Enterprises Align AI and Data Platforms to Scale AI Deployments with Accuracy, Compliance, ISG says

Enterprises are coordinating AI and data programs and adopting platforms that address both as they deploy AI for functions that require data from throughout the organization, according to new research from global AI-centered technology research and advisory firm Information Services Group (ISG) .

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As organizations take AI programs beyond experimentation, they need enterprise-grade software to build and maintain both AI models and the data those models use. The growing number of options includes integrated AI and data platforms and specialized tools.

The 2026 ISG Buyers Guides™ for AI and Data Platforms provide the rankings and ratings of 83 software providers and their products for using and managing AI models and data in an enterprise. The series includes Buyers Guides covering established and emerging providers of AI platforms, including AI agents and sovereign AI and data solutions, and Buyers Guides for established and emerging providers of data platforms. The research finds that many companies attempting to scale AI deployments struggle with siloed, inconsistent and inaccessible data that needs to be cleaned, organized and made compliant with regulations.

“As organizations take AI programs beyond experimentation, they need enterprise-grade software to build and maintain both AI models and the data those models use,” said Matt Aslett, director of research, Analytics and Data, at ISG. “There are a growing number of options, including integrated AI and data platforms and specialized tools that can be aligned with a coordinated AI strategy. Successful scaled deployments require careful planning and platform selection.”

Enterprises seek AI platforms that enable them to prepare, train, deploy and maintain AI models efficiently and accurately, the research finds. This includes accessing and preparing data used to build, compare and optimize models and apply governance and monitoring frameworks to ensure compliance. Companies implementing AI look to data platforms to maintain data validity and trust. As AI use cases demand higher volumes of data, raising infrastructure costs, coordination between AI and data platforms is essential for efficiency.

The growing need for real-time, personalized and contextually relevant results from applications is blurring the line between AI and data platforms, ISG finds. To provide results that may include pricing, recommendations, fraud detection and forecasting, applications now need continuous access to both data and analytical models. This has led enterprises to move away from batch-oriented analytic processes, which extract and load data from operational systems into separate analytics platforms, and toward using operational data platforms that can perform AI inferencing at scale.

Rapid adoption of generative and agentic AI is changing enterprise expectations for both AI and data platforms, the research finds. Data platform requirements have grown to include the storage and processing of vector embeddings, which help to enable natural language processing and make GenAI more accurate through retrieval-augmented generation. ISG expects data platform providers to prioritize development of hybrid operational and analytic processing for GenAI and agentic AI through 2028.

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