Generative AI Is Shifting Communications Value From Production To Perspective

For years, corporate communications relied on a kind of controlled friction. Product launches were carefully staged, messaging moved through layers of approvals, and the simple act of producing polished content required meaningful time and coordination. Generative AI has collapsed much of that friction in a matter of months. Now, almost anyone can generate a credible press release, executive statement, or brand narrative with a well-structured prompt. As content production becomes faster and cheaper, communications leaders are discovering that the value of the role no longer comes from producing more messaging than everyone else. It comes from making better decisions about what deserves attention, what strengthens differentiation, and what should never be published at all.

Shawn Dainas, Founder and Principal at BeCounted Media, has spent decades working through the communications industry’s major technology cycles. Over a 25-year career that included senior and VP-level roles at Riverbed Technology, Polycom, Yahoo!, and Sun Microsystems, Dainas watched organizations adapt to the dot-com boom, the rise of corporate blogs, and the social media transformation. He now sees generative AI as another inflection point, though one moving significantly faster than the transitions that came before it.

"Human interactions are going to continue to matter. Understanding and telling the narrative is super important. When you’re leveraging AI, you still need to understand how to use it to make sure you’re getting the most out of it and having a unique perspective," says Dainas. In his view, the responsibility now falls on communications leaders to create stronger editorial filters around what actually deserves to be published. Without that discipline, organizations risk overwhelming audiences with content that weakens clarity instead of strengthening it. In practice, many teams are discovering that strategic restraint and sharper judgment can create more value than simply maximizing output.

The premium on perspective

Large language models synthesize existing information, increasing the value of original thinking and human judgment. Dainas says his own workflow typically starts with a human point of view first, using AI either to help refine existing ideas or to break through moments of creative stagnation. In practice, he sees the technology as most valuable when it accelerates or sharpens thinking rather than replacing it entirely. "The best perspectives and thought leadership are going to be unique or counterintuitive views that maybe the LLMs are not going to be able to come up with," he notes. "I've used it both ways. With AI, it's not just about speed and efficiency, it's about innovative ideas and innovation sparks."

Those same tools become especially valuable when companies operate across highly technical products, industries, and buyer groups. AI allows communications teams to adapt a core narrative quickly for different audiences, tailoring language for specific verticals, executives, or technical decision-makers without rebuilding the message from scratch. As more employees gain access to AI-powered publishing tools, organizations also face a much larger coordination challenge around consistency, governance, and trust. Dainas says the underlying communication rules themselves have not fundamentally changed, even as the pace of content creation accelerates dramatically. "If you're a public company, you can't give out confidential information, so there needs to be clarity of what's allowed and what's not allowed. Many companies have policies on who could speak to the media, and those things aren't going to change. What changes is the speed."

Messaging in a new era

Managing large-scale messaging changes is familiar territory for Dainas. During his time at Sun Microsystems in the mid-2000s, he helped manage one of the early waves of corporate blogging, another period when employees suddenly gained public-facing voices connected to the brand. He sees clear, accessible narratives as the common thread between that era and today’s AI-driven communications environment, where organizations are once again rethinking how messaging gets created and distributed at scale. "That’s why great brand guidelines and high-level corporate narratives matter, so everyone understands and everybody is consistent with the messages they should be putting out there," says Dainas.

For experienced communications leaders, one of the biggest challenges is adapting to how quickly familiar workflows become outdated. Dainas sees generative AI as part of a much longer pattern of technological transitions, from the move from fax to email to the rise of blogs and social media. Each transition accelerated the speed of communication while forcing teams to rethink long-standing habits and approval processes. He recalls seeing similar concerns emerge during the shift to email in the mid-1990s, when communications cycles suddenly became much faster than organizations were used to managing. "Today, we talk about the speed of AI, but even back then, speed became a bigger issue because you previously had more time to fax something or mail it," he recalls. "In each stage, you need to sort of unlearn the ways you did it."

Leaning into the storm

Looking ahead, Dainas believes one of the biggest differentiators for communications leaders may simply be their willingness to experiment consistently as the technology evolves. He encourages professionals to move past the initial discomfort many people still feel around AI tools and treat them as part of an ongoing learning process rather than a one-time adoption decision. He also sees AI upskilling as something individuals can largely control for themselves, regardless of how quickly formal corporate training programs develop. "A few years ago we were all dabbling. I remember the first couple times, it almost felt like I was cheating," he recalls. "Now, if you're not using the tools and still relying on traditional ways, you're going to be left behind."

At BeCounted Media, Dainas uses AI as a way to expand his capabilities rather than simply automate existing work. He leverages tools like Loveable, Claude, and ChatGPT to prototype and revise the consultancy’s website himself instead of outsourcing design and development, while continuing to apply human judgment around positioning, messaging, and strategic direction. The experimentation also extends into his personal life, where he and his family use simple AI agents to help plan meals and handle routine decisions. For Dainas, regularly testing the tools in both professional and everyday settings helps build a clearer understanding of where automation adds value and where human perspective still matters most. "Lean into the storm, go straight at it," he concludes. "The most successful people are going to take their expertise, test and work with AI tools, and really be able to dig in."