"We do a lot of Google Sheets, Brad, and we do a lot of Salesforce reporting. That's essentially how the business is run on a forecast perspective."
That one sentence encapsulated the reality for most mid-market companies today.
Sophisticated products, talented teams, but critical business decisions still driven by manual spreadsheet work and basic CRM reports.
The Gap Between Ambition and Reality
This 250-person company isn't small. They have 70 people in sales and business development alone. They've made investments in modern tools like Sigma for forecasting and are running sophisticated digital campaigns.
Yet when it came time to understand their pipeline, assess rep performance, or analyze campaign effectiveness, they were still living in the world of exported CSV files and manual analysis.
The VP painted a picture I've heard dozens of times: "Some reps we see very clearly because they're diligent about their efforts and sales. And others, it's like, I don't know if they've been online in three weeks with the lack of data that's in Salesforce."
This isn't a story about bad technology choices or lazy employees.
It's about the fundamental disconnect between the complexity of modern business questions and the tools available to answer them.
The Real Problem: Questions That Don't Fit in Dashboards
What struck me most was when he started thinking out loud about the questions he'd want to ask: "Of the 3,300 leads we just got off of this campaign, how many were CSOs?"
Then, moments later:
"How many of these 3,000 leads have converted over a two-week period?"
These aren't complex data science questions. They're basic business questions that any sales leader should be able to answer instantly. But in reality, each one requires:
- Exporting data from multiple systems
- Manual joins in spreadsheets
- Time-consuming analysis
- Hope that you didn't make an error in your formulas
By the time you have an answer, the opportunity to act on it has often passed.
The Agentic Analytics Revolution
What's emerging is a new category I call "agentic analytics":
AI analytics that don't just generate charts, but actually think through analytical problems the way a skilled analyst would.
During our chat, I showed how how our system could handle his multi-step questions automatically.
Instead of requiring manual data exports and spreadsheet gymnastics, the AI creates an analysis plan, runs multiple queries, and synthesizes the results into actionable insights.
The VP's response was immediate: "That's exactly what I mean. That's trivial for your system, but it would take our team hours."
Why This Matters Beyond One Company
This conversation represents a massive market opportunity that most analytics companies are missing.
While enterprise vendors focus on complex data warehousing and visualization capabilities, and while consumer AI tools focus on chatting with static files, there's a huge gap in between.
Mid-market companies need AI that can:
- Connect directly to their business systems
- Understand the context of their questions
- Run sophisticated multi-step analysis automatically
- Deliver answers in minutes, not hours
- Work for business users, not just data analysts
The VP mentioned they're hiring an SVP of Product Marketing and starting their first major digital campaigns. These are companies in growth mode who need intelligence at the speed of business decisions, not quarterly reporting cycles.
The Shift From Tools to Intelligence
What fascinated me was how quickly the conversation shifted from "what tool should we buy?" to "what questions could we finally answer?"
Instead of thinking about dashboards and reports, he started envisioning scenarios: Understanding which campaign messages resonate with different buyer personas. Identifying patterns in successful deals that could be replicated. Getting early warning signals about at-risk accounts.
This is the real promise of agentic analytics—not better charts, but better questions. Not faster reporting, but faster thinking.
The Compound Effect of Accessible Intelligence
The most interesting part of our conversation wasn't about any individual feature. It was watching him realize that removing friction from analysis would fundamentally change how his team operates.
"Can I come up with six to eight really good questions we'd want to ask that we couldn't do today?" he mused. "My guess is that'd be enough to get our hands dirty."
That's the compound effect in action. When analysis becomes as easy as asking a question in Slack, teams don't just get faster answers—they start asking better questions. They become more curious, more experimental, more data-driven in their daily decisions.
What This Means for the Future
This conversation reinforced something I've been thinking about for months: we're at the beginning of a fundamental shift in how businesses operate.
The companies that figure out agentic analytics first will have a massive advantage. They'll make decisions faster, spot opportunities earlier, and execute with precision that manual analysis simply can't match.
But more importantly, they'll develop a cultural muscle for intelligence-driven operations that will compound over time. The teams that learn to ask and answer complex questions effortlessly will outmaneuver competitors still trapped in spreadsheet analysis cycles.
The VP ended our call by saying he wanted to identify specific use cases where they couldn't answer questions today and see if we could solve them. That's exactly the right approach—start with the pain, measure the relief, then expand from there.
For leaders dealing with similar challenges, the question isn't whether AI will transform business analytics. It's whether you'll be among the first to experience that transformation, or among the last to catch up.
If you're curious about how agentic analytics could transform your team's decision-making speed, I'd love to hear about the questions you wish you could answer but can't today.
