PR AI Update: New Tools Are Changing How PR Executes Campaigns And Measures Impact

(MENAFN- PRovoke) A new column by tech reporter Evan Zimmer tracking how artificial intelligence is shaping the PR industry. Contact Evan directly [email protected].

Key Takeaways:

• AI is beginning to handle parts of campaign execution, not just analysis and measurement.
• Brand discovery in AI environments is more limited, increasing competition for visibility.
• New tools are focused on execution, using AI to carry out workflows alongside human input.
• Performance signals are playing a larger role in how brands are surfaced and described.
• Generative engine optimization (GEO) is emerging as a way to assess and improve visibility in AI-driven environments.

A few weeks ago, we looked at how AI tools are changing how PR teams use data and understand audiences. This time, the focus shifts to generative engine optimization, or GEO, and to AI tools that are beginning to handle execution, including platforms that can support or even build campaigns for small teams or agencies with limited bandwidth.

Rather than supporting isolated tasks, these tools are being applied to execution, content development and the interpretation of PR outputs after publication. Several patterns are emerging as AI takes on more of that work, including the continued need for human oversight, how teams identify where brands should appear in generative results, and how success in PR is measured.

AI Tools Are Taking on Campaign Execution

It's no secret that generative AI is changing how work is executed. On April 28, The Marketing Cloud announced a new campaign for its Agent Cloud product, releasing a specialized marketing toolkit aimed at small to medium-sized business owners and in-house teams.

The platform is structured around a set of specialized agents that cover different parts of the workflow, including brand audits, campaign planning, copy refinement and social content creation, as well as tools for market research, sentiment tracking and message testing.

It also includes agents focused on discoverability and performance, such as auditing search visibility and translating outputs into formats suited for different platforms. The suite is built around what Amy Guenel, CMO at The Marketing Cloud, calls the“execution gap.”

“For a global agency, [the execution gap] is the 16 hours spent on a manual media audit. For an SMB, it's the founder who has a brilliant brand vision but no time to draft a single social post. Historically, this has been difficult to address because 'nuance' was a human-only trait. You either paid for expensive human hours or used rigid templates that lacked strategic depth,” she said, describing the platform as“expertise-as-a-service.”

The goal is to have AI handle parts of marketing workflows without losing strategic intent.

This approach is intended to help smaller businesses and internal teams execute campaigns without significantly increasing resources, while still keeping human oversight in place.

“Agent Cloud is a Command Center, not an autopilot,” Guenel said.“There is a mandatory human review stage for every output. We've designed the UI to be collaborative; a user can 'interject' at any point, refine a prompt, or ask an agent to pivot. You aren't just hitting 'print'; you are conducting a team of specialists.”

The platform is designed to take on what Guenel calls the“grunt work,” including research, initial drafts and data synthesis, while allowing teams to focus on relationship building and creative direction.

PR Outputs Are Being Filtered Differently

As execution changes, so does how PR work is evaluated. On April 29, Agility PR Solutions released its report Closing the AI Visibility Gap in 2026: Survey and Research Insights to Build a Stronger AI Brand Presence.

The research examined how brands are surfaced, described and cited within AI-generated responses, including which signals influence whether a brand is recommended and how accurately it is represented.

“According to our research, AI engines name an average of 7.5 brands per answer. That shortlist is where brand discovery now happens, and being left off is a signal to audit your visibility strategy,” said Osama Saeed, content marketing specialist at Agility PR Solutions.

“The digital shelf has shrunk significantly compared to the SEO era. Teams are now competing for a position on a highly exclusive shortlist,” he added.“That requires understanding what prompts your audience is asking, which engines they are using, and whether your brand's content and coverage footprint gives AI engines enough to work with when constructing their answers.”

This shift is already affecting how performance is measured and how content is developed. AI answer engine traffic increased 393% year-over-year in Q1 2026, according to an Adobe report, which also found that AI-driven visits converted 42% better than non-AI traffic in March.

“Good AI visibility for a brand involves being mentioned consistently and accurately across various prompts and AI engines,” Saeed said.“Given the limited number of brands per response, PR teams should target mentions across different query types and search engines to maximize visibility.”

In practice, that means aligning content and coverage with how AI systems evaluate and assemble responses, rather than focusing only on traditional search or media placement.

GEO Is Emerging as an Important Measurement Layer

On April 20, Russ Read-Barrow, a 20-year industry veteran, launched Known & Cited, a GEO-led content and strategy consultancy built for agencies and brands. According to Read-Barrow, GEO could offer a new way to measure success in PR.

“It's really hard to show how PR impacts sales or has a really clear business impact,” he said.“GEO is a massive opportunity for the PR industry; if they get it right, it could change that.”

Known & Cited's approach is built around understanding how brands show up across AI platforms. The company runs structured visibility audits using large sets of prompts across multiple models over time, rather than relying on one-off searches.

That work is designed to identify which brands are consistently recommended, which appear inconsistently and which are not surfaced at all, alongside the sources and signals influencing those outcomes.

The goal is not just to measure visibility, but to interpret it - building a clearer picture of how AI systems cite, recommend and position brands, and translating that into practical changes across content, media strategy and positioning.

Read-Barrow connected this to changing user behavior:“People are buying things and choosing things based on AI search... and AI search is being influenced... by earned media and third-party sources.”

Why This Matters

These developments point to a shift in how PR work is carried out and how its impact is understood.

Agentic tools are beginning to take on parts of the execution process, which changes how teams allocate time and where expertise is applied. At the same time, AI-generated responses are becoming a layer where PR outputs are interpreted, summarized and positioned, shaping how that work is evaluated.

GEO sits between those two changes, reflecting an attempt to understand how outputs move from creation to recommendation, and how PR contributes to that process.

There are also limitations to consider. AI-generated responses rely on existing data and citation patterns, which means they may not always reflect a complete view of a brand. These signals are best treated as part of a broader measurement approach rather than a standalone metric. AI-generated outputs can also introduce inaccuracies, which adds risk if execution is left entirely to automated systems.

As AI becomes more embedded in how information is surfaced, the emphasis shifts to how those outputs are interpreted and applied. The ability to understand how content is used, question results and apply that insight in context becomes central to how PR work is defined and evaluated.

Illustration: AI-generated

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