Bo Breuklander is principal at Breuklander Communications, instructor of public relations at the University of Tampa and creator of AI Comms Lab.
“Just let them go at it. We’ve been trying to make this conversation happen for a while.”
“I need to do more. My region is expanding and I’m getting lots of pressure to keep up.”
“Wow, we have way more challenges than I expected.”
These are real statements from communications leaders during conversations about AI.
One from a global B2B communications leader who works closely with her team. She’s sharp and engaged, but it was clear in that moment the processes and outcomes they were used to weren’t going to work the same way. Another from the head of communications at a nonprofit while two senior executives openly debated AI’s impact on the organization.
Across industries, we’re seeing the same pattern: random prompts, siloed experimentation and no clear line tying AI use to key business measures.
There’s an obvious gap between leadership and tactical teams, but the more dangerous gap is the spread between how confident team members feel about their AI knowledge and how effectively they can apply it to their actual work. Even with the same tools and training, siloed personal use cases create wildly uneven results.
You can’t close that gap by subscribing to another tool or platform. You close it with operational alignment. Here’s what that looks like in practice.
- Clarify the role of AI in communications work
If a tool doesn’t have a defined job, it becomes extra weight.
Just because you can use AI for something doesn’t mean you should.
Anchor AI to your existing workflows, not the other way around. When AI supports defined processes, it becomes a performance multiplier. When it floats around as an experiment, it becomes a distraction.
This starts with mapping a single core workflow end to end.
Take crisis monitoring as an example. It starts with a signal. You’re likely to have monitoring set up for certain keywords or mentions. Then you verify the information, send an internal alert, draft a message, identify and segment stakeholders if you haven’t done it already, then distribute and monitor your response.
Now identify the steps AI can support and what you can safely automate to improve speed to response. Maybe a custom GPT or Claude project drafts the first alert summary. Maybe it synthesizes 200 comments into five themes. Maybe it flags anomalies in sentiment.
Isolate friction points and test targeted improvements. The same applies to intake management, impact reporting, audience insights and executive briefings.
- Create guardrails that reduce fear and improve quality
Guardrails should not be red tape. They should build confidence through knowing where to step next.
Without guardrails, teams hesitate. Progress stalls when people are unsure what’s allowed. The absence of guidance caps performance long before the technology does.
First, publish an approved tool list, even if it’s a single page. The team knows the tools you’ve vetted and the acceptable use cases.
Second, create a simple AI usage guide if your organization does not have one. It doesn’t need to be a 20-page policy. It should define what not to input, how to validate outputs and where human review is required.
Third, define quality standards. For example, AI-generated drafts must be reviewed for tone alignment, data verification and stakeholder sensitivity before distribution.
Imagine a regional communications manager drafting a CEO memo. With the right guardrails, she knows she can safely use AI for structural drafting, but final messaging alignment stays human-led. This speeds up the process and reduces risk.
Don’t wait for enterprise-wide perfection. You can develop guardrails for your team tomorrow.
- Build shared confidence through repetition and practice
Confidence is built through repetition and knowing what to do next.
Communicators understand the power of consistent messaging externally. Internally, the same principle applies to skill development.
Uneven confidence is the hidden friction on team performance. Identify your internal champions. They’re not always managers, but they are the ones experimenting productively. They’re the ones who have “figured it out.”
Harness their curiosity and formalize that role. Let them host monthly 30-minute “AI in action” sessions to walk peers through a real use case.
Make it repeatable. Celebrate small wins. Encourage shared prompt libraries, or even better, share custom GPTs or projects aligned to your workflows.
Over time, the team will go from isolated experimentation to shared capability.
- Establish decision-grade intelligence
Call it new intelligence if you want, but don’t think of this as another data dashboard. Dashboards report metrics. Decision-grade intelligence provides context to guide leadership’s actions.
Modern communications cannot rely on static reporting. Leadership needs synthesized insight that informs actions.
AI is great for pattern recognition and completing repeatable steps. Use it to interpret signals across fragmented data sources to protect and strengthen stakeholder relationships. Discover patterns by combining media monitoring, social listening, website and email metrics, employee surveys and everything else you already track. Those patterns can help show share-of-voice gains in priority narratives, sentiment shifts and internal alignment.
This vantage point strengthens your role and positions communications as a strategic function rather than a service desk.
Closing the gap before it widens
The most dangerous AI gap isn’t technical, it’s operational. It’s already showing up as uneven confidence, misalignment, confusion and reporting that fails to guide decisions.
Communications leaders cannot afford to wait for the enterprise to close this gap. The stakes are too high. Reputation, speed, stakeholder trust and executive credibility all sit on the line.
The good news is that closing this gap does not require a massive overhaul. It requires clarity, structure, repetition and new intelligence.
Closing this gap is your strategic advantage.
