Different Lens, Same Disease

What Munich Re’s 2026 Tech Trend Radar Names—and What It Does Not

A Serious Document, Not a Brochure

Munich Re published its Tech Trend Radar 2026 on the fifteenth of April, and it arrives with the reassuring heft of a document that has been argued over in conference rooms rather than assembled in a marketing department. Ninety-six pages, twenty-three trends, four domains, each assigned a maturity rating from “Hold” to “Adopt.” It is co-published with ERGO and signed by the Chief Technology Officer. One senses, reading it, not the usual enthusiasm of futurism but something closer to institutional caution: a large and rather serious organisation deciding, in public, which parts of the future have already become the present.

The Decision That Does Not Travel

For the past seven months, I have been examining a particular condition in regulated markets, one that is neither new nor especially fashionable, and therefore easy to overlook. Data, as everyone now agrees, moves rather well between organisations. It is packaged, structured, transmitted, and received with increasing efficiency. What does not travel nearly as well is the decision made about that data. Who reviewed it, under what authority, applying which rules, and with what outcome—these are details that tend to remain firmly inside the walls of the institution that produced them. The data crosses the boundary; the judgment does not.

What Munich Re Names Well

The Munich Re report, to its credit, identifies many of the outward symptoms of this condition with admirable precision. It does so, however, in a manner that stops politely at the firm’s front door. What it captures brilliantly is how the problem appears from within a single organisation deploying modern technology. What it does not yet quite articulate is how the same problem behaves when several organisations are required to rely on one another. It is the difference between diagnosing a fever in one patient and noticing, perhaps belatedly, that the entire household is unwell.

Seven Symptoms in Search of One Mechanism

Seven of the report’s twenty-three trends map directly onto this pattern. The most straightforward of these is AI Governance, which Munich Re places at the highest maturity tier, “Adopt.” The accompanying text is refreshingly candid. Standards, it notes, are being written, but many are not yet available. Meanwhile, artificial intelligence systems are becoming more autonomous, reducing the degree of human oversight and thereby increasing the need for some form of machine-readable supervision. One can almost hear the polite clearing of a throat: the industry requires a way to record what a human did when an AI system made a recommendation, and that record must be portable. The report stops just short of saying that such a thing does not yet exist, though it comes very close.

AI Agents, also placed at “Adopt,” introduces the notion of “agent-ready APIs” and the requirement for grounding—ensuring that outputs are tied to verifiable sources. This is a sensible and necessary condition. Yet an autonomous system that depends on prior decisions made elsewhere will eventually require more than a source; it will require evidence of the decision itself. Again, the need is named. The mechanism is not.

Perhaps the most striking inclusion is Insurance API Standardisation, which observes, with a restraint bordering on understatement, that data exchange across the insurance value chain remains “very outdated” and lacks a widely accepted common format. This is not news to practitioners, who have been saying much the same thing for years, albeit sometimes with less decorum. The infrastructure exists, in a sense; the cargo moves along the rails. What is missing is a consistent way of sealing the boxcar—of indicating, on the outside, what has been done within.

Four further trends—AI-Augmented Software Engineering, Synthetic Data, Deepfake Defence, and the Digital Immune System—all point to similar requirements, albeit with different vocabularies. Traceability, provenance, verification protocols, and continuous validation. Each describes a slightly different facet of the same underlying need: a reliable, portable record of what has been assessed and accepted. One begins to suspect that the industry has, collectively, described the problem several times over, each time in a new dialect.

Where the Lens Stops

That accounts for roughly a third of the report. The remaining trends—ranging from artificial general intelligence to immersive experiences and quantum computing—belong to a different category altogether. They are bets on capability rather than observations about coordination. They are entirely appropriate to a document of this sort and, for present purposes, not especially relevant. The interest lies in the cluster of trends that quietly converge on the same missing piece.

What unites those seven is not merely their subject matter but their perspective. Each is framed from within the organisation. The question being asked, repeatedly and quite reasonably, is: what must this insurer do internally to deploy these technologies safely? It is a question that yields good answers, as the report demonstrates. It is also, increasingly, an incomplete question.

The Blueprint Two Parallel

Consider a scenario in which an insurer implements these recommendations flawlessly. Its governance is impeccable, its APIs are standardised, its AI systems are carefully supervised, and its data is well-provenanced. It will, by any internal measure, be a model of technological responsibility. And yet, when asked—by a partner, a regulator, or indeed a counterpart in the same market—what exactly it relied upon from upstream parties, and whether that reliance was justified, it may find itself at a loss. The difficulty does not arise from a failure within the firm, but from the absence of a shared mechanism between firms.

There is a historical parallel worth recalling. In 2020, the London Market undertook an extensive examination of its own processes, producing a document of considerable length and care. It correctly identified a series of coordination challenges: duplicated effort, fragmented data, delays in settlement, and so forth. Each issue was addressed with a corresponding solution, often involving some form of centralised infrastructure. Less clearly articulated was the underlying mechanism linking these symptoms—the absence of a portable record of acceptance. Without that unifying diagnosis, the solutions multiplied, each addressing a symptom rather than the cause. The programme that followed was ambitious, technically sophisticated, and, in the end, rather difficult to implement.

Munich Re’s report does not repeat that exercise. It is, however, exhibiting a similar diagnostic shape. The symptoms are identified with clarity. The maturity ratings suggest urgency. The absence of standards is acknowledged with unusual honesty. What remains unspoken is why, after many years of standard-setting activity, those standards have not yet fully materialised.

The Adopt-Tier Problem

One detail, in particular, deserves attention. Several of the trends are placed in the “Adopt” category, even as the report notes that the relevant standards are incomplete or unavailable. This is, on one level, entirely reasonable. Industries do not wait politely for standards to be finalised before proceeding; they build, experiment, and adapt. On another level, it creates a slightly precarious situation. Deploying at scale without common standards tends to produce a landscape of internally coherent but externally incompatible systems—perfectly functional in isolation, rather less so in combination.

It is a familiar pattern. One builds what one can, as best one can, within one’s own domain. Later, one discovers that everyone else has done the same, and that stitching the results together is more complicated than anticipated. There is no villain in this story, merely a sequence of sensible decisions whose cumulative effect is less than ideal.

The Seam Between Firms

None of this diminishes the value of the Tech Trend Radar 2026. On the contrary, its clarity and restraint make it unusually useful. It provides a reliable map of where the industry believes it stands and, perhaps more importantly, where it believes it must go next. It is also refreshingly candid about uncertainty, which is not always a common trait in documents of this genre.

If there is a gentle suggestion, it is that the next iteration of this line of thinking may benefit from a slightly wider lens. The questions now emerging—from regulators, from partners, from the markets themselves—are increasingly concerned with what happens between organisations rather than within them. They are, in effect, asking for a way to make decisions as portable as the data upon which they are based.

The Missing Seal

The missing piece is simpler than it sounds. When one firm reviews something and accepts it, the next firm should not have to start from scratch to understand what happened. It should be able to see a clear record: who accepted it, what they accepted, when they accepted it, and under what authority.

That record need not reveal every internal discussion or private judgment. It only needs to show the accepted outcome in a form that another authorised party can verify.

In older trade, that job was done by a seal—known formally as a sigillum. The seal did not explain the whole journey. It simply told the next person: this has been checked, accepted, and can be relied upon.

Modern insurance needs the digital version of that seal.