Score each question from 0 to 3. 0 means "not at all." 1 means "we've started thinking about it." 2 means "partially in place." 3 means "fully operational."

1. Can your systems talk to each other?

Not "do you have APIs" but "do your systems share context in real time?" Can an agent working on a ServiceNow ticket see the related Jira issues, the deployment history in GitHub, and the infrastructure status in AWS? If your systems are connected only through manual exports and scheduled batch jobs, your AI agents will operate with yesterday's information. Real-time cross-system connectivity is the foundation everything else depends on. Learn more

2. Can you trace ownership across your organization?

When something breaks in production, can you identify the owning team, the upstream dependencies, and the downstream impact in under five minutes? If ownership lives in spreadsheets, tribal knowledge, or "ask Dave, he knows," your AI agents will have the same visibility gaps your humans do.

3. Do you have a governance model for AI?

Not an ethics statement. An operational governance model. Who can deploy agents? What data can each agent access? How are agent actions audited? If the answer is "we'll figure that out later," later never comes. Governance debt compounds faster than technical debt. Learn more

4. Can you measure AI ROI at the use-case level?

When the CFO asks "what are we getting for our AI spend," can you answer with numbers? Not "it feels like things are faster" but "Agent X saved 120 hours of manual work this month, costing $2,400 in model spend to generate $45,000 in labor savings." If you can't measure per-agent ROI, you can't justify scaling.

5. Do your teams share context or operate in silos?

Engineering, IT, operations, compliance, support. Do these teams share a common understanding of your systems, or does each team maintain its own version of reality? Siloed teams produce siloed AI. An agent built by engineering that can't access IT's knowledge base is half-blind by design.

6. Is your data classified and governed?

Do you know which data is PII, which is regulated, which is public, and which requires specific access controls? AI agents that touch unclassified data are compliance incidents waiting to happen. Data classification is the prerequisite for agent-level access control.

7. Can you deploy software in days, not months?

This isn't strictly an AI question, but it's a proxy for organizational velocity. If deploying a new internal tool takes a three-month procurement cycle, deploying AI agents will follow the same timeline. Organizations with mature CI/CD, infrastructure-as-code, and self-serve deployment get AI to production faster.

8. Do you have a model strategy, or are you locked into one provider?

Are you using one LLM provider because it's the best fit, or because switching would require a rebuild? A model-agnostic architecture lets you adopt new models in hours. A single-provider dependency means every new model launch is a missed opportunity. Learn more

9. Is your cloud infrastructure flexible enough for AI workloads?

Can you spin up GPU instances, deploy containerized agents, and scale inference endpoints without a six-week infrastructure request? AI workloads are spiky, GPU-hungry, and latency-sensitive. Infrastructure that was designed for steady-state web applications often can't handle them without significant rework.

10. Does your organization have a clear AI mandate, or scattered experiments?

Is AI a strategic priority with executive sponsorship, budget, and cross-functional alignment? Or is it a collection of disconnected experiments run by individual teams with no coordination? Scattered experiments produce scattered results. A clear mandate, backed by infrastructure investment, produces compounding returns.