How AI Turns Your Press Releases Into Answers — And Why It Matters

AI isn’t just a future trend. It’s already reshaping how press releases are read, shared, and discovered today. The audience for your news is no longer only human. Journalists, analysts, and investors still matter, but now AI systems are scanning, indexing, and summarizing your announcements at scale.

Here are a few numbers that show the size of this shift:

• 78% of companies now use AI in at least one function (McKinsey, 2025)

• 92% of Fortune 500 companies are using OpenAI's technology (Financial Times) 

• The generative AI market is projected to reach $1.3 trillion by 2032 (Bloomberg Intelligence)

• Generative AI traffic to U.S. retail sites surged 1,200% in just seven months (Adobe, 2025)

• By 2026, 25% of search queries will shift from search engines to AI “answer engines” (Gartner)

• AI retrieval bot traffic jumped 49% in Q1 2025 compared to Q4 2024 (Washington Post)

What does this mean for you? Every press release now speaks to two audiences at once: people and machines. Serving this second audience—the AI systems—is the new frontier of corporate communications.

And this is just the beginning.

In our next article, we’ll reveal which AI companies (Anthropic, Google, Perplexity, and more) are crawling press releases the most, with charts that show the surge in traffic.

Two Ways Your Press Releases Power AI

Your news release, distributed through our SEO-optimized network, has a dual lifecycle. It doesn't just inform the public; it actively fuels the intelligence of the world's most advanced AI models. This happens in two critical phases.

1. Training AI with Your Press Releases

There are many popular AI models out there like ChatGPT, Gemini, Claude, Llama, Perplexity and Grok. These tools are built on large language models (LLMs), trained on massive amounts of text to understand and generate human-like language. And how do they learn? By reading. A lot. Trillions of words from across the internet. To do this, they rely on bots that crawl trusted sources on the web — including press release networks — to gather high-quality text. Once trained, though, their knowledge is fixed at that point in time. They don’t automatically update with new information. This means massive libraries of historic press releases have become part of the model’s long-term memory. These crawlers are indexing press releases for search.

At TMX Newsfile, we've studied the data and know with certainty that our releases are being crawled for use in AI models.

Your earnings release teaches an LLM what financial health looks like. A product launch helps it understand how companies position innovation. An M&A announcement becomes a case study in corporate strategy.

So in a real sense, every press release you publish is like another page in AI’s textbook on how the business world works.

2. Keeping AI Current with Your Press Releases (Grounding)

Training gives an AI its long-term memory, but it doesn’t keep the model up to date. That’s where grounding comes in. Grounding means connecting an AI system to real-time, live sources of truth so its answers reflect the latest facts. And one of the most important sources AI looks for? New press releases.

Takeaway: Press releases are no longer optional updates — they’re how you control your narrative with both humans and AI. When AI systems search for current, reliable information, your release is often what gets pulled in and used to shape the answer.

Grounding capabilities are quickly being implemented in most AI models. OpenAI has browsing built into ChatGPT, Anthropic’s Claude retrieves fresh facts through connected APIs, Google’s Gemini grounds its responses in Google Search and enterprise data, Perplexity is built entirely around grounded search, and Grok taps into real-time data from X (Twitter). Grounding is now the default way AI reduces errors and stays relevant.

Because your new press releases are instantly distributed across our 400+ channel partners and structured for discoverability, they’re often one of the first sources AI systems turn to for accurate, up-to-date answers.

If someone asks, “What did [Your Company] announce about Q3 earnings?”, the model finds your release, checks the facts, and responds with your official statement.

Without your timely press release in circulation, the AI will still try to answer. But, it may rely on incomplete, speculative, or less reliable sources.

How to Write for a Dual Audience: Optimizing Your Press Releases for Humans and AI

Knowing that AI is a primary audience requires a shift in how we write and structure news. The good news is that best practices for AI and humans largely overlap. AI values clarity, structure, and factual density. These are also the same qualities that a busy journalist or investor also appreciates. This approach is often called Answer Engine Optimization (AEO).

Here’s how to do it better than anyone else.

1. Structure for AI Scannability and Extraction

AI models look for predictable patterns to extract key information.

• Front-Load the 5 Ws: Your first paragraph should be a self-contained summary answering Who, What, When, Where, and Why. This is prime material for an AI-generated snippet.
  

• Concise summary bullets up top: Add 1-3 'what's new / why it matters' bullets before diving into details.
  

• Clear, hierarchical headings (for longer releases): While short news updates may not need them, longer earnings or investor releases benefit from bolded headings such as Key Takeaways, Financial Highlights, or FAQs.
  

• Use bullets or numbered lists for key data: Instead of burying metrics in a dense paragraph, break them out for easy scanning.

Before (Dense Narrative)	After (AI-Friendly)
"The company announced impressive third-quarter results, with revenues reaching $50 million, a 15% increase year-over-year, and a net profit of $5 million, while also growing its user base to over one million active subscribers."	Key third-quarter highlights include: Total Revenue: $50 million (a 15% YoY increase) Net Profit: $5 million Active Subscribers: Exceeded 1 million"

• Include an FAQ section (optional): Increasingly used in investor updates, a short Q&A makes complex releases easier for both readers and AI.

2. Prioritize Factual Density Over Marketing Hype

AI algorithms down-rank vague, promotional language and reward specifics. Instead of relying on adjectives, prove your point with names, dates, and verifiable facts.

Here’s how vague marketing language compares to fact-based statements:

Takeaway:

• Name entities consistently: Products, execs, partners, and locations build credibility.

• Anchor claims in facts: Use dates, client counts, or geographies.

• Cut hype: Let specifics speak louder than adjectives.

3. Leverage Multimedia and Metadata

AI doesn’t just read your text. It pulls signals from images, videos, and code-level markup to understand and surface your news.

• Descriptive alt text: Write literal descriptions. Instead of “Graph,” use “Bar chart showing 15% YoY revenue growth for Q3 2025.”

• Informative captions: Add context under photos, videos, and infographics so both humans and AI know what they represent.

• Schema markup: Wrap releases in NewsArticle schema so search engines and AI can instantly recognize it as an official news announcement.

• Multimedia assets: Charts, infographics, and PDFs make facts scannable and reinforce credibility.

Press releases are no longer optional updates. They are the way your brand teaches both humans and AI who you are. Ready to put your news in front of the audiences (and algorithms) that matter most? Contact us to learn how.