Enterprise data integration: from AI ambition to AI-ready foundations
Enterprise data integration for AI is the coordinated effort to connect, govern, and enrich data across business applications, infrastructure, and operational systems so that AI models can safely power mission-critical workflows at scale rather than remain isolated experiments. In most large organizations, AI data silos span legacy applications, departmental tools, and separate IT OT data ecosystems, leaving huge volumes of information locked away from modern analytics. New strategic partnerships are attacking this problem directly by treating data plumbing as a shared priority between infrastructure providers, software vendors, and consultants. Instead of ripping out existing systems, these alliances are focused on making current environments AI-ready: standardizing data models, enforcing governance, and synchronizing system-of-record information in near real time. The result is a shift from pilot projects to production-grade mission-critical AI systems that run where work actually happens.
IBM and ServiceNow: attacking AI-ready data and legacy application barriers
IBM and ServiceNow have expanded their collaboration to handle what they call two of the biggest blockers to enterprise AI adoption: the AI-ready data problem and the legacy application layer. The aim is to combine IBM’s AI, data, and automation tooling with the ServiceNow AI Platform so enterprises can modernize aging systems without ripping them out. According to IBM, the joint solutions will extend ServiceNow’s Workflow Data Fabric with IBM’s enterprise data capabilities and enable autonomous IT operations that can support agentic AI. This approach targets the heart of AI data silos inside large organizations: deeply connected yet outdated systems that hold rich historical data but are hard to expose to modern models. By giving customers an open, flexible foundation that can run any AI model on top of existing workflows, the partnership moves AI closer to core business processes instead of keeping it in isolated labs.
AVEVA, Snowflake, Siemens, and HighByte: unifying IT OT data ecosystems
On the industrial side, AI at scale depends on breaking down the split between IT systems and operational technology. AVEVA’s collaboration with Snowflake creates a direct, zero-copy integration between AVEVA CONNECT and Snowflake’s AI Data Cloud, allowing industrial firms to access and analyze operational and enterprise data without complex pipelines. This supports faster industrial AI deployment and gives customers Snowflake’s full governance stack for regulated sectors. Siemens and HighByte are tackling a related challenge inside factories and plants. HighByte Intelligence Hub is now available on the Siemens Industrial Edge Marketplace, where it connects, contextualizes, and transforms data from multiple IT and OT sources. By adding business context and flexible transformation rules to industrial data, the joint solution turns fragmented sensor and system feeds into standardized information that AI agents and analytics can act on reliably across production and enterprise environments.
Rocket Software, HPE, Infosys, and Valmet: operationalizing mission-critical AI systems
As data foundations improve, enterprises still need ways to move from analytics to dependable mission-critical AI systems. Rocket Software’s expanded collaboration with HPE focuses on this gap by pairing the Vertica analytics platform with HPE’s infrastructure and AI programs. Rocket DataEdge Data Replicate and Sync continuously and securely exposes system-of-record data to AI platforms in real time, so organizations can gain value without disrupting core business systems. Vertica’s in-database machine learning and high-performance queries help convert these streams into operational insight. Consulting-led collaborations are turning these capabilities into tailored transformations. Infosys and Valmet, for example, are working together to operationalize AI for sector-specific use cases, showing how service firms can package data integration, governance, and domain expertise into repeatable playbooks. Across these partnerships, the message is clear: AI at scale depends less on new models and more on disciplined, shared control of enterprise data integration.
