Published: Jun 20th, 2026 - 9 min read
One of the most expensive decisions an organization can make is to underestimate downtime.
Not because servers stop. Not because networks fail. Not because applications become unavailable. Those are only the visible symptoms.
The real cost begins long before the first alert appears. And it continues long after every dashboard turns green again.
It took me years to realize that.
Early in my career, I believed an incident ended when the service was restored. Experience taught me that the technical recovery is often the shortest part of the incident. The longer, more expensive part begins after, when the organization decides what kind of company it wants to be in response to what just happened.
This article is not about uptime. It is about what uptime actually protects.
Across banking, telecommunications, consulting, and enterprise technology, I have watched organizations respond to outages in remarkably similar ways. The technology changes. The architecture changes. The vendors change. The reaction rarely does.
Everyone focuses on restoring service. Very few stop to measure everything that was lost while the service was unavailable. The post mortem documents the timeline, the root cause, and the remediation steps. It almost never documents the full cost, because most organizations have never built a framework for what that cost actually includes.
Most outages last minutes. Their consequences can last quarters.
Downtime carries five distinct costs. Most organizations only measure one of them.
This is the cost everyone sees first, because it is the most immediate. Engineers pulled out of planned work into a war room. Escalations running across multiple teams and time zones. Emergency changes pushed through without the review process that exists precisely to prevent emergencies. Productivity lost not just for the people fixing the problem, but for everyone whose work depended on them.
Uptime Institute's 2026 Annual Outage Analysis, its eighth consecutive report on the subject, found that 57 percent of organizations said their most recent major outage cost more than $100,000. For the second year running, one in five reported costs exceeding $1 million. Those figures capture only the direct operational and financial response. They do not capture what comes next.
This is the cost finance departments already track, though usually only the most visible slice of it: lost revenue, SLA penalties, delayed transactions, the contractual cost of failing to meet a commitment.
What gets missed almost universally is opportunity cost: the features that did not ship, the roadmap that slipped, the strategic work that senior engineers did not do because they were managing an incident instead. That opportunity cost rarely appears in a financial statement, but it compounds every quarter it goes unmeasured.
Splunk's 2026 report, The Hidden Costs of Downtime, produced in partnership with Oxford Economics, surveyed 2,000 executives across the world's largest companies and put a figure on the full scale of this problem. Downtime now costs Global 2000 companies a combined $600 billion annually, a fifty percent increase over just two years. Average lost revenue per organization has reached $95 million a year, nearly double what it was in the same report two years earlier.
Numbers at that scale are easy to read past. The point is not the size of the figure. The point is the direction. This cost is not stabilizing. It is accelerating, even as infrastructure, tooling, and monitoring all continue to improve.
This is the cost that is hardest to measure and easiest to ignore, because it rarely shows up immediately. Customers do not usually churn during an outage. They churn months later, quietly, when the contract renews and they choose someone else, often without explaining why.
The same Splunk report found that 81 percent of technology leaders now cite customer loss as a direct consequence of downtime. Even more striking: 47 percent admit that their customers are often, or very often, the first to detect service degradation, ahead of their own monitoring systems.
That last figure deserves to be sat with for a moment. Nearly half of technology leaders are acknowledging that their customers see the problem before they do. That is not a monitoring gap. It is a trust gap, and trust gaps compound silently until the moment a customer simply does not renew.
This is the cost I find most consistently overlooked, and it is the one I believe matters most for how an organization performs over the following year, not just the following week.
For years, I underestimated this cost myself. I was the engineer celebrating when the fix worked. It took me longer to notice what the fix had cost us in caution, six months later, when nobody wanted to approve a routine change without three separate signatures.
When an organization lacks the observability to understand what is actually happening during an incident, every decision connected to that incident becomes slower, more conservative, and more expensive. Not just the decisions made during the outage itself, but every decision made afterward, by people trying to make sure it never happens again.
Approval layers get added. Change windows shrink. Deployment frequency drops. None of these reactions are irrational. Each one is a defensible response to a real incident. But stacked together, over multiple incidents, they produce an organization that has traded velocity for the appearance of safety, often without anyone deciding that tradeoff was worth making.
The real cost of downtime is rarely the incident itself. It is the overcorrection that follows it, repeated enough times that caution becomes the default culture.
The fifth cost is the one that compounds across all the others: leadership cost. Every incident spends down a reserve of confidence that took years to build. The board's confidence in the technology organization's ability to manage risk. Customers' confidence in the product they depend on. The team's confidence in its own judgment, particularly after a decision made under pressure turns out to be wrong.
I remember a room like that. The dashboards had already turned green. The room had not. Nobody was looking at the monitoring screens anymore. Everyone was looking at each other.
That reserve does not refill automatically when the dashboards turn green. It refills when leadership demonstrates, consistently and visibly, that it understands what happened, why, and what is actually being done about it, beyond a generic promise that it will not happen again.
This is where observability enters the conversation, and it is worth being precise about why it matters here.
Observability is not valuable because it produces dashboards. It is valuable because it reduces the cost of uncertainty. The faster an organization understands what is happening, the smaller the business impact becomes. Uncertainty, not the outage itself, is often the most expensive part of downtime.
Every one of the five costs described above is inflated by uncertainty. Operational cost rises the longer it takes to find the root cause. Financial cost rises the longer revenue-generating systems stay degraded. Customer trust erodes faster when customers cannot get a clear answer about what is happening. Decision cost rises when leaders cannot tell whether a fix actually worked. Leadership cost rises when nobody in the organization can speak with confidence about the current state of the system.
Observability done well does not eliminate incidents. It compresses the period of not knowing, and that compression is where most of the avoidable cost actually lives.
Artificial intelligence will eventually help organizations predict incidents before they occur. Anomaly detection, predictive capacity modeling, and automated root cause analysis are already moving in that direction, and the pace of improvement is real.
But prediction alone will never eliminate downtime. Complex systems will continue to fail in ways no model anticipated, because complex systems always do. What changes with AI is not whether uncertainty exists. It is how quickly an organization can move through that uncertainty toward a decision.
Organizations still need leaders capable of making sound decisions while uncertainty remains, because some uncertainty never fully resolves before a decision has to be made. Technology may predict. Leadership still decides.
After twenty years working in critical infrastructure, across banking, telecommunications, consulting, and enterprise technology, I no longer measure downtime by minutes.
I measure it by decisions.
Every minute of downtime is ultimately the consequence of decisions made before the incident, during the incident, and after it. The architecture decisions that determined how the system would fail. The decisions made in the first ten minutes of the incident about what to communicate and to whom. The decisions made in the weeks afterward about how much caution to add, and at what cost to velocity.
Downtime is a business problem disguised as a technical problem. The technology will keep failing, because all technology eventually does. What determines the actual cost is not the failure. It is the quality of the decisions made around it.
Technology restores systems. Leadership restores confidence.
How does your organization measure downtime: by availability, or by business impact?
Splunk, in partnership with Oxford Economics. The Hidden Costs of Downtime, 2026.
Uptime Institute. Annual Outage Analysis 2026 (8th edition).
Edwin Martin Salazar Vega
Helping organizations make better decisions through technology, observability, and complex systems thinking.