How we use AI agents to turn client work into useful outcomes

AI can help an agency prepare more material. The useful question is whether that material helps a client make a decision, launch an improvement, or serve a customer better.

My work connects product strategy, UX, design, development, and marketing. At Waymaker Agency, that perspective shapes how I use agents: start with the client’s outcome, give each task enough context, and make the result reviewable before it moves into use.

Here is the delivery process behind that approach, plus a practical brief you can use with your own team.

Start with one customer or business problem

A request such as “we need AI” is a starting conversation. A useful engagement needs a more specific result. Perhaps an interested prospect cannot understand the offer. Perhaps inquiries arrive without enough information. Perhaps the team repeatedly rebuilds the same campaign brief.

I look at the existing journey and tools before deciding what to change. That keeps the scope connected to the work people already do and makes it possible to define a useful finish line.

For example, a suitable finish line might be: “An inquiry reaches the right person with the information they need to choose the next action.” That is more useful than counting how many messages an agent drafted.

Give the work an owner and a clear handoff

These are roles in a process, not a requirement to create five independent agents. One assistant may handle several roles. Separate tasks when that makes the work easier to check; keep dependencies in order when one result needs another.

Give the agent the same context a colleague would need

A good working brief includes the audience, approved offer, existing pages, brand voice, relevant evidence, and boundaries. It identifies the systems the assistant may use and the actions that need a person’s decision.

I use tools across research, design, code, creative production, and operations. ChatGPT and Claude, Figma, coding agents, and video or voice tools each have a place when the task calls for them. Connections to a CRM or email platform are useful when the job depends on those systems. The process should fit the client’s setup and available access.

The handoff should be specific enough that the next person does not need to reconstruct the conversation. “Done” should arrive with the result, where it lives, what was checked, and what remains open.

A worked example: improve an inquiry handoff

This is an illustrative delivery example, not a claim about a completed client engagement or conversion result.

Imagine a service business receives inquiries, but the owner regularly has to ask three follow-up questions before deciding what to do next.

1. Inspect the current path. Review the public offer, form, confirmation, and internal handoff. Use approved examples with personal details removed.
2. Identify one change. Agree which missing information is genuinely needed and which questions would add unnecessary friction.
3. Prepare the experience. Draft clearer field labels, a useful confirmation, and a concise internal summary format.
4. Implement the agreed scope. Keep the existing platform where it can do the job. Do not add a new tool just to make the workflow look more advanced.
5. Try realistic cases. Check a complete inquiry, missing information, an unclear request, and an out-of-scope request. Confirm where each ends up.
6. Hand it over. Name the person responsible for follow-up, explain how exceptions are handled, and agree what to measure.

The evidence of completion is the observed handoff and the agreed instructions. Whether it improves bookings requires subsequent measurement.

Review the customer experience as well as the output

A technically successful automation can still produce a poor customer experience. A confirmation might arrive but fail to explain what happens next. An internal record might exist but lack an owner. A beautiful landing page might send visitors to the wrong destination.

I review the journey as a connected experience: message, action, confirmation, ownership, and follow-through. The review also covers facts, tone, mobile readability, and the behavior relevant to the scope.

For public writing, an agent should not fill missing evidence with invented testimonials or results. For an implementation, a generated file or passing build is one piece of evidence; it does not by itself establish that the customer-facing journey works.

Use this brief for your next AI-assisted task

Outcome: What should be easier or more reliable?
Audience: Who uses the result?
Starting point: Which existing pages, tools, and approved examples matter?
Scope: What is included in this change?
Deliverable: What should the assistant return, and where?
Review: Who approves it, and what must be checked?
Follow-through: Who owns the next step, and how will we know what happened?

Start with a task your team understands well enough to judge. Record the total effort, including context preparation and corrections. That helps you decide whether to repeat the workflow, simplify it, or choose a different task.

Turn the process into one useful improvement

If you want hands-on help, explore Waymaker Agency’s AI workflow services or team training. We can start with the work your team needs to finish and agree on an appropriate scope.

For the personal perspective behind this approach, read how I work with AI agents across my projects. For product teams and independent builders, see the path from an idea to a useful product.