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

Key Terms

generative ai technical

Generative AI is a type of computer technology that can create new content, like text, images, or music, on its own. It’s important because it can produce realistic and useful material quickly, which could change how we create art, write stories, or even develop new products. Think of it as a smart robot that can invent and produce things almost like a human.

agentic ai technical

Agentic AI refers to computer systems that can make their own decisions and take actions without needing someone to tell them what to do each time. It's like giving a robot a degree of independence to solve problems or achieve goals on its own, which matters because it could change how we work and interact with technology in everyday life.

retrieval-augmented generation technical

An AI method that combines a conversational language system with live access to external documents or databases, so the AI first fetches relevant facts and then uses them to form its answer. Think of it as an assistant that checks a file cabinet for source papers before replying, which helps reduce mistakes and reveal evidence. For investors it matters because it can produce more accurate, verifiable summaries of filings, news and research, speeding due diligence while still depending on the quality of the underlying data.

vector embeddings technical

A vector embedding is a compact numeric code that captures the meaning and relationships of text, documents, or items by placing them as coordinates in a multi‑dimensional space so that similar things sit near each other. For investors this matters because embeddings let systems quickly find, compare and group related filings, news or company disclosures—like arranging a library by topic—speeding research, automating screening and improving signal detection for trading and due diligence.

operational data platforms technical

Operational data platforms are software systems that collect, organize and deliver up-to-date information from a company’s day-to-day activities—like sales, production, customer interactions and inventory—so people and machines can use it immediately. For investors, they matter because timely, accurate operational data helps managers make faster decisions, reveals efficiency or risk issues early, and can drive revenue or cost improvements that affect future profitability and valuation; think of it as a central dashboard for running the business.

analytic data platforms technical

Analytic data platforms are software systems that gather, organize and analyze large amounts of business or market information to produce clear insights, visual dashboards and predictions. Think of them as a smart workshop that turns raw numbers into easy-to-use reports and signals; they matter to investors because they can speed decision-making, reveal risks or growth opportunities, and improve a company’s ability to measure performance and justify strategy.

ai governance and operations technical

AI governance and operations are the rules, oversight and day-to-day practices that guide how a company builds, tests, uses and monitors artificial intelligence systems. Think of it as the company’s rulebook and maintenance plan for AI — who is responsible, how safety and accuracy are checked, and how legal and ethical limits are enforced. Investors care because strong AI governance reduces risks like costly errors, regulatory fines, and reputational damage, and can protect long-term value and competitive advantage.

sovereign ai technical

An AI system described as "sovereign" is built, hosted, or operated under a country’s legal and technical control so that data, code, and decision-making stay within that jurisdiction. For investors, sovereign AI matters because it affects which vendors can sell to governments or regulated industries, imposes compliance and infrastructure costs, and can create protected local markets—similar to a factory that must follow a nation’s building codes and can only sell to certain buyers.

Companies seek tools to build and maintain cohesive systems for real-time, relevant, personalized AI results, new research says

STAMFORD, Conn.--(BUSINESS WIRE)-- 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) (Nasdaq: III).

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.

For the 2026 ISG Buyers Guides™ for AI and Data Platforms, ISG produced seven Buyers Guides: AI and Data Platforms, AI Platforms, AI Platforms Emerging Providers, AI Agents, Sovereign AI and Data, Data Platforms and Data Platforms Emerging Providers. Each covers the essential platform categories and products in the given technology segment. A total of 83 providers were assessed: Actian, Aerospike, Aiven, Alibaba Cloud, Altair, Alteryx, Anaconda, Anthropic, Automation Anywhere, AWS, Boomi, Broadcom, C3 AI, ClickHouse, Cloudera, Cockroach Labs, Cohere, Couchbase, Databricks, Dataiku, DataRobot, Domino Data Lab, Domo, Dremio, Druid AI, EDB, EdgeVerve, Exasol, Fractal, Google Cloud, Gravitee, H2O.ai, Hazelcast, Huawei Cloud, Hugging Face, IBM, Imply, Incorta, InfluxData, InterSystems, Jitterbit, KNIME, KX, MariaDB, MathWorks, Microsoft, MongoDB, Neo4j, NVIDIA, OpenAI, OpenText, Oracle, Palantir, Percona, Pinecone, PingCAP, Progress Software, Quantexa, Red Hat, Redis, Salesforce, SAP, SAS, ScyllaDB, ServiceNow, SingleStore, SnapLogic, Snowflake, Software AG, SQream, Starburst, StarTree, Supabase, Tencent Cloud, Teradata, Tiger Data, TigerGraph, Tray.ai, UiPath, VAST Data, Weights & Biases, Yellowbrick and Yugabyte.

For its Buyers Guide to AI Platforms, ISG evaluated software providers across three platform categories — AI Platforms, Agentic and Generative AI and AI Governance and Operations.

For the Buyers Guide to Data Platforms, ISG evaluated software providers across three platform categories: Data Platforms, Analytic Data Platforms and Operational Data Platforms.

