Definition · Buyer guide
AI dispatch software is the system that turns an incoming field-service request into a dispatched job: it reads the work order, drafts a price, ranks and routes contractors, and tracks the work to completion. Where traditional field-service-management software gives a human a calendar and rules to schedule by hand, AI dispatch software does the routine routing itself and escalates the judgment calls to a person.
Field-service dispatch is one loop, repeated all day: a request for work comes in, it gets priced, it gets routed to the right contractor, and it gets chased to done. For decades the software in that loop was a system of record — a place to store customers and tickets and a calendar to drag jobs around. A human did the actual dispatching.
AI dispatch softwaremoves the work, not just the record. An agentic layer reads the incoming request, drafts a not-to-exceed quote from the scope, ranks the contractors who could take it, and routes the job — then a person reviews the drafts and steps in where judgment is needed. The category name people search for is “AI dispatch software” or “AI work-order dispatch”; the underlying idea is an agentic control plane for field operations.
The physical world is unchanged — buildings still need plumbers, electricians, and HVAC technicians, and those technicians are still human. What changes is the coordination layer between the request and the person who does the work.
The contrast
Most tools sold as “field service management” — the ServiceTitan, Jobber, Housecall Pro, and Workiz tier — are rules-based scheduling systems: they give a human a calendar and configurable rules to dispatch by hand. AI dispatch software does the routine routing itself. The day-to-day is what differs, dimension by dimension.
The point is not that one is faster than the other by a fixed multiple — it is that the work moves off the person. Rules-based FSM gives a dispatcher a better surface to do the job by hand. AI dispatch software does the routine dispatching and asks for a human only where a person genuinely adds something.
How to evaluate
“AI” is now a label on a lot of rules engines. These five questions separate a real agentic dispatch system from a scheduling tool with a new badge. Ask what runs in production today, not what is on a roadmap.
Ask whether the software reads unstructured input — a scanned work order, an email thread, an API call — and produces a dispatchable job on its own, or whether a person still has to key every field. Reading the mess is the hard part; a system that only stores tickets you typed is FSM with a new label.
A trustworthy agentic system names where the person sits and what triggers an escalation. Look for an explicit confidence threshold: low-confidence cases routed to a human queue rather than forced through. Automation without a brake is a liability, not a feature.
Every routing and pricing decision should be recorded and inspectable after the fact. If a vendor cannot show you why a job went to a given contractor at a given price, the system is a black box — and a black box is hard to defend to a procurement team or a customer.
Dispatch quality is easy to assert and harder to show. Prefer a vendor that publishes operational outcomes — completion, quote accuracy, latency, override rate — over one that markets a single round number. A live, dated endpoint beats a slide.
Field operations rarely live in one tool. Ask whether dispatch is reachable from other software — an API, an MCP tool surface, an SDK — so it can be wired into the rest of your stack rather than trapping the work inside a single dashboard.
Where STEADYWRK fits
STEADYWRK runs an agentic dispatch system in production. The Dispatch Engine takes a work order in, drafts a not-to-exceed quote, ranks and routes contractors, and reconciles the outcome — held to the same five questions above. Its numbers are qualitative for current scale; the figures below are the ones it stands behind.
What it is
Dispatch Engine
Agentic work-order intake, NTE quoting, contractor routing, and outcome reconciliation.
Quote turnaround
<2hr target
A target, not a binding SLA — the figure STEADYWRK stands behind for current scale.
Completion rate
90%
Qualitative public posture; the live readings are published on the evals dashboard.
The surface
API · MCP · SDK
dispatch.quote, dispatch.order, dispatch.evals over OpenAPI 3.1, callable by other software.
Two things make the Dispatch Engine an agentic system rather than a dashboard. It is exposed as callable tools — dispatch.quote, dispatch.order, and dispatch.evals over an OpenAPI 3.1 surface and MCP — so other software can drive it. And it keeps a human in the loop by design: any decision below 70% confidence escalates to a person, and every decision is written to an append-only audit log. It sits inside STEADYWRK's broader agentic control plane, not as a standalone scheduling product.
AI dispatch software is the system that turns an incoming field-service request into a dispatched job: it reads the work order, drafts a price, ranks and routes contractors, and tracks the work to completion. Where traditional field-service-management software gives a human a calendar and rules to schedule by hand, AI dispatch software does the routine routing itself and escalates the judgment calls to a person.
Field-service-management (FSM) software — the ServiceTitan, Jobber, Housecall Pro, Workiz tier — is built around a human dispatcher: it stores customers, shows a calendar, and applies the rules a person configures, but the person still reads each ticket, picks the contractor, and types the quote. AI dispatch software inverts that. The agentic layer reads the work order, drafts the not-to-exceed price, and ranks the routing automatically; the human reviews and handles exceptions. FSM gives you a better dashboard to dispatch by hand. AI dispatch does the routine dispatching and asks for a person only where judgment is needed.
No, and a responsible system is explicit about that. AI dispatch software automates the repetitive middle of dispatch — reading intake, drafting quotes, ranking contractors — while keeping a confidence threshold. Anything the system is unsure about is escalated to a person rather than acted on automatically. STEADYWRK routes any decision below 70% confidence to a human queue and records every decision in an append-only audit log. The goal is leverage with a brake, not automation without a human in the loop.
Five things separate a real agentic dispatch system from a rules engine with an "AI" label: (1) Does it actually read unstructured intake — a PDF, an email, a webhook — and produce a dispatchable job, or does it just store tickets you key in? (2) Where is the human checkpoint, and is there a confidence threshold that escalates uncertain cases? (3) Is every decision recorded in an auditable log? (4) Are dispatch-quality outcomes published and inspectable rather than asserted? (5) Is the dispatch surface callable by other software — an API or MCP tools — or locked inside one dashboard? Ask what runs in production today, not what is on a roadmap.
STEADYWRK's Dispatch Engine is an agentic work-order dispatch system: intake, not-to-exceed quoting, contractor routing, and outcome reconciliation, exposed as callable tools (dispatch.quote, dispatch.order, dispatch.evals) over an OpenAPI 3.1 surface and MCP, with public evals. Its posture is qualitative for current scale — a <2hr target on quote turnaround and a 90% completion rate are the figures it stands behind — and decisions below 70% confidence escalate to a human. It sits inside STEADYWRK's broader agentic control plane rather than being a standalone scheduling dashboard.
Look at the mechanics and the published evaluations, or talk to the team that runs the dispatch operation.