An enterprise data migration can meet every technical target and still leave the business almost where it started. The data has moved, the dashboards are running again, and the program has closed. A few months later, teams may still be debating which numbers to trust and making decisions much as they did before. The project worked on paper, but it did little to improve the way the organization uses data.
For a long time, that was an acceptable outcome because migration was understood as infrastructure work. Move the data from the old system into the new one, reconcile the records, and consider the job complete. Whatever value there was would arrive later, from whatever the business built on the new platform. That assumption no longer holds, and the ISG Provider Lens® Advanced Analytics and AI Services – Large and Midsize 2025 U.S. Quadrant Report helps explain why the migration itself has become the work that decides what follows.
At a Glance
What’s on the Line
A data migration decision in 2026 affects four things beyond the platform itself:
- Whether AI initiatives will have data they can actually train on
- Whether governance and lineage arrive with the data or trail behind it
- Whether the estate stays portable when the cloud landscape changes again
- Whether decision-makers trust the reporting layer six months after go-live
Large Enterprises Are No Longer Moving Everything
The average large enterprise now operates close to 2,000 applications, according to the ISG Provider Lens 2025 Advanced Analytics and AI Services report, and an environment of that size cannot be rationalized after it has been moved. Migrating it unchanged relocates the same disorganization to a newer and more expensive location. The substantive work in modern data migration services now happens in the decisions made before anything moves, determining which systems are worth keeping as they are, which should be rebuilt, and which can be retired because the organization has not run a query against them in years.
This is why refactoring during migration has been growing faster than straightforward rehosting across the market. Organizations want the move to leave them in a better structural position, not simply relocated.
Governance Cannot Wait Until After the Move
For years, teams dealt with data governance in migration after the data had landed. That order is now being reversed. The ISG figures help explain the pressure: 76 percent of organizations say centralized governance speeds up insight delivery, while only about two-thirds have a consistent enterprise architecture to support it.
Migration offers one of the few opportunities to close that gap without creating a second program later. Cataloging, lineage tracking, and quality controls can be built in while systems and data flows are already being reworked. Adding them after go-live costs more and usually leaves gaps. In financial services, healthcare, and other regulated industries, governance is now part of the migration scope rather than an optional follow-on.
What Changed and Why
| What Has Changed |
What Is Driving It |
| Migration selectivity replaced comprehensive lift-and-shift |
Application sprawl made wholesale moves too expensive to justify |
| Governance moved to the front of the program |
Regulatory pressure and AI trust requirements shortened tolerance for phase-two fixes |
| AI readiness became a migration outcome |
Data quality gaps now stall models more often than modeling gaps do |
| Hybrid architectures became the default landing point |
Sovereignty rules and cost economics ended single-cloud strategies |
Migration Determines Whether the Data Is Ready for AI
The connection between data migration services and artificial intelligence has become direct. AI spending as a share of enterprise IT budgets has risen from roughly two percent to almost six percent within two years, per the ISG report, and that investment assumes a data foundation ready to support it. In many organizations, that foundation does not yet exist.
AI initiatives often stall because the data remains fragmented or only partly migrated. A model cannot repair a foundation that teams do not trust. By the time the move is complete, the organization has either created a usable base for AI or carried the same weaknesses into the new environment.
In one Hexaware engagement, a mining company completed a cloud data migration, replacing an aging on-premises Teradata warehouse with Snowflake on Azure. Data loads became three times faster, and the total cost of ownership fell by half. More importantly, consolidating the data gave the company a dependable base for real-time analytics across sensor feeds from hundreds of haul trucks, along with an analytical environment that could support AI in ways the legacy platform could not.
Three Questions
- How large is the estate we are migrating?
Close to 2,000 applications on average for large enterprises
- How much of the IT budget now depends on data being ready?
AI’s share of enterprise IT budgets rose from roughly 2 percent to almost 6 percent in two years
- How wide is the gap between governance intent and governance capability?
76 percent of organizations believe centralized governance accelerates insight delivery, while only about two-thirds have the architecture to support it
Source: ISG Provider Lens 2025 Advanced Analytics and AI Services, U.S. edition.
Automation Is Now the Baseline for Data Migration Services
The way migrations are executed has also changed. Manual discovery, hand-written schema conversion, and visual data checks have given way to automation as a standard expectation of data and cloud migration services in 2026.
Amaze® shows how automated data migration can accelerate much of the migration process. The platform reduces assessment from weeks to days, converts code and schemas, and uses AI-driven validation to identify discrepancies before they reach production. That approach was used when a global brewer brought a disconnected group of data centers onto Azure. Automated discovery, assessment, and planning gave the team a workable way to manage a program of that scale without interrupting the business.
Most Migrations Now End in Hybrid Environments
Few enterprise migrations now end with everything in one cloud, making hybrid cloud migration the default approach. Workloads are commonly spread across public, private, and sovereign environments to manage cost, control, and increasingly strict data residency rules. That makes portability part of the original design. The estate being moved today may need to move again, so cloud-agnostic architectures are less likely to replace one dependency with another.
The Cost of Migrating as Though It Were 2019
Treating migration the old way in a 2026 environment carries consequences that show up later:
- AI programs that stall at pilot because the data foundation is not ready
- Governance debt that regulators are increasingly unwilling to accept
- Cloud bills that preserve the fragmentation at higher rates
- Vendor lock-in that narrows options as sovereignty rules tighten
The Practical Takeaway for 2026
Data migration services belong within the wider data modernization program rather than being treated as a separate infrastructure exercise. The decisions made during the data migration strategy affect governance, AI readiness, and whether the business trusts the data once it is in the cloud. When those questions are handled as part of the migration, the organization gains more from the program than a completed transfer and a different infrastructure bill.
Explore, Hexaware’s data migration services here.
