How Comms Teams Can Use AI Without Losing Control
The promise of artificial intelligence in communications is not transformation for its own sake, but targeted enhancement of the work comms teams are already doing. In recent years, conversations around AI have tilted toward extremes: automation versus human nuance, speed versus ethics, disruption versus tradition. But most media teams aren’t looking to reinvent their workflows.
They are looking to make them more manageable.
Integrating AI into Everyday Work
AI has entered the communications space gradually, first as transcription and scheduling tools, then as summarization aids, and now increasingly as generators of content, sentiment analysis and media-planning recommendations. These developments have created opportunities, but also uncertainty. For comms leaders, the central challenge is no longer whether to use AI, but how to do so in a way that supports accuracy, consistency and trust.
The most effective applications of AI in communications today are not the headline-grabbing ones. Instead, they are the quiet efficiencies: rapid transcription of interviews, automated tagging of media coverage, summarizing long email threads and creating draft coverage reports for review. These are time-consuming tasks where precision matters but creative discretion is often limited.
Used correctly, AI can reduce administrative burden and free up experienced communicators to focus on judgment-driven work: advising leadership, framing messaging and navigating stakeholder relationships. The aim is not to replace communications expertise but to clear space for it.
That said, even low-stakes automation demands oversight. A flawed summary of a media interview or an incorrectly tagged article can lead to missteps that harm reputation. The more AI is embedded in routine workflows, the more important it becomes to build systems that validate and track its output.
Not all gains are worth the trade. Comms teams need to be wary of over-reliance on AI-generated copy or sentiment analysis tools that lack transparency. A perfectly formatted but contextually misleading briefing is more dangerous than an incomplete one. What AI can do is not the same as what a communications team should delegate.
Accuracy, tone and institutional memory still require human review. Draft statements, for example, might be usefully composed by an AI assistant, but they should never go out without a review for unintended implications, consistency with prior messaging, and cultural nuance.
Efficiency becomes a liability if it obscures errors instead of revealing them.
The Case for Structured Oversight
The goal isn’t to use AI everywhere. It’s to use it where it adds value without undermining judgment. This is the approach Broadsight takes internally. The platform leverages AI to streamline tedious, repetitive tasks like coverage tagging and summarization, but it doesn’t rely on automation to make final decisions. AI-generated outputs are always presented alongside relevant history and context, helping teams assess them critically rather than adopt them uncritically. In this way, Broadsight treats AI as a tool within a human-driven process, not a replacement for it.
The more AI enters the day-to-day work of media teams, the more critical it is to create visibility around how communications decisions are made. Comms teams must be able to retrace where a statement originated, what reference points were used and whether any material was generated by or amended through AI tools. This isn’t about compliance. It is about control.
When AI tools are used to assist with summaries or reporting, teams can compare the generated material against past communications and ensure alignment. Structured archives also provide a layer of defence against overdependence: They offer the context necessary to interpret AI output accurately, instead of accepting it at face value.
AI is not neutral. It reflects the data it is trained on, the prompts it receives, and the gaps in both. Without institutional knowledge to counterbalance its output, even a technically impressive tool can lead teams astray. A structured system gives teams the ability to question, verify and correct, rather than operate on assumption.
Choosing the Right Thresholds
There will always be a temptation to let automation scale. A successful pilot project becomes a team-wide workflow. A helpful assistant becomes a decision-maker. But communications work is not inherently high-volume—it’s inherently high-consequence. The threshold for trust must remain high.
Smart comms teams are not the ones automating the most. They are the ones choosing carefully what to automate, and when to intervene. AI can help create space for strategic work, but only if its role remains transparent and its output consistently reviewed.
The goal is not to slow down innovation, but to channel it. With the right systems in place, AI becomes a collaborator, not a risk. But without those guardrails, speed becomes noise and automation becomes guesswork. Structured oversight keeps teams grounded in the facts, anchored in context and aligned with their broader communications strategy.
The question isn’t whether AI will change the way media teams work. It already has. The question is whether those changes will make teams more effective or merely more efficient. The answer depends entirely on how well those teams understand the tools they use and how well they remember the work they’ve already done.
Broadsight remembers the work you’ve done and helps you keep the AI in check. To see how, visit broadsight.ca.
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