Readiness has almost nothing to do with how excited your team is about AI, and almost everything to do with five unglamorous conditions. We run this checklist before we agree to build, because the projects that fail are rarely the ones with a bad idea — they're the ones that started before the ground under them was solid. Meet four of the five and you should build now. Meet two and building first is the expensive way to discover which three were missing.
That gap is most of the market right now: 91% of mid-market firms use generative AI, but only 25% have it integrated into core operations (RSM, 2025). The distance between those two numbers is this checklist.
1. A workflow that already costs you real money
Not a workflow that's annoying — one with a number attached. The threshold we use is roughly 15 hours a week of paid time on a repeated task. Below that, even a perfect agent struggles to clear its own cost. At 20 hours a week and a $75 loaded hourly rate, you're spending about $78,000 a year on that one workflow, and a 50–70% automation rate is worth real money against a flat build fee.
The test isn't whether the work is tedious. It's whether you can name the hours and the rate without guessing. If you can't, that's not a disqualification — it's the first thing to go measure. Our ROI calculator does the arithmetic once you have the inputs.
2. The right answer is knowable
There has to be a correct outcome that a competent person could confirm. *Was this ticket routed to the right team? Was this invoice matched to the right PO? Was this lead qualified correctly?* — all checkable. *Did we write the best possible marketing strategy?* — not checkable, and therefore not something you can hold a system accountable to.
This matters more than it sounds, because a knowable right answer is what makes an eval suite possible, and evals are the gate everything else depends on. If nobody can say whether a given output was correct, nobody can say whether the system is working — and you'll be relying on impressions forever.
3. Your systems can actually be reached
An agent that can't touch your systems is a chatbot with extra steps. The question is whether software can authenticate into the tools where the work happens — helpdesk, CRM, billing, database — and write, not just read.
Most modern SaaS clears this easily. The places it breaks: a homegrown internal system with no API, a vendor whose integration tier costs more than the agent, or a process that runs through a shared inbox and a spreadsheet nobody owns. None of these are fatal, but each one adds weeks, and it's better to find out now than in month two. Data quality is the related trap — poor data readiness is the top obstacle data leaders name for moving pilots to production (Informatica, 2025).
4. One person will own the number
Not a committee, not a sponsor, not your vendor — one named individual whose job is affected by whether the metric moves. This is the least technical item on the list and the one that most reliably predicts the outcome.
If you can't name that person today, you are not ready, and no amount of engineering fixes it. A system with no owner has no one to notice it degrading, no one to defend its budget at renewal, and no one for whom shipping it is genuinely urgent. We've written about how this failure compounds in why your AI pilot never reached production.
5. You can live with the worst case
Every autonomous system will be wrong sometimes. Readiness means you've decided in advance what happens when it is: what the agent is allowed to touch, what it must escalate, and what the damage looks like on a bad day.
The useful exercise is to state the worst outcome in one sentence. *It routes a ticket to the wrong queue and a human re-routes it* is a worst case you can ship against. *It issues a refund to the wrong account* needs an approval step before it goes near production. Teams that skip this conversation don't avoid it — they just have it later, in a security review, with a finished build waiting.
What a partial score means
Most companies we talk to have three of five, and the pattern is consistent: signs 1 and 2 are usually solid, and one of 3, 4, or 5 is the gap. The response depends on which:
- Missing the workflow (1) — don't build yet. Go measure two or three candidate workflows properly; the winner is usually not the one people nominate.
- Missing knowability (2) — pick a different workflow. This one isn't an agent problem.
- Missing integration (3) — buildable, but scope it honestly. This is where budget overruns come from.
- Missing an owner (4) — the cheapest and hardest fix. It costs nothing and requires a real decision.
- Missing risk tolerance (5) — usually solved by narrowing the agent's authority rather than improving the model.
If you want this scored rather than self-assessed, the AI Readiness Assessment comes at it from the operational side — data, process, tooling, team, and alignment — and returns a score with a recommended first move. The five signs above are the workflow test; that one is the organisational test, and you want both to come back clean. When the score is good, a Gigabit Agents build is a flat fee from $8,000 per agent, deployed in about 90 days — and when it isn't, we'd rather tell you before you spend it.
