For the past decade, organizations have invested heavily in connecting their marketing data. However, even in some of the world’s most sophisticated brands and agencies, data literacy has stalled. In my experience working across global data and media technology teams, the same pattern appears again and again: the majority of people who rely on this data still can’t retrieve it safely and reliably in a way that intuitively makes sense.
The gap between data and action isn’t a motivation problem or an interpretation problem. It’s an access problem. If people can’t get to the data independently, they can’t act on it. Adverity’s AI vision is built to remove that barrier entirely by turning data into an everyday, accessible language.
Why has data literacy stalled?
Data literacy isn’t one skill. It consists of four capabilities:
- Access - Can I retrieve the data myself?
- Interpretation - Do I understand what it means?
- Communication - Can I explain it clearly?
- Application - Can I use this insight to make a decision?
Across the industry, non-technical teams understand their business metrics and how to interpret them. They know how to communicate and act on these insights to make high stakes decisions, but what they can’t do is reach the data independently.
In my experience, access is the single biggest barrier preventing organizations from becoming truly data-driven. In fact, according to our research, improving data access is the top data governance priority, with 28% saying this is their biggest focus for the coming year.
Typically less than half of staff are genuinely empowered by the data their organization works so hard to centralize. Everyone else depends on analysts, as they’re the gatekeepers of SQL knowledge, BI tools, and complex dashboards. The delays caused by dependence on more technically-minded team members mean that by the time an answer is provided, the moment for action or decision has passed.
As a result, employees spend hours pulling data and assembling slides. They struggle to find basic information, such as a simple spend number, which requires navigating through multiple complex dashboards. Ultimately, this lack of access means businesses are systemically underusing their most valuable asset.
Lack of access is stamping out curiosity
The industry often frames this challenge as a data literacy issue. But that narrative doesn’t reflect reality inside marketing teams. People aren’t confused by the data, they’re just blocked from getting to the data.
This creates a predictable and damaging cycle:
1. Dependency on analysts
Everyone who can’t self-serve becomes reliant on BI teams for every question, large or small.
2. The pre-selection effect
Teams start to avoid asking questions they think won’t be answered quickly. Curiosity shrinks.
3. Insights narrow instead of expanding
Organizations unintentionally optimize for the simplest possible questions, not the best ones.
4. Data maturity stalls
Dashboards become static endpoints. Analysts become fulfillment teams, and insight velocity collapses.
To move forward, organizations need a new model of access, one which allows for complex queries that expand curiosity rather than stamping it out.
Moving from dependency to autonomy
Adverity’s AI vision is rooted in a simple but transformative principle. Data should feel like an interactive language that anyone can speak, not a gated system that only analysts can enter.
The end goal is an organization where:
- Non-technical teams explore data independently
- Dashboards become interactive conversations, not static reports
- Stories rooted in data can be created instantly, beautifully, and consistently
- Analysts shift from gatekeepers to enablers, focusing on governance, templates, and strategic work
- Curiosity expands instead of contracting
Getting there requires a full rethinking of the user experience, relying on AI and automation to handle so much of the complexity that has historically been a barrier to actually using data.
AI that expands human capability and curiosity
Our AI agents are designed to unlock each part of data literacy by eliminating the first barrier of access, and making exploration, explanation, and execution much less complex. Here’s a quick snapshot of how we’re doing that.
1. Data Conversations: Ask anything
Natural-language questions like:
"How much did we spend on Meta last week?"
"Which products underperformed in LATAM in Q3?"
Impact:
- Reduces time-to-answer from days → seconds
- Turns Slack messages into insight channels
2. Data Notebooks: Explore anything
For multi-step reasoning and “why” questions without SQL or Python.
Impact:
- Makes complex analysis accessible to strategists, category teams, and brand managers
- Turns hypotheses into insights within an hour
3. Dashboard Conversations: Unlock anything
A conversational layer over trusted dashboards.
Impact:
4. Dashboard Creator: Build anything
Explain what you want. AI builds the dashboard.
Impact:
5. Presentation Creator: Tell the story
The last mile of insight: automated slides, updated branded decks, and instant reporting.
Impact:
- Standardizes quality across teams
- Turns everyone into a compelling data storyteller
Together, these AI agents and environments reshape how people think about and interact with data itself. Non-technical teams finally move past queries being answered with "I'll get back to you" to provide real-time answers, shrinking the decision cycle from days down to minutes. This efficiency extends to reporting and analysis, as QBRs and monthly decks are built in minutes, and complex hypotheses are tested instantly rather than waiting for days. This not only empowers creatives with on-demand performance insights but fundamentally unblocks the speed and agility of the entire business.
Beyond speeding up individual workflows, this shift also elevates the organization’s entire operating model. Teams across the business begin working from the same trusted data language, removing the misalignment that slows collaboration today. And while non-technical teams gain autonomy, analysts gain breathing room. Their time is freed from manual fulfillment so they can focus on governance, modeling, and strategic enablement. The result is a company where insight flows freely, decisions happen quickly, and teams stay in sync.
MCP lets you bring your own AI application to Adverity Data
We’re also preparing Adverity Data to support MCP connections, which means these agents are only the beginning. As MCP becomes the standard for connecting AI tools to business systems, customers will be able to bring their own agents or preferred AI applications and plug them directly into their Adverity data. Whether a team prefers Claude, Gemini or something they've built internally, they’ll be able to safely connect those tools directly to their Adverity data without extra integration work.
None of it works without a strong data foundation
While AI dramatically increases the velocity of turning data into intelligent action, this acceleration is only possible when the data powering AI is consistent, well-governed, and secure. This is what Adverity has historically been known for, and we’re continuing to invest in making this process as simple as possible while giving maximum flexibility for marketers with Adverity Data.
AI has long been a part of how we help marketers connect, clean and prep data as efficiently as possible, whether that’s smart naming conventions, AI powered transformations or automated data monitoring. We’ve been in the business of making marketing data usable for a decade now, and most of that work hasn’t been glamorous. It’s meant navigating constant API changes, reconciling mismatched metrics, and building guardrails that keep data trustworthy at scale.
That kind of experience is what’s given us a sharp sense of where complexity hides and how to remove it without compromising quality. And that’s exactly why our approach to AI works. We’re not layering intelligence on top of chaos. We’ve already spent years unraveling that chaos and building the foundations that make real-time, reliable insights possible. And with that foundation of good data quality and data governance in place, AI stops being a risk or a novelty. It becomes a practical way for every marketer to explore, understand, and act on their data with confidence.
The future: Data as a shared language
The organizations that thrive in the next era of marketing will be the ones where data is used widely rather than held within a small group. When teams can explore information freely, insight begins to move at the pace decisions are made. Curiosity becomes something people act on rather than something they suppress, and conversations across the business start to share a common context. In those environments, data is no longer a specialist task. It becomes part of how people think.
The priority is no longer to centralize or collect more data. It is to unlock the collective ability to work with it. That is the core of Adverity’s AI vision: making data a language that everyone can speak with confidence, regardless of role or technical skill.
