What I’m Seeing on the Front Lines: How Generative AI Is Actually Changing Marketing, AdTech, and RevOps Data Work
Written by Tara Patoile, Adobe data & insights practitioner
The Bite-Sized Breakdown
Hot take…many marketing, advertising, and revenue teams are getting ahead of the data problem, but they still have an insight problem. Despite powerful platforms and endless dashboards, getting from signal to understanding still takes too long. From my perspective working at Adobe and with many high-tech customers, generative AI is finally changing that. By enabling teams to ask natural-language questions, auto-generate insight narratives, and connect web, media, and revenue data more intuitively, GenAI is shifting analytics from reporting to reasoning. The real impact is helping humans understand their data faster, communicate insights more clearly, and make better decisions across the entire customer journey.
Changing How Humans Interact with Data
If you asked me a few years ago what teams needed most from their data, I probably would’ve said better, more connected dashboards.
Today, I’d tell you something very different.
What teams really want is clarity and quickly surfaced insights.
I spend my days talking with marketing technology, advertising technology, and revenue operations leaders who are drowning in data but starving for insight. They have Adobe Analytics data, CDP profiles, CRM data, media performance metrics, signal data, product usage data — sometimes “connected,” yet still incredibly hard to interpret.
Generative AI is the first thing I’ve seen that meaningfully changes that equation.
Not because it replaces analytics or dashboards… there’s a time and place for those, but because it changes how humans interact with data.
From Reporting to Reasoning: Why GenAI Feels Different
One common, regurgitated line is:
“We have the data. We just don’t know what it’s telling us.”
Traditional analytics tools still assume the user knows:
What question to ask
Where the data lives
How to interpret the result
Generative AI breaks that assumption.
Inside tools like Adobe Analytics and Customer Journey Analytics, GenAI-powered experiences are shifting analysis from navigation to conversation. Instead of clicking through dimensions and segments, teams can ask:
“Why did conversions drop among returning visitors last week?”
“What’s different about users who convert after three visits versus one?”
“Which behaviors actually correlate with pipeline creation, not just engagement?”
What matters here isn’t novelty. It’s speed to understanding.
I’ve watched marketers who never touched Analysis Workspace suddenly explore insights confidently—because the system meets them in their language, not the other way around.
AI-Generated Insights That Actually Get Read
Let’s talk about reporting for a second.
Most reports fail for one simple reason: no one has time to interpret them.
With generative AI layered into Adobe’s analytics and customer data platforms, we’re starting to see reporting shift from static outputs to narrative explanations:
Automated summaries of weekly performance
Plain-language explanations of anomalies
Callouts on what changed, what didn’t, and why it matters
For example:
Adobe Analytics surfacing why a key metric moved, not just that it moved
Customer Journey Analytics explaining cross-channel drop-off points across web, app, and offline interactions
AI-assisted insights highlighting which journeys are accelerating—or stalling—revenue outcomes
For RevOps teams, this is a big deal. Instead of stitching together marketing and sales reports manually, GenAI helps translate behavioral data into business impact stories executives can actually act on.
Making Sense of Web and App Behavior at Human Speed
Web and app data is some of the richest, and messiest, data organizations have.
Page views, events, clicks, scroll depth, feature usage… it’s powerful, but overwhelming.
This is where I see GenAI adding immediate value inside tools like Customer Journey Analytics and Real-Time CDP:
Summarizing top user journeys without manually building flow reports
Identifying emerging behaviors you didn’t think to look for
Translating event-level noise into insight-level signal
Instead of asking, “What happened on this page?”
Teams start asking, “What does this behavior tell us about intent?”
That’s a massive mindset shift.
Connecting Marketing, Media, and Revenue Signals
One of the hardest problems in marketing technology has always been connecting the dots across the funnel.
Media data lives over here.
Web behavior lives over there.
CRM and pipeline data live somewhere else entirely.
Generative AI doesn’t magically fix data architecture, but when applied on top of platforms like Adobe Experience Platform, it helps teams reason across those connections faster.
I’m seeing:
Media teams using AI-assisted insights to understand which audiences drive downstream value, not just clicks
Marketing ops teams identifying behavioral signals that correlate with pipeline creation
RevOps teams explaining revenue performance in plain language backed by data
The breakthrough isn’t more data—it’s shared understanding across teams that used to speak entirely different analytical languages.
Analysts Aren’t Being Replaced — They’re Finally Being Unblocked
This is important to say clearly: Generative AI is not replacing analysts.
What it’s doing is removing the friction that kept analysts stuck doing:
Repetitive report pulls
Basic exploratory analysis
Endless “can you explain this chart?” requests
With GenAI assisting in exploration and storytelling, analysts can focus on:
Data quality and governance
Advanced modeling and experimentation
Strategic interpretation and recommendations
In other words, GenAI raises the floor for everyone, and raises the ceiling for experts.
Trust, Governance, and Responsibility Still Matter
One thing I’m very vocal about internally and externally: AI-generated insights are only as valuable as they are trusted.
As teams adopt GenAI across Adobe platforms, the strongest implementations:
Respect consent and data usage policies
Make it clear how insights are generated
Keep humans in the loop for critical decisions
Speed without trust doesn’t scale. Insight without governance doesn’t last.
The Big Shift I’m Watching Happen
If I zoom out, here’s what I think is really changing: We’re moving from data as an asset to insight as the interface.
Instead of asking people to adapt to tools, we’re finally adapting tools to how people think, question, and decide.
For MarTech, AdTech, and RevOps professionals, generative AI isn’t about doing more reporting—it’s about getting to the meaning faster, so teams can focus on outcomes instead of outputs.
That’s the shift I’m most excited about, and honestly, the one that feels long overdue.
Reference Links for Light Reading
If you’re curious to dig more into best practices for any of the tools references in this article, check out the links below: