Systems
AI readiness, governance and implementation
Where AI belongs, how far to rely on it, how to govern it, and how to design and implement it around the people who will use it: readiness, governance, procurement, implementation and human-AI teaming. For emergency services, healthcare, government and corporations.
Why it matters
Governance that holds up on the floor, not only on paper
Every organization has two versions of its work: the work as imagined in policy and procedure, and the work as actually done by the people on shift (Hollnagel et al., 2013). Safety lives in the gap between them. Good governance closes that gap from both sides: it writes policy that fits the real work, it learns from the work instead of only judging it, and it builds a just culture where people can raise what is going wrong without fear (Dekker, 2018). Governance, culture and learning are one circle of safety, and a weak link anywhere shows up everywhere.
Appropriate reliance, not maximum trust
AI makes all of this more urgent. Organizations adopting AI decision support need people to rely on it when it is right and question it when it is not. Research on trust in automation describes two ways this goes wrong: misuse, where people lean on a tool past the point it deserves, and disuse, where they set aside a tool that would have helped (Parasuraman & Riley, 1997; Lee & See, 2004). Automation also changes the human's job, often in ways that make the hardest moments harder (Bainbridge, 1983). Getting this right is a human factors problem before it is a technology problem.
Services
Governance and safety culture
Every engagement starts with how your work is really done. Each service says what the problem is and what you get.
Board and oversight governance
The problem: the board or council signs off on policy it has never seen tested on a shift. What you get: a governance review against how the work is really done, the questions oversight should be asking, and board training that turns the sign-off into a working practice. How long: a bounded review in six weeks.
Policy and practice reviews
The problem: rules that do not fit the job, workarounds that have become normal, and nobody deciding whether to change the policy or the practice. What you get: a map of the gaps between policy and practice for one area of work, with a decision on each: change the rule, change the work, or accept it and say so.
Just culture and learning from events
The problem: people stop reporting because the last report found a person. What you get: a just culture policy and a decision aid for supervisors, learning teams for the recurring themes, and learning reviews and post-event support through After the Call.
Services
AI readiness, governance and implementation
Where AI belongs, how far to rely on it, how to govern it, and how to design and implement it around the people who will use it. Each service says what the problem is and what you get.
Pre-deployment assessment
The problem: a tool is arriving, or already in use, and nobody has decided where it belongs. What you get: a written readiness report on the decisions it will touch and how it can fail, what the people using it need to know, and a Do Not Touch list of where AI should not go. How long: six weeks.
Trust and appropriate reliance
The problem: people lean on the tool past what it deserves, or set aside one that would have helped. What you get: a measure of how your people rely on the tool and why, and the workflow and interface changes that make good judgment easier.
Reliability and safety assessment
The problem: the vendor's benchmark is not your busy night. What you get: a test plan and results for the tool against the conditions it will meet, the unusual call and the degraded system included, with the failure modes written down.
AI governance frameworks
The problem: an AI policy that reads well in the report to council and does nothing on shift. What you get: policy, accountability, procurement criteria and oversight for AI, and a package for the board or council. How long: six weeks for a bounded review; longer when the scope is the whole service.
In-service monitoring
The problem: the tool is live and nobody is watching overrides, errors or drift. What you get: a monitoring design your service can run itself, the measures, and a first review of what they show.
Systems design and implementation
The problem: the tool was bought, then the workflow and the training were bolted on. What you get: the tool, the workflow and the training designed together with the people who will use them, a pilot in real workflows before scaling, and change management built in from the start.
Start here
The AI Decision Architecture Workshop
One day, on site, with your leadership team in the room. In six structured sessions we map how decisions actually get made in your organization, identify where AI and automation fit well and where they do not, and produce a Do Not Touch list specific to your operations. You receive the deliverables and a 90-day roadmap within five business days. The workshop stands on its own, and for organizations ready to go further it becomes the scoping document for a full engagement. Not about AI? Ask about a governance review, a policy and practice review, or a learning team session.
AI Decision Architecture Workshop
Ask about the workshop
Tell us roughly how many leaders would attend and what you most want to look at. We will come back with an outline and the fee.
Our basis
Grounded in research and practice
This practice area draws on Scott Ramey's 35 years in paramedicine and the fire service, including executive fire leadership, leading a department through CFAI accreditation from 2022 to 2025 and continuing as a CPSE peer assessor, and a Master of Science in Healthcare Quality Improvement and Human Factors. It draws on his governance work: board member of MedicAlert Foundation Canada, where he sits on the Human Resources and Technology Committees; the Canadian Association of Fire Chiefs' Data Committee and Science and Technology Committee; and, until 2026, the IWK Health Board Quality Committee. It is grounded in his doctoral research at the University of Waterloo on trust calibration and appropriate reliance in AI-assisted decision support. Our work uses published science, never unpublished study data. The Human Factor is independent of the University of Waterloo.
Not sure where to start?
The AI Readiness Assessment takes about five minutes and gives you a personalized report. Or tell us what you are weighing and we will set up a 30-minute call.
Book a 30-minute call
Talk to us about governance, trust and safety
What decision, policy or tool are you weighing, and who will carry it? Thirty minutes, no charge.
References
Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779.
Dekker, S. (2018). Just culture: Restoring trust and accountability in your organization (3rd ed.). CRC Press.
Hollnagel, E., Leonhardt, J., Licu, T., & Shorrock, S. (2013). From Safety-I to Safety-II: A white paper. EUROCONTROL.
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80.
Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253.