AI & Automation

How Long Does an AI Implementation Actually Take?

Realistic timelines for GTM and AI automation projects, what makes them slip, and the milestones to agree before anyone starts building.

Mauricio Esparza By ·Published ·2 min read
mithub.club
Short answer

A first useful result usually takes two to four weeks, a production system with an owner six to ten, and a multi unit rollout one to two quarters. The build is rarely the slow part. Access, undocumented processes, data quality and compliance registration cause most delays, and all four can be started before any building begins.

Estimates for this kind of work miss because they price the build. The build is the predictable part.

The realistic shape

Weeks 1 to 2: access and reality. Getting seats, keys and a sandbox. Reading real records rather than documentation. Mapping the process as it actually runs, which is never how it is described. This phase looks like nothing is happening and it predicts everything that follows.

Weeks 2 to 4: first useful result. One team or one location, in production, doing something that was not happening before. Narrow on purpose. The guidance from people who build these systems is consistent: start with the simplest thing that works and add autonomy only where it earns its cost.

Weeks 4 to 10: it survives its author. Error routes, alerting, a runbook, a named owner, reporting somebody actually reads. This is the phase that gets cut under pressure, and cutting it is what turns a system into a dependency.

Quarter 2: rollout. Fast only if a location is a configuration rather than a build. If unit two requires rebuilding, this phase expands without limit.

The four things that cause delay

Access. Roughly half of all slippage in these projects is waiting for a seat, an API key or a sandbox. It is administrative and entirely avoidable.

An undocumented process. If nobody can describe what should happen, the first weeks go to discovering it. Worth doing, but it is discovery time, not build time, and it should be planned as such.

Data quality. Every plan assumes cleaner data than exists. Duplicates, inconsistent fields, contact details older than the records holding them. Pull fifty real records before the estimate, not after.

Compliance registration. In the United States, application to person messaging requires brand and campaign registration with the carriers. Until it is approved, texting is blocked. Voice usually is not blocked by the same gate, so the sequencing matters: ship voice while the registration is pending.

What to agree before anyone builds

  • One result that will be true at the end, named in business terms
  • Who grants access, and by when
  • The definition of done: what a person opens to verify the business result, not an execution log
  • The handover date and the owner

Projects with those four written down land inside their estimates far more often, because the estimate is describing the same work everyone is doing.

A note on measuring too early

You will see directional movement on speed and coverage metrics inside a month. Attribution takes a quarter, because it needs enough closed deals to be more than noise. Judging a revenue system on four weeks of attribution data produces confident conclusions from nothing, and non deterministic systems in particular have to be measured by running real data through them rather than reasoned about theoretically.

If the blocker is inside your systems

That is a specific job. MitHub publishes open profiles of engineers who have taken this work from pilot to production, with the deployments written out.

See the engineers.

Frequently asked questions

Why do estimates slip so often?

Because they estimate the build, which is the predictable part. Access, undocumented process, data quality and compliance are the unpredictable parts, and they sit before the build rather than inside it.

Can we go faster by skipping documentation?

You can reach the first result faster and you will pay for it at the rollout, when a second person has to operate something only one person understands. The documentation is what makes unit two cheap.

What can we do this week to shorten it?

Four things, none of which need the vendor or the engineer: grant the access, write down the process as it actually runs, pull fifty real records and look at them honestly, and start any compliance registration.

How long before we see a number move?

Expect a directional signal inside the first month on speed and coverage metrics, and a quarter before revenue attribution is trustworthy, because attribution needs enough closed deals to be more than noise.

Sources

  1. Building effective agents · Anthropic (accessed 2026-09-22)
  2. Handle errors gracefully · n8n Docs (accessed 2026-09-22)
  3. A2P 10DLC registration · Twilio Docs (accessed 2026-09-22)
AI & AutomationImplementationProject Planning
Mauricio Esparza
Mauricio EsparzaGTM Systems Lead · Revenue Engineer · Founder of MitHub. Designs and runs revenue systems for multi-location businesses: AI voice campaigns, enrichment, CRM automation and attribution. Founded MitHub to teach the method in the open.

Part of AI & Automation on MitHub.

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