How We Use AI to Run Our Own Agency

Most agencies selling AI transformation haven't transformed their own operations. They talk about automation at client dinners and then go back to managing projects in spreadsheets. We decided early on that if we were going to help companies run on AI, we needed to do it ourselves first. Here is exactly how we use AI to run AXI, from the first client touchpoint to the final delivery.

Why Internal AI Adoption Matters

Before we get into the specifics, the framing matters. We didn't automate our operations to cut headcount. We did it to free up the team for the work that actually requires a human: creative decisions, client strategy, quality judgment, and building things that have never been built before.

The result: our team handles a significantly higher volume of work per person than a comparable agency. We move faster on client timelines. And we catch errors earlier, because automated checks run constantly rather than at the end of a project.

That's the real value of internal AI. Not replacing people. Replacing the parts of the job that drain them.

Intake and Discovery

Every new client engagement starts with a structured intake process. Historically, this meant someone on our team manually reviewing a form submission, scheduling a call, prepping notes, and synthesizing everything into a brief. That chain had four handoffs and took about two hours per lead.

Now, when a new inquiry comes in, an AI agent pulls the form data, cross-references it with any prior context we have on the company (LinkedIn, their current site, news), generates a pre-call research brief, and drops it into our project management system with the call linked and prep notes ready.

The result: discovery calls are tighter. We show up knowing the prospect's tech stack, their likely bottlenecks, and the three or four questions we need answered before we can scope a project. Calls that used to run 60 minutes now close in 35.

Scoping and Estimation

Scoping is where agencies lose margin. A project that looks like 40 hours turns into 120 because the original estimate missed a dependency. We've trained an internal AI tool on our historical project data to flag common underestimates in new scopes.

When a project manager drafts a scope, the tool runs a comparison against similar past projects, surfaces scope items that have historically been underestimated, and suggests additions with the reasoning attached. It doesn't override judgment. It gives the PM a second opinion in about 30 seconds.

This has reduced scope creep across our delivery work by roughly 40% since we deployed it. Projects stay on budget more often. Clients are happier. The team isn't scrambling.

Design and Content Production

AI has changed our production workflow more than any other part of the business. A few specific examples:

Copy drafts. For blog content, landing pages, and ad copy, a writer gives a brief and a set of constraints. An AI draft comes back in minutes. The writer's job shifts from generating to editing and sharpening. Output volume is roughly 3x what it was 18 months ago with the same headcount.

Design feedback loops. We use AI-assisted review at two points in our design process: after initial concepts (checking for accessibility, consistency, and alignment with brand guidelines) and before client delivery (checking spec accuracy against the Figma brief). These checks catch about 60% of feedback before it ever reaches the client.

Component documentation. For development projects, we auto-generate component documentation from the codebase at every major merge. The AI parses the code, writes descriptions, flags undocumented props, and keeps the internal wiki current. No one has to remember to update it.

Project Management and Communication

We run project management through a combination of Linear and a custom AI layer built on top of it. Every morning, an agent reviews open tickets, flags blockers, surfaces tasks that are behind schedule, and prepares a plain-English status summary for each active project.

Client updates used to require a PM to manually pull status from five different places and write a recap. Now the agent drafts the update and the PM reviews and sends. Time per update: from 25 minutes to under five.

We also use AI to monitor Slack threads across client channels. If a decision is made in a message thread that has downstream project implications, the agent flags it and creates a ticket. This has nearly eliminated the class of problem where something was "decided in chat" and never made it into the actual work.

QA and Delivery Checks

Before anything ships to a client, our delivery checklist runs automatically. This includes:

• Design QA: pixel-level comparison between Figma specs and implemented components
• Performance checks: Lighthouse scores against our minimum thresholds
• Copy review: grammar, brand voice consistency, and readability scoring
• Link and asset checks: every link tested, every image confirmed to load

A human reviewer still signs off on everything. But they're reviewing a clean pass, not hunting for bugs. The QA process used to be the most inconsistent part of our delivery. It's now the most consistent.

What We've Learned

A few things we got wrong early and had to fix:

Automating too early. We tried to automate processes before they were stable. An automation built on top of a broken process is just a faster broken process. Map the workflow manually first. Run it a dozen times. Then automate.

Not enough human review on client-facing outputs. In our first six months of using AI for copy drafts, we let a few pieces go out with tone that was technically correct but felt off. Clients noticed. Now everything client-facing gets a human read before it leaves the building.

Underestimating maintenance. AI tools need tuning. Models get updated. Prompts that worked in January sometimes behave differently in July. Budget time to maintain your AI systems the same way you'd maintain software.

The Honest Takeaway

Using AI to run an agency is not a silver bullet. It requires the same discipline as any other operational system: clear processes, good data, and a team willing to iterate. The difference is that when you get it right, the compound effect is significant. You move faster, catch more, and give your best people more time for the work only they can do.

If you're trying to figure out where to start in your own business, the answer is almost always the same: pick one workflow that has clear inputs and outputs, automate that, and learn from it before you expand.

That's exactly what we help companies do at AXI. If you want to talk through what that looks like for your team, get in touch.