The cost of AI automation depends mainly on how many processes are automated, how many systems must be connected, the quality of your data and documents, the accuracy you need, and the ongoing cost of AI model usage. Starting with one well-defined process is the most reliable way to control cost and prove value.
The main cost drivers
- Scope: the number of processes and steps being automated
- Integrations: how many systems are involved and whether they have usable APIs
- Data and content: whether documents and knowledge are clean, current and accessible
- Accuracy and review: how much human review and testing the process needs
- Security and compliance requirements
- Running costs: AI model usage, hosting and monitoring
One-off vs ongoing costs
One-off costs cover discovery, design, building, integration and testing. Ongoing costs include AI usage (often priced per volume of text processed), hosting, monitoring and continuous improvement as processes change.
How to keep costs under control
- Start with one process that has a clear, measurable cost today.
- Reuse existing systems through integration rather than replacing them.
- Use rule-based automation where possible and AI only where it adds value.
- Measure results before extending to more processes.
Estimating the value side
Compare the cost with what the process costs today: hours spent, delays, errors and missed opportunities. An assessment of your workflows can identify which automations offer the clearest return before you commit.
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