Introducing the Clu Work Model™: Five Years in the Making
Clu Labs research release, November 2026
Clu Labs today completes the Clu Work Model™, the result of a research programme Clu began in 2021.
By extending Clu Cognitive Fingerprinting™ to workflows and value chains, Clu can now convert any organisation’s job-based model, which describes the workforce through job titles, teams, and reporting lines, into a work-based model that describes it through the work being done and the outcomes that work delivers.
Clu clients have built work models from skills and tasks since 2025. This release completes the model. The conversion is deterministic, so the same data always produces the same result, and you can trace every result back to the individual skills it was built from.
Why this matters now
Organisations are deciding how to share work between people and machines. Those decisions are being made in restructures, automation programmes and AI investments today, and they will shape how organisations operate for decades.
Most of these decisions are still planned on a job model. A job model shows how many people an organisation employs and where they sit. It doesn't show what work each role carries, which outcomes depend on that work, or what happens when a role is automated or removed. Planning the shift to work shared with machines on a model that cannot see the work leaves the biggest risks out of view.
Five years of research
2021. Clu begins research into a defensible way to describe and model work.
January 2023. The Clu Atomic Skill Taxonomy™, in which every skill describes exactly one capability.
September 2025. Clu Cognitive Fingerprinting™, describing every skill in the same structured way.
June 2026. Task fingerprinting, letting people describe work in tasks while analysis stays anchored in skills.
October 2026. Clu Coverage™, a workforce risk metric showing whether critical work survives change.
November 2026. Workflow and value chain fingerprinting, completing the Clu Work Model™.
How the Clu Work Model is built
A workflow in Clu is a set of tasks delivered together to achieve an outcome, such as processing an insurance renewal or onboarding a new supplier. Each workflow carries a fingerprint, anchored in the fingerprints of its tasks, which are in turn anchored in atomic skills. Value chains group workflows by the part of the organisation’s value they support, such as service and support or governance, risk and compliance.
Every level traces back down to the skill layer and has a risk, cost and augmentation score. You can trace a result at the value chain level step by step to the individual skills it was built from.
Clu identifies which workflows exist in an organisation by reading the tasks across its roles against Clu’s workflow library. Clients can also define their own workflows and model new ones to see whether they will work.
Clu does not publish the fingerprinting or linking methods, but they are available under NDA to clients.
Results
Workflows identified from task data matched client-validated process maps in 86% of 120 workflows tested across 7 organisations.
Re-running the same organisational data produced the same work model in 100% of cases.
Most roles tested contributed to between three and seven workflows, which a job model cannot show.
What the Clu Work Model shows
With every role, task, workflow, and value chain anchored in fingerprinted atomic skills, the Clu Work Model produces ten workforce indicators defensibly, without generative AI. Each one can be traced back to the underlying skills.
Right-sizing. Whether each team's work matches its assigned roles, so headcount decisions start with the work instead of the org chart.
Duplication. Where the same work sits in several roles, and whether that overlap supports the same or different value chains, wastes effort, or protects critical work.
SPOFs. Points where a workflow depends on a single role or hand-off, slowing the outcome it delivers.
Development. The gap between the skills a role uses today and the skills its work is moving towards, and the closest routes to close it.
Agility. How easily work could move between roles, based on how closely their skill fingerprints match and how long it would take.
Cognitive overwhelm. Roles whose design concentrates so much cognitive strain that burnout becomes more likely.
Manager suitability. Whether each manager role’s work fits the business areas it supports, and whether there is a gap at the work level between a manager role and the roles that report to it.
Integration. In a merger or acquisition, where the work of two organisations overlaps, conflicts or leaves gaps.
Capability. Whether the organisation has the skills its work requires, and where it doesn't.
Coverage. Whether critical work is held by enough roles to survive change, through Clu Coverage.
None of these indicators assesses individuals. Each describes how work is designed and distributed, something an organisation can change.
Seven of the indicators are available in Clu as dedicated views. Cognitive overwhelm, manager suitability and integration can be produced from any Clu Work Model today, with dedicated views to follow.
Until now, most of these indicators have relied on surveys, consultant judgement or inference models. In Clu, each is produced by the same deterministic method, from the same data, and gives the same answer every time.
A human-augmented future of work
Clu’s position is that the future of work is human-augmented. Machines will take on a growing share of tasks, but large parts of most organisations’ work depend on judgement, accountability and relationships that tools cannot currently provide.
The Clu Work Model makes that position testable for any organisation. Because every workflow is anchored in atomic skills, and every skill carries a score for how much of it AI, automation or robotics could do, Clu can show:
which parts of each workflow tools could take on, and which kind of tool
which parts depend on people, and how much of each outcome rests on them
where automating a role would remove work that no tool or other role could pick up
In pilots, 41% of the work in roles earmarked for automation depended on skills that tools could only partly perform. Leaders saw that before making the decision, not after.
Clu does not decide whether a role should exist. It gives leadership teams the evidence to decide how far to automate, where to augment people instead, and the risk of each choice.
What comes next
This release completes Clu Labs’ methodological research programme. From here, Clu’s development will focus on product features and functionality built on the Clu Work Model and continual improvement of our AI models. The underlying method will remain stable, so results produced today remain comparable to results produced in the future.
Frequently asked questions
What is the Clu Work Model? A description of an organisation built from the work it does and the outcomes that work delivers, rather than from job titles and reporting lines. Every level, from value chains down to tasks, is anchored in fingerprinted atomic skills.
How does Clu convert a job model into a work model? Clu reads the tasks across an organisation’s roles, links them to the workflows and value chains they serve, and anchors every level in fingerprinted atomic skills. The conversion is deterministic and traceable.
What can the Clu Work Model measure? Today, it measures ten indicators: right-sizing, duplication, blockers, development, agility, cognitive overwhelm, manager suitability, integration, capability, and coverage. None assess individuals.
Can Clu show which work should stay with people? Clu shows which work tools cannot currently do, and where removing people would put critical work at risk. Leadership teams use that evidence to decide.
Does it use generative AI? No. The Clu platform uses no generative AI. The same input always produces the same output.
To see your organisation’s Clu Work Model, you can contact Clu's research team directly at labs@getaclu.io.
About Clu
Clu provides work intelligence for regulated organisations. It helps leadership teams identify and reduce the risk of their biggest workforce decisions, including restructuring, AI transformation, M&A and changes to capability models, using the structural data they already hold. Clu Labs is Clu's research arm.
