When I began putting pen to paper to write down my thoughts on this topic – yes, I still think best with pen in hand – there seemed to be a veritable explosion of AI development hitting the mainstream conversation.
And when I read about an AI ad airing during the NBA finals (with the painfully appropriate tagline “the world’s gone mad”), I dropped my pen in frustration and pushed away from my desk.
I mean, come on.
With the rapid shift in public expectations, it’s no surprise that pressure is mounting within organizations as well, where AI is already redefining how we work, plan, and build.
These days, expertise might feel a little … slippery. With the right prompt and polish, anyone can sound the part. And when confidence wins out over credibility, it shapes what gets heard – and what gets overlooked.
Look at how much more efficient we can be! Look at how easy the complex is now! Can’t we just AI this?
I’m hearing versions of this tension in nearly every conversation these days. As the pendulum swings further into the AI space, the question of how we communicate expertise – and act as a trusted advisor in a world of AI-generated everything – becomes even more urgent.
When did it get so complicated? The shifting trust in data communication.
Given the speed of change in how we communicate with data, it’s interesting to look back and see where things started to change.
Five to ten years ago, data storytelling was about clarity and translation. Visual design was celebrated, and bridging the literacy gap between analysts and decision-makers was the unicorn skill everyone wanted.
Then came the brief but transformational era of data democratization – and COVID.
As organizations pushed for more data-informed decisions, they invested in self-serve analytics, dashboards, and literacy programs. COVID accelerated the shift. Suddenly, every Zoom call included a “can you see my screen?” moment, as chart sharing became the name of the game, creating a level of analytic comfort and expectation we’d never seen before.
And just as we were getting settled into, or stressed out by, that new normal, AI hit the mainstream.
Now, we’re flooded with content that appears authoritative but lacks strategic context. (Case in point – what does your LinkedIn feed look like these days?) This makes stakeholders more comfortable challenging conclusions, and more skeptical about what’s real, what’s manipulated, and what’s actionable.
In short, just a few years ago, the storyteller’s job was to demonstrate the “so what.” Today, that same storyteller must convince skeptical audiences that their interpretation is both valid and meaningful – and provide perspective on where to focus in a growing sea of information.
At the same time, they need to show confident humility: the ability to stand by their narrative, while recognizing other points of view may hold merit too.
And that level of nuance? No AI tool can truly replicate it.
The bar has been raised: here’s what that means for Storytellers
A note for savvy readers before we dive in: none of the skills you see below are “new,” nor are they mutually exclusive. Strong data communicators have always leaned on empathy, judgement, and persuasive delivery.
But what has changed is the environment. And those changes have raised the stakes for data communication.
“Sounding smart” is easy; breaking through is harder. The best data storytellers I work with understand this to their core, and know:
- Their value is no longer simply clean logic or slides – it’s relevance and resonance
- Their superpower is no longer simply clarity – it’s cutting through noise with conviction
- Their goal is no longer simply understanding – it’s making decisions easier
What sets trusted data storytellers apart today
I see these instincts show up most often in three human skills – ones that help data storytellers stay relevant, trusted, and heard when it matters most. In a crowded, AI-shaped conversation, these are the competencies that tend to carry weight.
1. Adapting to the audience lens, not just audience priorities
AI can mimic structure, logic, and even tone, but it can’t model audience awareness. Sounding right but landing flat isn’t going to break through the continuous hum of noise surrounding decision makers.
This goes beyond the traditional “know your audience” definition. We’re talking about anticipating skeptical audiences, knowing your story, speaking to outcomes, and adapting the message accordingly. Audience-centric persuasion is a non-negotiable trait.
Imagine this: You’ve built a detailed forecast for a marketing investment, but know your CFO is laser-focused on fiscal constraint. Instead of leading with ROI, you start by acknowledging the budget pressures and point to how your plan helps manage risk down the line. It’s framed as a strategic trade-off, not a spending ask – your story told through their lens, not yours.
