Chaos: The Blue Blob and the Missing Bottom Rung
Discovery: the first rung is the first junior experience step to be figured out. What will be next in this step where AI now sits for this company. Find out more...
Discovery: the first rung is the first junior experience step to be figured out. What will be next in this step where AI now sits for this company. Find out more...
A career ladder without the first rung is not more modern, it is just harder to climb the ladder. Early-career work is where people build judgement by doing small, imperfect, repetitive things.
Guides
A quick career ladder check to help reduce the risk that tomorrow's experts do not arrive with possible knowledge gaps. A good ladder check does not reject AI, it makes sure AI does not accidentally eat or gloss over the practice field.
This company's AI appears to have removed the first rung of the ladder in this room, leaving what appears to be a floating ladder with no foundation. Where will this discovery lead us?
In many ways, the job description and title were likely mostly fictional, do not automate the job away to AI before checking what the humans actually do. The task unbundling may shock you.
Guides
No need to panic if you understand at task level what AI is changing. Roles slowly reorganise around what remains, what gets assisted, what gets automated, and what becomes more important because AI made the easy bit cheaper.
Guides
There are empty bubbles and productive bubbles, which bubble does the AI fall under? The practical checklist may surprise you because it is more boring than is being discussed right now.
More AI Usage does not always equal more AI Value, it can bring value but the speed is moving faster than most organisations can absorb. Value discipline is needed to ensure realistic value and ROI estimations.
Playbook helps separate soft benefits, useful productivity, exit-rate value, and bankable impact. AI programs often count activity, usage, or claimed time savings long before the business can bank value. The missing discipline is a value ledger that links AI activity to an actual business mechanism.
Use this playbook to help review conditions to be met before an AI workflow, copilot, automation, or agent is allowed to move from pilot into live use.
AI will impact jobs but not in a linear way. Use this Signal linked to the Agent or Not? Playbook to understand how mapping the work increases job impact visibility.
Exploring the Modern enterprise workplace from Operator lens with something for everyone interested in modern enterprise. Guides, Signals, Playbooks and Chaotic blue blob.
Markets can price the destination before anyone has built the road. A huge IPO can make a future feel more concrete simply because a public market is now pricing it.
AI with orbiting data centers sounds weightless until the bill arrives as power, cooling, chips, launches, satellites, maintenance, permits and supply chains.
When does this need to become real for the valuation or strategy to make sense? A 20-year dream priced like a 3-year certainty deserves a raised eyebrow.
TAM is very useful, as we celebrate the record breaking IPO, the 'addressable' should not quietly start behaving like 'imaginable.'
TAM is a useful way to think about market scale, maybe vision. A big market does not automatically mean a business can reach it, serve it, win it or make money from it.
TAM becomes slippery when a reachable, an imaginable, and a narrative market all sit inside one number and everyone politely pretends the number is neutral.
The mystery of the AI Agents managing AI Agents and being monitored by human supervisor managing the performance of all the AI Agents. What thread is to be pulled here?
AI Agents change where supervision happens, design the supervision layers before the the agent becomes another thing multiple people and tools have to babysit.
AI creates supervision layers before the work is transformed because AI does not only automate work
The mystery of the 5 dashboards discovery, 5 dashboards, one business question, many charts, where will this discovery go?
Digital work produces dashboards because every function wants visibility. AI will make this variation louder because it can generate more analysis faster than teams can decide which source wins.
Does your organisation have definitions of the questions each dashboard is supposed to answer? Who owns it, and when it should be retired?