AI is sitting in on your earnings call, and it's already rewriting the story

Reporters, analysts and investors are asking smarter questions.

That’s what I keep hearing from CEOs, CFOs and communications leaders when we talk about how Earnings Day is changing. The press release still matters. The Q&A still matters. But AI is changing how earnings materials are read, analyzed and interpreted.

Stakeholders are walking in better prepared. And they’re walking out with their take already written, drafted by AI in real time, and sometimes released before the CEO and CFO have finished the Q&A.  

That’s because what used to take hours of manual work, from reviewing transcripts to cross-referencing filings and prior commentary, now happens in minutes. AI tools surface patterns, compare language across quarters and pressure-test management’s narrative in real time.

AI isn’t replacing human judgment. But it is increasingly shaping the first read of a company’s performance story, even before many journalists and analysts have read the full transcript themselves.  

Earnings Day Has a New Translator

Earnings calls have always been high-stakes moments. They can move stock prices, shape investor confidence, and influence how a company’s leadership, strategy and long-term outlook are perceived.

What’s new is the interpretive layer sitting between the call and the audience: AI tools that summarize, compare and repackage the story before anyone else weighs in.

That matters because markets don’t respond to numbers alone. A company can beat expectations and still see its stock move lower if investors are focused on guidance, margins, capital allocation, leadership tone, geopolitical risk or broader macro uncertainty.

So the central question for Earnings Day is no longer only, “How did we perform?” It is also, “How will this performance be interpreted?”

For communications and investor relations teams, that changes the work. Earnings preparation can’t stop at the script, press release or anticipated Q&A. Companies need to understand how their story will land with people, platforms and AI-powered tools, and build a narrative disciplined enough to hold up across all three.

Three shifts stand out for me.

1.) A single phrase can define the quarter

Companies used to have a window. Stakeholders would digest earnings materials, listen to the call and develop their read of the quarter over hours or even days. Today, AI-powered tools can identify key themes, compare current commentary with prior quarters and surface potential issues much more quickly.  

That speed matters. In a real-time media and market environment, early interpretations can travel at a breathtaking pace. A single phrase about guidance, demand, margin pressure or macro uncertainty can be pulled forward, summarized and repeated before the company has had a chance to reinforce the broader context.

That speed creates a serious risk: narrative-compounding. One narrow interpretation gets repeated, summarized and redistributed until it begins to shape the broader read of the quarter.  

Companies still need to report the facts clearly, but they also need to anticipate which facts, phrases or inconsistencies are most likely to be elevated by AI-enabled analysis.

2.) Organizations are fighting AI with AI

To stay ahead of this shift, communications and investor relations teams are increasingly exploring how AI can help them prepare. They’re beginning to fight AI with AI, not by outsourcing judgment to a model but by using AI as a pattern-recognition tool.

In practice, that means identifying likely questions, surfacing potential inconsistencies, analyzing sentiment, comparing current messaging with prior commentary and pressure-testing how a quarter might be interpreted by different audiences.

If a company misses expectations in one area but has a strong underlying strategic story, AI can help teams identify where the narrative may become muddied, whether around guidance, margins, demand, capital allocation, leadership credibility or sector pressure. The earlier those pressure points are identified, the better prepared leadership and communications teams are to address them directly.

AI doesn’t give companies the answer. It helps them see where the market, media or other stakeholders may look first.

3.) Proactive companies ensure AI can understand the right story from the start

AI can help companies identify inconsistencies and pressure-test likely interpretations. That’s great, but pressure-testing is reactive. The more proactive opportunity is ensuring the company’s narrative is clear, consistent and easy to understand from the start.

When my colleagues and I help clients prepare for Earnings Day, one of the first things we look at is their Generative Engine Optimization, or GEO.

In a communications context, GEO means structuring public-facing content so generative AI tools can more easily find, understand and cite accurate information about a brand or organization. Owned content, earned media, executive statements, investor materials and public disclosures should all reinforce the same core story.

The goal is not to “game” AI. It’s to reduce ambiguity.

Then we go over some key questions.  Does the messaging hold up across the release, script, Q&A and investor materials? Are short-term headwinds clearly separated from long-term fundamentals? Would an AI-generated summary reflect the story stakeholders are meant to take away?

Companies that answer those questions well will be better positioned in an environment where earnings narratives are interpreted faster, summarized more widely and pressure-tested more aggressively than ever before.

The story doesn't end when the call does

AI does not change the core responsibility of Earnings Day. It raises the standard.

The traditional muscles of financial communications still matter: market fluency, investor understanding, executive counsel, message discipline and media judgment.

But as AI changes how information is found, interpreted and distributed, those muscles need to work alongside new ones: technology fluency, data awareness and a real understanding of how narratives move across both human and machine-powered channels.

That is the shift I’m seeing across the industry.  

I’ve had the benefit of working across very different agency models, from large global firms to smaller, specialized boutiques to a midsize global agency built around technology, complexity and senior-level partnership. That perspective has shaped how I think about this moment. The strongest client partners in this environment will be the ones that can connect financial communications, technology fluency and senior counsel.

Earnings preparation is just one part of the process. Once the call ends, the story keeps moving through headlines, analyst notes, investor conversations, internal channels and AI-powered tools.

This isn't theoretical.

I saw it firsthand recently at the BlackRock and Global Infrastructure Partners (GIP) conference in Washington, D.C., where I heard directly from Larry Fink, Sam Altman, legislators and other leaders. The conversation has already moved past the question of whether AI will reshape industries. The focus now is on infrastructure, capital, policy and competitive dynamics already forming around it.

And that’s still largely just the generative AI conversation. The next phase, from agentic AI to more complex emerging models, will only add new layers to how information is found, interpreted and acted on.

For financial communications, the signal is clear: The early days are behind us. We’re in the middle days now. The advantage will belong to communications partners who understand how stories move across markets, media and machines, and who are already building the muscles for what comes next.