Modernization projects don’t look like modernization projects anymore. The scope has expanded; the sequencing has been rewritten; and half of what used to constitute a modernization engagement has been redesigned to accommodate a technology that barely registered eighteen months ago. That technology is, of course, AI. The interesting story is not that AI arrived. It is what AI has done to the assumptions underneath every serious enterprise data modernization and to what modern data modernization services are now being built to deliver.
The ISG Provider Lens® Advanced Analytics and AI Services – Large and Midsize 2025 U.S. Quadrant Report captures the enterprise data behind the change. Five shifts, in particular, define what a serious enterprise data modernization program now looks like.
Five Shifts at a Glance
Data modernization services in 2026 have been reshaped by five specific shifts:
- AI-ready platforms designed from the outset, not adapted from BI foundations
- Agentic AI running inside the modernization engagement itself
- The lakehouse pattern as the market default target architecture
- Financial visibility and value tracing built into the platform layer
- Real-time as the baseline latency expectation, no longer premium
Shift 1. AI-ready Is the Design Goal, Not a Downstream Consideration
AI-ready data platforms are now being architected for artificial intelligence workloads from the outset, rather than adapted for AI after being built for reporting. This is the most consequential change in modernization design since the move to cloud, and the pressure driving it is visible in the numbers. 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. That level of investment can no longer sit on top of a foundation designed for historical reporting.
Modernization engagements now begin with the AI use cases the platform will need to support and work backward into ingestion, storage, semantic modeling, and consumption. Vector storage, embedding pipelines, and retrieval-augmented data products, and other AI infrastructure components are no longer premium additions. They are standard requirements.
Shift 2. Agentic AI Is Entering the Modernization Engagement Itself
AI is no longer only a workload that modern data platforms need to support. The role of agentic AI in data modernization is expanding, with agentic tools now used to carry out the modernization itself. Agentic tools now handle tasks such as data profiling, schema mapping, quality checks, and metadata creation, with much less manual review than they required a year ago.
The pressure behind this change is severe. The ISG report identifies data usability for AI as the top enterprise challenge, ahead of integration, scalability, and security. Manual approaches to closing that usability gap have run out of runway at enterprise scale, where a single AI initiative can affect more than 1,600 employees. Agentic automation is the answer providers are converging on because human review cannot keep pace with the volume of decisions a governed modernization program now generates.
Shift 3. The Lakehouse Pattern Has Become the Market Convention
Most enterprises have moved past the warehouse-versus-lake argument. In current modernization programs, the work usually leads to a data lakehouse, whether it runs on Databricks, Snowflake, Microsoft Fabric, or another comparable platform. That reflects the data companies now have to handle. Operational records sit alongside contracts, emails, images, transcripts, and other unstructured sources. Generative AI needs access to both, and managing them under separate governance models adds further complexity.
The gap remains substantial. The ISG report says fewer than one-third of large organizations have consistent data architecture standards across the business. That becomes harder to correct when a typical large enterprise runs close to 2,000 applications. Modernization services are increasingly being used to reduce that fragmentation, retire overlapping platforms, and move data onto a common lakehouse foundation with shared governance.
The Modernization Engagement, Then and Now
| The 2024 engagement |
The 2026 engagement |
| Platform designed for BI, adapted for AI later |
Platform designed for AI from the outset |
| Manual quality and metadata work |
Agentic automation across quality and metadata |
| Batch-first with real-time as premium |
Unified streaming and batch as baseline |
| Cost governance addressed after go-live |
Financial visibility engineered into the platform |
Shift 4. Financial Visibility and Value Tracing Are Built Into the Platform
Cloud data platforms gave enterprises more room to scale, but they also made spending harder to control. One poorly written query, a recursive AI process left running, or a forgotten pipeline can burn through budget within hours. Modernization work in 2026 is starting to address that problem in the platform itself, with cost controls, usage tracking, and links between spending and specific business results built into the engineering.
The ISG report explains why this has become urgent. Many enterprises are still falling short of their own targets for savings, revenue, and productivity, and often cannot connect those results to individual data programs. That is pushing more buyers toward consumption-based and outcome-linked pricing. Providers now need to build the measurement into the platform from the beginning, rather than trying to piece it together at the end of the year.
Shift 5. Real-time Has Become the Baseline, Not the Premium
Overnight batch refresh is being retired across enterprise data platforms. Unified streaming-and-batch pipelines, in which the same business logic runs over historical and live data interchangeably, are the assumed default of new modernization engagements. The reason is competitive rather than technical. When the ISG report notes that 87 percent of enterprises have already enabled analytics and BI use cases, the differentiation among enterprises is no longer whether they have reporting. It is how current that reporting is at the moment a decision is made.
Modernization services in 2026 are therefore engineered around latency as a first-class design constraint, and not an addition made once the platform has stabilized.
What These Shifts Mean for Enterprise Data Leaders
Enterprise data modernization in 2026 is not a rebadged migration exercise. It is the design of the environment in which an enterprise will run its analytics, its artificial intelligence, and increasingly its operations. Modern data modernization services now scope, price, and deliver all five shifts as an integrated program.
Any credible data modernization strategy in 2026 has to reckon with all five, and buyers evaluating providers should look for evidence of each, and for the connective architecture that lets the five reinforce each other rather than working independently.
All statistics cited in this article are drawn from the ISG Provider Lens 2025 Advanced Analytics and AI Services report, U.S. edition.
