Everyone wants to know whether their AI rollout is working. The first instinct is almost always to look at the dashboard.

• How many licenses have been activated?

• How many users logged in?

• How many prompts were submitted?

Those metrics are easy to collect, but after spending weeks supporting a global enterprise AI rollout, we think they're measuring the wrong thing.

Every week, associates brought their questions. During the first weeks alone, we received more than fifty questions a day. At first glance, they looked like support tickets. But when we analyzed them, they revealed something much more valuable: a real-time view of organizational readiness for AI.

As we reviewed the conversations, four clear themes emerged:

• Enablement – "How do I actually use AI in my role?"

• Access – "How do I get my team enabled?"

• Technical – "Something isn't working as expected."

• Strategy – "How should we responsibly use AI?"

  
  

Those four categories told us far more about adoption than login statistics ever could.

Enablement: People Needed Confidence More Than Training

The most common questions weren't about technology, rather they were about confidence. People wanted examples relevant to their jobs. They wanted help writing prompts, brainstorming use cases, and understanding where AI fit into existing workflows.

That told us something important: People don't adopt AI because it's available. They adopt it when they can picture themselves using it successfully. The response wasn't more documentation. It was role-based examples, prompt libraries, FAQs, and Office Hours that evolved into reusable learning assets.

Access: People Wanted to Participate, They Just Didn't Know How

Questions about access revealed something unexpected. There wasn't a lack of interest in AI, there was a lack of clarity around how to get started.

Associates wanted to know how to obtain licenses, when rollout waves would reach their teams, whether they were eligible, and where to go when they encountered login or regional access issues.

Rather than signaling resistance, these questions demonstrated enthusiasm. While leadership did encourage adoption of AI from the “top-down,” there was just as much if not more enthusiasm coming from the “bottom-up” of Individual Contributors wanting to supercharge their work with AI.

The lesson was clear: organizations should make access as easy to understand as the technology itself. Centralized intake processes, self-service guidance, clear escalation paths, and transparent rollout status all help remove unnecessary friction before it slows adoption.

Technical: Early Friction Is a Sign of Adoption, Not Failure

Technical questions increased almost immediately, but that wasn't a bad sign. We realized it meant people were experimenting.

Associates encountered browser issues, integration challenges, upload limitations, unexpected platform behavior, and questions about how data was being handled. Real-world usage exposed edge cases far more quickly than testing environments ever could.

Instead of viewing these questions as problems, organizations should view them as evidence that people are actively incorporating AI into their work. The priority becomes responding quickly with clear ownership, documented known issues, streamlined escalation paths, and a repeatable triage process that keeps confidence high while the platform matures.

Strategy: The Conversation Quickly Shifted from "Can We?" to "Should We?"

Perhaps the most interesting questions weren't technical at all, they were the strategic ones. 

Leaders and associates alike wanted guidance on appropriate use cases, responsible AI practices, governance, data privacy, measuring productivity, and how AI should reshape existing workflows.

These questions signaled that the organization was moving beyond experimentation and beginning to think about long-term transformation. Requests for tips and tricks dwindled, and questions about environmental impact and ethical use ramped up.

This is where AI adoption becomes less about technology and more about operating models. Organizations need responsible and sustainable governance frameworks, role-based playbooks, leadership enablement, use-case prioritization, and a clear vision for how AI will support the business over time. 

What We Learned as a Communication Team

Looking back, Office Hours became much more than a support function. They became one of the most valuable sources of organizational intelligence during the rollout. They became a listening mechanism. The questions highlighted where communications were unclear, where governance needed strengthening, where workflows could be transformed, and where future AI champions were already emerging.

Perhaps most importantly, they gave leadership something dashboards couldn't: A "Voice of the Associate." That's a far richer indicator of adoption than login metrics alone.

Areas We'd Invest in First

If you're preparing to roll out AI across your organization, here are the areas we'd invest in first:

• Scalable enablement: Convert recurring questions into reusable learning assets, FAQs, prompt libraries, and role-based guidance.

  
  

• Operational intelligence: Treat Office Hours as a "Voice of the Associate" feedback loop that surfaces friction and unmet needs. Keep track of the questions asked, and the evolution of answers that can be given.

  
  

• Governance and risk visibility: Use strategic questions to identify policy and governance gaps before inconsistent AI usage scales.

  
  

• Workflow transformation: Capture emerging use cases and identify opportunities to improve how work gets done. Highlight the ingenuity of the users to build your champion network.

  
  

• Champion networks: Identify early adopters who can help scale support across the organization.

  
  

• Enterprise change management: Focus on building confidence and behavior change, not just access to the technology.

  
  

• Measure beyond usage: Track confidence, readiness, friction points, and adoption maturity—not just login metrics.

We still believe usage metrics matter, but they tell you what people did. Questions tell you what people need. If you're serious about enterprise AI adoption, don't just watch the dashboard, listen to the conversations.