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Human-AI Cognition & Performance / Human Performance and Wellbeing

SUB-0065 · Story

Sustainable Performance

By the third lesson, Maya could see which students the new support helped and which students had simply learned to hide their confusion. Practice and emerging evidence suggest that sustainable performance is a distinct determinant of outcomes within human performance and wellbeing, but current approaches are inconsistent.

In Japan, Maya's team at a regional health service had been asked to explore sustainable Performance. The immediate pressure was practical: current approaches to sustainable performance are fragmented, poorly calibrated or insufficiently measured, making it difficult to distinguish real benefit from substitution, novelty or surveillance effects. People could see activity, outputs and confident recommendations, but those signals did not establish that capability, safety or agency had improved.

Maya resisted turning the scenario into a success story too early. As a nurse unit manager, Maya knew that a memorable example can clarify a research problem, but it cannot validate a causal claim. The team therefore framed one answerable question: How can organisations improve output without accumulating hidden human debt? The story gave the work human stakes; the question gave it a boundary.

The working hypothesis was specific enough to fail: Performance systems that track recovery, learning and error debt will outperform utilisation-focused management. That wording changed the conversation. Instead of asking whether the idea sounded beneficial, the team had to compare conditions, define what improvement meant, and decide what evidence would count against the intervention. They also had to test whether a short-term gain concealed dependence, reduced understanding, new exclusion or a difficult handback when assistance disappeared.

The proposed study centred on longitudinal cohort studies, wearable and workload telemetry, diary studies, organisational pilots. The design varied Primary variables: utilisation, recovery, learning time, error debt and observed quality trend, turnover intention, incident rate, skill growth, recovery. Subgroup and accessibility analysis were not treated as optional additions. A result that helped an average participant while predictably harming a smaller group would not satisfy the programme's definition of success.

During the imagined pilot, the most useful moment was not a dramatic breakthrough. It was a disagreement. One participant completed the task faster but reported less control; another moved more slowly yet retained the process after support was withdrawn. Maya asked the team to record both observations without choosing a preferred ending. They were scenario prompts, not findings, and they exposed why performance alone could not carry the evaluation.

The team built recovery into the protocol. Participants could challenge a recommendation, inspect relevant reasoning, pause the intervention and resume unaided. Failure scenarios tested changed conditions and incomplete information. Delayed follow-up asked whether any advantage persisted and whether people could still act independently. This made the study less theatrical and more useful: the system had to support correction and handback, not merely produce an impressive first result.

The unknowns remained visible: Effect size, causal mechanism, subgroup variation, optimal dose, long-term persistence, transfer beyond the test context, implementation cost.. The principal risks included worker surveillance, medicalisation of normal stress, coercive optimisation, privacy breach. None could be resolved by the narrative itself. They required sourced literature, approved ethics and accessibility review, a pre-registered protocol, traceable evidence and reproducible analysis.

If the hypothesis is supported, the value could extend beyond one pilot in health and care. Target: improve outcomes relating to sustainable performance while preserving agency, skill, dignity, accessibility and sustainable human capability. The same evidence could inform product requirements, assurance services, training, procurement criteria and policy guidance. If the hypothesis is not supported, that result would still be valuable by preventing a weak approach from scaling behind attractive claims.

At the closing review, Maya replaced the original programme claim with a more honest sentence: “We know what must be tested next.” Integrated assurance protocol for sustainable performance linking immediate performance, retained human capability, agency, wellbeing, equity, longitudinal adaptation, safe handback and recovery. For the people represented by the story, progress would not mean a system doing more. It would mean a person remaining more capable when the system stepped back.

Reflection

What did we learn?: The scenario shows why sustainable Performance must be evaluated as a human-capability claim, not inferred from activity or short-term output. It also shows why assistance, burden, agency, subgroup effects, handback and recovery belong in the same evaluation.

Why does this matter?: Sustainable Performance may materially affect human capability, independence, confidence, safety and productivity. Poorly designed assistance can create hidden costs even where short-term output appears to improve.

What research does this connect to?: This subtopic sits within Human Performance and Wellbeing and draws on occupational psychology, behavioural science, wellbeing research and organisational performance. Existing work provides useful foundations but rarely integrates individual differences, AI behaviour, long-term adaptation and measurable human outcomes in one programme. Related subtopics: Productivity Measurement; Peak Performance Conditions; Burnout Prevention.

What should happen next?: Complete primary-source review for Sustainable Performance; appoint owner; define benchmark, comparison and measures; convene affected-user and expert review; pre-register protocol; establish handback, adverse-effect and recovery tests.

Research connection

Hypothesis: Performance systems that track recovery, learning and error debt will outperform utilisation-focused management.

Scientific uncertainty: Effect size; causal mechanism; subgroup variation; optimal dose; long-term persistence; transfer beyond the test context; implementation cost.

Variables: Primary variables: utilisation; recovery; learning time; error debt; AI workload; outcome variables: long-term output; turnover; incidents; capability growth; contextual and control variables: baseline capability; prior exposure; age; motivation; context; technology access; implementation fidelity.

Research methods: Longitudinal cohort studies; wearable and workload telemetry; diary studies; organisational pilots; surveys; performance analysis; pre-registered analysis; active comparison; subgroup and accessibility analysis; delayed retention or longitudinal follow-up; adverse-effect capture; reproducibility testing; participant debrief.

Evidence: Validated instruments for quality trend; turnover intention; incident rate; skill growth; recovery; pre-registered protocol; representative sample; baseline and comparison condition; raw and derived data; analysis code; consent and ethics records; subgroup results; limitations; authoritative primary sources; representative and accessible samples; documented comparison; analysis code; raw and derived data; subgroup analysis; delayed retention or longitudinal evidence; adverse-effect, handback and recovery records.

Frameworks: Demand–Resource–State–Performance–Recovery model applied to Sustainable Performance, linking baseline capability, context, AI intervention, observable outcome, subjective burden, retained skill, handback and recovery.

Links: WHO Mental Health at Work — https://www.who.int/; NIOSH Total Worker Health — https://www.cdc.gov/niosh/twh/; ISO 45003 — https://www.iso.org/; OECD Job Quality — https://www.oecd.org/.

Commercialisation and public value

Products: Sustainable performance dashboard; fatigue and recovery coach; burnout risk monitor; workload planner; team wellbeing system; Sustainable Performance assessment module; Sustainable Performance intervention toolkit.

Services: Enterprise, education and consumer subscriptions; adaptive-assistance modules; analytics and assurance services; benchmark licensing; implementation support; training and certification; sector-specific human-performance solutions.

Industries: Workplaces; hybrid work; shift work; entrepreneurship; caregiving; high-pressure and safety-critical roles.

Government: Workers; employers; occupational health teams; HR leaders; unions; insurers; clinicians; regulators; occupational performance specialists; insurers.

Policy: Psychosocial safety; right to disconnect; fatigue management; privacy of worker data; fair performance monitoring.

Future research: Complete primary-source review for Sustainable Performance; appoint owner; define benchmark, comparison and measures; convene affected-user and expert review; pre-register protocol; establish handback, adverse-effect and recovery tests.

Business opportunity: Develop and validate a sustainable performance balance sheet; package the evidence into research cards, implementation guidance, assessment instruments and reusable data assets.

Scenario narrative — not an empirical finding.