The Essential AI Glossary for PR Pros

From personalized outreach to media monitoring, AI systems have turned the PR industry on its head. 

While there’s a lot to be excited about, our new artificial intelligence world has come with a flood of confusing terms and high-brow jargon that can overwhelm even the most savvy PR professionals.

If you can’t quite keep up, you’re in good company: Over half of professionals say it feels like “another job” to get to grips with AI. Around 33% say they feel embarrassed by how little they understand.

Sit tight. We’ve got you. The definitions in this glossary will help you master the language of machine learning platforms and stay ahead in your field.

Key AI terms for communications and PR professionals

Agentic AI

No, we’re not in the Matrix (yet), but AI agents are already among us. An agentic AI system is one where AI can make autonomous decisions, reason through problems like a pro, and adapt its strategy when situations change.

For the PR function, this means AI systems are capable of: 

• Real-time analysis. 

• Action based on predefined instructions.

• Helping you refine messages as events evolve.

But be warned: while there’s a lot of hype around agentic AI right now, not every tool delivers on its promises. If you plan to use this technology, make sure you select an AI agent designed explicitly for PR pros.

AI-driven media monitoring

Artificial intelligence is a game-changer for how PR pros can monitor and analyze media. Machine learning algorithms now make it incredibly easy to track news coverage in real-time. These platforms, powered by AI systems, can also alert you to mentions, criticism and shifts in sentiment as they happen. 

Many PR pros also use these tools to automate outreach to journalists, but this is where things can get tricky. While large language model tools can suggest possible target journalists for your pitch, they don’t grasp the nuance or the actual relevance of your content to a journalist’s interests. If you use these tools blindly, you risk coming across as insensitive or even spammy. 

Today’s top PR teams integrate AI-driven media monitoring and outreach within a broader, human-led PR strategy.

AI Overviews

Google’s AI Overviews are AI-generated summaries that appear at the top of Google search results, which give users quick insights into the topics they search for. These snippets of information have fundamentally changed how PR content is discovered and ranked in search.

Traditional SEO depends a lot on backlinks, keywords, and domain authority. AI Overviews focus more on brand mentions, relevant context, and how clearly information is shown. Traditional SEO signals still matter, but AI Overviews have reprioritized them.

To optimize your PR content for AI Overviews, you must go beyond technical SEO and create authoritative, brand-focused content that clearly answers user questions. Read our go-to Google AI Overviews guide for more on how to master this new frontier in search.

AI-powered predictive analytics

AI-powered predictive analytics refers to the process of using artificial intelligence and machine learning to analyze large datasets, identify patterns, and forecast future outcomes. AI’s speed, scale and adaptability make it easier for PR pros to spot media trends, plan campaigns, and manage crises. 

For example, if your beverage brand plans to launch a new plant-based product, AI systems can analyze past campaigns, social media sentiment and similar product launches to guide your strategy. Using this information, the system can generate a model's prediction of which messages or campaigns are likely to resonate most with your audience. The data may suggest that sustainability messages could be dismissed as greenwashing, while nutrition benefits could be a stronger focus for your campaign. 

Though still in the early stages of adoption, AI-driven predictive analytics are set to become a core part of modern digital PR.

AI sentiment analysis

AI-powered sentiment analysis tools now make it easy for PR teams to instantly scan thousands of social posts, reviews and mentions to track sentiment in real-time. A key advance in this field was natural language processing, which enables AI to extract insights from human-written content.

With the addition of deep learning and more advanced models, these tools are now pretty good at detecting nuances (e.g., context, sarcasm, or empathy). In other words, they’re way more powerful than older tools that only classified sentiment as positive, negative, or neutral. To get there, large language models rely on training data, thousands of real social media posts already tagged as positive, negative, or neutral. This helps AI systems interpret new messages.

AI-powered sentiment analysis tools can help you spot reputational risks early, adapt campaigns to what resonates with target customers, and address negative sentiment before a situation escalates into a crisis.

Deepfake

As if public relations didn’t have enough challenges to contend with, we’ve now entered the era where AI systems can convincingly create video or audio of anyone on the planet. These are called “deepfakes.” 

The potential consequences? Huge reputational damage and next-level scams.

