Buyer’s guide · Evaluation criteria

How to choose AI dispatch software

Picking an AI tool for field operations is not about finding the “best” one on a list. It is about scoring each option on the criteria that decide whether it fits your operation: how much it acts on its own, whether it can prove what it did, how it connects to your stack, where a human steps in, what you actually pay for, and where your data lives. This guide walks those six criteria neutrally — with the trade-offs spelled out, and STEADYWRK measured on the same axes, including where it is the wrong choice.

01Autonomy level
02Accountability & audit trail
03Integration & MCP-openness
04Human-in-the-loop design
05Pricing model
06Data residency & operations location

Score the tool, not the listicle

Most “top AI dispatch tools” lists hand you a ranking and skip the reasoning. The order often reflects who paid for placement or who tuned hardest for the search term — not which tool fits the way you run jobs. A ranking ages badly and rarely survives contact with your actual constraints.

A criteria-based evaluation travels better. If you know the six axes below and can put the same sharp question to every vendor, you can judge any tool — including ones no list mentions — against your own operation. The work shifts from trusting someone else’s verdict to making your own, on evidence you can check.

The honest position on each axis is usually “it depends.” A heavier tool with a long audit trail and bundled operators is right for a regulated, high-stakes operation and overkill for a single van. Below, each criterion names where each end of the spectrum wins — and where STEADYWRK, named on the same terms, is and is not the right call.

The framework

Six criteria that decide the fit

For each one: why it matters, the question to put to every vendor, the real spectrum of answers, where STEADYWRK sits, and a case where the other end of the spectrum is the right call.

Autonomy level

AI field-ops tools sit somewhere on a spectrum from "suggests, you click" to "acts, then tells you." The right point depends on how costly a wrong action is in your operation. A mis-routed marketing email is cheap; a wrongly dispatched emergency repair is not. Knowing where a tool sits — and whether you can move that line — matters more than the headline word "autonomous."

The question to ask

Which decisions does the system take on its own, which does it draft for a human, and can I set that threshold myself?

The spectrum of answers: At one end, copilots draft everything and a person approves each step. At the other, fully autonomous agents act and only surface exceptions. Most credible tools land in between, with a confidence threshold that decides when to escalate. The honest answer is rarely "fully autonomous" for anything with real-world cost.
Where STEADYWRK sits: STEADYWRK runs agentic intake, quoting, and contractor routing, with a policy gate on every decision; anything the system scores below its confidence threshold is escalated to a human operator rather than acted on automatically. Its self-reported, estimated human-override rate is 3% — a figure from its own production telemetry, not an audited benchmark.
Where the other end wins: If you want a pure self-serve copilot that never picks up a phone, a lighter scheduling assistant fits better. STEADYWRK pairs automation with human operators on the call; that is overhead you do not need if a human voice is not part of the job.

Accountability & audit trail

When an AI system makes operational decisions, "why did it do that?" stops being academic. For regulated facilities, insurance, and disputes, you need a durable, queryable record of what was decided, by which version of the system, and on what inputs. A tool that cannot reconstruct a decision after the fact moves risk onto you.

The question to ask

Is every decision logged immutably, how long is it retained, and can I query that log through an API rather than a support ticket?

The spectrum of answers: Some tools keep a rolling activity feed that ages out in weeks. Others keep an append-only log for years and expose it programmatically. The difference shows up the day a customer disputes a charge or an auditor asks for evidence.
Where STEADYWRK sits: STEADYWRK keeps a 7-year append-only audit log and publishes a live evals dashboard at /evals; both are designed to be queryable rather than screenshot-and-email. Its dispatch metrics are self-reported and estimated from production telemetry, and the page says so — transparency about provenance is part of the criterion, not a substitute for an external audit.
Where the other end wins: A 7-year audit posture is weight a one-van operator may not want to carry or pay for. If your jobs never reach a dispute or a compliance review, a simpler tool with a short activity log is a reasonable, cheaper choice.

