Why Training Alone Isn’t Enough
When organizations roll out AI initiatives, training is usually the first lever they pull. It’s visible, it’s measurable, and it feels like progress. But training without governance is like teaching someone to drive without giving them any road rules.
Samantha Stark, chief strategist and founder of Phyusion, has identified this as one of the core stalling points organizations are hitting right now. The issue isn’t that people can’t learn to use AI tools—it’s that they don’t have a clear framework for when to use them, how to use them responsibly, or what guardrails exist to protect the organization.
For communications teams specifically, this gap is high-stakes. Comms professionals handle sensitive messaging, brand voice, crisis response, and internal narratives. Introducing AI into that workflow without clear policy creates real risk—reputational, legal, and cultural.
The Three Pillars: Governance, Readiness, and Culture
Sustainable AI adoption inside a communications function rests on three things working together. Miss one and the whole structure wobbles.
Governance: The Policy Foundation
Governance is the part most organizations skip or delay. It’s less exciting than the tools themselves, but it’s what makes everything else work.
For comms teams, governance means having documented answers to questions like:
- What types of content can AI assist with, and what requires full human authorship?
- Who owns the output when AI is involved in drafting?
- How do we handle AI-generated content in regulated or legally sensitive communications?
- What data can be fed into AI tools, and what’s off-limits?
Without these answers, individual team members are left making judgment calls that should be organizational decisions. That inconsistency creates risk and erodes trust in the AI initiative itself.
Good governance doesn’t have to be a 40-page policy document. It can start as a one-page framework that gets refined over time. The goal is clarity, not bureaucracy.
Readiness: Honest Assessment Before Scaling
Readiness is about knowing where your team actually stands before you push forward. This means assessing skills, workflows, and infrastructure—not just asking whether people have completed a training module.
A genuine readiness assessment for a comms team might look at:
- Skill distribution: Are AI capabilities concentrated in a few individuals, or spread across the team? Concentrated capability creates bottlenecks and dependency.
- Workflow integration: Have existing processes been mapped to identify where AI genuinely adds value versus where it creates friction?
- Tool-to-task alignment: Are the tools being adopted actually matched to the specific tasks comms teams perform—drafting, research, media monitoring, internal messaging?
- Leadership alignment: Do managers and senior communicators understand enough about AI to make informed decisions about its use?
Skipping this assessment and jumping straight to deployment is one of the most common reasons AI initiatives underperform. Teams end up with tools they don’t fully use, or use incorrectly, because the foundation wasn’t there.
Culture: The Invisible Accelerator
Culture is the hardest pillar to build and the easiest to underestimate. You can mandate tool adoption. You can’t mandate genuine engagement.
For AI to take root inside a communications team, people need to understand the “why” behind it—not just the “what.” Why does AI fit into this team’s work? What problems does it actually solve? What does it mean for individual roles and career trajectories?
When these questions go unanswered, you get passive resistance. People comply with training requirements but don’t change how they work. They use AI tools for low-stakes tasks and avoid them for anything that matters.
Building an AI-positive culture in comms means creating space for honest conversation about concerns—including job security, quality control, and ethical questions. It means celebrating early wins visibly. And it means making sure leadership models the behavior they’re asking for.
Where Communications Teams Are Getting Stuck
Beyond the three pillars, there are specific friction points that show up repeatedly in comms AI adoption.
The governance vacuum. Teams are using AI tools—often shadow tools that IT doesn’t know about—because official guidance hasn’t arrived. This isn’t defiance; it’s pragmatism. Communicators have deadlines. If the organization doesn’t provide sanctioned tools and clear policy, people find their own solutions. The risk is that those solutions may not meet data security or compliance standards.
The training-to-workflow gap. Generic AI training doesn’t translate to comms-specific workflows. A course on prompt engineering is useful, but it doesn’t tell a media relations manager how to use AI to draft a press release while maintaining brand voice, or how to use it to monitor sentiment without over-relying on automated interpretation.
The measurement problem. Many teams can’t demonstrate AI’s impact because they haven’t defined what success looks like. Time saved? Content volume? Response speed? Quality scores? Without baseline metrics and clear KPIs, it’s hard to make the case for continued investment—or to identify where adoption is actually breaking down.
The change fatigue factor. Communications teams have often been through multiple technology transitions in recent years. AI can feel like another wave of disruption layered on top of existing workload. Acknowledging this fatigue openly, rather than pushing through it, tends to produce better outcomes.
Practical Steps to Move Forward
If your communications team is somewhere in the middle of this—past the initial excitement, not yet at confident adoption—here’s where to focus energy.
Start with a governance sprint, not a governance project. Convene a small working group of comms leaders, legal, and IT for a focused two-week effort to produce a working AI policy framework. It doesn’t need to be perfect. It needs to exist and be communicated clearly.
Map your highest-value use cases first. Don’t try to AI-enable everything at once. Identify two or three specific tasks where AI can meaningfully reduce time or improve quality—first drafts, media list research, internal newsletter production—and build competency there before expanding.
Create feedback loops. Build in regular check-ins where team members can share what’s working, what isn’t, and what questions have come up. This surfaces problems early and signals that leadership is genuinely invested in making adoption work, not just checking a box.
Invest in comms-specific AI literacy. Generic AI training has its place, but communications teams benefit most from learning that’s grounded in their actual work. Case studies from other comms functions, hands-on practice with relevant content types, and guidance on maintaining brand voice and editorial judgment alongside AI assistance—these are the things that build real capability.
Tie AI adoption to team goals, not just organizational mandates. When AI is framed as something the organization wants, it feels like compliance. When it’s framed as something that helps the team hit its own goals faster and better, it becomes something people actually want to engage with.
The Bigger Picture for Enterprise Comms
Communications functions sit at the intersection of brand, culture, and information flow. That makes them both high-value targets for AI adoption and high-risk environments if adoption goes wrong.
A poorly governed AI rollout in comms doesn’t just create inefficiency—it can produce inconsistent messaging, compliance exposure, or content that undermines the brand. The stakes are real.
But the opportunity is equally real. Teams that get governance, readiness, and culture right are positioned to move faster, produce more, and focus human judgment where it matters most—strategy, relationships, and the nuanced decisions that AI genuinely can’t make.
The organizations that will get the most from AI in communications aren’t necessarily the ones with the most sophisticated tools. They’re the ones that did the foundational work first.
That’s the part that doesn’t show up in a product demo. It’s also the part that determines whether any of this actually works.
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