April 16, 2026
Key takeaways:
- Tighter budgets are forcing teams to scrutinize what they’re paying for and what actually delivers value
- Many systems look good during evaluation but don’t hold up in day-to-day use
- When software doesn’t fit the work, teams find ways around it
- Real value shows up in solutions that stay embedded in the workflow and get used consistently
- The reset is exposing a clear gap between perceived value and actual impact
Over the past year, the market has sent a clear signal: software is being re-evaluated, and value is being measured much more aggressively than before. In one of the clearest signs of that shift, roughly $285 billion in software market value disappeared in just 48 hours in February 2026.
This is not a normal downturn. It is a reset.
The reason is simple: buyers are no longer asking, “What software do we have?” They are asking, “What is this software actually doing for us?”
The stack is being cut, but that’s not the real story
Across field service and other asset-heavy industries, the response has been predictable. Teams are reducing overlap, cutting tools that do not clearly deliver value, and simplifying the stack. In many cases, that is the right move, but it doesn’t solve the underlying problem.
A lot of software promised visibility, insight, and better decisions. In practice, it often delivered incomplete data, disconnected systems, and outputs that never changed how work actually got done. So when buyers cut tools, they are not just reacting to cost pressure. They are cutting software that never earned its place.
This is why the reset feels so abrupt. It is not just budget pressure. It is a credibility problem.
What’s actually breaking
What’s breaking is not SaaS itself. It is the model of software that was never tied to real outcomes.
For years, the industry rewarded growth, feature expansion, and surface-level visibility. That created a wave of tools that could capture information, but not consistently turn it into action. As a result, many products became part of the workflow without ever becoming essential to it.
That model is now being tested, and it’s not holding up.
The divide in this reset is becoming clear. Tools built on fragmented or incomplete data, tools that rely on manual input to stay accurate, and systems that create visibility without improving decisions are the ones being cut. They may still function, but they do not hold up when teams are asked to prove value, which is why they are being consolidated or replaced.
At the same time, platforms built on structured, continuously improving data are becoming more central. These systems capture what is actually happening in the field and make that data usable across workflows, decisions, and systems. Because they are tied directly to outcomes, they do not get removed during consolidation. They expand their role as other tools are eliminated and become foundational to how the business operates.
That is the shift this reset is driving. Some tools will not survive it, while others will become the foundation everything else depends on.
The real problem
The problem is not too many tools. It’s too many tools built on unusable data. Most software did not fail because it lacked features. It failed because the data behind it was too fragmented, too inconsistent, or too hard to use.
Asset data is often:
- scattered across systems
- encoded in model numbers
- inconsistent across manufacturers
- incomplete at the point of use
So even when systems “have data,” they still cannot answer the questions that matter most:
- What assets do we actually have?
- What should we be doing next?
- Where is risk building across the portfolio?
In practice, that shows up in simple ways. A technician arrives on site without a clear understanding of the equipment configuration. A manager cannot confidently identify which assets are approaching failure. Teams make decisions based on partial information and experience instead of data.
And now AI is making that gap even more obvious. AI does not fix bad data. It exposes it, and in doing so, is redefining where value lives in software.
In an AI-driven market, the advantage is no longer the interface or the feature set. It’s the depth, structure, and usability of the data underneath. Nearly half of organizations struggle with the searchability and reusability of data, limiting how effectively they can use AI and automation. That’s what makes usable, proprietary data the defining moat, while tools built on weak data are increasingly easy to replace.
What survives this reset
The tools that survive will not be the ones with the most features. They will be the ones built on data that actually works.
That means data that is
- complete enough to understand the asset
- structured enough to use across systems
- connected enough to reflect real-world performance
- continuously improving over time
That kind of data is built over time by capturing what’s happening in the field, enriching it with context, and connecting it across systems. It reflects not just what assets are, but how they perform over time. The systems that do this become the foundation everything else depends on, continuously improving how work gets done and how assets are understood.
Because when the data works, everything else starts to work. Service improves, sales becomes proactive, and planning becomes predictable. Without that foundation, every tool in the stack is limited.
Where XOi fits
XOi is not just another tool in the stack. It is the data layer that the rest of the stack depends on, becoming more central as other tools are cut.
In a market where software is being re-evaluated, XOi sits in a different category. It is becoming the system of record for asset intelligence, capturing and structuring the data that other systems rely on but were never designed to create.
XOi captures raw jobsite data and transforms it into standardized, usable asset data across equipment types, manufacturers, and portfolios. That data is continuously enriched and structured so teams can clearly understand what assets they have, how they are performing, and what actions to take next.
Because that data is consistent and connected, it becomes the foundation for how service is executed, how assets are evaluated, and how decisions are made across the lifecycle. Instead of operating as another tool in the workflow, XOi defines the layer that the rest of the stack runs on.
This is why it expands as other tools are consolidated or replaced. Without a system that creates and maintains usable asset data, the rest of the stack continues to operate on incomplete and unreliable information.
The shift ahead
This is not the end of SaaS. It is the end of software that cannot prove value.
Many teams evaluating their tech stack are still asking the same questions: What should we keep? What should we cut?
But those aren’t the right questions. The better question is: Is the data behind our tools actually usable?
Because if it’s not, removing tools won’t fix the problem, and adding new ones won’t fix it either. The value of your tech stack is only as strong as the data it runs on.
That’s what this reset is really revealing.
The companies that win this reset will not necessarily have fewer tools. They will have better tools, built on better data, connected to real workflows, and capable of producing measurable outcomes.
In a market where AI is accelerating the scrutiny on software, the real question is no longer whether a tool is smart. It is whether the data underneath it is usable enough to matter.
And that is where XOi stands out: as the data intelligence layer that enables field service teams to move from fragmented information to real operational value.
We’ve broken down what it actually takes to make asset data usable at scale, and what that unlocks.
