There is a 1965 Soviet cartoon called “Vovka in the Far Far Away Kingdom.” Its hero dreams of a fairy-tale life in which he never has to do anything himself. Then the “two from the casket” appear: helpers ready to carry out any command quickly and without questions. But they do not understand where the task ends. They fetch the sweets — and then start eating them too. Vovka protests: “What, are you going to eat the candies for me too?” (Wikipedia)
The scene shows how easily a sensible request can turn into a meaningless ritual. The command is carried out literally, confidently, and quickly, yet the purpose is lost. The helpers cannot see why the candies were needed in the first place.
In 2026, AI is creating much the same situation for IT service companies.
The dream seems to have come true. AI can already triage alerts, summarize incidents, suggest likely causes of degradation, reconstruct timelines, draft postmortems, spot suspicious patterns in logs, and even query telemetry in something close to natural language. For SRE and operations teams, the appeal is obvious: here is the long-awaited assistant that never gets tired or complains about a night on call.
But AI is a large language model, not a large meaning model. It was built to continue text, not to discover purpose.
When meaning appears in an answer, much of it comes from the meaningful writing on which the model was trained — not all of it meaningful, obviously. Then we put a saddle on this wild cow: agents, tool calling, runbooks, production integrations, and guardrails. The rodeo begins. A saddle helps the rider stay on; it does not choose the direction. A human still has to hold on to the purpose of the task.
The danger begins when fluent text is mistaken for meaning. A polished paragraph, a confident RCA, or a neat timeline proves nothing on its own. There is no shortage of meaningless text. In operations, it is especially treacherous: it sounds like a decision until someone checks what action it actually leads to.
AI did not replace SRE. It took over part of the routine
AI is good at the dirty, noisy, exhausting part of operational work: assembling the picture, filtering the noise, reconstructing the sequence of events, and producing a first draft.
Engineers used to spend hours piecing together an incident from logs, metrics, traces, deployment events, and chat history. Now part of that work takes minutes. According to Elastic, 85% of organizations already use GenAI in observability in 2026, and they expect adoption to reach 98% within two years. The same report includes an important caveat: standalone tools without context are useful only for one-off investigations. In production, the useful platforms are those that understand service dependencies, deployment patterns, and the company’s own data. (Elastic)
For a service company, the benefit is practical. Clients do not pay for heroic log scrolling at three in the morning. They pay to have the problem found and isolated sooner. AI is useful in the initial investigation: gathering signals, connecting symptoms, surfacing likely hypotheses, and making sure important telemetry is not overlooked.
The “two from the casket” really can be first on the scene. They answer quickly and are sometimes even right. But fast text is not yet a decision, so they should not be making one.
The most valuable thing in 2026 is the boundary of automation
Mistakes cost more in operations than they do in a polished demo. If AI gets an article wrong, that is annoying. If it confidently sends an incident team in the wrong direction, you lose hours, money, and the client’s trust. A language model can elaborate on a false account of events with remarkable confidence.
Human-in-the-loop has gone from a nice-to-have to a requirement. Elastic reports that teams increasingly assess guardrails and human oversight when choosing observability platforms, while security remains the top concern for 61% of organizations adopting GenAI. (Elastic)
In SRE, you cannot simply say, “Let the agent handle it.” You can delegate signal triage, context gathering, a draft RCA, and the search for similar incidents. But the authority to change the system — especially during an outage — must remain tightly controlled. So must the authority to say, “This polished answer leads nowhere.”
Otherwise, AI will eventually do more than help you investigate production. It will start making mistakes in production on your behalf — confidently, coherently, and with no awareness that the task has already lost its purpose.
That would be a much less amusing remake of Vovka.
AI is changing both incident response and service economics
AI changes the cost of routine work for service companies.
Tasks that once took several hours of an experienced engineer’s time increasingly take 15 minutes when the engineer has good context and enough time to verify the result. The old billing trick of presenting large amounts of manual activity as large amounts of value is becoming harder to sustain.
This is especially visible in support and managed services. If AI can summarize a stream of events, reduce alert fatigue, retrieve relevant context, and suggest standard next steps, clients begin to expect more than an on-call team: they expect a faster, more mature service. SolarWinds’ 2026 report links AI-driven monitoring to reduced alert fatigue, faster incident response, and the need for unified visibility across hybrid infrastructure. It also notes that 51% of environments remain mostly or entirely on-premises. The familiar pain of hybrid infrastructure is not going away. (SolarWinds)
It used to be possible to sell effort. Increasingly, service companies will have to sell operational maturity.
Not “we have engineers” and not “we have AI,” but: “We have a process in which engineers preserve the purpose and AI reduces the manual workload. Together, they lower MTTR and noise, restore service faster, and avoid compromising security along the way.”
AI makes SRE faster. It does not reduce responsibility
In 2026, AI already makes a capable and energetic junior on-call engineer. It reads, searches, summarizes, and proposes — all very quickly. Sometimes alarmingly quickly. But the speed of text is not the speed of meaning. The value of a mature IT service company still depends on human judgment: distinguishing symptoms from causes, noise from signal, a plausible hypothesis from dangerous nonsense, useful automation from automated stupidity, and fluent language from a meaningful decision.
A strong service business is therefore built not around the slogan “we use AI,” but around a less exciting and much more profitable formula:
we know what can be delegated to a language model and how to preserve the purpose of the process.
For years, the industry dreamed of an assistant that would take some of the operational burden away. It has finally arrived: summarizing logs, finding correlations, drawing timelines, and suggesting hypotheses. A beautiful thing. Just do not mistake its fluency for understanding.