Check automation readiness
AI Automation Readiness Framework
The AI Automation Readiness Framework checks whether a marketing process is ready to be automated with AI on six conditions: process stability, volume, data quality, error cost, reversibility and monitoring.
The model
- Process stability
- Volume
- Data quality
- Error cost
- Reversibility
- Monitoring
Developed by Markaigen · Updated
What each part means
- Process stability
- The steps and rules do not change every week; exceptions are known and listed.
- Volume
- The process runs often enough that automation pays for its set-up and monitoring.
- Data quality
- The inputs are complete and consistent; the AI does not have to guess.
- Error cost
- A mistake costs little, or is caught before it reaches a customer.
- Reversibility
- Outputs can be undone or corrected without lasting damage.
- Monitoring
- Someone sees volumes, errors and anomalies, and can pause the process.
How to use it
- Check readiness per process, after the collaboration framework has shown that the task may be automated.
- Fix the failing conditions before you build; a workflow does not fix unstable rules or bad data.
- Reassess when the process, the data or the tools change.
Check a process
Answer six questions for one process. The result says whether it is ready, nearly ready or not yet, and why.
Answers are saved in this browser so you can return to them.
Download or e-mail this framework
Get a branded PDF or Word document, with your answers or as an empty template to fill in.