Why One or Two People Shouldn’t Own Your Business Insights
In most growing companies, there’s a person — sometimes two — who everyone goes to when they need to understand what’s happening in the business. They know how to pull the reports. They know where the data lives. They know how to make sense of it. And they are, almost always, completely overwhelmed.
This is the analysis bottleneck. It’s one of the most common structural problems in companies between 50 and 250 employees, and it tends to get worse as the business grows, not better. More data comes in, more functions need answers, and the same one or two people are expected to handle all of it on top of everything else they’re already doing.
The bottleneck doesn’t just slow things down. It shapes what questions get asked in the first place.
How the Bottleneck Forms
The analysis bottleneck usually starts with the founder. In the early days of a business, the person who built it is also the person who understands it best. They know the numbers intuitively. They can look at a dashboard and see what’s wrong. They make decisions quickly because the whole picture is in their head.
As the company grows, that approach stops scaling. There are too many functions, too many data sources, too many decisions happening at too many levels for one person to stay on top of all of it. So either the founder stays in the bottleneck and becomes a constraint on the business, or they hand it off to someone else, usually an ops or finance person, who then becomes the constraint instead.
Pete Caputa, CEO of Databox, describes the pattern clearly. You’ve got a senior person trying to manage the business who’s also doing analysis. Or you’ve got an ops team that’s responsible for processes, tools, and billing, and they’re squeezing in analysis on the side. Either way, there’s always a bottleneck. The analysis gets done when someone has time for it, which means it gets done infrequently, incompletely, or both.
What the Bottleneck Actually Costs
The most obvious cost is speed. When analysis has to run through one person, decisions wait for that person. A marketing manager who wants to understand whether a campaign is working has to ask someone. A sales manager who wants to see how a rep’s activity compares to the rest of the team has to ask someone. A product team trying to understand usage patterns has to ask someone. Every one of those requests adds to a queue that’s already backed up.
The less obvious cost is the questions that never get asked. When analysis is scarce, people learn to ration their requests. They ask the questions that feel most urgent and most defensible. They don’t run exploratory analyses, don’t investigate hunches, don’t dig into anomalies that might turn out to be nothing. The analytical surface area of the business shrinks to whatever the bottleneck person has bandwidth to handle.
And the least obvious cost is the quality of decision-making at every level of the org. When front-line managers and team members can’t access analysis themselves, they make decisions on instinct, habit, or whatever information happened to come their way. Some of those decisions will be fine. Some of them won’t. And the people making them have no way to know the difference until the results come in.
The Shift AI Makes Possible
What’s changed in the last year or so is that AI, combined with the right data infrastructure, makes it genuinely possible for anyone in a company to run meaningful analysis without needing to know how to write a query or build a report.
Caputa describes what this looks like in practice at Databox. He used to run a monthly process where managers reported up through directors and VPs to him, and he’d sit with all of that information and identify what needed attention. Now he logs into Claude, which is connected to Databox’s own MCP server, and runs analysis directly. He’s defined skills inside the system that know how the business is built and can pull real-time analysis across every function. What used to take a monthly cycle of reporting now happens on demand.
The broader implication isn’t just that the CEO can move faster. It’s that the same capability can be extended to anyone in the org. A product manager can analyze feature usage without waiting for a data analyst. A marketing manager can investigate a traffic drop without filing a request. A sales manager can look at pipeline health across the team without asking ops to pull a report.
When analysis is no longer scarce, the frequency of good decisions goes up. Problems get caught earlier. Opportunities get spotted by the people who are closest to them. And the one or two people who used to carry the analytical weight of the entire company can focus on the harder, higher-leverage work that actually requires their expertise.
What This Requires to Work
Distributing analysis across an org doesn’t happen automatically. A few things need to be in place first.
The data has to be there. If your CRM only captures closed deals, if your marketing tools aren’t connected to your revenue data, if each function is living in its own system with no way to see across them, there’s nothing to analyze. The foundation is clean, connected, well-captured data.
The metrics have to be defined. AI can sequence a set of analytical steps and explain what it finds, but it needs a system that knows how to calculate your specific metrics correctly. Engagement rates, conversion rates, return on ad spend — these require specific calculations, and getting them wrong produces analysis that looks authoritative and leads you in the wrong direction.
And the culture has to support it. If leadership is used to being the sole interpreter of business performance, distributing that capability requires a real shift. It means trusting that your team can handle real information about how the business is doing, and that the transparency will produce better decisions rather than confusion or conflict.
None of those things are easy. But for companies that get them right, the payoff isn’t just faster analysis. It’s a fundamentally different kind of organization, one where insight isn’t rationed and decisions don’t have to wait for the one person who knows how to pull the report.
Listen to the full podcast episode here.