For most Belgian insurers, the conversation about modernization has shifted from "if" to "how soon, and how painfully?" Legacy policy administration platforms are aging out. Consolidation continues. Product portfolios are being rationalized. Regulatory expectations keep rising. And somewhere in the middle of all of this sits the data – decades of it – that has to make the trip from the old world to the new.

This is where modernization programs tend to either succeed quietly or fail loudly.

Migration Is a Data Problem? Mostly It Is Not.

Insurance data migration is rarely defeated by the technical work of moving records from A to B. The pipes connect. The adapters adapt. What grinds projects to a halt is something more uncomfortable: Nobody is entirely sure what the data actually means anymore.

A premium field looks straightforward until you realize that, across twenty years of product evolution, three acquisitions, and one regulatory regime change, the same column has carried at least four different definitions. Reserves are calculated according to logic that lives partially in a system, partially in a spreadsheet, and partially in the head of a senior actuary who is six months from retirement. Claim status codes were last documented in a Word file that nobody can find.

This is what makes insurance migration genuinely different from migrating, say, a CRM system. The data is financially sensitive, semantically loaded, and tied directly to obligations toward policyholders, reinsurers, and the regulator. Moving it is the easy part. Understanding it is the actual challenge.

Why the Traditional Delivery Model Cannot Keep Up

The classical migration playbook is well known to anyone who has lived through one. Workshops with SMEs. Mapping spreadsheets that grow alarmingly large. Manually written transformation pipelines. A first test run six months after the design phase that surfaces problems nobody anticipated. Reconciliation issues discovered – inevitably – three weeks before go-live, when changing pipelines has become risky and the test backlog is already groaning under its own weight.

This model has not aged well, for three reasons.

First, it depends on manual interpretation of fragmented knowledge. Documentation is incomplete, scattered, or contradictory. The institutional memory you need sits with people who already have day jobs.

Second, it is structurally slow. Engineers spend most of their time building scaffolding – pipelines, plumbing, test harnesses – rather than working on the questions that actually matter. Does this transformation preserve financial equivalence? Why does the new reserve differ from the old by 3.2 percent? Is that difference correct, expected, and defensible?

Third, it discovers problems too late. Hidden truths in legacy data tend to surface during the first realistic dry run, at which point the cost of correction has already multiplied.

The unflattering summary: Traditional migration delivery spends most of its energy in the wrong places.

The Platform Underneath the Method

Our point of view is that migration delivery needs a different operating model, and that this model needs a platform built for it from the ground up – not an off-the-shelf data tool with consultants improvising on top.

Concretely, we have developed a AI-native integration platform with three connected layers.

A data infrastructure layer that handles the unglamorous-but-essential foundation: A high-performance database, pipeline orchestration, secure access management, and adapters that read from any source system or file format and write into any target.

A business semantics portal where migration analysts and business experts capture the inputs that actually drive a migration: data models, semantics, constraints, mapping and transformation rules, data quality rules, target validation rules and reconciliation rules. Crucially, all of this is captured in a structured way, not in free-form documents that nobody will reread. The same portal is where issues are surfaced into actionable information: data quality dashboards, integrated issue tracking, progress KPIs.

An AI-native development environment where small anonymized test datasets can be pulled into local environments for fast iterative development. Engineers and AI agents can see pipeline output – including data quality and reconciliation results – at any moment, not only when a scheduled test run of the full dataset finally gives them the ability to look. The principle is straightforward: Surface problems as early as possible, while changing things is still cheap.

AI, Embedded Across the Lifecycle

The reason the platform looks the way it does is that every layer was designed to enable AI-augmented delivery. This is the part where a certain amount of skepticism is warranted, so let us be specific about what AI actually does here.

In discovery, AI agents extract data models, business semantics, and constraints from source documentation, mapping spreadsheets, and system specifications. The output is structured and goes straight into the platform. Analysts validate and amend. Nobody starts from a blank page.

In design, AI proposes business term definitions, semantic alignments, transformation rules, data quality rules, and reconciliation rules – all derived from the structured inputs already captured in the portal. Humans remain the editors-in-chief.

In build, AI generates transformation code, data quality tests, and reconciliation assets based on those validated specifications. This is where the time savings are most visible and most quantifiable.

In troubleshooting, AI traces failed tests and reconciliation breaks back through lineage, identifies probable root causes, assesses downstream impact, and proposes fixes. The engineer still decides whether to apply them.

The headline is not that AI does any single one of these steps well – plenty of tools can claim that. The headline is that AI is embedded across the full lifecycle, working from a semantic backbone on our migration platform that was built from the grounds up to enable AI-augmented delivery. That is what separates a productivity gimmick from a genuine change in operating model.

Reconciliation: Where Insurance Migration Is Actually Won or Lost

Technical completeness – every record moved, every field populated – is necessary but nowhere near sufficient. In insurance, what matters is financial equivalence. Total reserves before and after must reconcile, or be explained. Earned premium must reconcile, or be explained. Claim provisions, IBNR, recoverables – all of it must reconcile, or be explained.

This is why reconciliation has to be embedded into the delivery framework, not bolted on at the end. It is also where insurance domain expertise is once more proven to not be optional. Knowing why a reserve calculation produces a different result on the new platform – and being able to defend that difference to internal audit, to the actuarial function, and ultimately to the regulator – is not a generic data engineering skill. It is an insurance-specific one.

This is precisely the work we want our consultants spending their time on. Not writing yet another pipeline. Not hand-coding yet another test. The focus should be on explaining the deltas. Understanding the products. Defending the numbers.

Humans Stay in Charge

In our delivery model, AI augments experts; it does not replace them. Engineers remain accountable for delivery decisions. Business experts remain accountable for semantic correctness. Our platform makes the routine work faster so that the judgment work gets the attention it deserves.

There is also a longer-term benefit worth flagging. The structured semantic assets created during a migration – business term dictionaries, mapping logic, reconciliation rules – do not have to be discarded when the project closes. They become reusable organizational intelligence, useful well beyond the migration itself.

What This Means for You

If your organization is preparing for a core system replacement, a post-merger consolidation, or a broader platform rationalization, the useful question is not whether AI can play a role in your migration. It certainly can. The question is whether your delivery partner has built AI into the spine of how migration actually gets done – or whether it has been added as a feature alongside an otherwise conventional approach.

The difference shows up in the timeline. It shows up in the reconciliation report. And it shows up in how confidently your team can answer the auditor's next question.

If any of this resonates – or if you are simply curious to see our platform in action – we are happy to walk through it with you. Reach out to Mattijs Augustijnen and Sebastiaan Van Baelen for a demo.