A dashboard showing 92% CPU utilization may look alarming. It may also mean that infrastructure is being used efficiently while the service meets its SLA, transactions complete normally and automatic scaling has adequate headroom. A system running at 35% CPU, meanwhile, can still be experiencing a serious customer-facing failure.
Both numbers matter to engineers. Neither tells an executive whether customers are getting the service they expect, whether the company is losing money or whether management needs to intervene.
This distinction is becoming increasingly important as businesses depend more heavily on digital services. IT teams already collect enormous amounts of telemetry, from infrastructure metrics and application logs to distributed traces and incident data. The challenge is making those signals useful outside the engineering organization.
For business leaders, the useful measure of observability is not how much of the technology stack can be monitored. It is whether a change in that stack can be connected to a customer journey, a business process and eventually a financial or operational outcome.
Technical severity and business severity are different things
Consider a payment API that normally responds in 300 milliseconds. Following a software release, response time increases to 1.2 seconds.
The infrastructure may still look healthy. Availability remains at 99.99%, CPU and memory are within normal ranges, and very few requests fail outright. Based on a conventional infrastructure dashboard, the incident may not appear particularly serious.
The business experience can look very different.
If the API sits in the authorization path for card payments, additional latency may push some requests beyond downstream timeouts. Customers experience slower payments, retries or occasional declines. Merchants and mobile applications retry transactions, adding further load to the system. Payment completion begins to fall, abandoned transactions increase and support receives more complaints.
A service that appears technically available can therefore be failing at the job the business needs it to perform.
This is why technical severity and business severity need to be treated as separate dimensions. A relatively small degradation in a high-volume payment service may have a larger business impact than the complete outage of a rarely used internal application. This also aligns with established SRE practice, where service objectives are defined around what users experience rather than the infrastructure metrics that are easiest to collect. Infrastructure metrics remain essential for diagnosis, but they do not establish business priority on their own.
For senior management, the relevant questions differ from those asked by the engineer investigating the incident. How many customers were affected? How much transaction value is at risk? Is the impact increasing? How quickly can the service recover? Does the situation justify intervention or additional investment?
This also means an executive observability view should remain deliberately small. In practice, 8–12 measures can often provide more management value than a dashboard containing dozens of technical indicators. Critical-service availability, customer journey success, user-impacting latency, the number and business impact of major incidents, recovery time, change failure rate, recurring problems and technology cost relative to demand provide a useful core. Technical debt or lifecycle exposure can be included where it represents material business risk.
The objective is not to hide technical complexity from management. It is to present the level of information at which management can plan.
An engineering dashboard might report 87% CPU utilization, 1.4-second latency, a 2.1% error rate and 17 restarted pods. An executive view of the same event should look closer to this: payment success is 97.9% against a 99.5% target, approximately 8,400 customers are affected, around €1.2 million in transaction value is exposed, the current risk is approximately €70,000 per hour, and recovery is expected within 35 minutes.
The second version tells management what the first cannot: how serious the event is, which part of the business is affected, how quickly the impact is accumulating and whether intervention is required.
More telemetry will not create the missing connection
A sophisticated observability stack does not guarantee business insight. A company can have application performance monitoring, distributed tracing, centralized logging, infrastructure monitoring and real-time dashboards while still struggling to explain what a technical event means commercially.
The missing element is usually the mapping between technology and the business services it supports.
Infrastructure is naturally modeled in hosts, containers, APIs, databases and dependencies. The business operates in payments, orders, deliveries, claims, customer logins and other journeys. Unless these two views are connected, observability reaches the technology boundary and stops there.
I would not start by buying another observability platform. I would start by establishing a chain that the organization can follow consistently:
Business outcome → customer or business journey → service → applications and dependencies → telemetry
Once this exists, incidents, changes, costs and business outcomes can be connected to the same model.
Database latency, for example, is primarily an engineering signal. If that latency increases checkout API response time, which then reduces payment completion by 1.8%, the organization has established service impact. Add the estimated transaction value at risk and the same event becomes something management can evaluate.
The underlying technical measurement has not changed. Its context has.
Someone has to own the translation layer
Connecting these worlds becomes difficult because the relevant information usually belongs to different parts of the organization.
Infrastructure teams manage infrastructure metrics. Application teams understand application telemetry. Service management tracks incidents. Finance owns cost information. Product and business teams understand customers, revenue and operational outcomes.
Each dataset can be accurate and useful independently while the organization remains unable to explain the business consequence of a technical event.
This also creates an incentive problem. Infrastructure may optimize utilization, operations may optimize incident closure, development may optimize delivery speed, and product may optimize conversion. Every function can perform well against its own KPIs while the end-to-end service deteriorates.
The translation between technical and business performance therefore cannot sit entirely with an observability or DevOps team. Those teams can build excellent monitoring capabilities, but they can rarely decide why a 1% degradation in one process represents a material financial risk while a much larger degradation elsewhere has little business consequence.
Business leadership cannot own the technical translation alone either. Executives should determine what matters and what level of risk is acceptable, but they should not need to interpret distributed traces or determine which microservice caused a decline in conversion.
The model I have seen work best is shared but accountable. Business defines what matters. Product or service owners own the outcome. Engineering and SRE establish telemetry and technical causality. Data connects those signals to measurable business impact. One service owner remains accountable for the complete chain.
Business observability must be designed before the incident
Trying to calculate business impact after an outage is often too late.
An incident can reveal relationships that were not previously understood, but it cannot recreate information that was never collected. If telemetry contains the application, endpoint, timestamp and exception type but no way to correlate the event with a transaction, channel, customer journey or outcome, the business impact may be extremely difficult to reconstruct afterwards.
The relationship between system performance and business KPIs should therefore be established during service and product design.
