Appendix

120 principles, side by side

Each administrative principle with its human expression, its artificial equivalent, and a classification: universal, adapted, or exclusively human.

The consequence matters more than the count. 87 of 120 principles survive the change of occupant untouched — most of the administrative discipline does not disappear with AI. 20 keep their intent and change mechanism. Only 13 belong to the human condition and must never be transferred to software by a naive metaphor.

120 / 120

#	Principle	Human resource	Artificial resource	Class
1	Every post must have a purpose	The occupant must understand why the post exists and what value it creates.	The AI Employee must have an explicit, stable operational mission.	U
2	Formal job description	Documented job description.	Versioned AI Job Description / Role Contract.	U
3	Defined responsibilities	Results and duties assigned with clarity.	Processes, decisions and results under explicit ownership.	U
4	Limits of the post	It must be clear what does not belong to it.	Prohibited actions, domains, data and decisions.	U
5	Defined authority	It is specified what can be decided without approval.	Autonomous actions, thresholds and approvals are specified.	U
6	Responsibility and authority must be aligned	No result is demanded without sufficient faculties or resources.	No KPI is demanded without tools, permissions, data and budget.	U
7	Unity of command and clear accountability	There must be a manager accountable for performance.	Exactly one accountable manager, even when work comes from several areas.	U
8	Known chain of command and escalation	Knows whom to escalate an exception or conflict to.	Explicit escalation tree by type, risk and urgency.	U
9	Reasonable span of control	A manager should not have more reports than they can effectively direct.	Scale may be larger, but requires tooling, dashboards and supervision limits.	A
10	Division of labour and specialisation	Roles organised by competencies and results.	AI Employees or subagents specialised by function.	U
11	Avoid duplicated ownership	There must not be two ambiguous owners of the same result.	Avoid several AI Employees acting on the same object without coordination or lock.	U
12	Explicit interdependencies	Inputs, outputs and dependencies between posts are known.	Handoffs, APIs, humans, other AI Employees and systems identified.	U
13	Define the post before selecting the occupant	First design the role; then look for the person.	First design the role; then choose model, agent and configuration.	U
14	Select on required competencies	Skills, experience, knowledge and behaviours.	Model, reasoning, tools, memory, context and integrations.	U
15	Validate competencies before hiring	Interviews, tests, references and assessment.	Benchmarks, evals, simulations, sandbox and role red-teaming.	U
16	Do not pay for capability the post does not need	Avoid costly or poorly used overqualification.	Do not use the most expensive model if a smaller one meets the SLA.	U
17	Post–resource fit	Person-job fit and person-organisation fit.	Model/agent-role fit and architecture-role fit.	U
18	Probationary period	Probation with supervision and success criteria.	Shadow mode, sandbox or production with reinforced approvals.	U
19	Prior verification	Background, references and applicable trust requirements.	Security review, vendor/model evaluation, provenance and supply-chain review.	A
20	Induction to the organisation	History, purpose, strategy and structure.	Business context, structure, objectives and policies loaded into the knowledge layer.	U
21	Know products and services	Training on offer, customers and value proposition.	Knowledge base / RAG / context on products, pricing, customers and constraints.	U
22	Know policies	Handbook, internal policies and obligations.	Policy layer, system policies and compliance rules.	U
23	Know procedures	SOPs, checklists and ways of working.	Executable SOPs, instructions and authorised workflows.	U
24	Know colleagues and structure	Org chart, stakeholders and responsibilities.	Organisational graph with humans, AI Employees, roles, channels and ownership.	U
25	Know the supervisor	Assigned manager and expectations of the relationship.	Accountable manager registered as part of the role.	U
26	Know communication channels	Email, Slack/Teams, meetings, tickets and protocols.	Authorised channels, routing, recipients and communication rules.	U
