AyEye Papers
Ideas worth more than a daily briefing — rewritten in plain English with explicit evidence, limitations, alternative explanations and future considerations.
From fragmented processes to adaptive workforce capability
A direction to explore, not a fixed maturity ladder. The steps overlap and repeat as work changes. Select a part of the diagram to see its planned paper below.
- 01Simplify & embedFrom separate HR transactions to support within everyday work.Workforce experience
- 02Connect evidenceFrom course and activity records to an evolving view of capability.Capability intelligence
- 03Use skills in decisionsConnect skills and validated competence to tasks, mobility and outcomes.Skills & future work
- 04Support performanceTurn conversations and shared evidence into practical development.Performance support
- 05Adapt work allocationReassess roles and responsibilities across people and AI agents.The disappearing job
From Capacity to Performance: the evidence architecture for the human–agent enterprise
Thesis: Organisations need a dynamic model connecting business performance, group and system performance, individual performance, capacity, capability, competence, validated competence, continuing competence assurance and skills — with work and tasks providing the bridge between organisational demand and available human or agent supply. Proposition: these should become connected, evidence-bearing data objects rather than separate HR measures. A living graph of objectives, outcomes, work, tasks, skills, actors, evidence, competence, validation, currency, authority, capacity, allocation and performance could allow AI to support skills and task management, workforce planning, learning, work allocation and assurance while actual performance continuously renews the evidence. The paper will define the purpose and value of each layer, distinguish actor capability from group/system capability and business performance, and explore how a common architecture can support people and AI agents without treating them as equivalent.
Assuring competence in the human–agent workforce
Why model benchmarks may be the wrong unit of assurance for deployed agents, and what regulated human competency systems can teach us.
From learning management to capability intelligence
Why course completion is a weak proxy for capability, and how persistent evidence could change learning, talent and workforce planning.
The disappearing job
What happens when a role stops being a stable bundle of human tasks and becomes a dynamic allocation of work across people and agents?
From annual appraisal to continuous performance support
Could an AI agent help people perform better through support in the flow of work, capture agreed evidence from performance conversations, and make periodic reviews more useful and fair? Examining the case for continuous development, the link to customer and business outcomes, and the limits of AI judgement, with employee control and a clear distinction between feedback and verified evidence.
Beyond the skills-based organisation: capability and work in a human–AI workforce
How can organisations make skills-based talent management work in practice while preparing for a changing human–AI workforce? Exploring the cultural shift in hiring, development, internal mobility and work allocation: manager incentives, employee trust and the practical governance needed to turn skills into decisions. Connecting skills ontologies, inferred skills, validated competence and compliance requirements to capability, tasks and outcomes, with evidence renewed as work evolves and people and AI agents take on changing responsibilities.
From HR transactions to workforce experience
How can hiring, development, support and mobility become part of everyday business workflows, with less administrative effort and clearer accountability? Exploring the process, technology and cultural changes needed to embed support in work while preserving specialist help, confidential channels and employee voice.
Beyond headcount: demonstrating workforce value
How should organisations assess investment in people and AI agents through capability, service quality, resilience, cost and customer outcomes? Examining how to connect workforce interventions to measurable value, account for implementation costs and distinguish contribution from other explanations for improvement.
From human capital management to workforce capability: a trajectory for the human–AI enterprise
An overarching proposition connecting workforce experience, skills, performance evidence, work allocation and governance to business outcomes. Exploring a practical trajectory from simplifying fragmented processes to embedding support, connecting evidence to decisions and adapting work across people and AI agents, with explicit ownership, changed behaviours and evidence of value at each stage.
Available research notes
Agent Competency Assurance — evolving framework →
Could a company grow from a seed? — a worked futures example →
The longer papers listed above remain planned. These links open the actual research notes currently available.