Are we treating AI like a hammer that makes everything look like a nail?

In a world where generative AI is exploding, the possibilities seem infinite. But this abundance creates a paradox: the tasks that used to make us feel like specialists, such as deep data analysis and personalized reporting, are becoming faster to do and, consequently, harder to land.

Our clients’ inboxes are noisier than ever.

So, how do we cut through that noise? We must transition from being data extractors to strategic enablers. 

In this article, I’ll share how to build data narratives that influence effectively. You’ll discover how to ruthlessly prioritize your focus, the psychology behind effective data visualization, and how to position yourself as the partner your clients simply cannot afford to lose.

The shifting landscape of skills

I have the privilege of seeing skill trends from 175 million learners across the globe at Coursera. The data is staggering. While technology and data skills remain crucial, we’re seeing a massive explosion in generative AI learning: a 195% year-on-year growth.

What does this mean for us? It means the administrative burden is lifting, but the bar for strategic value is rising.

Data shared in a recent conference talk by Ashvin Vaidyanathan, VP Customer Success & Services at LinkedIn, and Solany Kapur, Director of Customer Insights at LinkedIn highlights a significant shift in the customer success function. 

Between 2020 and 2025, the skills rising most rapidly in importance aren’t technical troubleshooting or data entry. They’re client satisfaction and customer engagement.

Underneath those buckets, the specific keywords driving adoption on our platform are:

  1. Storytelling
  2. Relationship building
  3. Influencing

This validates what I see daily. To remain indispensable, we must pivot from simply reporting on metrics to articulating value through storytelling.

The trap of the word "obviously"

I recently had a discussion with my team about filler words. We used a tool to analyze our calls and discovered that 34% of our conversations contained the word "obviously."

This was jarring. We use it colloquially to say things like, "Our feature obviously leads to X," or "From this platform usage, you can obviously deduce Y."

Here is the hard truth: It’s not obvious.

We sit within our terminology all day – logins, clicks, engagement scores. Our customers do not. They’re in a noisy environment filled with competitive chatter and internal pressure. 

If we assume the value is obvious, we fail to translate our data into a narrative they can take to their board, share with shareholders, or use to defend the partnership when we aren't in the room.

Value doesn't live in the data points. It lives in our ability to translate them.

Ruthless prioritization: Follow the money

In this infinite world of AI possibilities, we must ruthlessly focus. When preparing for a Business Review (QBR/EBR), we need to ensure we’re driving more valuable, impactful use of our solutions based on the client's specific outcomes.

To do this, we must "follow the money." Every data story we tell should answer one of these three questions:

  • Did we help the client make money?
  • Did we help the client save money?
  • Did we help them improve efficiency?

If your narrative doesn't clearly align with one of these pillars, you risk becoming just another noise in their inbox.