For a critical customer journey, teams should know which applications and dependencies support it, and which technical signals indicate degradation. Telemetry needs enough context to correlate performance with transaction type, service, channel and outcome. Business systems can then provide the financial or operational information required to quantify the impact without placing sensitive business data directly into technical logs.
Baselines are equally important. A statement such as “latency exceeded one second” becomes considerably more useful once historical data shows what typically happens to completion, abandonment or support demand at that threshold.
This is what I mean by designing business observability alongside technical observability. The translation should exist before somebody needs it in the middle of an incident.
Business criticality should determine what gets observed most closely
Not every system deserves the same level of observability investment.
Technical complexity is an unreliable way to establish priority. A sophisticated platform may be fascinating from an engineering perspective while having relatively little effect on customers or core operations. A much simpler service may sit directly in the path of every payment, shipment, customer login or regulatory process.
Observability priorities should therefore follow business criticality. Revenue exposure is one consideration, but it is not the only one. Customer dependency, operational importance, regulatory obligations, the number of processes dependent on a service, and the consequences of failure can all change how closely a system needs to be monitored. The same principle appears in established cloud architecture guidance, which recommends identifying and scoring user and system flows according to their importance to business requirements and using that criticality to guide design, testing and incident management.
This creates a more defensible basis for investment. Instead of trying to achieve the same observability maturity everywhere, organizations can concentrate engineering effort where degradation would produce the greatest business consequence.
Better observability can change where the business invests
I saw the practical value of this approach on a high-volume banking and payment platform where performance deteriorated during peak periods.
The initial evidence suggested a capacity problem. Transaction response times increased, occasional timeouts appeared and infrastructure monitoring showed elevated utilization. Increasing compute capacity seemed like the natural response, along with the additional infrastructure budget required to support it.
Application and transaction-level observability changed that conclusion.
By correlating infrastructure metrics with API latency, transaction processing times, database activity, external-system response times, error rates and successful and failed transactions, we found that overall compute capacity was not the primary constraint.
Most transactions were still processed quickly. The degradation was concentrated in a relatively small subset that followed a path involving synchronous calls to another system. Under peak load, those calls created queueing and connection-pool contention, which then propagated into the main transaction flow.
The decision changed from adding infrastructure to redesigning a specific part of the transaction path.
This is where observability becomes more than an incident-response capability. It can provide evidence for architecture decisions, capacity planning and technology investment. Instead of asking for budget because infrastructure utilization looks high, a CIO can show which business process is approaching a limit, what impact that limit creates and whether a proposed investment addresses the actual constraint.
DevOps practices and automation strengthen this model by shortening the feedback loop. The objective is not simply to collect operational data more frequently. It is to reduce the time between a change in technology, understanding its business consequence and taking the appropriate action. When deployment, telemetry and business outcomes can be correlated consistently, observability becomes useful for continuous decision-making rather than primarily for explaining incidents after they occur.
Reliability is becoming an economic variable
The same logic applies to reliability itself. Technology teams have traditionally aimed for high availability, but each additional level of resilience carries a cost. Moving a service from 99.9% to 99.99% availability may require substantial architectural investment, additional redundancy, operational capacity and engineering work. Whether that investment makes sense depends on what the service does.
For a critical payment platform, the additional availability may protect enough transaction value, customer trust or regulatory exposure to justify the cost. For a lower-priority internal service, the same investment could produce little meaningful business value.
This changes the reliability conversation. Instead of asking how close the organization can get to maximum availability, leadership can ask what another 0.01% of availability costs and what business loss it is expected to prevent. AWS reliability guidance makes the same trade-off explicit: higher availability typically increases cost, making it important to establish the actual availability a workload requires before investing in additional resilience.
Historical observability data can support that decision. It can show how transaction volume is changing, whether latency is approaching agreed thresholds during peak periods, when current capacity is likely to become insufficient, and what previous incidents cost the business. The same data can later show whether an architecture or capacity investment delivered the expected improvement.
As digital services become inseparable from business operations, reliability data will increasingly belong in financial planning and risk management as well as engineering. A payment-platform failure stops payments. A slow e-commerce checkout can affect conversion. A logistics-system outage can disrupt physical operations. In these environments, system performance and business performance are increasingly difficult to manage separately.
AI can interpret observability data, but it still needs context
AI will make it easier to consume the enormous volume of information generated by modern technology environments. Its most useful role may not be generating additional alerts, but correlating telemetry, changes, incidents, dependencies and business data into explanations that different audiences can act on.
An engineer will still need access to metrics, logs and traces. Management could receive a very different interpretation: payment completion declined following yesterday’s release; the impact is concentrated in the mobile channel and correlates with authentication latency; an estimated number of transactions have been affected; rollback is recommended.
That is a significant improvement over asking executives to interpret a technical dashboard.
It still depends on the underlying organizational model.
AI cannot infer reliable business meaning from technical data if the organization has never established which systems support which processes, which customer journeys matter, who owns them and which KPIs define acceptable performance. Without that context, applying AI to billions of technical measurements may produce more sophisticated analysis without producing better business decisions.
Start with the decision, not the dashboard
The question for a CIO is no longer how much telemetry an organization can collect. Modern IT environments already generate more technical information than any executive could reasonably consume.
The useful question is whether a technical change can be followed through the organization to the customer journey, business process, financial outcome or material risk it affects.
Engineers will always need detailed technical observability. That layer should remain detailed because diagnosis demands it. Executive observability serves a different purpose: helping leadership understand whether something matters, how much it matters and what decision should follow.
If 92% CPU utilization cannot tell management whether to act, the dashboard is doing exactly what it was designed to do. The organization simply needs a business layer above it.
Read More from This Article: Observability should start with business outcomes, not infrastructure
Source: News