AI Onboarding · The Methodology
Scoped to a real job. Supervised by someone who owns the outcome. Accountable to the numbers it touches. Most teams don't have an AI problem — they have a workflow problem that someone proposed AI for. We start with the workflow.
How We Onboard AI
Each phase has a gate, and each gate can stop the project — that's the point. Better to kill a bad use case in the audit than in production. The loop closes: operating well feeds the next audit.
The Phases
That's a feature. An onboarding process that can't say no isn't a process — it's a sales funnel. Ours is built to kill weak use cases early and ship strong ones fast.
01
We sit inside your workflows: where the volume is, where the judgment lives, where the data behind each decision actually comes from. Decades of enterprise and SMB eCommerce operations mean we find the highest-ROI targets fast — and kill the proposed use cases that would never pay for themselves. Most die here. Better here than in production.
GateA workload worth automating, with a number attached
02
Which systems it reads, which sources count as primary, and which data stays out of reach entirely — customer PII, card data, credentials. An AI grounded in the wrong inputs is confidently wrong at scale. The perimeter is defined before a single build decision is made.
GateA defined data perimeter, in writing
03
We pick a single scoped process, measure it before AI touches it, and run a proprietary build against that baseline — no resold SaaS, no cookie-cutter templates. Live in two weeks on your real data. If we can't measure the before, we don't ship the after.
GateMeasured results against the baseline
04
No parallel tools nobody opens. The AI shows up inside the existing workflow — Shopify, Google Ads, CRM, Slack — with review steps where the stakes warrant them, and a human sign-off anywhere money moves.
GateGuardrails and sign-offs in place
05
Because it will be. A review cadence, a working definition of failure, and an escalation path your team actually follows — with monthly strategy reviews as coverage expands. Onboarding ends when the AI has a manager, not when the invoice clears. And what we learn operating feeds the next audit.
The loop closesOperate feeds the next audit
House Rules
These rules govern our data-driven editorial series — and they govern client work identically. Whether we're publishing a chart or wiring a model into your stack, the bar doesn't move.
Rule One
Every claim about what the AI improved traces back to a measured baseline. No before-and-after story without a before.
Rule Two
We'd rather tell you what a capability can't do than sell you a punchy line we can't defend. Vendor theater is an operational liability.
Rule Three
Autonomy is earned per workflow, never assumed. Anywhere a decision touches revenue, payments, or a customer, a person owns the final call.
Start With the Audit
Where does the work pile up? Who touches it? What would "better" look like in a number you already track? If AI is the answer, the audit will show it. If it isn't, that finding is worth just as much.
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