Ideas
Five frameworks for talent after skills
Each one frames a management question that a skills inventory is not built to answer. They are working tools, not a taxonomy.
These came out of the same question the book asks. A skills inventory describes what people can do today, but says much less about how quickly they could do something else, how much of what they do is exposed to automation, or where they are heading.
These five frameworks are developed in The Death of Skills. What follows is an overview, not the book.
How to read these
They are conceptual models, developed from practice and set out in the book. Each exists to frame a management question more usefully. None of them is a validated measurement instrument, and none should be treated as one.
Potential Stack
I use this one to read a person in layers. Demonstrated skills sit on top, and underneath them sit learning capability, adaptability and the conditions in which that person actually performs well.
Time-to-Skill
This one treats capability as a duration rather than a state. If a team can pick something up in six weeks, it is a planning question. If it takes three years, it becomes a strategic one, and hiring, buying or partnering start to look different.
Fortress · Front Line · Laboratory
Not every part of an organisation should meet AI the same way. Some work must be protected and stabilised (Fortress). Some is where AI meets customers and volume, and has to be industrialised carefully (Front Line). Some exists to run controlled experiments whose failure is acceptable (Laboratory). A single posture applied everywhere risks being reckless in one part of the organisation and paralysing in another.
Trajectory Radar
Reads direction rather than position. Two people with identical current profiles can be moving in opposite directions: one accumulating capability that is compounding, the other accumulating capability that is depreciating. Direction is a different question from position, and not one that performance ratings are designed to answer.
Pay-for-Agility
Reward systems are typically built around a defined job, in a context where the work can change faster than the grade. Pay-for-Agility asks what happens when reward recognises redeployability and learning velocity alongside current scope, and what the second-order effects of that would be.
How they connect to AI
AI is changing how long a skill stays relevant and what it costs to acquire a new one. That puts pressure on an assumption sitting underneath a lot of talent work: that what people can do today is a fair guide to what they will be able to do next.
In the book I use these five to look at what holds up better when that assumption weakens.
The book
The full argument, the reasoning behind it and how the five fit together.
In a room
These frameworks work well as a keynote or a masterclass with an executive team.
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