From Enterprise Data Silos to AI-Ready Foundations
Enterprise data silos are fragmented, disconnected stores of business information that prevent organizations from applying consistent AI-ready data governance, real-time analytics, and automated decision-making across their operations. For many enterprises, these silos sit on top of decades of legacy systems and manual data pipelines that block production-scale AI. The result is a familiar pattern: pilots succeed in isolated areas, but models cannot be trusted or scaled because data remains inconsistent, poorly governed, or locked inside old applications. Strategic partnerships between cloud, AI, and enterprise software providers are now trying to change that. By building direct platform integrations, shared governance layers, and pre-built workflows, these alliances aim to turn scattered IT, OT, and business data into unified ecosystems that can support agentic AI, autonomous operations, and continuous insight delivery across the organization.
IBM–ServiceNow: Modernizing Legacy Systems into an AI Control Plane
IBM and ServiceNow are focusing on the two problems that most often stall enterprise AI deployment: AI-ready data governance and legacy system modernization. Decades of interconnected applications sit at the core of many enterprises, making it hard to standardize data or embed AI into workflows without major disruption. Their multi-year collaboration combines IBM’s AI, data, and automation stack with the ServiceNow AI Platform to evolve these systems instead of replacing them. IBM Bob, Enterprise Application Runtime for Java, and watsonx.data will scan and refactor aging applications, while extending ServiceNow’s Workflow Data Fabric. “Most enterprises have the ambition to deploy agentic AI, but lack the foundation to run it at scale,” said John Aisien of ServiceNow. The goal is an open, flexible foundation where autonomous IT operations can rely on governed data catalogs, master data management, and observable data quality.
AVEVA–Snowflake: Unifying IT OT Data Integration for Industrial AI
Industrial firms face a distinct challenge: operational technology systems generate high-frequency plant and asset data that rarely connects cleanly with enterprise IT platforms. This divide blocks industrial AI from using a complete picture of operations. AVEVA’s collaboration with Snowflake targets IT OT data integration by creating a direct, zero-copy link between the AVEVA CONNECT industrial intelligence platform and Snowflake’s AI Data Cloud. Instead of moving data through complex pipelines, customers can access and activate operational and enterprise datasets in place, cutting technical debt and speeding industrial AI deployment. Joint customers inherit Snowflake’s governance controls, including column-level security and dynamic data masking, to keep OT data compliant and secure. With a governed, enterprise-wide data foundation, AI agents can reason across production, engineering, and business signals in real time, helping organizations shift from fragmented dashboards to AI-driven operational decisions.
Infosys–Valmet: AI-First IT Operations for Process Industries
Infosys and Valmet are bringing the AI-first model to process industries by transforming IT operations into a governed, automated backbone for digital services. Infosys will modernize Valmet’s core IT services and deliver end-to-end IT transformation that aligns technology operations with business priorities such as cost reduction, resource optimization, and resilience. The collaboration uses Infosys Topaz Fabric, an open agentic services suite, to embed intelligence into IT workflows with a human-in-the-loop approach for transparency and control. Infosys Cobalt provides cloud services and platforms to build scalable, secure foundations for legacy system modernization and AI deployment. According to Infosys, these capabilities support an AI-first operating model that improves long-term business agility and operational efficiency. For process manufacturers, this kind of unified, AI-aware IT layer is a key step toward moving from isolated AI tools to reliable enterprise AI deployment across plants and services.
Toward Unified Governance and Real-Time AI at Scale
Across these partnerships, a clear pattern is emerging: enterprises cannot scale AI without unified governance and real-time insight flows across all data domains. IBM–ServiceNow aim to make workflow platforms the control tower for governed AI-ready data; AVEVA–Snowflake focus on removing IT/OT data barriers through shared governance stacks; Infosys–Valmet concentrate on building AI-aware IT foundations for process industries. These multi-year collaborations point to a future of platform consolidation, where pre-built integration paths and shared data models replace custom pipelines and isolated pilots. To move beyond experimentation, organizations need cataloged, observable, and secure data that feeds AI agents and copilots continuously, not in batch projects. Breaking down enterprise data silos is no longer a side project; it is becoming the central strategy for any enterprise that wants reliable, production-scale AI deployment.
