Revenue Engineering

What to Automate First in a Revenue Team

A prioritisation method that ranks automation candidates by hours returned, error cost and effort, plus the five that pay back fastest in most companies.

Mauricio Esparza By ·Published ·2 min read
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Short answer

Rank candidates by frequency times minutes saved, weighted by what an error costs, divided by build effort. In most revenue teams the five that pay back fastest are lead routing, enrichment before contact, the after contact write back, follow up scheduling and the recurring report somebody assembles by hand. Start with routing, because it is the cheapest to build and it changes a number you already care about.

Everyone wants to start with the impressive automation. The one that pays back fastest is almost always boring.

The ranking

For each candidate:

Value = (times per month x minutes each) x error weight ÷ build effort

Where error weight is 1 if a mistake is annoying, 3 if it costs a deal, and 10 if it reaches a customer in a way you would have to apologise for.

Fill this in for ten candidates and the order usually stops being controversial.

The five that usually win

1. Lead routing. Happens on every lead, takes seconds of human attention each time, and delays contact by hours when it waits for a person. Cheapest thing on the list to build and it moves speed to lead immediately.

2. Enrichment before contact. Removes the research step that delays every first call and improves every conversation that follows. High frequency, easy to verify, and it makes the next three automations better.

3. The after contact write back. Outcome, summary and next step onto the record, every time. Stops customers repeating themselves, which is the failure they actually remember.

4. Follow up scheduling. The second and third touches that currently depend on memory. Low effort, and it recovers deals that were already paid for.

5. The recurring report. Somebody spends part of Monday assembling it. Automating it returns those hours and, more usefully, makes the number current rather than three days old.

What to leave for later

Anything irreversible without a checkpoint. Automate up to it, put a human approval there, automate after it.

Anything where the process is still in debate. You would be encoding one side of an argument.

Anything touching data you do not trust. Fix the data or route the bad records into a flagged path first.

The impressive one. Usually the agent that does everything. It generally depends on two or three of the boring five working first, and building it early means debugging several things at once.

Start narrow on purpose

Take the top candidate, build the smallest version that produces the result, run it for a week, then extend. The guidance from people who build these systems is the same: simplest thing that works first, autonomy added only where it earns its cost.

Before you build any of them

Write down how you will know it worked. Not the execution log, the business result: open the record, and it is either there or it is not. Teams that decide this before building find their failures in days. Teams that decide it after find them in months.

Frequently asked questions

Why not start with the biggest opportunity?

Because the biggest opportunity usually depends on smaller things working first, and because an early visible win buys the trust the bigger project will need. Sequence matters as much as selection.

How do we estimate build effort honestly?

Ask what has to be true for this to work, and count the dependencies rather than the steps. Automations are rarely hard to build and often hard to integrate, and the integration is where the time goes.

What if everything feels urgent?

Then rank by what an error costs rather than by hours saved. Urgency is a poor signal and error cost is a good one, because it captures the failures that are expensive rather than merely annoying.

Sources

  1. Building effective agents · Anthropic (accessed 2026-09-22)
  2. Handle errors gracefully · n8n Docs (accessed 2026-09-22)
  3. Why evaluate AI workflows · n8n Docs (accessed 2026-09-22)
Revenue EngineeringAutomationOperations
Mauricio Esparza
Mauricio EsparzaGTM Systems Lead · Revenue Engineer · Founder of MitHub. Designs and runs revenue systems for multi-location businesses: AI voice campaigns, enrichment, CRM automation and attribution. Founded MitHub to teach the method in the open.

Part of Revenue Engineering on MitHub.

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