Integration & MCP-openness

Field operations already run on a stack — a CMMS or FSM system, accounting, a messaging layer. An AI tool earns its place by fitting that stack, not by demanding you rebuild around it. The newer question is whether the tool is reachable by your own AI agents: an open API and a Model Context Protocol (MCP) surface let an LLM call the tool directly, instead of a human clicking through a UI.

The question to ask

Do you expose a documented API and an MCP server, or only a closed UI and a fixed list of native integrations?

The spectrum of answers: Closed suites offer a curated set of integrations and little beyond them. Open platforms publish an API, webhooks, and increasingly an MCP server plus SDKs, so you can wire the tool into systems they never anticipated — including agent-to-agent calls.
Where STEADYWRK sits: STEADYWRK exposes its dispatch actions as MCP tools and an OpenAPI 3.1 spec, with TypeScript and Python SDKs, so an external agent can call dispatch.quote, dispatch.order, and dispatch.evals directly. Openness is verifiable from the spec itself rather than a claim.
Where the other end wins: If your team will only ever use a point-and-click UI and values a large catalogue of pre-built native integrations, a mature closed FSM suite may cover more of your existing tools out of the box than an API-first platform does on day one.

Human-in-the-loop design

The physical world stays human: buildings still need plumbers, and a tense customer call still needs a person. The question is not whether humans are involved but where, and whether the hand-off is designed or accidental. A tool that automates the easy 80% and dumps the hard 20% on an untrained user has not removed the work — it has hidden it.

The question to ask

When the system is unsure, what exactly happens — who gets the exception, with what context, and is that person trained for it?

The spectrum of answers: Some tools escalate to whoever happens to be logged in, with thin context. Others route exceptions to a defined role with the full decision history attached. The mature pattern is an explicit confidence threshold feeding a trained human, not a silent failure or an over-confident auto-action.
Where STEADYWRK sits: STEADYWRK’s model is automation for the repetitive coordination — reading tickets, drafting quotes, ranking contractors — with a trained operator owning the call, the negotiation, and the outcome. Below its confidence threshold, the decision goes to that operator. The role itself is documented in plain terms at /what-is-an-ai-operator.
Where the other end wins: If you already have your own staff to handle exceptions and just want a software layer underneath them, a tool that ships only the software — no operators — may be a cleaner fit than a model that bundles trained people into the service.

Pricing model

Pricing shape decides whether a tool scales with you or punishes you for growing. Per-seat SaaS, per-dispatch usage, and percentage-of-spend each reward a different shape of operation. The headline number matters less than how the meter runs as your volume and headcount change — and whether setup fees front-load the cost.

The question to ask

What is the unit you pay for — seats, dispatches, or a cut of spend — and what does my bill do as volume and team size grow?

The spectrum of answers: Per-user monthly pricing is predictable but climbs with headcount and can penalise putting more people on the tool. Usage pricing tracks activity, which suits variable or seasonal volume but is harder to forecast. Percentage-of-spend aligns the vendor with throughput but can get expensive at scale.
Where STEADYWRK sits: STEADYWRK uses per-dispatch pricing: the meter is the dispatch, not the seat, with no per-user fees and no setup cost. That favours teams that want to add people, or absorb seasonal swings, without adding licence cost.
Where the other end wins: Per-dispatch pricing is not always cheapest. A low, steady volume of jobs handled by a fixed small team can cost less on a flat per-seat plan than on usage pricing. Model your own volume before assuming usage-based is the better deal.

Data residency & operations location

Where your operational data lives — and where the people touching it sit — is a procurement and compliance question, not a detail. Some buyers are bound to keep data in a specific jurisdiction; others care about who is on the other end of a customer call. A tool should be able to tell you plainly where data is processed and where its operations run.

The question to ask

In which jurisdictions is my data stored and processed, and where are the people who handle it located?

The spectrum of answers: Answers range from "single-region, data never leaves the country" to globally distributed processing with operations staff across several countries. Neither is wrong in the abstract; the right answer is the one that matches your contractual and regulatory constraints.
Where STEADYWRK sits: STEADYWRK is built in Aqaba, Jordan, and operates across the United States; its operator workforce is based in Aqaba. That is stated openly so a buyer can weigh it against their own requirements — detail at /nearshore-jordan.
Where the other end wins: If your contracts require that data is processed only in-country and handled only by domestic staff, a cross-border operating model is a genuine constraint to weigh. STEADYWRK names its model plainly precisely so that buyers with strict residency rules can rule it in or out on the facts.

