Workflow Automation
Not every repetitive task should be automated. The question is not "can we automate this?" — the answer is almost always yes. The question is "what is the cost of an error in this process?" That answer determines how much human oversight you need.
The error-cost framework
Categorize your processes by what happens when something goes wrong:
Low error cost — wrong output is caught quickly and easily corrected (formatting a report, sending a draft for review, creating a calendar event). These are safe to automate fully.
Medium error cost — wrong output affects a customer or costs money to fix but is reversible (sending a follow-up email, updating a CRM record, generating an invoice draft). Automate with a human review step before final action.
High error cost — wrong output is hard to reverse or creates legal, financial, or reputational exposure (publishing to customers, processing payments, sending legal documents). Keep a human in the final approval loop regardless of how confident the automation is.
The data quality question
Automation is only as reliable as its inputs. Before automating a process, ask: is the data feeding this process consistently structured and complete? If the answer is no, you will spend more time fixing automation errors than you would doing the task manually. Fix data quality first.
The explainability test
For any process that affects a customer or a financial outcome, you need to be able to explain the output if asked. "The algorithm decided" is not an explanation. If you cannot trace the logic from input to output, that process needs human oversight until you can.
Building a simple decision log
Every automation decision your business makes should be traceable. Not a formal legal record — a plain log that records what decision was made, what data it was based on, and who approved it. This protects you when something goes wrong and helps you improve the process over time.
Want to map your current automation decisions and identify where you need better documentation? The AI Readiness Workflow includes a structured risk-scan step built for exactly this.