27	Receive working tools	Equipment, software, accounts and operational access.	Tools, APIs, credentials, browser, DBs, ERP/CRM and other connectors.	U
28	Receive only necessary access	RBAC and least privilege.	RBAC, least privilege, isolated secrets and minimum scopes.	U
29	Clear objectives	Concrete expectations about what must be achieved.	Explicit, verifiable objectives tied to the Role Contract.	U
30	Defined KPIs	Performance metrics for the post.	KPIs, SLAs, quality, cost and risk of the AI Employee.	U
31	Align KPIs with business objectives	Avoid vanity metrics or perverse incentives.	Measure outcomes, not tool calls, tokens or valueless activity.	U
32	Achievable targets	Realistic targets given resources and capacity.	Realistic targets given model, context, tools and authority.	U
33	Frequent feedback	Performance conversations and course correction.	Manager feedback feeds configuration, examples, policies, evals or prompts.	A
34	Periodic evaluation	Formal or continuous performance review.	AI Performance Review with metrics, incidents and quality samples.	U
35	Evaluate results, not mere activity	Outcome and quality above visible hours.	Outcome and reliability above tokens, messages or steps executed.	U
36	Compare result against a standard	Quality bar, SLA or professional standard.	Test sets, golden datasets, thresholds and policy checks.	U
37	Responsibility for performance	Employee and manager both participate in the result.	The AI executes; the accountable human retains business and governance responsibility.	A
38	Correct underperformance	Coaching, training, PIP or redesign of the post.	Modify instructions, knowledge, model, tools, workflow or scope.	A
39	Recognise high performance	Recognition, promotion, compensation or more autonomy.	Wider scope, autonomy, authority limits or assignment to more critical processes.	A
40	Supervision proportional to competence and risk	A junior needs more supervision; an expert can receive more autonomy.	Autonomy increases only with evidence of reliability and according to risk class.	U
41	Explicit delegation	The manager defines what is delegated and what is retained.	Every delegated decision or action must appear in policy or authority matrix.	U
42	Management by exception	The manager intervenes especially on deviations and exceptions.	The AI resolves routine within limits and escalates exceptions.	U
43	Defined escalation	Criteria for requesting help or approval.	Confidence/risk thresholds, timers, exception classes and human escalation.	U
44	Separation of duties	Reduces fraud, error and undue concentration of power.	The AI that initiates a payment should not approve it; maker/checker roles separated.	U
45	Four-eyes principle	Sensitive decisions require additional review.	AI+human, AI+AI+human or other approval proportional to risk.	A
46	Do not grant more authority than necessary	Minimum delegation compatible with the work.	Least authority and transactional limits.	U
47	Authority must be revocable	Suspension or withdrawal of faculties when risk changes.	Kill switch, revoke credentials, disable tools or downgrade autonomy.	U
48	Continuous training	Training to maintain and extend capabilities.	Updates to knowledge, tools, model, examples, policies and evals.	U
49	Identify competence gaps	Skills gap analysis.	Capability gap from eval failures, incidents and unsupported tasks.	U
50	Development plan	Career path and Individual Development Plan.	Capability roadmap and criteria for widening scope or autonomy.	A
51	Coaching	The manager helps improve judgement and execution.	Human feedback transforms configuration, context, examples and policy.	A
52	Learn from mistakes	Lessons learned and corrective actions.	Postmortems, regression evals, controlled memory and new guardrails.	U
53	Knowledge management	Capture and share critical knowledge.	Shared knowledge layer, provenance, versioning and retrieval.	U
54	Update on policy change	Retrain when rules, products or context change.	Update policy/context immediately and verify comprehension with evals.	U
55	Clear communication of expectations	Reduce ambiguity in objectives and standards.	Unambiguous Role Contract, prompts, policies and definitions of done.	U
56	Defined official channels	The organisation determines where each type of communication happens.	Channels and tools authorised by type of interaction.	U
57	Sufficient context to decide	The person needs relevant and timely information.	Context engineering, retrieval and memory sufficient, without overexposure.	U
