The communications industry currently faces a critical inflection point where the rapid evolution of artificial intelligence is outpacing the organizational capacity to integrate it effectively. As Jeff DeMarrais, Chief Communications Officer at Cognizant, recently argued, the primary threat to communications professionals is not the technology itself, but rather the "velocity gap"—the widening chasm between the speed of AI development and the slower adaptation of professional skills, internal systems, and corporate governance. To bridge this gap, communications leaders must transition their teams from passive users of third-party tools into "AI builders" who can construct, govern, and refine their own automated workflows.
The Current Landscape of AI Adoption in Communications
The shift toward AI integration is already underway, but it remains fragmented across the industry. According to a recent comprehensive survey conducted by Ragan’s Center for AI Strategy, fewer than 20% of communications teams have successfully integrated AI into their core workflows and broader organizational strategies. While many teams have experimented with generative AI for surface-level tasks—such as drafting social media posts, generating headlines, or conducting preliminary research—the deeper, more strategic applications remain underutilized.
Currently, the industry sees a significant drop-off in AI usage when moving from content production to more complex functions. These include stakeholder targeting, predictive crisis preparation, and advanced audience sentiment analysis. The data suggests that while the "creative" side of AI has seen rapid adoption, the "analytical" and "operational" sides are lagging. This disparity creates a strategic vulnerability: teams are using AI to generate more content faster, but they are not necessarily using it to make that content more effective or to protect the brand’s reputation more proactively.
Chronology of the AI Evolution in Public Relations
The trajectory of AI in the communications sector has moved through three distinct phases over the past several years:
- The Experimental Phase (2022–2023): Following the public release of large language models (LLMs) like ChatGPT, communications teams began unsanctioned experimentation. This period was defined by "shadow AI," where individual employees used personal accounts to assist with writing tasks, often without formal guidelines or data security protocols.
- The Governance Phase (2023–2024): Organizations began implementing formal AI policies. This era saw the rise of enterprise-grade AI tools that prioritized data privacy. CCOs focused on mitigating risks such as hallucinations, copyright infringement, and the leakage of proprietary information.
- The Integration Phase (2024–Present): The industry is now entering a period where the focus has shifted from "if" AI should be used to "how" it can be architected into the daily operations of a communications department. This is the era of the "AI Builder," where the goal is to create bespoke, repeatable, and governed processes that augment human judgment.
Move 1: The One-Week Audit Sprint
Before a communications department can scale its AI capabilities, it must first gain a clear understanding of its existing technological footprint. Many teams suffer from "tool fatigue," where multiple subscriptions are used for overlapping tasks, leading to inefficient spend and fragmented data.
The "Audit Sprint" is a high-intensity, five-day documentation process. During this week, team leads are tasked with identifying every point where AI currently touches a workflow. This includes identifying "hidden" AI features within existing software stacks, such as transcription services in video conferencing tools or predictive text in email clients.
The objective of the audit is twofold: to identify redundancies and to pinpoint high-value opportunities for new builds. By documenting current usage, leaders can move away from the reactive purchase of new tools and instead focus on prototyping solutions that solve specific, recurring bottlenecks. For example, if the audit reveals that the team spends 15 hours a week manually tagging media mentions, that becomes a primary candidate for an AI-assisted rebuild.
Move 2: Prototyping Recurring Deliverables
The transition from a bystander to a builder requires a shift in mindset from broad implementation to targeted prototyping. A common mistake among communications departments is attempting a department-wide rollout of a complex AI system without first testing it on a micro-scale.
Industry experts recommend selecting one high-stakes, recurring deliverable—such as a monthly executive summary, a weekly media monitoring report, or a stakeholder newsletter—to serve as a test case. The benefit of choosing a recurring deliverable is that the team already possesses a "gold standard" for what a successful version looks like.
During the prototyping phase, the team should run the AI-assisted process in parallel with the traditional manual process for at least three cycles. This allows for a direct comparison of accuracy, tone, and efficiency. It also exposes "failure points"—situations where the AI might struggle with industry-specific jargon or fail to capture the nuance of a particular corporate narrative. By focusing on a single deliverable, the team can refine the prompts and data inputs until the AI output meets or exceeds human standards.
