May 27, 2026
Key takeaways:
- Poor asset data slows decisions, which directly reduces the amount of work that converts into revenue.
- Basic visibility into assets does not provide enough context to guide repair, replacement, or capital spend decisions.
- Inconsistent or incomplete data forces teams to verify information, delaying quotes, approvals, and execution.
- The speed at which teams move from identifying an issue to making a decision determines whether revenue is captured or lost.
- Structured, reliable asset data gives teams the clarity needed to plan spend, justify decisions, and move work forward with confidence.
Customer expectations are rising, costs are tightening, and regulatory pressure is increasing. At the same time, many organizations are still operating with fragmented asset data, inconsistent workflows, and service history that never becomes anything more than documentation.
This gap is often described as operational, but it has real financial implications.
It came through clearly in a recent conversation between Pete Shimkus, who leads data strategy at XOi, and Irv Aristy, who runs service operations at Farmer & Irwin.
Seeing the asset is not the same as understanding where money is going
Most teams believe they already have visibility into their assets. They can find a record. They can pull a model number. They can look up past work. But that is not the same as understanding the asset.
In most systems, asset information is fragmented. Some of it lives in job history, some in customer records, and some in notes or attachments. It exists, but it is hard to use when a decision needs to be made. You cannot clearly see how the asset is configured, what it runs on, how it has performed over time, or what it is likely to require next. Without that context, decisions default to general assumptions.
When asset data is enriched and structured, the picture sharpens quickly. At Farmer & Irwin, Irv and his team saw this play out in real time. Within a few months, they uncovered that 15 percent of the assets they were servicing were already beyond 80 percent of useful life, and 10 percent were at or past the end of life. They also identified R22 still active across multiple sites.
The equipment did not change. The level of understanding did. And that is what makes it possible to decide what to fix, what to replace, and where to spend.
That shift turns a list of assets into a clear view of where money is going and where future cost is building.
In many portfolios, that number is even higher. It is not uncommon to see 50 to 60 percent of assets at or beyond useful life once the data is fully understood. Most teams simply do not have the structure or depth to see it clearly.
At that point, the conversation changes. It is no longer just about understanding your own assets. It becomes possible to understand how they compare.
Knowing an asset is aging is useful. Knowing whether it is aging faster than it should is what actually drives action.
When the data does not hold up, deals slow and revenue follows
Understanding only creates value when it can be trusted at the moment a decision needs to be made.
This is where most organizations fall short. The issue is not access to data, but consistency.
If assets are captured differently across jobs, if details vary, or if work is not reliably tied to the correct unit, the record weakens. That weakness shows up when work needs to move forward.
Quotes take longer because information must be verified. Answers are delayed because context is incomplete. Recommendations lose strength because they cannot be supported clearly.
As a result, decisions slow down.
Work gets deferred, opportunities stay open longer than they should, and teams fall back to short-term fixes because they are easier to approve. These decisions are not made because they are right, but because the data does not hold up well enough to support something better.
Every delay slows down deals and impacts revenue.
Speed determines whether identified work turns into revenue
When asset data is consistent and complete, the operating model changes.
Information is available when it is needed, without reconstruction. Irv described a request for documentation that would have previously taken days to assemble. Instead, it was delivered in under two minutes, complete with photos, videos, and service context tied directly to the asset.
That level of clarity changes how work progresses.
Approvals can happen while the technician is still on site because the information needed to decide is already there. The gap between identifying an issue and acting on it disappears, which reduces delays and removes the need for follow-up visits.
This is what determines whether identified work actually converts into revenue.
When decisions can be made quickly, work moves forward. When they can’t, opportunities start to slip.
Service history only matters when it changes what happens next
As structured data accumulates, its value compounds.
Teams begin to see how assets behave over time, including which units fail repeatedly, where spend is increasing without improving performance, and which assets are no longer worth fixing.
This is where service history becomes useful.
As Irv put it, ‘service history is gold.’ Its value is not in the record itself, but in what it enables. It allows teams to explain not only what has happened, but what should happen next.
That is when the conversation shifts from another repair to a decision about replacement.
As that understanding builds, it extends beyond individual assets. Teams can see how the portfolio behaves as a whole, where failures are concentrated, and where action should be prioritized.
This is where teams move from reacting to guiding.
Waiting slows deals and makes revenue harder to win
The impact of delay is already showing up in the numbers.
Teams are seeing proposals that were sent a year ago return at 10 to 12 percent higher cost. The work has not changed, but the timing has.
Equipment costs have increased, constraints have tightened, and options have narrowed.
When decisions are delayed, deals become harder to close. Customers hesitate, and work that could have been planned becomes reactive.
That changes how the work is sold.
Instead of a structured conversation about timing and replacement, it becomes an urgent fix. That compresses margin and reduces control over the outcome.
Waiting does not simply increase cost. It slows deals, makes revenue harder to win, and limits the ability to execute work on the right terms.
The difference shows up in what you can say when someone asks what to do next
The shift is not about adding more systems or collecting more data. It is about the quality of the answer when a decision needs to be made.
Most teams can explain what happened. Fewer can clearly explain what it means for the asset. Fewer still can support what should happen next with enough confidence to move a decision forward.
When the data is clear and complete, the conversation changes.
It becomes possible to explain that an asset has been repaired multiple times, that spend is increasing, and that continuing to invest in it no longer makes sense.
That is what makes the answer credible.
The real shift
This is what separates teams that find work from teams that close it.
The teams that get this right move faster, close more of what they find, and stay in control of how revenue comes in instead of chasing it.
Because in the end, this starts in operations and shows up in the numbers.
