How Agency Growth Implements ChatGPT: A Worked Example

This page is a demonstration. The company described below is hypothetical. There is no real client behind it, no real numbers, and no results we are claiming. What is real is the method. This is the same process we follow when we deliver a ChatGPT Implementation Plan for an actual business.

We built this page because most people evaluating an implementation have never seen one. It is hard to buy a process you cannot picture. So here is the process, worked through end to end.

Picture a small consulting firm. Around fifteen people: a few partners, project managers, analysts, and one operations lead. They sell advisory engagements to mid-sized companies.

Their week is full of repeatable knowledge work. It is a common starting point: a few people use ChatGPT on personal accounts, each in their own way, with no shared setup, no shared standards, and no connection to how the firm actually operates.

We do not start in ChatGPT. We start with how the firm works. Through interviews and document review, we build a plain map of the business: what it sells, who does what, which processes repeat, which tools hold the important information, and where the hours actually go.

The deliverable is a process map. Generic form: a one page inventory per recurring process, listing the trigger, the steps, the people involved, the systems touched, and the documents produced. No AI yet. Just an accurate picture.

Not every process deserves AI. We score each mapped process on four things: how often it runs, how much time it takes, how costly errors are, and how much human judgment it truly requires.

For this hypothetical firm, client onboarding scores well. It runs every time a deal closes, follows the same shape each time, is well documented, and the cost of a sloppy first week with a new client is high. So onboarding becomes the first workflow. Proposals and meeting summaries go on the roadmap behind it.

The discipline here matters. We pick one first workflow, not ten. A working system that the team trusts beats a broad rollout nobody uses.

Now the platform work begins. The firm moves off scattered personal accounts and onto a ChatGPT business plan with a shared workspace. OpenAI offers business plans with workspace management and admin controls, and it states that business plan data is not used to train its models by default.

We configure the foundation deliberately: who gets a seat, who administers the workspace, what the roles and permissions are, and what the firm's usage rules say. Exact admin features can vary by plan, so part of this step is confirming what the chosen plan actually provides and recording it.

ChatGPT can only reflect the company it has been shown. So we collect the documents the tools will rely on: service descriptions, the onboarding checklist, proposal and email templates, a tone and style guide, standard boilerplate, and examples of strong past work with anything confidential removed.

Most firms discover their documentation is messier than they thought. That is normal, and fixing it is part of the job. We consolidate duplicates, retire stale versions, and produce a clean knowledge set with a named owner for each document.

The generic deliverable: a knowledge inventory. One list of every source document, where it lives, who owns it, and which AI asset uses it.

ChatGPT supports custom GPTs: reusable assistants with their own instructions and attached knowledge files that can be shared inside a workspace. This is where the firm's way of working gets encoded so nobody starts from a blank prompt.

For the hypothetical firm, the first library is small and specific. Each custom GPT gets written instructions, the relevant knowledge files, and worked examples of good output.

Depending on plan, ChatGPT can connect to sources such as cloud drives, so it can reference approved company files where that is enabled. Custom GPTs can also be configured with actions that call external systems. We treat every connection as a decision, not a default.

For the example firm, we would connect only the vetted knowledge folders, confirm what the connection can read, and document it. Anything that writes to another system keeps a human approval step. Connector availability changes over time, so we verify against current documentation during setup rather than assuming.

Here is the onboarding workflow, end to end. Trigger: a new agreement is signed. Input: the agreement details and the completed intake form. The project manager gives these to the Onboarding Assistant.

Example input, in generic form: engagement type, scope summary, key contacts, start date, and any special terms. Example output, in generic form: a draft welcome email in the firm's voice, a kickoff meeting agenda, an internal setup checklist for the operations lead, and a one page project brief for the delivery team.

What stays human: the project manager reviews and edits every draft, decides what actually gets sent, and handles anything unusual about the client or the deal. The AI produces the first version. People own the judgment and the send button.

Before the team relies on any of this, we test it against reality. We run several past onboardings through the workflow and compare the drafts to what the team actually produced and sent at the time.

The first pass is never perfect. Instructions get revised, knowledge files get corrected, and edge cases get documented. We repeat until the team agrees the drafts meet a written acceptance standard: correct facts, right tone, nothing missing that the checklist requires. The test log itself becomes a deliverable, so there is a record of what was checked and what changed.

Tools that nobody uses are just settings. Training for this firm is hands on: working sessions where each person runs their own real task through the relevant custom GPT, with someone alongside to correct habits early.

Each asset ships with a one page usage guide: when to use it, what to give it, what to check before anything leaves the building. One person is named owner of each GPT, responsible for keeping instructions and knowledge current. A simple feedback loop and a review cadence keep the system improving after we step back.

Every implementation produces artifacts the business keeps. In generic form, the set for this example would be:

Notice what the work actually was. Creating the account took an afternoon. Mapping the business, organizing the knowledge, encoding the firm's standards, testing against real work, and training the team took the rest. Access is not implementation.

Agency Growth is an AI business setup and implementation company. For every engagement, the first deliverable is the same: an Implementation Plan built on your business, your processes, and your documents, not a hypothetical one. If you want to see what this method finds in your company, that is where we start.