks across 44 professions — agentic models performed as well as or better than human professionals in 80% of cases, up from about 50% six months earlier. Source: OpenAI's GDPval benchmark, reported by Marco Argenti in Harvard Business Review, June 2026 His conclusion is the one I'd underline for anyone running a small operation: many of the skills people spent careers building can now be executed by agents, and the question of how to adapt is a pressing one for the people who do that work. 02 ## The mindset half A senior banker asked Argenti which 10% of his job he should hold on to, the part AI would never be able to do. The answer was to let go of that 10% entirely. Argenti's comparison: an experienced horse rider learning to drive a car. Which horse-riding skills should they keep in order to drive well? Probably none. What carries over is instinct and judgment — reflexes built over years, pointed at a different machine. His prescription for the banker was a change in stance: from making every line of the pitch deck to giving clear instructions, setting the controls, and supervising the result. From individual contributor to supervisor and mentor. From the outside Mindset, not skillset — Marco Argenti, Harvard Business Review The piece argues that the workplace question has moved from which skills to keep to how you think about your own role: don't just reskill, reimagine skills and build new habits. Argenti's line on the argument underneath it — if we don't know what good looks like, neither humans nor AIs will know how to take the right steps towards success — is the same observation this practice is built on. [To Thrive Alongside AI, Focus on Mindset—Not Skillset](https://hbr.org/2026/06/to-thrive-alongside-ai-focus-on-mindset-not-skillset), Harvard Business Review, 12 June 2026 I spent thirty years running newsrooms, which turns out to have been an agent system before anyone used the word. An editor holds the judgment, briefs the reporter, and checks the result. When a draft came back to me, I read it against the standard I already carried and said exactly where it missed, and that read is the part the reporter could not do yet. It is why the person in this seat usually knows more than they can currently say. That is the shift this program asks for, stated plainly: **you already know what good looks like, and you prove it every time you look at a draft and know it's off.** The machine cannot see that standard, because it has never been said out loud. 03 ## The habits The mindset is the decision. The habits are what carry it, and they run against how most of us were taught to adopt new software: learn the tool first, find the perfect course, wait until you feel ready. Argenti's line is the whole instruction — reimagine skills and build new habits. The behavioural research adds two mechanics that any program has to respect. The first is anxiety: the fear of looking incompetent in front of colleagues suppresses experimentation, and experimentation is the only thing that builds capability, so the room has to be safe for imperfect work. The second is the trigger — a specific moment where reaching for the machine is the decided first step, rather than a general intention to use AI more. Habit then takes repetition in context, which the research puts at four to eight weeks on real work. A one-day workshop cannot deliver that, and neither can a course. Four habits do most of the work. 01 Build something small A course teaches you the tool; a project teaches you the job. Pick one real job, run it end to end on a real piece of work, and let it be rough 02 Learn the fundamentals, then pick the tool Tools change every quarter and the fundamentals don't. Understand how the work gets described, delegated and checked, and the tool you already pay for is usually enough to start 03 Think it through before you search The answer to your problem is usually in your own judgment, waiting to be said precisely. Working out what you want is the part that makes any tool useful 04 Fail faster The first version will miss, and the miss is information. Run it, read the output against your own standard, and let each attempt sharpen the statement None of this needs a bigger budget or a new subscription. It needs a job you care about and the willingness to say what good looks like before you hand it over. Every job we set up gets its trigger named out loud for the same reason — when this lands, the first step is this — because a behaviour with a moment attached is the one that survives a busy week. 04 ## The clarity half The mindset gets you into the room. The brief is what makes the work good. In practice it is one page, written in your own words, that a person or a machine can work from. I call it the statement, and it has five fields. The fifth is the one most people skip. 01 Goal What the finished work is for, in one or two sentences 02 Must include The three to five things that have to be in the output 03 Must never The failure modes you would send back without thinking twice 04 Voice How it should read, described the way you'd describe it to a colleague 05 The check What you look at, how often, and what makes you take over Written down, that page does something a conversation cannot: it survives. It briefs the next assistant, the next hire, and the colleague covering for you on Friday, and it gives you something concrete to judge the work against. The clarity of your judgment stops living in your hands and becomes something you can point at. 05 ## The method The practice runs on one job at a time, in three beats. The same question — what does good look like here — gets asked in three places. The System Say it you say what good looks like, out loud, and it gets written down Delegate it the machine takes the doing; you keep the judgment and the check Optimize it you check the work, sharpen the statement, and move one level up **Say it** is the session. We walk through your week — what you produce, what eats the time — and pick the job with the clearest rules. The statement gets written field by field, out of what you say. **Delegate it** is where the machine takes the doing. You describe the outcome and the machine produces the outputs. Stay at that level: the moment you start specifying how, the division breaks and you are back to fixing drafts by hand. **Optimize it** is the rhythm after that. Run it, check it, sharpen the statement. Expect the first two weeks to feel slower; that dip is the climb, and each cycle moves you a level up. Set up one job, not an agent. One job