Move 3: Implementing the 90-Day Governance Hard Stop
One of the greatest risks in AI-assisted workflows is "model drift" or "process decay." Because AI models are updated frequently by their developers and because corporate strategies are constantly evolving, a workflow that is effective in January may be inaccurate or misaligned by April.
To combat this, the "AI Builder" framework requires a mandatory review date for every automated workflow before it goes live. This is not a casual check-in but a "hard stop." Every 90 days, the workflow must be audited against three criteria:
- Accuracy: Is the AI still producing factually correct information?
- Relevance: Does the output still align with the current quarterly goals and brand voice?
- Efficiency: Is the human oversight required for this task increasing or decreasing?
If a workflow fails any of these criteria, it is taken offline and recalibrated. This rigorous governance ensures that the comms team remains in control of the technology, rather than allowing outdated automated processes to run on autopilot.
Move 4: Inclusive Rollouts and Human-Centric Design
The final move in becoming an AI-driven team involves the human element. There is a persistent anxiety within the communications profession that AI will replace the need for human storytellers. Jeff DeMarrais addresses this by emphasizing that the core of communications—crafting narratives, building trust, and navigating the complexities of human emotion—cannot be reduced to an algorithm.
An inclusive rollout involves bringing the end-users into the development process early. When a new AI-assisted workflow is ready for adoption, the "builders" must walk the rest of the team through the "why" and the "how." This includes being transparent about what the AI can do (e.g., summarizing 500 news articles in 30 seconds) and what it cannot do (e.g., determining the political sensitivity of a specific media outlet’s coverage).
By framing AI as a "co-pilot" that handles the high-volume, low-nuance tasks, leaders can free up their staff to focus on high-value strategic work. This move is essential for cultural adoption; a technically perfect AI tool is useless if the team is too skeptical or fearful to use it.
Supporting Data: The Productivity Impact
Supporting data from the McKinsey Global Institute suggests that generative AI could increase productivity in marketing and communications by as much as 15% to 45% of total work tasks. However, this productivity gain is only realized when the technology is integrated into a structured workflow.
Furthermore, a 2024 study by Muck Rack found that while 64% of PR pros are now using AI, only 22% feel they have been "adequately trained" on it. This discrepancy highlights the necessity of the "builder" approach. Training alone is insufficient; teams need to be involved in the construction of the tools they use to truly master them.
Broader Implications and Strategic Analysis
The shift toward becoming AI builders has profound implications for the future of the Chief Communications Officer (CCO) role. Historically, the CCO has been the "conscience of the company" and the master of the narrative. In the AI era, the CCO must also become a technologist and an architect of information systems.
The "velocity gap" described by DeMarrais suggests that the competitive advantage in communications will no longer belong to the teams with the largest budgets, but to the teams with the highest "AI fluency." Those who can quickly prototype, test, and govern AI workflows will be able to respond to crises in real-time, personalize stakeholder engagement at scale, and provide the C-suite with data-driven insights that were previously impossible to obtain.
However, this transition also brings ethical responsibilities. As comms teams become builders, they must also become the guardians of AI ethics. This includes ensuring that AI-generated content is transparently disclosed when necessary and that the data used to train internal models is free from bias. The preservation of human judgment is not just a professional preference; it is a strategic requirement for maintaining brand trust in an increasingly automated world.
Conclusion and Future Outlook
The transition from being a consumer of AI to a builder of AI is the defining challenge for communications teams in the mid-2020s. By following the four-move framework—auditing the current stack, prototyping recurring deliverables, enforcing strict governance cycles, and prioritizing inclusive rollouts—comms leaders can bridge the velocity gap.
As the technology continues to evolve at an exponential rate, the goal is not to reach a final destination of "full automation." Instead, the goal is to build a flexible, resilient, and human-led system that can adapt as quickly as the tools themselves. The future of communications belongs to those who do not just use the tools, but who understand how to build the systems that power them.