58	Right information, right actor	Need-to-know principle.	Need-to-know enforced by RBAC, retrieval filters and data scopes.	U
59	Document important decisions	Record for continuity, control and audit.	Structured logs, tool traces and decision records.	U
60	Clear handoffs	Explicit transfer between people or teams.	Agent-to-agent and AI-to-human handoffs with state, context and ownership.	U
61	Personal sense of purpose	Can affect motivation, commitment and retention.	Does not exist as subjective experience of the software.	H
62	Intrinsic motivation	Interest, mastery, autonomy and meaning can drive performance.	Not applicable as a psychological state.	H
63	Extrinsic motivation	Pay, recognition, incentives and consequences.	A reward/optimisation function may exist, but is not human motivation.	A
64	Engagement	Psychological commitment to work and organisation.	Does not exist as demonstrable subjective experience.	H
65	Job satisfaction	Matters for human health, retention and performance.	Not applicable to the artificial resource.	H
66	Sense of belonging	Social and psychological relationship with the group.	Not applicable ontologically; can only simulate social conduct.	H
67	Salary	Economic consideration for work.	No salary; there are model, SaaS, infrastructure, licence and support costs.	A
68	Fair compensation	Internal, external and legal equity.	Economic optimisation applies, but not as a right of the software.	A
69	Performance bonuses	Economic incentive tied to results.	No psychological incentive required; technical reward functions may be used.	A
70	Benefits	Health, pension, insurance, holidays and other entitlements.	Not applicable to software.	H
71	Total cost of the employee	Salary + charges + benefits + equipment + administration.	Total Cost of AI Employment: models + infrastructure + integrations + supervision + errors + governance.	U
72	Occupational health	Protection of physical and mental health.	Not applicable as AI wellbeing; operational safety requirements do exist.	H
73	Rest	Biological need and labour protection.	Not applicable biologically; replaced by maintenance windows, quotas and capacity management.	A
74	Working hours	Human protection over working time.	May operate 24/7 subject to capacity, budget and operating rules.	H
75	Burnout	Human risk from sustained stress.	Not a subjective state; degradation, context pollution, saturation or error accumulation do exist.	A
76	Psychological safety	Allows speaking, disagreeing and admitting error without undue fear.	Not applicable as AI experience, though it matters for the humans working with it.	H
77	Code of conduct	Expected standards of behaviour.	Behavioural policies, output constraints and conduct rules.	U
78	Confidentiality	Duty of discretion and care of information.	Data access, disclosure policies, DLP and constraints.	U
79	Conflicts of interest	Identify and manage incompatible interests.	Manage conflicts between vendor, data source, goals, tools or roles.	A
80	Non-discrimination	Ethical and legal obligation in decisions about people.	Fairness testing, policy constraints and human review of sensitive decisions.	U
81	Honesty and integrity	Do not deceive, falsify or deliberately conceal.	Policies against fabrication, impersonation and unsupported claims.	U
82	Protection of information	Custody and appropriate use of data.	Data minimisation, encryption, access control and retention.	U
83	Regulatory compliance	Respect for applicable laws, policies and standards.	Compliance-by-design plus human accountability.	U
84	Freedom of association	Human right of labour association.	Not applicable to software.	H
85	Collective bargaining	Right of human workers and unions.	Not applicable to software.	H
86	Grievance procedure	Channel for a person to contest decisions or conditions.	Not applicable subjectively to the AI; human channels must exist to contest its actions.	H
87	Protection against harassment	Human right to an environment free of harassment.	Not applicable to the AI as victim; its outputs must be governed so as not to harass humans.	H
88	Due disciplinary process	Human protection against disciplinary measures.	Not a right of software; operationally replaced by incident review and change control.	A
89	Segregation of access	Limit and separate privileges by role.	Fundamental: identity, RBAC, scopes and separated secrets.	U
90	Audit	Independent review of processes and decisions.	Logs, traces, event history, evals and reproducibility.	U
