When to automate and when to assist
Decision framework for revenue workflow automation
Context
Not every revenue workflow should be fully automated. Some benefit from AI assistance, others from full automation.
The wrong choice wastes resources and frustrates teams. Full automation in the wrong place creates quality problems.
Leaders need a clear framework to decide where AI should replace, assist or stay out of the way.
What changed with AI systems
AI can now handle complex workflows that previously required human judgment: qualification, personalization, objection handling.
The question is no longer 'can AI do this?' but 'should AI own this decision or support a human?'
New architectures enable human-in-the-loop at specific decision points, not binary automation or manual.
How to approach this in your organisation
- →Map each workflow step: what decisions are made, what data is needed, what risks exist.
- →Apply decision criteria: Is the task repeatable? High volume? High risk? Customer-facing?
- →Choose the right level: Full automation for routine, high-volume, low-risk. Assist for complex, high-value. Manual for strategic or sensitive.
- →Pilot in controlled environments: test automation on a subset before full rollout.
- →Measure impact: track time saved, quality maintained and team satisfaction.
Metrics and risks
Key metrics
- →Automation rate: percentage of workflow steps handled without human input.
- →Quality comparison: automated vs manual outputs measured against defined standards.
- →Time savings: hours per week freed for higher-value work.
- →Error rate: mistakes requiring correction or customer follow-up.
Risks to consider
- →Over-automation: removing human judgment where it adds value.
- →Under-automation: wasting team time on routine tasks that could be automated.
- →Poor handoffs: AI systems that escalate too early or too late.
- →Team resistance: automation that makes work harder or less satisfying.
Want to discuss this topic?
Talk to us about implementing these approaches in your organisation.