Designing work where people and AI both contribute
Human-AI Work
Adding AI changes more than who or what completes a task.
A first draft may move to AI. A person may spend more time checking evidence. A decision may happen earlier. A handover may disappear. Responsibility may stay with the same person even when much of the work is now produced elsewhere.
Human-AI Performance gives teams a way to examine those changes and decide how the work should operate.
It starts with one specific piece of work and asks a simple question:
Did adding AI make the work better?
Human-AI Performance Field Guide
The Field Guide brings the method together in one place.
It is designed for someone working on a real piece of work where people and AI both affect the result.
PDF · Free to use for your own work
Look at the work between the person and the AI
Writing “Human + AI” on a workflow leaves a lot unresolved.
It doesn’t tell us what the person sees before making a decision, how much authority the AI has, what gets checked, when the work returns to a person, or who still owns the result.
Human-AI Performance looks closely at that join.
The HAP Map separates the work into:
Human
What people notice, decide, judge, check and remain responsible for.
Interaction + Control
How information, work and authority move between the person and the AI. This includes checking, approval, correction, hand-back and ownership.
AI System
What the AI retrieves, recommends, produces or does, along with the limits that affect the work.
Study what happens, not only what was intended
A designed workflow and a working workflow are rarely identical.
Someone may start checking every output. An AI system may continue when it should return control. A nominal approval step may become a rubber stamp. A team may discover that AI saves time in one part of the work and creates more checking somewhere else.
HAP compares the intended arrangement with what people and AI actually do.
That gives the team something concrete to improve.
Six questions
The current method follows six questions.
Understand the work
What work are we trying to improve?
How does the work happen now?
Design the arrangement
How should humans and AI work together?
What could stop that design working well?
Learn from reality
What happens when the design meets reality?
What should change, and is the work better?
Better can mean several things
A faster AI output does not necessarily mean faster work.
A team may save time generating something and spend most of that time checking it. Quality may improve while exceptions are handled worse. A process may become cheaper while important human capability starts to weaken.
HAP asks teams to define performance for the piece of work they are studying.
That may include time, quality, rework, risk, escalation, judgement or learning. The measures depend on the work.
The comparison should be against a credible alternative, such as the previous human-only process or another way of allocating the work.
The result does not have to be more AI
Human-AI Performance does not assume that a joint human-AI arrangement is the best answer.
The work may suggest giving AI more authority in one part of the process and less in another. A human review step may need changing or removing. A task may be better kept human. Another may work well as AI-only within clear bounds.
The method keeps those options open until there is enough evidence to make a judgement.
Human-AI Performance Field Guide
The Field Guide brings the method together in one place.
It is designed for someone working on a real piece of work where people and AI both affect the result.
PDF · Free to use for your own work
Human-AI Performance
© 2026 Alterkind Ltd. All rights reserved.
Human-AI Performance™ is a proprietary methodology developed by BehaviourStudio using our Behaviour Thinking® framework. All content, tools, systems, and resources presented on this site are the exclusive intellectual property of Alterkind Ltd.
You’re welcome to use, share, and adapt these materials for personal learning and non-commercial team use.
For any commercial use, redistribution, or integration into client work, services, or paid products, please contact lauren@laurenakelly.com to discuss licensing terms.
Icons by Creative Mahira, The Noun Project.
Thanks to Nicholas Edell, Valentina Tan and multiple VPs implementing AI for your feedback during development.
LICENSE
Based on work by Lauren A Kelly.
For commercial licensing contact: lauren@laurenakelly.com