In one high-profile case, the U.K. engineering firm Arup was duped out of $25 million when an employee was tricked by a deepfake video and audio impersonation of a senior executive, which appeared to be a legitimate video call. 

It’s become critical to monitor news and social channels for suspicious deepfake content. Fortunately, AI-powered media monitoring tools can now flag new mentions and footage in real time. 

Deep learning

Deep learning is a branch of machine learning where artificial neural networks with many layers are used to recognize patterns in large and complex datasets. Unlike simpler AI models, deep learning can handle nuance, subtle connections, and large volumes of information without human intervention at every step.

In PR, deep learning helps tools understand the sentiment behind thousands of social media posts, identify emerging trends in coverage, or even suggest how to craft messages that resonate with audiences. By feeding these models rich training data, they improve over time, becoming more accurate at predicting what content will perform best.

Generative AI

Generative AI (GenAI) is the blanket term for artificial intelligence tools that can aid in content creation, creating new text, images, video and audio. 

It has a wide range of public relations applications (for example, artificial intelligence can help draft press releases, assist with crisis communications, analyze research and uncover campaign insights), which opens up new possibilities for creativity and efficiency in communications. 

These AI systems are trained on vast amounts of training data, which allows them to generate content that feels natural and relevant to humans.

Hallucinations

Generative AI is incredibly powerful, but it isn’t infallible. Like a human brain under stress, it can sometimes “hallucinate” and produce confident but false information.

A good example is Microsoft’s travel chatbot, which famously recommended a food bank as a travel destination. Another case involved Google Bard. The tool claimed that the James Webb Space Telescope had captured the first images of planets outside of our own solar system. NASA clarified that the first images of "exoplanets" were actually taken in 2004, 20 years earlier. This bluder sparked widespread concern over AI’s factual reliability and caused Google's market value to decrease by $100 billion.

Unfortunately, there’s proof that artificial intelligence hallucinations are becoming more common as the models get smarter. 

Human-in-the-Loop

AI systems can learn without human input, but that doesn’t mean they always should. When people actively train and monitor machine learning platforms to improve performance, it’s called “Human-in-the-Loop” (HITL).

HITL helps artificial intelligence pick up on nuance and context that machines often miss. The process may involve labeling training data (e.g., flagging which images depict cats or plants) or checking AI models’ outputs to find mistakes.

For PR professionals, this opens up new job opportunities. As PRDaily recently noted, roles focused on guiding and refining AI have emerged. “AI wrangling” is now a valuable skill to have.

Large Language Models (LLMs)

Large Language Models (LLMs) are AI systems built on neural networks and trained on large amounts of text from the internet that can understand and generate human-like responses. Think of them as incredibly sophisticated virtual assistants that are powered by complex neural networks that can answer questions, write content, and have conversations by drawing on patterns they've learned from millions of web pages, articles, and documents.

Popular LLMs include ChatGPT, Claude, Google's Bard, and others. When someone asks these AI systems a question about your brand, industry, or clients, the LLM searches through its training data to provide an answer.

This is where the power of public relations becomes crucial.

Unlike search engines that show a list of links, LLMs give users direct answers by synthesizing information from multiple sources online. This means that your brand needs to be present in the content these models learn from. The AI systems will reference the most authoritative, frequently mentioned, and relevant information it has encountered during training.

For PR professionals, this creates both an opportunity and a challenge. Your earned media coverage, thought leadership content, press releases and brand mentions in trusted publications become the foundation for how AI systems understand and talk about your brand. The stronger your presence in quality content across the web, the more likely LLMs will accurately represent your brand when people ask about it.

LLM Optimization

AI Overview optimization know-how isn’t the only new skill PR pros need. It’s now just as essential to ensure brand content appears in Large Language Models (LLMs) like ChatGPT and Claude. Say hello to LLM optimization (LLMO). 

Instead of traditional rankings, LLMs surface content based on signals such as the context of brand mentions and the trustworthiness of the sources that discuss them. That makes user-generated content like reviews and organic mentions especially valuable. 

The good news is that LLMO still leans heavily on established PR and SEO principles, such as E-E-A-T and content relevance, which makes it a natural extension of what the best PR pros and digital marketers already do.

