Enterprise Data Silos: The Hidden Barrier to AI at Scale
Enterprise data silos in AI are fragmented information stores across applications, business units, and industrial systems that stop organisations from building the unified, governed data foundation needed to deploy trustworthy AI at scale. As companies rush to experiment with generative models and AI agents, most discover that data readiness, not algorithms, is the main constraint. Decades of legacy system integrations, overlapping tools, and local operational databases mean that critical context is scattered or inconsistent. This blocks effective AI-ready data governance and prevents AI from moving beyond pilots into core workflows. Strategic enterprise AI partnerships are emerging to solve this problem by unifying IT and OT data, modernising legacy applications without full replacement, and embedding governance into workflow platforms. Multi-year collaboration models show that AI success now depends on coordinated infrastructure, data, and operating-model change rather than isolated tool choices.
IBM and ServiceNow: From Legacy Systems to Autonomous IT Operations
IBM and ServiceNow are forming a multi-year collaboration aimed at two stubborn obstacles: enterprise data silos in AI and the legacy application layer. Instead of forcing rip-and-replace strategies, the partners will use IBM Bob, Enterprise Application Runtime for Java, and IBM watsonx.data to scan, refactor, and modernise existing systems so they can support AI agents. ServiceNow’s Workflow Data Fabric will be extended with IBM’s enterprise data tools to keep information AI-ready as it flows into business workflows, with catalog-driven quality, observability, and master data management. As John Aisien of ServiceNow notes, most enterprises want agentic AI but lack the foundation to run it at scale. The collaboration also targets autonomous IT operations by integrating Red Hat Ansible, IBM Bob, Instana, HashiCorp Terraform, and HashiCorp Vault into ServiceNow IT workflows to detect and remediate issues before they hit the business.
AVEVA and Snowflake: Unifying IT/OT Data for Industrial AI
In industrial settings, IT OT data integration is essential because operational technology data has often lived in separate plants, historians, and control systems. AVEVA and Snowflake are addressing this by creating a direct, zero-copy integration between AVEVA CONNECT and Snowflake’s AI Data Cloud. Industrial organisations can access and activate both operational and enterprise data without complex pipelines or repeated copying, lowering technical debt and speeding industrial AI deployment. According to AVEVA, the collaboration lets customers move from fragmented IT and OT systems to a governed, enterprise-wide data foundation, inheriting Snowflake’s governance stack with column-level security, dynamic masking, object tagging, and fine-grained access controls. With higher-quality, trusted datasets in the cloud, companies can build AI agents that reason across operations, corporate data, and external signals, supporting use cases like energy optimisation, predictive maintenance, and more informed, compliant decision-making across plants and head offices.
Infosys and Valmet: Consulting-Led AI-First Transformation
The Infosys and Valmet partnership shows how enterprise AI partnerships increasingly blend strategic consulting with platform modernisation. Infosys will deliver a long-term, end-to-end IT transformation for Valmet, modernising core IT services to support the company’s Lead the Way strategy. Using Infosys Topaz Fabric, an open agentic services suite, the collaboration will embed intelligence across IT operations with a human-in-the-loop approach to maintain governance, transparency, and accuracy while improving productivity and resilience. Infosys Cobalt will support an AI-first operating model by creating scalable, secure cloud foundations for legacy system modernization and future initiatives. The focus is not only technology uplift but closer alignment between IT operations and business priorities, from lowering operating costs to enabling proactive management of enterprise-wide IT. Taken together, these moves show that AI-ready data governance, resilient infrastructure, and organisational change are now treated as a single transformation agenda.
Why Data Readiness and Governance Now Define AI Success
Across these collaborations, a pattern is clear: data readiness and governance are becoming the primary barriers enterprises must solve before scaling AI. IBM and ServiceNow are embedding data quality, observability, and workflow-aware governance into their platforms. AVEVA and Snowflake are showing how governed IT/OT data integration can replace brittle pipelines with secure sharing. Infosys and Valmet are pairing AI-first strategies with cloud foundations and human-in-the-loop controls. Multi-year enterprise AI partnerships signal that companies now see modernising infrastructure, applications, and governance frameworks as prerequisites, not side projects. AI agents and copilots cannot deliver value if they sit on top of fragmented data silos, inconsistent definitions, and opaque controls. The emerging strategy is to treat AI as an outcome of disciplined data platforms and operating models, turning enterprise data silos into connected, governed foundations that can support AI at scale over the long term.
