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AI agent for operations managers

Automation Candidate Scoring Agent

A ranked, evidence-based list of automation candidates with pilots chosen by the manager

Automation Candidate Scoring Agent: what goes in, what the agent does and what you get

What it does

Automation ideas are chosen by enthusiasm rather than value. This agent measures volume, handling time and error rate of manual tasks from logs. It scores each task for value and feasibility, and checks the data behind each score. When the data is doubtful, it asks for a sample, for example a week of timings, and rescoring. It produces a ranked list with the reasoning and the expected payback. The manager approves which pilots go ahead. The list shows the evidence for each score so the manager can challenge it. Edge case: a task has high volume but changes every month, so the agent lowers its feasibility score.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueYes, continueApprovedNoNo 1 STARTS WHEN Quarterly review of manual tasks 2 USES A TOOL Load task logs and timings 3 DOES Calculate volume, time and error cost per task 4 CHECKS THE RESULT Is the data good enough for each task? If not: request a sample of timings or count andrecompute. Back to step 2. 5 DOES Score feasibility from stability and data access 6 DOES Rank tasks by value and feasibility 7 DOES Estimate payback for the top candidates 8 CHECKS THE RESULT Is the payback inside the target for each pilot? If not: narrow the scope or drop the candidate andrescore. Back to step 5. 9 YOU APPROVE Manager approves the pilots 10 RESULT Ranked list and pilot plan filed
Read the steps as a list
  1. Quarterly review of manual tasks
  2. Load task logs and timings
  3. Calculate volume, time and error cost per task
  4. Is the data good enough for each task?If not: request a sample of timings or count and recompute. Back to step 2.
  5. Score feasibility from stability and data access
  6. Rank tasks by value and feasibility
  7. Estimate payback for the top candidates
  8. Is the payback inside the target for each pilot?If not: narrow the scope or drop the candidate and rescore. Back to step 5.
  9. Manager approves the pilotsThe agent waits here for your OK.
  10. Ranked list and pilot plan filed

How it decides

Value equals volume times time saved and errors avoided. Feasibility depends on rule stability and data access. Data under the quality limit triggers a sample.

  • Require at least 4 weeks of data
  • Lower feasibility when rules change monthly
  • Drop candidates with payback over 18 months
  • Rank by value times feasibility

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Payback limit (default 18 months)
  • Data period
  • Scoring weights
  • Task areas

What keeps you in control

It always asks you first

  • Pilots chosen

Hard limits

  • Never start a pilot
  • Never use personal data beyond task counts

It stops when

  • Done: pilots approved
  • Stop: no usable task logs

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensInvoice matching had 9,000 items a month at 3 minutes. The data check failed because 1,200 records had no time. A one week sample gave 2.6 minutes. Payback was 11 months and it ranked first. Returns handling scored high on volume but low on stability. The manager approved the invoice pilot.

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