Machine Learning

Machine learning is a way of training computers to recognize patterns by showing them thousands of examples, rather than programming them with specific rules.

Instead of giving a system detailed instructions for every possible scenario, you feed it large amounts of data and let it identify patterns on its own. For example, to build a spam filter, you'd show the system thousands of emails labeled as "spam" or "not spam" until it learns to recognize the characteristics that distinguish between them.

The key advantage is that machine learning systems improve their accuracy over time as they process more data. They can also identify subtle patterns that might not be obvious to human observers. What makes machine learning particularly powerful is its ability to adapt and refine its understanding without constant human intervention.

For PR professionals, machine learning powers tools can automatically monitor brand mentions across thousands of sources, analyze sentiment in social media conversations, or predict which content topics will generate the most engagement. Most importantly, they can do all of this by learning from historical data patterns rather than following preset rules. Some more advanced machine learning applications can even help optimize press release distribution timing or identify emerging trends before they become mainstream topics.

Natural language processing (NLP)

Talking to AI has become much easier thanks to natural language processing (NLP), a subfield of machine learning, that allows generative tools to interpret and converse in human language. 

In practice, NLP enables users to type a question into ChatGPT and receive a detailed, human-like response.

NLP is also behind the rise of AI agents. These virtual assistants can understand instructions in plain language, use reasoning to complete tasks and report back with clear answers.

For PR professionals, NLP makes AI tools more straightforward to use (you can talk to the tool like you would to a colleague). As the tools can help with everyday tasks like sentiment monitoring and content production, they’ve quickly become part of daily workflows. 

To remain current in digital PR, it’s essential to understand not just what AI can do, but how and when to use it effectively.

Neural Network

Neural networks are the backbone of many AI systems you already use, including generative AI. Inspired by how the human brain works, they process information through layers of interconnected “neurons” that pass signals and refine patterns with each step.

For PR professionals, this is what enables AI to go beyond simple keyword matching. A neural network powered by machine learning can detect tone in a journalist’s article, recognize a logo in a sea of social images, or summarize complex reports into digestible insights. The more training data it’s fed, the better it becomes at identifying context, nuance, and meaning.

Deep learning, a type of neural network with many layers, powers some of the most advanced PR solutions today, from real-time media monitoring to predictive analytics on campaign performance.

Prompt engineering

Prompt engineering is the practice of designing inputs for generative AI tools, like ChatGPT or Perplexity, to get accurate and detailed outputs. 

Anyone with a computer can do prompt engineering. The key is to be as specific and clear as possible. Writing a press release? Cover elements such as tone, target audience and even the type of sources you want cited for statistics or quotes to win at prompt engineering.

Note that you can refine outputs through multi-step prompting, where each prompt builds on the last. For example, start with an outline tailored to your audience and tone and then ask your AI tool to expand subsections or sharpen key points. OpenAI’s GPTs now make this process even easier. 

That’s just the tip of the iceberg. If you’d like to delve deeper, detailed guides on prompt engineering are available from reputable sources like PRDaily and Spin Sucks.

Stay ahead with AI in public relations 

Now that you're armed with the terms in this AI glossary for PR pros, you already have a strong foundation for any discussion about AI and its impact on your profession. But understanding the language is just the beginning, real mastery comes from hands-on application.

That's exactly what PR.co's AI Agent was built for.

Our AI Agent transforms how you create and manage PR campaigns by helping you build structured, search-optimized press materials that journalists and search engines love. Instead of starting from scratch every time, you'll have an intelligent assistant that understands PR best practices and can adapt to your brand's unique voice and goals.

Here's how PR.co's AI Agent can help you streamline daily tasks:

• Pitch emails that actually get opened: Our AI-Agent crafts personalized pitch emails using your brand voice, release content, and target audience insights.

• Create global campaigns without the budget drain: Our AI Agent translates your releases while preserving your brand voice and following advanced PR localization guidelines, creating campaigns that truly resonate with local audiences.

• Measure your coverage in a way that matters: Generic sentiment tools miss the nuances that matter to your brand. Our AI Agent analyzes your media coverage through your brand's specific lens, delivering insights that drive real decisions.

The future of PR is here, and it's powered by AI that actually understands communications strategy.

Ready to see it in action? Reach out and let’s show you what’s possible.