What you need to know to scale AI securely
- Tracey Cesen
- 4 hours ago
- 2 min read
The fastest way to setup an AI initiative to fail? Scale a pilot before you understand the risks.
Everything can look perfect in testing, and still fall apart the minute it hits the real world. That's why you need to be clear on business risk and impact before you move from experimentation to production.
4 questions to ask before you scale AI
Here are the questions my team always looks at before giving clients the green light to scale:
1. What can the AI see? 👀
What data will it pull from once it’s live? And what changes when the guardrails of a test environment disappear?
2. What can the AI touch? 🤝
What systems can it write to or update? What access does it get in production that you might not have accounted for in testing?
3. What can the AI trigger? ⚙️
When it connects to other systems, what actions can it set off? What downstream impacts might you be missing?
4. How fast can a mistake spread? 🏃
Will you know if something goes wrong? Can you quickly contain it or revert to manual? Or can it cascade across teams, systems, or customers before you catch it?
These are the parts teams can skip when they’re excited to scale, and the exact things that will come back to bite you. Because building something that works? That’s great, but it’s not enough. Understanding how it behaves inside your business is what actually makes it safe to scale.
How ServiceNow can help
The good news is that ServiceNow tools (think the AI Control Tower, their built-in agentic AI, and Moveworks) let you build in governance and control early, so you can move forward with more confidence.
Remember, there’s real opportunity here - but there’s also real risk. And it usually comes down to whether you’re asking the right questions early enough. If you’re working on this right now, I'm happy to talk it through with you. Use my link above to book a meeting!
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