91	Traceability	Know who did what, when and under what authority.	Actor ID + action + timestamp + context + tool + approval.	U
92	Accountability	There must be a person responsible for decisions and results.	Never orphaned: an accountable human answers for deployment, authority and outcomes.	U
93	Incident management	Detect, contain, investigate and learn from failures.	AI incident management with kill switch, rollback, postmortem and remediation.	U
94	Data protection	Privacy, access, minimisation and retention principles.	Data governance, consent, retrieval filters, retention and deletion.	U
95	Risk management	Identify, assess, mitigate and monitor exposure.	Risk classification by role, tool, data and action; proportional controls.	U
96	Promotion	More responsibility, scope, status or compensation.	More scope, autonomy, budget or authority after evidence.	A
97	Post transfer	Change of function or unit.	New Role Contract, tools, context, permissions and manager.	U
98	Succession plan	Prepare a replacement for critical talent.	Fallback agent/model/version and replacement runbook.	U
99	Cross-training	Develop flexibility to cover other functions.	Multi-capability, backup agents or ensembles, minding separation of duties.	U
100	Retention of critical talent and knowledge	Reduce loss of capabilities and know-how.	Reduce vendor/model lock-in; preserve prompts, policies, evals, memory and artifacts.	A
101	Termination criteria	Underperformance, restructuring, breach or other causes.	Obsolescence, cost, risk, incidents, underperformance or architecture change.	U
102	Offboarding	Recover equipment, access, obligations and responsibilities.	Revoke credentials, disable tools, remove schedules, queues and integrations.	U
103	Knowledge transfer	Avoid loss of information on exit.	Export approved memory, context, artifacts, runbooks and outstanding tasks.	U
104	Information protection after exit	Confidentiality and closing of access.	Retention/deletion policies, revocation and secret rotation.	U
105	Historical record	Employee file and evidence of performance.	Versioned audit/performance record for governance and learning.	U
106	Plan future capacity	Headcount, skills and load required by the strategy.	Human + AI capacity planning, concurrency and workload forecasting.	U
107	Make vs. buy	Hire, outsource or develop capability internally.	Build agent vs. SaaS/vendor vs. managed service vs. open source.	U
108	Sizing	Number and mix of people required.	Instances, concurrency, model tiers and required capacity.	U
109	Design the workforce mix	Full-time, part-time, contractors, outsourcing.	Human, AI and hybrid role allocation by risk, cost and comparative advantage.	U
110	Productivity per resource	Output/FTE and value generated.	Outcome/AI Employee, cost per outcome and human review load.	U
111	Measure productivity	Quantity or value of output per resource.	Outcomes per unit of cost/time of the AI Employee.	U
112	Measure quality	Quality against the standard of the post.	Accuracy, acceptance rate, QA score and policy compliance.	U
113	Measure cost	Total and marginal cost of operating the post.	Model + compute + tools + integrations + supervision + error cost.	U
114	Measure errors	Error rate, rework and incidents.	Hallucination/error rate, exception rate, incidents and recovery cost.	U
115	Measure availability	Attendance and coverage of the human resource.	Uptime, queue readiness, dependency availability.	U
116	Measure utilisation	Capacity used versus available.	Runtime/concurrency/tool utilisation and idle capacity.	U
117	Measure time to competence	Time-to-productivity of a new hire.	Time-to-autonomy: from instantiation to reliable performance.	U
118	Measure turnover and replacement	Turnover and its causes and costs.	Model/agent replacement rate, architecture churn and migration cost.	A
119	Benchmarking	Compare performance between people, teams or market.	Compare models, configurations, prompts, agent versions and vendors.	U
120	Continuous improvement	Optimise processes and workforce with evidence.	Continuous evals, optimisation, policy iteration and process redesign.	U

The matrix is an original synthesis of organisational design, HR, performance management, governance, workforce planning and risk practice. It is not a transcription of any single existing standard, and the classification is a working proposal of this framework — administrative doctrine, not a legal claim about the employment of software.