We Automated Hours of Repetitive Agency Work With AI: Three Real Examples

Every agency loses hours to work nobody wants to do twice: reconciling invoices line by line, translating a website page by page, logging into a client's WordPress admin to change one sentence. None of it is hard, but all of it eats time that should go to strategy, design and the client relationship.

Over the past year we automated three of these processes for client work at Ruler. Below are the workflows we built, what broke along the way, and what we would tell another small business considering the same move.

Why repetitive work is the right place to start with AI

Before picking a project to automate, we look for three things: the task happens often, the inputs are structured (or can be), and a mistake is cheap to catch. Repetitive admin work usually ticks all three. Creative and strategic work rarely does, which is why we have not tried to automate copywriting judgment, design direction or client negotiation. We use AI for the mechanical parts of the job and leave the parts that need taste to people.

The same applies to AI search visibility. When a business searches for "AI automation for small business," the answers that rank and get quoted by tools like ChatGPT are the ones that describe a real process instead of promising vague "efficiency gains." Here is ours, process by process.

Example 1: automating invoice reconciliation for an ecommerce client

One of our long-running ecommerce clients needed every sales invoice and credit note matched against their internal bookkeeping system. For over a year this had been a manual export-and-match job in a spreadsheet: someone pulled the numbers, someone else checked them against the books, and discrepancies were chased by hand.

We built a sync that pulls invoice and credit-note data directly into the client's BigQuery reporting environment on a schedule, so nobody has to re-key totals every month. We got there in stages. It started as a spreadsheet-based process, moved to a working BigQuery pipeline, and only reached a full historical backfill after several rounds of fixing edge cases the first version missed, such as partial refunds, currency rounding, and records that arrived out of order.

Our advice to another business is that a reconciliation pipeline can run without errors and still be wrong. Our client's team still audits the output against the source system periodically, and that audit is what caught the edge cases early. Build the checks in from day one.

Example 2: AI-agent translation for a bilingual website launch

A client launching a new site needed the full page set translated into Greek on a tight timeline, without paying for a full professional translation pass on every future content update. The usual agency workflow is to brief a translator, wait for a draft, and route it through a review cycle for every single page.

We used an AI agent to produce the first-pass Greek translation directly from the English source content, and someone on our team reviewed it before anything went live. The site launched on schedule with both language versions complete. Most of the time saved was in the first draft. For a translator starting from a blank page, 15 to 20 pages of content is a multi-day job; reviewing and correcting an AI-agent draft for tone and accuracy takes a few hours.

This only worked because we kept the human review step. An AI translation that goes live unreviewed is a liability, especially for client-facing text in a formal register.

Example 3: managing WordPress content changes through an AI agent

Small content changes (a new service line, an address update, a pricing page tweak) are the most repetitive request we get from support clients, and until now each one meant a developer opening the WordPress admin, finding the right field, and making the edit by hand.

We set up an integration that lets an AI agent make these changes directly through WordPress's own management layer. Each change is described to the client in plain terms and confirmed before anything publishes. For a client with frequent small updates, this turns "email support, wait for a developer, wait for confirmation" into a same-day change with a lighter review step on our side.

Ruler's own internal AI tooling, which we wrote about separately, works the same way. In both cases AI does the mechanical retrieval and editing, and a person owns the final check before anything goes to a client or the public.

What made these automations worth building

Two choices kept these projects from turning into another maintenance headache. First, we picked tasks with a clear, checkable output: a matched invoice total, a translated sentence, a published page. When the definition of "done right" is fuzzy, an automated process is much harder to trust. Second, every one of these keeps a human checkpoint before anything client-facing goes out. None of them run fully unsupervised, and we do not think they should.

The time we get back goes into the parts of client work that need a person: reading a client's tone correctly, catching a strategic problem in a brief, deciding whether a design direction is right. We follow the same principle in our AI consulting and transformation work for clients, automating the mechanical layer and keeping judgment with people.

If your team is buried in the same kind of repetitive admin, whether it is invoicing, translation, or content updates on a custom website, the fastest way to find out if AI can help is to pick one process, map its inputs and outputs precisely, and test a small version before committing to a full build. We started all three of the examples above that way.

FAQ

Does automating repetitive work with AI mean removing people from the process?

No. In all three examples here, a person still reviews the output before it reaches a client or goes live. AI handles the mechanical first pass, and the judgment call stays with a person.