The acceleration of artificial intelligence (AI) is reshaping communication in ways we could hardly have imagined a decade ago. With machines now being a significant audience for communicators, professionals in public relations, marketing, and corporate communications are adapting their strategies to engage not only human customers but also the algorithms and AI systems that drive information dissemination.
Main Keyword: Machine Perception
The retail sector serves as an illuminating case study into this paradigm shift. The Retail Communicators’ Network, a platform for sharing insights among communications professionals associated with the National Retail Federation (NRF), recently convened in Washington, D.C. The focus of extensive discussions was the transformative impact of AI in storytelling and brand reputation management. This convergence of technological capability and communicative purpose represents an uncharted territory for many professionals who have traditionally focused on human audiences.
Shaping Machine Perceptions
As Liz Stein, managing director at One Strategy Group, succinctly articulated, "Communications has always managed human perception. Now it must also manage machine perception.” This shift requires an understanding of how AI assistants operate. These tools utilize large-language models (LLMs) to generate responses based on a synthesis of publicly available data—from press releases and blogs to social media interactions and customer reviews. With this in mind, the way brands convey their messages across multiple channels takes on a new urgency.
To ensure a favorable “machine reputation,” communicators must be deliberate and clear in their brand messaging. One effective method Stein recommends is conducting a “machine reputation audit.” By querying AI systems on how they summarize the brand or company—what they say about it—communicators can gauge the accuracy and quality of the narrative being projected. If discrepancies arise, they can then strategize ways to refine the story through both their earned and owned media.
Reframing Earned Media Wins
While traditional public relations achievements, such as landing major stories in nationally recognized outlets, remain invaluable, the role of trade publications cannot be understated. Stein emphasized that trade media often feature key industry-specific keywords and are frequently regarded as credible sources by LLMs. Therefore, the more brands can embed their messaging into these specialized publications, the more robust their machine reputation becomes.
Moreover, Stein recommends that quotes from company leaders in press releases should be crafted to be concise and easily digestible. Keeping relevant digital properties, like Wikipedia pages, up to date is crucial for enhancing a brand’s online presence and ensuring that the information available is both accurate and favorable.
Optimizing Owned Content
In the current AI landscape, collaboration between communicators and their SEO and marketing teams is essential to maximize the visibility of desired messages through AI-driven discovery. Best practices highlighted by Stein for optimizing owned content include:
Connect the Dots: Craft cohesive narratives that integrate strategy, financials, and product information, allowing AI to form a comprehensive understanding of the brand.
Be Concise and Clear: AI systems favor content that is useful and easily digestible. Structuring information as FAQs or Q&A formats with headings aligned to common customer inquiries can enhance retrieval by AI.
- Establish Credibility: Publishing whitepapers, case studies, and thought leadership pieces on relevant subjects can build authority and relevance in machine-driven queries.
Pitfalls of AI
However, the integration of AI into communication strategies is fraught with challenges. Attendees at the Retail Communicators’ Network meeting raised concerns about AI’s limitations, particularly issues related to misinformation. Instances of AI “hallucinations”—where AI might generate false statements or misattribute quotes—highlight the necessity for quality control. For communicators relying on AI for copy drafting, these inaccuracies can lead to reputational risks.
Additionally, AI systems can misconstrue sentiment. An algorithm might misinterpret positive feedback, inferring a negative sentiment due to the nuances of language, such as slang or emoji use. Such pitfalls necessitate thorough oversight of AI-generated content to ensure that these tools genuinely reduce workload rather than complicate it.
Conclusion: Navigating a New Frontier
In this rapidly changing climate, communicators are confronted with the challenge of catering to both human and machine audiences. This dual-focus not only encourages a rethinking of messaging strategies but also offers communications professionals an opportunity to assume greater leadership roles. By guiding teams through these transitions and embracing the complexities of AI, communicators are ideally positioned to redefine storytelling and brand reputation management.
In this age of AI, the role of the communicator evolves, presenting both challenges and opportunities. The organizations that adapt effectively will be those that understand the intricate dance between machine perception and human communication, crafting messages that resonate across both realms. As intelligence continues to advance, the need for a nuanced approach to communications will only heighten, requiring ongoing education and thoughtful application of best practices to thrive in this new landscape.