needs the context around it, the two tools it touches, the steps, and one way to check the work. Starting is the point — a perfect agent that never ran is just a plan. 06 ## What the machine needs Delegating the doing means setting the machine up to take it. Four things have to be in place before it can run. 01 Context What it knows: your business, the job, your rules. Argenti's version of this is blunt — agents are unable to operate without context, and they revert to chatbots 02 Skills What it knows how to do: the steps, written down the way you would do them 03 Access What it can reach: your email, your calendar, your files 04 The agreement How it runs with you: what it must never do, when it works on its own, and how you check it This is where the work gets honest about your own material. An assistant can only work with what it can see, so the state of your files, your versions and your notes decides how much of the job can actually move. On my own operation all four layers run on one rented server, and the architecture, the tools and what they cost are [documented here](/workflow-architecture/). 07 ## The workshop One full day with your team, working on the jobs that actually eat the week. The morning is about seeing the shift. By early afternoon everyone is working on their own job. The weeks afterwards are where it becomes a habit, which is why the day comes with a thirty-day check attached. Nothing on the agenda is hypothetical, and nobody leaves with a prompt library. 09:00 The shift Where adoption stalls in your team and why, then each person names the job that eats their week and says what good looks like on it, out loud 10:30 The statement The five fields, demonstrated live on one volunteer's job, then written: goal, must include, must never, voice, the check 12:00 The test in pairs A statement is only finished when someone else can produce the work from it — so each brief gets handed to a colleague and tried 13:30 The handover Everyone hands one job to the machine with their statement as the brief. When the output misses, the rule is to sharpen the brief rather than rewrite the draft 15:00 The check and the trigger Each person writes what they will look at, how often, and what sends work back — plus the moment in the week where handing work over becomes the decided first step 16:15 The team standard The room picks one recurring output it produces every week and writes the shared standard for it, so the practice survives the day The day runs 09:00 to 17:00 with the people who produce the work and whoever owns the outcome, in person in Singapore or over video for teams elsewhere. For a larger organisation I run it team by team and the shared standard is written once. ## What the team leaves with Everyone leaves with their own judgment written down, in a form they can use on Monday, and the team leaves with one standard it agreed on together. 01 A statement per person One page, in their own words: what the work is for, what must be in it, what must never happen, how it should read, and how they will check it 02 One shared standard The recurring output your team produces every week, written the way it should be produced, with someone's name on it 03 A trigger each The moment in the week where handing the work over becomes the first step, rather than a good intention 04 The check What gets looked at, how often, and what sends the work back Thirty days later we run the check together: what held, what drifted, what you actually needed to look at. The work runs to ninety days, because a new habit takes weeks of repetition on real work before it stops being effortful. I ask whoever leads the team to run the System on their own job first, because people copy what they watch their boss do. The day suits a team that produces something every week and is ready to change how it gets made. If the tools are still being chosen, start with the one-to-one work at [AI coaching sessions](/ai-sessions/) — better to specify the job with one person before asking a whole team to change. 08 ## Questions I get asked **Is this prompt training?** The tools are the easy part, and most teams already have access to one. The day is about the brief: saying what good looks like, plainly enough that a person or a machine can produce it and you can check it. People leave with their own judgment written down, not a prompt library. **Do we need to be using AI already?** You need one recurring job your team produces every week and the last real version of it. Everything else happens in the room, on the AI tool you already pay for. **Who should be in the room?** The people who produce the work, plus whoever owns the outcome. The day runs on your real jobs, so observers learn much less. For a larger organisation we run it team by team and write the shared standard once. **What happens after the day?** Each person leaves with one job to hand over that week and a date on it. Thirty days later we run the check: what held, what drifted, what needs sharpening. The follow-through runs to ninety days, because a new habit needs weeks of repetition in context before it stops being effortful. **How is this different from the AI training our L&D team buys?** Most of that training transfers information about what AI can do, which produces awareness, and awareness fades within weeks. This day is designed as behaviour change: practice on real work inside the day, a named trigger for each person, and a follow-through long enough for the habit to take. **Can you run it remotely?** Yes, over video for teams outside Singapore. The in-person day works better for the handover, because people see each other's briefs and the standard gets argued out in the room. The bottleneck is never the model — it's whether you can describe, plain and specific, what done means. **That's the whole skill, and what this day exists to build.** If your team produces something every week — reports, briefs, proposals, content — and AI is not carrying any of it yet, one day is enough to change how that work gets made. Tell me what your team produces and what is eating the week; I'll tell you straight whether the day is the right fit. Individuals start with [the coaching sessions](/ai-sessions/). [Email me about a workshop date](mailto:alan@fractals.sg?subject=Fractals%20System%20workshop)[Book a 20-minute call](https://calendar.app.google/zWVfEHW6c9KccTNM8) Fractals Collective Made deliberately small in Singapore. [fractals.sg](/)[alan@fractals.sg](mailto:alan@fractals.sg)[LinkedIn](https://www.linkedin.com/in/alansoon/)