AI-Driven Feedback Systems, Digital Labour, and Silent Quitting: Transforming African Workplaces

TEXT START: The current trend of digitalisation has revolutionised the organisation of work and the way it is measured and performed across the globe, with AI becoming more common for managing labour and performance, as well as employee communication.

The Dissection

This paper frames AI as an HR control layer: sentiment analysis, pulse surveys, chatbots, dashboards, and predictive analytics convert worker emotion and behavior into continuously harvested management data. “Silent quitting” is treated as a detectable engagement failure requiring intervention.

Its African-centred discussion of inequality, weak infrastructure, privacy, bias, and surveillance identifies real implementation hazards. But the abstract offers an agenda and recommendations, not demonstrated evidence that these systems improve productivity, retention, or worker welfare. Its deeper function is to domesticate AI: surveillance becomes “continuous listening,” and labor control becomes “employee communication.”

The Core Fallacy

The paper assumes that the central problem is insufficient engagement inside a durable human workplace. Under the Discontinuity Thesis, that is the wrong battlefield.

When AI becomes cheaper and better at cognitive work, feedback systems do not restore the wage-to-consumption circuit. They make the remaining workforce more legible, more intensely managed, and easier to rank, replace, or discard. Detecting disengagement does not create economically necessary human labor. It merely improves extraction from residual workers and accelerates selection among them.

“Responsible” deployment can limit abuse and delay the damage. It cannot defeat P1, P2, or P3.

Hidden Assumptions

• Human labor will remain broadly necessary even as AI manages and automates cognitive work.
• Disengagement is primarily an attitude problem rather than a rational response to weak bargaining power, surveillance, stagnant rewards, or impending redundancy.
• Sentiment and performance data reliably capture contribution across highly varied African workplaces.
• More measurement produces better communication rather than retaliation, gaming, and distrust.
• Managers can intervene early enough to preserve participation instead of identifying workers for replacement.
• Privacy rules, ethical frameworks, and policy recommendations can reconcile competitive pressure with worker protection.
• Digital inequality and infrastructure shortages are temporary adoption barriers rather than factors that determine who controls the new productive system.

Social Function

Classification: partial truth, transition management, prestige signaling, and ideological anesthetic.

The paper correctly names surveillance, algorithmic bias, and uneven infrastructure. Its anesthetic effect is to reduce a structural employment crisis to an engagement dashboard. Organizations receive a respectable vocabulary for managing a shrinking labor pool while preserving the fiction that better listening can preserve mass participation.

For African workplaces, infrastructure, verification, maintenance, governance, and local implementation may create transitional niches. Those niches are not evidence that the old employment order survives. They are lag defenses and intermediary positions around the emerging control system.

The Verdict

This is a useful account of how AI will monitor and discipline workers, but a weak account of the future of work. It measures the symptoms of productive participation collapse while avoiding the mechanism that causes it. AI feedback systems are not an antidote to silent quitting; they are the surveillance architecture of a workplace where fewer humans remain economically indispensable.

The paper can help manage the transition. It cannot preserve the post-WWII wage-consumption system.