Analyze PR and AI Research Findings from the Simmons University Gwen Ifill School
Understand the Current State of AI Integration in USA PR Firms
The intersection of artificial intelligence and public relations represents one of the most significant operational shifts in the USA communications landscape. Recent PR and AI research news highlights a critical disconnect between the desire to adopt new technologies and the actual operational standardization of these tools. Public relations firms across the country recognize that they can no longer delay their adoption of AI, yet many remain stuck in the preliminary stages of implementation.
Industry leaders understand there is a distinct opportunity to leverage their existing expertise and engage in meaningful conversations with clients regarding AI capabilities. However, recognizing an opportunity and executing a structured strategy are two entirely different challenges. As agencies attempt to integrate these complex systems into their daily workflows, they are encountering unexpected roadblocks that prevent them from maximizing their return on investment.
Navigating these new technologies requires a clear understanding of both the tools and the internal dynamics of an agency. Schedule a free consultation to learn more about how strategic planning can bridge the gap between AI adoption and actual workflow integration.
Identify the Internal Capability Gap in Public Relations
One of the most compelling findings from recent academic-industry collaborations is the presence of a pronounced internal capability gap within PR organizations. Research spearheaded by Ammina Kothari, Dean of the Gwen Ifill School of Media, Humanities, and Social Sciences at Simmons University, reveals that AI familiarity is heavily concentrated at the top of the organizational hierarchy.
Senior leaders and executives possess a strong conceptual understanding of AI technologies. They feel confident discussing these tools with clients, proposing AI-augmented strategies, and forecasting the future impact of machine learning on the industry. Conversely, mid-level managers, supervisors, and entry-level employees—the professionals closest to the day-to-day workflow and client execution—often lack this same level of familiarity and comfort with the technology.
This dynamic creates a distinct operational bottleneck. Agency leaders are making promises and setting expectations based on a high-level understanding of AI, while the staff responsible for fulfilling those expectations lack the specific training or guidelines required to execute the vision. As Bret Werner, a partner at the PR firm MikeWorldWide and a collaborator on the research, notes, the most significant AI gap in public relations exists inside the agencies themselves, not between agencies and their clients.
Compare Modern AI Adoption to Early Social Media Integration
This internal capability gap represents a fascinating reversal of historical trends in the PR industry. When social media platforms first emerged, the knowledge dynamic was inverted. Entry-level employees and junior staff were the digital natives who understood the nuances of Facebook, Twitter, and Instagram better than many C-suite executives. Agencies had to rely on their youngest employees to educate leadership on digital strategy.
With AI, the paradigm has shifted. The cost of accessing large language models, the strategic implications of data privacy, and the high-level understanding of algorithmic capabilities are currently resonating more strongly with senior leadership. Mid-level and junior employees, who are focused on immediate task execution, are largely being left out of the initial AI strategy conversations, which hinders an agency’s ability to operationalize the technology effectively.
Review the Methodology Behind This PR and AI Research
The credibility of these findings stems from a rigorous, mixed-methods research approach that combined academic scrutiny with real-world industry perspectives. Funded by Pennsylvania State University’s Arthur W. Page Center for Integrity in Public Communication, the project brought together Kothari, Joon Kim (an Associate Professor of Public Relations at the University of Rhode Island), and industry veteran Bret Werner.
To capture a comprehensive view of the USA PR landscape, the research team utilized two primary data sources: focus groups and nationwide surveys. The focus groups provided deep, qualitative insights into the nuances of different types of PR organizations. By engaging with five distinct companies of varying sizes, geographic locations, and client portfolios, the researchers could ask targeted questions about agency-level AI strategies, tool usage, and the specific barriers preventing investment or implementation.
To broaden the scope of their findings, the team partnered with Qualtrics to deploy a nationwide survey targeting communications professionals. This quantitative data provided a macro-level view of AI adoption across the industry, validating the qualitative insights gathered from the focus groups and ensuring the research accurately reflected national trends.
Examine the “Nuance Gap” in Large Language Models
Beyond internal organizational gaps, the research highlights a fundamental technological limitation known as the “nuance gap.” Public relations relies heavily on context, tone, emotional intelligence, and brand voice—elements that current large language models (LLMs) like ChatGPT struggle to replicate accurately.
Because LLMs generate output based solely on their training data and the prompts provided, they lack the inherent human judgment required for sensitive communications. Crisis management, corporate social responsibility messaging, and nuanced media pitching require a deep understanding of current cultural contexts and interpersonal dynamics that AI simply cannot provide. Consequently, AI-generated content requires strict human verification before it is suitable for publication or client delivery.
