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Building an AI agency: repeatable systems and business discovery

In this class, I frame an agency as a business that solves problems through systems it can deliver again. The idea comes from a practical difficulty: if each client requires starting from scratch, growth can mean accumulating work that is difficult to sustain.

By Eric Muriel3 min read

English article adapted with AI assistance from the transcript of my Spanish video. About this adaptation · Explore my channel

Eric Muriel · Own content · original Spanish video · Published on January 19, 2026. YouTube loads when you press play.

01First decide whether the problem needs AI

The video emphasises examining processes, data and metrics before building. Knowing how to use a tool does not make it necessary in every case. Sometimes the problem is confusing organisation or information that is not ready.

Ask which task repeats, how much effort it takes and which improvement is wanted. If you cannot describe that improvement, discovery is incomplete. Suggesting another approach or postponing implementation can also be a sound decision.

Reference [1]: Eric Muriel · YouTube

02Connect niche, problem and offer

The roadmap starts by choosing a field and recognising a specific problem. It then becomes an offer explaining the expected result, scope and implementation time. Those elements need to fit the company's actual situation.

A clear sentence helps start a conversation but should not hide conditions. If the proposal promises an improvement, state how it will be checked and what the client must contribute. Do not turn a whiteboard example into a guarantee for every business.

03Design delivery before multiplying clients

In the class, I describe onboarding, necessary information, checks, metrics and reports. These steps help ensure a delivery does not rely solely on remembering everything.

Reusing a foundation does not mean changing a logo is enough. Each business may have different permissions, data and exceptions. Keep the tested process and the checks you understand, carefully adapting what changes.

04A real case improves the next version of the offer

The next step is documenting the before and after of early deliveries. That evidence may show an improvement or an unforeseen limitation. Both help explain the service more accurately.

Describe what was implemented, the observation period and other possible influences. Social proof is more useful when it helps explain a case than when it merely displays a number without context.

05Acquisition channels lead to a conversation

The video compares content, a personal profile, sales conversations and ads. In each case, my practical goal is to reach a discovery conversation where fit can be assessed. A reply or click is not yet a sale.

Content can show how you think and which problems you work on. A conversation requires listening before presenting the whole solution. An advertising test needs a message and a way to measure what happens after initial interest.

06Review the system through a complete delivery

To apply the roadmap, follow one case from the first question to subsequent support. Mark where time is lost, which information is requested repeatedly and which checks can be documented. That review shows what is worth standardising.

Custom work can also make sense; it needs appropriate scope and pricing. The choice concerns how you want to organise the service and your capacity to deliver it, not using the agency label as a guarantee that it will scale.

Sources and further reading

These references expand on the concepts indicated. The examples and exercises are original editorial material.

  1. [1] Eric Muriel · YouTube

    Building an AI agency: repeatable systems and business discovery ↗

    Primary source: the author’s Spanish video and supplied transcript. Personal experiences retain their original context and date.

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How we use sources, quotes and images

Companion video · YouTube

Building an AI agency: repeatable systems and business discovery ↗

Eric Muriel | IA ·

English translation of an AI-assisted adaptation of the transcript supplied by Eric Muriel. The original video is in Spanish. Verbal fillers and transcription errors have been edited, and examples distinguished from promises of results. Suggested exercises are editorial additions. Tools and interfaces reflect the recording date, except for updates identified with sources.

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