Honest positioning

Where STEADYWRK fits — and where it doesn’t

Measured on the same six criteria, here is the plain read — the cases it suits, and the cases where another category of tool is the better answer.

A strong fit when…

You want agentic automation with a human operator on the call, not a UI-only copilot.
You are audit- or dispute-conscious and value a long, queryable decision log.
You are building an API-first or agent-driven stack and need MCP and OpenAPI access, not just native integrations.
Your job volume varies and per-dispatch pricing with no per-seat cost suits you better than flat licences.

A weaker fit when…

Your contracts require single-region data handling and only domestic staff touching it.
You want software alone, with your own team handling every exception and no bundled operators.
You run a low, steady job volume where a flat per-seat plan may simply cost less.
You need a large catalogue of pre-built native integrations on day one more than an open API.

Note on numbers: STEADYWRK’s dispatch figures — completion 90%, override 3%, NTE variance ±9% — are self-reported and estimated from its own production telemetry, published live at /evals and tracked over time at /state-of-ai-dispatch. They are not an independently audited third-party benchmark. Whatever tool you evaluate, ask for the same transparency before you trust a metric.

Apply the criteria to STEADYWRK

The pages below are where STEADYWRK shows its own answer to each criterion — use them to verify the claims on this guide rather than take them on faith.

The control plane

Autonomy, the policy gate, and how decisions are made — the system behind the criteria.

Live evals

The self-reported, estimated dispatch metrics, published and dated — check the accountability claim.

Security & trust

Audit log, security posture, and how operational data is handled.

For FM clients

How the model maps to a facility-management buyer’s pricing and integration needs.

Frequently asked questions

What should I look for when choosing AI dispatch or field-ops software?+

Evaluate six things, not a vendor’s ranking: how much autonomy the system has and whether you can set that threshold; whether every decision is logged immutably and is queryable; whether it exposes an open API and MCP server or only a closed UI; how it hands exceptions to a trained human; what unit you pay for and how the bill scales; and where your data is stored and who handles it. Score each tool on the same axes rather than trusting a "best of" list.

Is a fully autonomous dispatch agent better than a human-in-the-loop one?+

Not inherently. Full autonomy reduces human effort but raises the cost of a wrong action, which matters when a mis-dispatch has real-world consequences. A human-in-the-loop design with an explicit confidence threshold escalates uncertain decisions to a trained person. The right level depends on how costly an error is in your operation, not on which sounds more advanced.

Why does MCP-openness matter for field-ops tools?+

The Model Context Protocol (MCP) lets an AI agent call a tool’s functions directly, rather than a human clicking through its interface. A tool that exposes an MCP server plus a documented API can be wired into your own agents and into systems the vendor never anticipated. A closed UI with a fixed integration list cannot. As more operations are run by agents, that openness decides whether the tool fits a future stack or boxes you in.

How is this guide different from a "top AI dispatch tools" list?+

A ranked listicle tells you a winner; it rarely tells you the criteria or the trade-offs, and the order often reflects who paid or who optimised for the keyword. This guide gives you the evaluation framework so you can score any tool yourself — including ones not mentioned here — against your own operation’s constraints. It names STEADYWRK on the same axes, with its trade-offs stated, not as a ranking.

Where does STEADYWRK fit on these criteria?+

STEADYWRK is an agentic control plane for field operations: it runs intake, quoting, and contractor routing with a policy gate that escalates low-confidence decisions to a trained operator, keeps a 7-year append-only audit log, exposes its dispatch actions as MCP tools and an OpenAPI 3.1 spec, prices per dispatch with no per-user or setup fees, and is built in Aqaba, Jordan while operating across the US. Its dispatch metrics are self-reported and estimated from production telemetry. It is a strong fit for API-first, audit-conscious teams and a weaker fit if you need single-region data handling or a UI-only tool with no operators.

How to choose AI dispatch software

Score the tool, not the listicle