ISG rates software providers in five evaluation categories: Overall, Product Experience (incorporating Capability and Platform) and Customer Experience. Providers ranked in the top three for each evaluation category are named as Leaders. Within each platform category, those that meet the greatest proportion of our evaluation criteria are named as Overall Leaders.

Oracle was named the top Overall Leader in all AI and data platform categories that evaluated established providers. Other providers named as Overall Leaders within these platform categories were Databricks, IBM, AWS and InterSystems.

In the Buyers Guide for AI Platforms Emerging Providers, Domino Data Lab, H20.ai and Hugging Face were the Overall Leaders. In the guide for Data Platforms Emerging Providers, MariaDB, Aerospike and PingCAP were the Overall Leaders.

In addition, the following providers were rated as Exemplary or Innovative:

AI and Data Platforms: AWS, Databricks, Google Cloud, IBM, Oracle, SAP and Teradata were rated Exemplary. Alibaba Cloud, Microsoft and Snowflake were rated Innovative.

AI Platforms: AWS, Databricks, Dataiku, Google Cloud, IBM, Microsoft, Oracle, SAP and Teradata were rated Exemplary. Alibaba Cloud, DataRobot, MathWorks, Snowflake and Tencent Cloud were rated Innovative.

Agentic and Generative AI: AWS, Databricks, Dataiku, Google Cloud, IBM, Microsoft, Oracle, Salesforce, ServiceNow, Teradata and UiPath were rated Exemplary. Alibaba Cloud, DataRobot, OpenAI, Palantir, Snowflake and Tencent Cloud were rated Innovative.

AI Governance and Operations: AWS, Cloudera, Databricks, Dataiku, Google Cloud, IBM, Microsoft, Oracle, SAP and Teradata were rated Exemplary. Alibaba Cloud, DataRobot, MathWorks, SAS, Snowflake and Tencent Cloud were rated Innovative.

AI Platforms Emerging Providers: Domino Data Lab and KNIME were rated Exemplary. H2O.ai, Hugging Face and Quantexa were rated Innovative.

AI Agents: Automation Anywhere, AWS, Boomi, Databricks, Dataiku, Google Cloud, IBM, Microsoft, Oracle, Salesforce, ServiceNow, Teradata and UiPath were rated Exemplary. Alibaba Cloud, Snowflake and Tencent Cloud were rated Innovative.

Sovereign AI and Data: AWS, Databricks, Google Cloud, IBM, Oracle and Teradata were rated Exemplary. Microsoft and Snowflake were rated Innovative.

Data Platforms: AWS, Databricks, Google Cloud, IBM, InterSystems, Microsoft, Oracle, Progress Software and SAP were rated Exemplary. Alibaba Cloud, Cloudera and Snowflake were rated Innovative.

Analytic Data Platforms: AWS, Cloudera, Databricks, Dremio, Google Cloud, IBM, InterSystems, Microsoft, Oracle, Progress Software, SAP and Teradata were rated Exemplary. Alibaba Cloud, ClickHouse and Snowflake were rated Innovative.

Operational Data Platforms: AWS, Cloudera, Databricks, Google Cloud, IBM, InterSystems, Microsoft, MongoDB, Oracle, Progress Software and SAP were rated Exemplary. Cockroach Labs, EDB and Yugabyte were rated Innovative.

Data Platforms Emerging Providers: Aerospike, Exasol, Hazelcast, MariaDB and PingCAP were rated Exemplary. Supabase, Tiger Data and Yellowbrick were rated Innovative.

“Enterprises can no longer afford fragmented approaches to AI and data. They need comprehensive strategies to ensure their AI investments deliver useful, accurate results,” said David Menninger, executive director, software research, ISG. “This research provides independent insights, informed by extensive research, on platforms for all aspects of AI deployment and maintenance and the data foundations underlying those functions.”

The ISG Buyers Guides™ for AI and Data Platforms are the distillation of more than a year of market and product research efforts. The research is not sponsored nor influenced by software providers and is conducted solely to help enterprises optimize their business and IT software investments.

Visit this webpage to learn more about the ISG Buyers Guides™ for AI and Data Platforms and read the executive summaries of each report. The complete reports, including provider rankings across seven product and customer experience dimensions and detailed research findings on each provider, are available by contacting ISG.

About ISG

ISG (Nasdaq: III) is a global AI-centered technology research and advisory firm. A trusted partner to more than 900 clients, including 75 of the world’s top 100 enterprises, ISG is a long-time leader in technology and business services that is now at the forefront of leveraging AI to help organizations achieve operational excellence and faster growth. The firm, founded in 2006, is known for its proprietary market data and research, in-depth knowledge and governance of provider ecosystems, and the expertise of its 1,500 professionals worldwide working together to help clients maximize the value of their technology investments.

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Press Contacts:

Laura Hupprich, ISG

+1 203 517 3100

laura.hupprich@isg-one.com

Eric Arvidson, Matter Communications for ISG

+1 978-518-4542

isg@matternow.com

Source: Information Services Group, Inc.