2. Bringing judgment to the data, not just the facts
Where AI can predict patterns, flag anomalies, and even generate hypotheses, it still does not have the strategic discernment to provide genuine judgement and identify what matters most to this audience at this moment, and why.
Too often, “most surprising stat” is confused with “most important takeaway.” These are not the same thing. AI can surface anomalies, but it takes human judgment to know which ones actually signal risk, change, or opportunity.
A point of view is essential. Find it and share it.
Imagine this: You’ve filtered through 48 different metrics about customer retention. Stepping back, you recognize that declining repeat purchases aren’t just a loyalty issue – they signal a fundamental shift in how your customers use your product category. While others focus on the surprising 15% decline and suggest tactical fixes, you recognize a deeper risk: this trend threatens your company’s newly developed subscription model. Your story shines light on the risk and speaks to the strategic pivot needed, not just the retention tactics.
3. Advocating with confident humility, not false certainty
Today’s AI environment presents a double-edged sword: these tools foster false confidence, while those with real judgement often feel unsure about how to advocate in the face of strong(er) voices. It’s the Dunning-Kruger effect in action.
I suspect we’ve all seen this play out – strong opinions and incomplete facts competing with credible findings for attention. That’s why informed voices need to show up with clarity, translating the complex into a confident, credible story that can break through.
At the end of the day, the pretty slide isn’t the one that wins. The slide that wins is the one that earns the presenter the right to be in the room.
The strength of a storyteller increasingly lies in defending a recommendation under pressure, and seeing questions as the gift they are – an opportunity to show depth, flexibility, and intent. This kind of exchange drives the right conversation for the business, leading to better, faster decisions for the business.
Imagine this: You’re on the agenda to discuss changes to a long-standing leadership program. A senior executive cuts in: “I think we can gain some time back in our agenda here. There’s no evidence we need to change. Candidates tell us it’s why they join, and the latest engagement survey rates it among our highest.” You see agreement around the table.
You stay grounded. “The program still scores well, but those scores have dropped for two years, and we’re seeing more negative than neutral feedback from later-stage participants. In follow-up sessions, it’s clear expectations have shifted. The intent is right – the changes we’re recommending keep the spirit, but evolve the delivery to meet today’s needs and maintain its competitive edge.”
You don’t challenge the facts. You reframe with context you own, and bring the conversation back to what matters now.
These three examples aren’t templates — they’re glimpses of how human nuance continues to matter. And in a world that rewards speed and polish, it’s the steady, trusted voices that carry weight.
Data storytelling isn’t going anywhere – but it has evolved
Currently, generative and agentic AI can write, summarize, visualize, and even simulate structured data narratives.
It’s no surprise that some see this as the future of data storytelling – yet another function destined to be made obsolete by technology.
But I see it differently. AI won’t replace data storytelling, but it may mark the end of passive storytelling, the kind that expects audiences to draw their own conclusions based on templates, visualizations, and retrospective narratives. (Honestly, no one will miss that.)
AI models can’t truly listen, adapt, or persuade in the moment, with actual stakeholders and real decisions on the line. They can’t anticipate reactions or navigate the complexities of change – all necessary skills to move from introspection and inertia to alignment and action.
This is where trusted advisors still stand apart. The best will tap into AI to sharpen their thinking – e.g. test ideas faster, explore framing, and/or pressure-test findings – but using it still requires something AI can’t replicate: judgement about what matters most, when, and why.
The bottom line: the human edge still matters
In an age of growing mistrust and strongly held opinions, we need trusted advisors to help us make sense of what’s in front of us. But trust is harder to earn and easier to lose. Credibility isn’t just about content anymore (if it ever was). It’s about judgment, empathy, and timing.
The reality is the next era of data storytelling won’t be defined by slides or stats. It will be defined by trust, discernment, and real-time persuasion.
Developing and flexing these storytelling muscles sets the groundwork to thrive as a trusted advisor in the world of AI. That’s where the real value lies, and that’s where we work.
The future of data storytelling is human. For more than a decade, Storylytics has helped teams grow into confident, credible advisors. If you’re ready to help your team thrive in the age of AI, let’s start the conversation.
