Workforce & Role Design

Why the AI doom loop in recruitment is a measurement failure, not a behaviour problem The term doing the rounds is the AI doom loop. Candidates use AI to generate applications at volume. Employers use AI to filter them. Both sides optimise against an algorithm rather than against the job. Reported application volumes now sit in the hundreds per role, and most practitioners will tell you privately that the published averages understate it. The diagnosis is accurate. The prescr

Why contact centres are the test case for PwC's two-track thesis, and why the track is a decision, not a destiny PwC's 2026 Global AI Jobs Barometer, drawn from over a billion job advertisements, makes an argument that deserves to be taken seriously. AI is not eliminating jobs at the rate the forecasts promised. It is splitting them. Some roles are being “Professionalised”. AI absorbs the routine tasks and leaves the human with the harder, more expert work. Others are being D

The meeting is in a small room near HR. The agent resigned nine days ago and has mentally left already. They are polite, because references matter, and vague, because burning bridges does not. They say something about career growth. They thank you for the opportunity. Someone types it into a form, the form joins four hundred other forms, and next quarter the attrition number is exactly where it was. This is the exit interview, the contact centre industry's standard instrument

The Illusion of Progress: Profit Over People Every customer operations boardroom in 2026 is celebrating high automated containment rates. On paper, deflecting routine transactional volume to conversational AI appears to be an unassailable financial victory. But beneath the corporate dashboards, a severe operational crisis is unfolding. AI is widely being misused. When deployed purely as a cost-cutting tool to slash headcount and maximise immediate profit, it creates an illusi

In my previous article, we cut through the vendor marketing hype to define what true Agentic AI looks like in the contact centre. We established that moving from rigid decision trees to dynamic reasoning engines requires a robust 3 to 6 month runway built on clean data, standardised standard operating procedures (SOPs), and strict API execution guardrails. However, as organisations begin mapping out this operational runway, a much larger macro shift is unfolding. Recent data

AI governance is often discussed as if it lives in a separate world from the day-to-day operation of a business. In practice, it does not. Governance shows up in the moment a customer issue is handled, a recommendation is accepted, a decision is overridden, or an exception is escalated. That is why so many AI programs struggle not because the technology is weak, but because the role design around it is unclear. If people do not know what AI can do, what humans remain accounta

Most traditional position descriptions are built around tasks. They describe what a person does, what system they use, and what process they follow. That made sense in a world where humans handled most work manually and technology played a supporting role. AI changes that. In an AI-enabled operating model, routine tasks are increasingly handled by automation, copilots, chatbots and workflow tools. That means the human role is no longer defined primarily by task completion. It

For years, position descriptions were treated as administrative documents. They outlined: responsibilities, tasks, reporting lines, and required experience. In many organisations, they changed very little over time because the nature of work itself changed slowly. That is no longer the case. As AI becomes embedded into customer operations, contact centres and enterprise workflows, organisations are discovering that many existing position descriptions were designed for work th