An AI automation demo can look impressive in five minutes. The harder question is what happens after six months, when the workflow contains customer data, several integrations and a process your staff now depends on.
Before choosing a vendor or platform, look beyond the demo and ask how the system behaves in normal operation, failure and exit.
1. What data does the system receive?
List the actual categories of data: customer messages, names, financial fields, internal documents, CRM records and so on.
Then ask:
- Where is the data processed and stored?
- How long is it retained?
- Is it used to train provider models?
- Can retention or training use be disabled where needed?
- Who can access logs and conversation history?
Do not settle for “enterprise-grade security” as the entire answer.
2. What permissions does the integration need?
If the workflow only needs to read new support tickets, why does it require full administrator access to the CRM?
OWASP’s guidance on Excessive Agency is directly relevant: keep tools and permissions as narrow as the task allows.
3. What happens when the AI is uncertain?
Look for explicit fallback behavior. Can the system stop, ask for clarification or route to a person? Or does it always try to produce an answer?
4. How are errors detected and reviewed?
Ask whether you can inspect logs, replay failures, see which source was used and identify who approved a consequential action.
Without observability, a workflow can fail quietly.
5. How dependent are you on one vendor?
Find out whether you can export your data, prompts, knowledge sources and workflow configuration in a usable form. If you leave the platform, can the business continue operating?
An exit plan is not pessimism. It is basic operational resilience.
6. What is the real pricing model?
Check for usage limits, per-seat fees, model charges, integration costs and premium features required for security or governance. A cheap pilot can become expensive when volume increases.
7. What support and change controls exist?
AI models and APIs change. Ask how the vendor communicates material changes and what happens if an integration breaks.
The NIST AI Risk Management Framework provides a useful lens for evaluating governance, measurement and monitoring around AI systems.
Choose for the workflow you actually have
A smaller tool with clear permissions and reliable exports may be a better fit than a broad platform with dozens of capabilities you do not need.
Our AI Business Automation service starts with the business process and risk boundaries first, then chooses technology to fit that process—not the other way around.