07.22.26 By John Castleman
Try this exercise: pull up your company’s tech spend from the last five years and add it up. Include everything from your data storage, CRM, Martech and RevOps tooling to custom builds and digital transformation initiatives.
Now ask finance how much of that spending drove revenue.
Most leadership teams can’t draw a clean parallel between spend and revenue growth. This is not because the tools don’t work; in fact, they do exactly what they were bought to do. The problem lies in what they were bought to do in the first place.
The reality is that most tools get purchased to solve somebody’s internal headache. Perhaps finance wants a cleaner pipeline report, or sales ops wants a field that makes their numbers easier to defend, or the product team wants to close out a backlog item that’s been open since 2022. Each purchase is reasonable in and of itself, but likely none of them were scoped against the most important question every organization should keep top of mind: does this enhancement change what the customer experiences?
The situation gets worse with mergers and acquisitions. Tool sprawl multiplies, and companies sometimes find themselves running multiple instances of the same platform without realizing it.
On the surface, everything may seem fine because the company is functioning, but what you don’t get is a company that’s winning. Those are two different things, and most companies have quietly settled for a setup that functions when they need to focus on a system that wins.
This is where it gets tempting to sprinkle some AI initiatives into the setup, but it’s important to resist the urge. Bolting AI onto a fragmented setup will not fix the plumbing, it will just move the same disconnected water faster.
The actual decision in front of most leadership teams right now is whether to keep layering AI onto a system that was never built to drive growth or to fix what the system is pointed at first. We built Bridgenext to help enterprises tackle the second option. We call it a Growth OS: one integrated system, engineered around what the customer experiences, not what’s easiest to report on internally. It’s the difference between a setup that functions and a system that wins.
According to Gartner, 74% of the average enterprise IT budget goes to “run” activities (maintenance, upkeep, keeping systems operational). That’s not growth spend. It’s the quiet drag of licensing renewals, integration patches, and the IT hours spent making fragmented platforms talk to each other. The companies that break that cycle don’t just free up budget; they redirect attention and change what’s possible.
A U.S.-based commercial insurance and risk advisory firm we worked with has more than 1,500 agents spread across distributed teams. Before every client conversation, those agents were doing the same thing: manually piecing together fragmented data from multiple systems, sometimes taking days to prepare a single risk profile. The result was a sales conversation that stayed reactive, focused on walking through the current coverage, talking about the renewal and answering one-off questions.
Buyers, meanwhile, were often deciding based on precedent and market norms, not a clear view of their actual risk exposure. While nobody was doing anything wrong, the process wasn’t engineered to produce a different kind of conversation.
Today, an AI-generated brief assembles an agent’s risk profile in about 30 minutes instead of days, pulling client data, industry context, and risk modeling into one view before the call even starts. Pre-sales prep time is down 70%. But the bigger shift is what happens on the call itself: agents walk in with sharper, more specific questions, and the conversation moves from “here’s your coverage” to “here’s your actual exposure, and here’s what to do about it.”
Same agents, same client relationships. A completely different conversation – and one that’s turning transactional buyers into more confident, better-informed ones.
That’s a functioning setup turned into a winning system. The industry doesn’t matter as much as the question the setup was designed to answer.
Most client leadership teams I talk to already sense the gap – between what the stack cost and what it’s actually delivering.
The question they can’t quite answer is whether they’re running a setup that functions or a system that wins. Those two things can look identical from the inside, right up until a competitor who’s rebuilt around the second one starts showing up differently in front of your customers.
That’s the exercise worth doing before the next AI initiative or software implementation plan lands on the roadmap: not “what should we add?” but “what is our current setup actually pointed at?”
Reference
Gartner, Beyond the IT Budget: The Enterprise IT Financial Plan, May 2026
