AI Implementation Consultant for Agencies

01 · The gap

Why AI projects die in implementation

By now you have probably tried something. A ChatGPT workflow someone championed. An AI feature switched on inside a tool you already pay for. A pilot that looked great on the demo call. Six months later, almost none of it is part of daily delivery. Nobody decided to stop. It just faded.

The cause is rarely the model or the vendor. It is the gap between a working demo and a working process. A demo needs one enthusiastic person and a happy path. A process has to survive a Thursday with three deadlines, a new hire who never saw the demo, and a client who will notice if quality slips. Getting from one to the other is implementation, and it is a different discipline from picking tools.

I have spent twelve years running delivery operations for marketing agencies, and I have watched more AI projects die in the landing than in the build.

02 · What landing takes

What landing AI in delivery actually covers

An implementation is finished when the AI step runs inside live client work, the team uses it unprompted, and quality holds after launch attention fades. Five things make that happen.

• The process is defined before the AI arrives

  Which step it sits in, what feeds it, what leaves it, who checks it. If the process does not exist yet, we define it first.

• The tooling runs inside your existing stack

  I configure AI steps in the tools your team already lives in and connect them with n8n. A new subscription has to earn its place; most of the time it does not.

• Human checkpoints are designed in, not bolted on

  Someone reviews AI output before it reaches a client, and the review is deliberately cheaper than doing the task manually. If it is not, the workflow gets cut.

• The team is trained on the workflow, not the tool

  SOPs, examples of good and bad output, and a clear answer to what do I do when it looks wrong. Adoption is a design goal, not a hope.

• Someone owns it after launch

  Models update and behaviour shifts. Every workflow ships with documentation and a named owner, because an unowned automation degrades quietly until a client notices.

03 · Scope

Implementation is a wider job than building automations

The distinction matters when you are deciding who to hire. An automation consultant builds the workflows themselves; my AI automation consulting page covers where AI genuinely helps inside agency delivery and what gets built. Implementation is the end-to-end job around that build: the process the AI sits inside, the checkpoints that protect quality, the training that gets your team using it, and the maintenance that keeps it alive. Both sit inside the same delivery operations practice for agencies.

04 · Method

How I land AI without breaking delivery

The sequence is boring on purpose. Boring survives contact with a real delivery week.

1. 01

   Map the delivery process as it actually runs

   Not as the org chart says it runs. Where the hours go, where work queues, who touches what.

2. 02

   Pick the steps where AI earns its place

   Defined input, defined output, cheap to verify. Everything else waits or stays manual.

3. 03

   Pilot inside one team or one account

   With the human checkpoint in place from day one, run by the people who will live with the workflow.

4. 04

   Measure against the manual baseline

   Counting review time. If the AI version is not clearly cheaper for the person doing the work, it gets cut before rollout.

5. 05

   Document, train, and hand over ownership

   The workflow has to survive its champion leaving.

6. 06

   Maintain

   Models update, tools change under you, and standards drift once nobody is watching closely. Maintenance is scheduled work, not a favour.

Every step exists because I have watched its absence kill a project. Skip the baseline and review burden moves instead of disappearing. Skip the documentation and quality drifts once launch attention fades. Skip the ownership and your team quietly abandons a tool that costs more time than it saves.

05 · Pricing

How it works

• Agency Ops Audit

  Two weeks. I review your delivery workflow, identify which steps are ready for AI and which are not, and deliver a written 90-day operations roadmap. One 60-minute readout call to walk through it. This is the entry point.

  $1,500 fixed

  Book the audit

• Fractional operations retainer

  10–15 hours per week, 90-day minimum. I run the implementation end to end, keep the workflows maintained, and adjust as your tools and team evolve.

  $4,000–$6,000/month

• Implementation project

  SOP library, tool configuration, and AI workflows built, documented, and handed over with a trained owner. Scoped after the audit so you know exactly what you are buying.

  $8,000–$14,000 fixed

06 · Proof

Proof

• 12Years
• 79Engagements
• 4.9/5Across 63 ratings
• 2,900+Hours billed

Case studies →

07 · Fit

Who this is not for

I would rather say so in the audit than take a project that is not ready.

08 · Questions

FAQ

• What does an AI implementation consultant do for an agency?

  I take AI from pilot to production inside agency delivery: mapping the process, configuring AI steps in the tools you already run, designing human checkpoints, writing SOPs, training the team, and setting up ownership so the workflow survives model updates and staff changes. The output is not a tool recommendation. It is AI running inside live client work with quality intact.

• How is this different from AI automation consulting?

  An automation consultant builds the automations: the workflows, the connections, the AI steps themselves. Implementation is the wider job. It covers the process the automation sits inside, the checkpoints that protect quality, the documentation and training that get your team using it, and the maintenance that keeps it alive. Building is one phase of implementation, and usually the easiest one.

• How long does it take to implement AI inside an agency?

  The first workflow typically goes from mapping to daily use in four to six weeks: two weeks to map and choose, a two-week pilot with a human checkpoint, then documentation and rollout. Full adoption across a team takes about a quarter, because habits change more slowly than tools. Anyone promising a transformed agency in a fortnight is selling the demo, not the landing.

• How do you get a team to actually adopt AI tools?

  By making the AI version genuinely cheaper than the manual version for the person doing the work. Teams abandon tools that cost more time than they save, and they are right to. I pilot with the people who will live with the workflow, measure against the manual baseline including review time, and cut anything that does not clear it. Adoption follows self-interest, not mandates.

• Who maintains the AI workflows after implementation?

  Someone has to, and we name that person before launch. Models update, tools change behaviour, and an unmaintained workflow degrades quietly. Every implementation ships with documentation and a trained owner on your team. If you would rather I keep running and adjusting it, that is what the fractional operations retainer covers.