Safety Framework for the AI Era

Human, AI, & Organizational Performance

A framework for work systems where humans, organizations, and AI all shape operational outcomes.

HAOP extends Human & Organizational Performance into AI-enabled work by treating AI not as a passive tool but as a performing element inside the system. It must be designed, governed, verified, and constrained.

Explore the Framework Run the Free Diagnostic
Jaina Ko, CSP / HAOP.ai / Framework under active development
HUMAN adapts to conditions ORGANIZATION designs the container AI acts on representation HANDOFF who controls what? BOUNDARY where must work pause? VERIFICATION GATE representation checked against reality OPERATIONAL REALITY grounding is a control

Verify at the boundary

Ensure work pauses where it must.

Test representation against conditions

Align models with reality.

Accountability follows control

Assign accountability clearly.

Design for safe adaptation

Build systems that adapt safely.

Framework

HAOP starts where ordinary AI governance becomes too narrow.

Most AI governance asks whether the model is accurate, secure, private, explainable, or compliant. Those questions matter. They are not enough when AI begins to classify risk, prioritize signals, route work, recommend controls, approve action, or shape what a human reviewer sees first.

HAOP looks at the work system: human adaptation, AI optimization, and organizational signaling. It asks whether the system remains grounded in operational reality before its outputs become consequential.

The three performers

Different performers. Different failure signatures. One operating system.

01

Human performer

Perceives, adapts, hesitates, compensates, speaks up, stays silent, and makes tradeoffs under real operating conditions.

Adapts
02

AI performer

Classifies, predicts, generates, routes, recommends, prioritizes, escalates, suppresses, and optimizes based on signals and architecture.

Optimizes
03

Organizational performer

Authorizes, funds, measures, rewards, constrains, validates, ignores, normalizes, and assigns accountability.

Signals
Start Here

The True Function Diagnostic is the entry point.

The True Function Alignment Map is the first public HAOP tool. It gives teams a starting point for understanding where an AI-enabled workflow actually is — not where the policy, dashboard, vendor deck, or implementation plan says it is.

It is the beginning of HAOP implementation: a way to surface signal-objective mismatch, weak grounding, symbolic oversight, missing pause points, and accountability gaps before the workflow becomes consequential.

Run the True Function Diagnostic Read the White Paper
01Identify the true function

What outcome is the workflow supposed to produce in the real world?

02Test the representation

What does the AI see, miss, compress, distort, or prioritize?

03Map control

Who controls the signal, constraint, permission, metric, verification point, and pause path?

04Begin implementation

Use the findings to design verification, grounding, boundaries, and accountability into the workflow.

White Paper

HAOP: A Safety Framework for the AI Era

June 14, 2026 / PDF

Read the paper introducing HAOP, the three performers, distinct failure signatures, accountability-by-control, grounding, pause authority, and the True Function Test.

View White Paper Download PDF
Roadmap

The Alignment Map is the start. More HAOP tools are in development.

HAOP is being built as a practical framework, not just a concept. The current public release includes the white paper and the True Function Alignment Map. Additional diagnostic, implementation, and assurance tools are under development.

The goal is to help teams move from “we have AI governance” to a more concrete question: does this work system have the grounding, verification, constraints, control mapping, and pause authority needed to remain safe as AI changes the speed and shape of work?

Available now

True Function Alignment Map

Beginning diagnostic for understanding whether the workflow is aligned with its stated safety purpose.

In development

Accountability-by-Control Map

Maps control over actions, signals, constraints, permissions, metrics, verification points, escalation paths, and deployment decisions.

In development

Human Oversight Reality Check

Tests whether oversight has time, competence, authority, source access, and the protected ability to pause or intervene.

In development

Workflow Boundary Map

Defines where the workflow begins, where AI enters, what decisions it shapes, and where consequences land.

In development

Verification Gate Builder

Designs necessary friction into AI-enabled workflows so optimization cannot outrun verification.

More coming

Additional HAOP tools

Additional diagnostics, implementation aids, and field guides are planned as the framework develops.

BOOK IN DEVELOPMENT
Forthcoming

A book on AI deployment risk through the lens of occupational and operational safety.

The larger HAOP project includes a book examining the current state of AI deployment risk: what happens when organizations use AI to accelerate work, extract expertise, compress judgment, scale weak signals, and move operational consequences faster than human adaptation can absorb.

The frame starts with occupational and operational safety, but the concern is broader: how humans, organizations, and performing technology interact inside systems shaped by incentives, control, contribution, extraction, and reality.

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For collaboration, speaking, or early discussion of HAOP in AI-enabled safety-critical work systems.