This requirement for a “human in the loop” significantly impacts the return on investment for smaller PR firms. For agencies with limited staff, investing time and resources into AI tools may not make sense if the output still requires extensive human editing. The operational risk associated with publishing unverified AI content is high, making smaller firms particularly cautious about widespread standardization.
Share your experiences in the comments below regarding how your organization handles the nuance gap and verifies AI-generated content.
Develop Strategies to Operationalize AI in PR Workflows
Addressing the internal capability gap and the nuance gap requires PR leaders to move beyond generalized conversations and establish concrete, operationalized strategies. Agency leaders must initiate organization-wide dialogues that specifically address the concerns, fears, and practical needs of entry-level and junior employees.
Establish Clear Internal AI Policies
Research indicates that many agencies lack formal, in-house AI policies. Developing these guidelines is a crucial first step. Policies should clearly define which AI tools are approved for use, what types of tasks AI can assist with (such as initial brainstorming, data analysis, or draft generation), and the mandatory review processes required before any AI-assisted work leaves the agency.
Implement Targeted Training Programs
To close the capability gap, agencies must invest in targeted training for mid-level and junior staff. Training should not focus solely on how to use specific AI platforms, but rather on AI literacy—understanding how these tools generate information, recognizing their biases and limitations, and learning how to write effective prompts that yield useful, rather than generic, results.
Evaluate Return on Investment Realistically
PR leaders must shift their perspective on AI ROI. Instead of viewing AI as a replacement for human labor, agencies should evaluate how AI can augment human expertise. Measuring success should focus on metrics like time saved during the initial research phase, the volume of data successfully analyzed, or the efficiency of drafting standard communications, rather than expecting AI to produce final, client-ready copy.
Explore our related articles for further reading on creating effective technology policies and measuring ROI in communications.
Apply AI Research Insights to Public Relations Education
The implications of this research extend far beyond active PR agencies; they directly influence how universities must prepare the next generation of communication professionals. At the Gwen Ifill School of Media, Humanities, and Social Sciences, Kothari is actively integrating these research findings into the curriculum to ensure students are equipped for the realities of the modern workplace.
Higher education institutions bear the responsibility of producing graduates who are not only AI-literate but also deeply grounded in the fundamental skills that AI cannot replicate. Interpersonal communication, creativity, emotional intelligence, ethical judgment, and complex problem-solving remain the core competencies of a successful PR professional. AI tools must be taught as supplements to these skills, not replacements for them.
Joon Kim’s classroom practices at the University of Rhode Island offer an excellent model for this pedagogical approach. In his social media courses, Kim requires students to conduct manual comparative analyses of brand strategies on platforms like Instagram before they are allowed to use any AI tools. Once the students have established their own findings, they generate an AI-based comparative analysis and compare the results. This exercise practically demonstrates the limitations of AI, showing students that while AI-generated analysis can be useful, it frequently misses the deeper contextual story that human analysis uncovers.
Furthermore, the research suggests that teaching students why an agency might choose not to use AI in certain situations is just as valuable as teaching them how to use it. Understanding the risks, the nuance gap, and the client-specific contexts where AI is inappropriate is a critical component of modern PR education.
Anticipate Future Trends in AI and PR Research
The work initiated by Kothari, Kim, and Werner is far from concluded. As the technology evolves, so too must the research tracking its impact on the industry. The team has already presented their focus group findings at the Association for Education in Journalism and Mass Communication (AEJMC) conference, with plans to present the comprehensive survey results at the International Association for Business & Society (IABS) conference.
Looking ahead, the researchers plan to expand their findings into peer-reviewed academic articles, ensuring the data enters the permanent scholarly record. Additionally, they are developing high-level, practical content designed specifically for PR industry leaders to help them navigate the standardization process.
As AI continues to embed itself into the fabric of USA PR operations, the divide between agencies that merely talk about AI and those that successfully operationalize it will widen. By addressing internal capability gaps, acknowledging the nuance limitations of large language models, and prioritizing comprehensive education, the public relations industry can harness these tools responsibly and effectively.
For students and professionals looking to stay ahead of these industry shifts, gaining a formal education that addresses both the theoretical and practical aspects of AI in media is essential. Submit your application today to join programs that prioritize cutting-edge industry research and responsible AI integration. If you want to discuss how these trends might impact your specific career trajectory, have questions? Write to us!
