Support, Augment, Automate: How Safety Professionals Can Make Better Decisions with Heath
Safety professionals make dozens of decisions every day. Which hazard gets fixed first? Is this control good enough? What should the crew hear in this morning's toolbox talk? Does this near miss need a full investigation? Most of those decisions get made with too little time, scattered information, and a lot of experience doing the heavy lifting.
AI is changing that, but not by replacing the safety professional. Gartner frames the shift well in its model for reengineering decision making with humans and machines. It describes three levels of collaboration, sitting on a spectrum from mostly human to mostly machine:
- Decision support: humans decide, and machines provide visualizations, exploration, and alerts.
- Decision augmentation: machines generate recommendations, and humans validate, choose, or refine them.
- Decision automation: machines make the decision, and humans manage the risk by setting guardrails and monitoring for exceptions.
The question isn't whether AI belongs in safety. It's which decisions belong at which level. Here's how that model maps onto everyday safety work, and where Heath fits at each step.
Source: Gartner, "Reengineering Decision Making With Humans and Machines."
The three levels at a glance
| Level | Who makes the call | What Heath does | Safety examples |
|---|---|---|---|
| Decision support | You | Pulls together hazards, controls, and requirements so you can see the full picture | Researching hazards for a new task, checking OSHA minimums, reviewing past risk assessments |
| Decision augmentation | You, from Heath's recommendations | Suggests options and drafts, ranked and explained for you to validate | Choosing controls, fixing a near-miss hazard, turning a JHA into a toolbox talk |
| Decision automation | Heath, within your guardrails | Handles routine, low-risk, repeatable tasks and flags exceptions | Tagging and coding near-miss reports, categorizing controls, formatting documents |
Decision support: Heath informs, you decide
At this level, the safety professional is fully in charge. Gartner notes that human decisions draw on principles and ethics, experience, logic, skills, and style, along with emotion and bias. The machine's job is to make sure those decisions are well informed.
This is where much of a safety professional's time disappears: digging through old JHAs, flipping between the regulation and the company standard, trying to remember how a similar job was handled two years ago. Heath shortens that search.
- Ask about a task and get the full picture. Ask "what are the hazards and controls for replacing a rooftop exhaust fan?" and get a consolidated answer, instead of hunting through individual risk assessments.
- See the minimum and the better. Heath lays out the OSHA requirements alongside best-practice controls that go beyond the standard, so you know where compliance ends and good practice begins.
- Draw on experience you don't have yet. A newer safety professional gets access to the kind of reasoning a seasoned expert would apply, while still making the call themselves.
You still weigh cost, production, worker burden, and site culture. Heath just makes sure you're not deciding blind.
Decision augmentation: Heath recommends, you validate
In the middle of the spectrum, the machine does more of the thinking up front. Gartner describes AI generating recommendations for humans to validate and explore, with people choosing from the machine's suggestions or working alongside it to reach a decision. Each decision has its own dynamic.
This is where Heath does its most valuable work for safety teams.
- Control options, ranked by effectiveness. Heath suggests controls and explains where each one sits in the Hierarchy of Risk Treatment, from avoiding and eliminating the hazard down to administrative controls and PPE. You pick what fits, and the framing nudges every decision up the hierarchy.
- Near-miss fixes on the spot. A worker snaps a photo of a hazardous condition, and Heath suggests fixes someone on the floor could put in place right away. A person still decides which fix to apply.
- Toolbox talks from your own documents. Hand Heath a JHA, risk assessment, or SOP and get a one-page toolbox talk to review and deliver. You edit it, own it, and tailor it to the crew in front of you.
Augmentation doesn't take the decision away from you. It means you start from a strong draft instead of a blank page.
Decision automation: Heath handles the routine, you set the guardrails
At the far end of the spectrum, the machine makes decisions on its own using rules, predictions, and other AI. The human role shifts to managing risk: setting the guardrails and watching for the exceptions.
In safety, this level should be reserved for decisions that are routine, repeatable, and low-consequence if they're occasionally wrong. That's still a lot of work. It's the administrative load that eats into time safety professionals could spend in the field.
- Coding and tagging reports. Heath can categorize near-miss reports by hazard type and control, so trends surface without someone manually coding every entry.
- Classifying controls. Every control can be tagged by its level in the Hierarchy of Risk Treatment automatically, giving you a consistent picture of how much your program leans on PPE and administrative controls.
- Formatting and first drafts. Turning notes into a consistent template, or a long procedure into a short summary, doesn't need a human decision at every step.
You set the rules, review the output, and step in when something looks off. That's what managing the guardrails means in practice.
Match the level to the stakes
Safety professionals already know how to think about this, because it's a risk decision. The higher the consequence of getting a decision wrong, the more human judgment it needs.
- Life-safety calls stay human. Stopping work, approving a rescue plan, signing off on a confined space entry, or accepting residual risk on a high-hazard task should sit at the support or augmentation level. Heath informs and recommends; a qualified person decides.
- Judgment calls get augmented. Selecting controls, prioritizing corrective actions, and tailoring training are where AI recommendations save the most time without removing accountability.
- Routine work gets automated. Tagging, sorting, formatting, and summarizing are where automation frees up hours with little downside.
A good rule of thumb: if you'd want a competent person's name on the decision, keep a human in the loop. Heath is built to make that person faster and better informed, not to replace them.
How Heath can help
Heath is a virtual health and safety expert built for the way safety professionals actually work. It supports, augments, and automates decisions at the right level, keeping you in control where it matters most.
- Support: ask about any task and get the hazards, controls, and requirements in one place.
- Augment: get control recommendations ranked by the Hierarchy of Risk Treatment, near-miss fixes from a single photo, and toolbox talks drafted from your own documents.
- Automate: let Heath handle the tagging, coding, and formatting so you can spend more time in the field.
- Live help: when a decision needs a second set of expert eyes, connect with a safety professional.
Better decisions, made faster, with your judgment where it counts.