Everyone’s Talking About AI, But How Do You Actually Get Heard? The Reality Check for Your AI Pitch

Here’s how to make sure reporters are listening as the world of AI continues evolving.

Later this month marks the three-year anniversary of ChatGPT’s public debut. Back then, we wondered what it was – maybe even feared it – and how it would change the future. Looking back, it now lives on as a monumental moment that marked artificial intelligence becoming mainstream.

Here’s what’s changed: In 2022, journalists were hungry for experts with insights on AI, and companies experimenting with it. Fast forward to today, everyone has thoughts on AI and virtually every organization claims to have an AI story to tell. While the AI market grows more saturated, newsrooms are shrinking and news cycles are getting shorter. The old playbook to pitching AI is no longer cutting it.  

As AI continues to evolve at breakthrough pace, we also need to evolve how we tell these stories to break through the noise.

Cutting Through the AI Noise

Every technology rolls through the phases of the hype cycle. In fact, the growth of AI took off so quickly that Gartner now has a dedicated annual hype cycle for AI.

In fact, American reporters collectively published 7.1million stories on AI from March 2023 to March 2024. As everyone strives to be a part of the conversation, this number will likely continue to grow (hint:that means more AI pitches in reporters’ inboxes).

For communicators tasked with telling the stories, this means staying educated and staying honest. Understanding how AI actually works is now table stakes. It’s not enough to say your client “uses AI.” You need to know how it works, what problems it solves, and why it matters.

The bottom line: how you position your client’s AI matters just as much as what the technology does.

1) Differentiate in the Post-ChatGPT World

ChatGPT may have ignited the AI revolution, but it’s not the only player. The ecosystem has exploded with models advancing toward multimodality, deeper reasoning, and industry-specific specialization. Modern systems are better at understanding context, turning insights into action, and generating domain-specific results.

So, what? When pitching, skip blanket statements about AI capabilities. Instead, articulate what kind of AI you’re using — large language model, generative image system, recommendation engine — and what makes it uniquely effective.

Specificity signals credibility, and the more precise you can be about what your AI does, the more likely your story will rise above the generic claims out there.

2) Call It What It Is, Don’t ‘AI Wash’

The AI hype is driving pressure to innovate, even when you’re still experimenting. But overpromising backfires. In early 2024, the term ‘AI-washing’ gained recognition after the Securities and Exchange Commission warned against misleading claims about AI use.

Why it matters: AI-washing isn’t just a marketing blip, it’s an ethical and legal responsibility. When you pitch, be explicit about what your AI can do and transparent about what it can’t.

As journalist Agam Shah said, “If someone tells me their AI is accurate, I move on. No AI is 100% accurate.” Honesty builds long-term credibility, and helps your story hold up under journalistic scrutiny.

3) Break the Echo Chamber with a Fresh POV

Journalists crave fresh perspectives, and can spot a recycled narrative instantly. Stay away from echoing herdthink. The strongest AI stories come from companies with a unique thesis on how AI will change their industry and why they’re approaching it differently – backed by users and concrete metrics.

Encourage executives to document their AI philosophy and test it with trusted advisors. Some of the questions you should be able to answer are:

• What problem are you solving that others aren’t?  

• How is your model trained, validated, and updated?

• What challenges or limitations are you managing?

• What measurable impact have users seen as a result of your AI?

• How does your approach differ from competitors models available today?

Even if your prediction misses, a clear log of intent, experiments, and user evidence wins more trust than vague market-speak.

4) Win with Proof, Not Promises

The media wants a story with outcomes. Lead with proof instead of product features. Bring customers into the conversation to demonstrate real-world impact, using metrics, case studies, and testimonials that demonstrate measurable results. A single quote from a user describing how your AI solved a measurable problem will almost always resonate more than a soundbite from a Chief Technology Officer.

What Edelman is saying: According to their 2025 Trust Barometer, distrust of business leaders and media have both grown. As the trust gap widens, credibility depends on evidence.

Make sure your public claims with your actual capabilities. Consistency between what you say and what you deliver is the foundation of long-term media trust.

Credibility Wins Headlines

Every AI model makes mistakes and every organization experiences trials and tribulations. It’s better to communicate the setbacks transparently than to hide them. And, your credibility as a communicator depends on how you navigate those realities. Be clear about what stage your AI is in, back it with data, and frame progress as part of the journey.

As the AI landscape continues to evolve, the brands that break through the noise to win coverage — and trust — will be those that combine expertise with honesty. In the end, the most compelling story isn’t the one with the perfect path to success. It’s the one about how organizations learn, adapt, and deliver real impact, proving why their AI truly matters.

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