Winning programs pick workflows that are repeatable, can be standardized at the boundary, and have bounded blast radius when wrong — then they add measurement, not magic.
AI accelerates defined work — it does not substitute for missing process design.
Where AI stacks beat brittle RPA alone
| Scenario | What you automate | Controls |
|---|---|---|
| Documents | field extraction, doc classification | rules + QA sampling + change audit |
| Ticketing | tagging, routing, agent summaries | confidence thresholds + human escalation |
| Comms | draft replies, meeting notes | mandatory review on legal/finance topics |
Architecture that stays auditable
- input/output logs and prompt/config versioning
- single sources of truth — the LLM is not your database
- human review on high-risk outputs
- regression tests when policies or integrations change
Scale checklist
- Baseline KPIs before AI.
- Define acceptance thresholds and escalation paths.
- Wire destination systems — avoid orphan pilots.
- Expand scope only when metrics hold.
Related
- AI in business strategy
- AI chatbot on the website
- Cost of AI implementation
- Marketing automation context
FAQ
TagsAI & Machine LearningStrategy
Frequently Asked Questions
- Often complements it — RPA for rigid steps, AI for unstructured text and classification, with shared quality monitoring.
- Weeks to months depending on data quality and integrations — models without integrations rarely stick.
- Process owner plus technical owner for integrations/tests — otherwise quality drifts.