Imagine a client adviser whose AI assistant now drafts responses, summarises conversations and flags patterns in customer data. On paper, the role is unchanged. In practice, the adviser spends less time assembling information and more time deciding what it means, recognising when a client needs a human conversation, and taking responsibility when the machine is wrong. The manager’s job changes too: how should quality be checked, exceptions escalated and capability developed? A faster task has quietly redesigned an entire chain of work.
Many organisations are already living through this change without naming it. They buy a tool, measure time saved and leave the old job descriptions, reporting lines and incentives in place. People are then asked to deliver a new kind of value inside a structure built for the old one. That is why an AI rollout can look successful in a pilot while creating confusion, rework or anxiety across the wider business.
Start with the system, not the software.
In Future Shaping, I describe four forces that can fragment an organisation when change is left on autopilot: Disruption, Disconnection, Discontent and Degradation. A technology-led redesign can trigger all four. Work changes faster than people can adapt; teams lose the relationships that make complex decisions possible; employees struggle to see meaning in what remains; and short-term gains consume the capabilities the organisation will need later.
The alternative is Generative Sustainability: deliberately creating value across Prosperity, People, Purpose and Planet. Applied to work redesign, that means asking whether a new operating model improves customer and financial value, develops rather than depletes people, gives the work a clear purpose and accounts for its wider long-term effects. Efficiency matters. It becomes strategic when it strengthens the system that produces it.
Tasks, customer needs, technology, regulation and skills.
Roles, hand-offs, decision rights, trust and workload.
Human–AI partnerships, fluid teams and learning pathways.
Prosperity, People, Purpose and Planet over time.
Adapted as a practical decision sequence from the Generative Sustainability and organisational agility ideas in Future Shaping.
Three changes have to move together.
My research on generative agility identifies three connected orientations. Structural orientation asks how teams form and where decisions are made. If every customer need crosses four departments and waits for approval, adding AI to each department may simply accelerate the queue. Teams can instead form around customer journeys or opportunities, with decision rights close to the people who hold the relevant information.
Resource orientation asks how skills, time, funding and expertise move. Annual allocations and fixed headcount can trap scarce capability inside silos. Leaders need a way to direct resources toward emerging value while making the trade-offs visible. This is especially important when a promising AI use case depends on people from operations, technology, risk and HR working together.
Technology orientation asks whether digital tools replace human activity or multiply human capability. Routine processing may be automated with clear oversight; judgement-intensive work may be augmented with better evidence. These are different designs, with different accountability and development needs. Buying the same tool for both does not make them the same job.
The orientations reinforce one another. Changing one in isolation leaves the old constraints in place.
Design a partnership between people and AI.
Future Shaping sets out four modes of symbiotic intelligence: guided automation, human-led AI augmentation, symbiotic emergence and collaborative intelligence. They are not a ladder that every organisation must climb. They help a leadership team choose the right relationship between human agency and technological capability for a particular decision. A chatbot that handles routine enquiries with human escalation needs different controls from a design team that iterates with AI on complex options. A hiring recommendation calls for explicit human authority and scrutiny of bias. The design question is always: who understands the context, who can challenge the output, and who remains accountable?
That choice reshapes skills. Rather than treating a job title as a fixed box, map the capabilities that create value across roles: interpretation, empathy, problem framing, technical fluency, ethical judgement and collaboration. The book’s idea of the infinity-shaped employee describes a continuous learning loop between distinctively human strengths and AI-assisted analysis. It is a useful prompt for development, but it also places a responsibility on the organisation to create time, opportunity and pathways for people to grow.
A practical route from diagnosis to delivery.
- Map the work as it is lived. Follow a customer or employee journey from start to finish. Record tasks, hand-offs, decisions, exceptions and the knowledge people use that no process chart captures.
- Separate automation from augmentation. Identify repetitive work that can be reliably handled by technology, the decisions that require human judgement, and the activities where collaboration produces something better than either could alone.
- Redraw roles and decision rights. Define who owns an outcome, who can override an AI suggestion, how a sensitive case escalates and which teams need to work across boundaries.
- Build the capability before expecting the result. Give people practice with the new tools, but also develop critical thinking, communication, resilience and the confidence to question an output. Learning should use the real problems teams face.
- Measure the value created. Track quality, customer experience, speed and cost together with trust, wellbeing, mobility and capability growth. Revisit the design as evidence changes.
Consider a hypothetical regional service team introducing AI summaries. A narrow project would measure how many minutes each adviser saves. A strategic project would also examine whether advisers spot more complex client needs, whether managers can review exceptions, whether customers receive better support, and whether freed capacity is invested in relationship-building. The first project optimises a task. The second reshapes the organisation’s ability to serve.
Which jobs have already changed in practice? Where are decisions still trapped in old structures? What would your people create if technology gave them more capacity?
Organisational redesign is a choice about the future you want to make possible. Start with evidence about your work and people, test the wider consequences, then design the structure, resources and capabilities together. That is how a technology investment becomes a more adaptable—and more human—organisation.