Through 2026, more than one-third of enterprises will deploy cloud-native AI and data platforms in information architectures, a new ISG report says.

Enterprises are turning to the cloud to access the resources they need to execute their AI strategies, according to a new research report from leading global technology research and advisory firm Information Services Group (ISG) (Nasdaq: III).

The ISG Buyers Guide™ for Cloud-Native AI and Data Platforms, produced by ISG Software Research, projects that through 2026, more than one-third of enterprises will deploy cloud-native AI and data platforms in their information architectures. Most leading cloud providers offer both AI and data capabilities, while independent software providers offer AI and data platforms built to operate natively in and across cloud environments, the report says.

The report points out that AI requires large amounts of high-quality data that needs to be prepared, engineered and organized to feed and train AI models. The volumes of data necessary for accurate AI models place a significant demand on computing resources, which can often be best met with elastic cloud platforms. The cost of these systems can be significant, and any inefficiencies in the process can exacerbate the costs, the report says.

“AI workloads are highly variable and require large amounts of compute resources, making them ideal for the cloud environment,” said David Menninger, executive director, ISG Software Research. “Cloud, AI and data platforms should be evaluated together, rather than independently, to make the most informed decisions when selecting software providers.”

The increasing importance of intelligent operational applications driven by AI insights is blurring the lines that have traditionally divided the requirements for AI platforms and data platforms, the report says. While there have always been general-purpose databases that could be used for both analytic and operational workloads, traditional architectures have involved the extraction, transformation and loading of data from the operational data platform into an external analytic or AI platform. This enables the operational and analytic workloads to run concurrently without adversely impacting each other, protecting the performance of both.

The report notes the importance of coordinating cloud, AI and data efforts. Cloud platform providers have recognized the opportunity to help enterprises with this convergence, and all the top cloud providers are offering platforms that combine AI and data capabilities.

The Cloud-Native AI and Data Platform Buyers Guide includes an evaluation of platforms that provide three sets of capabilities: cloud, AI and data. To be considered for inclusion in this Buyers Guide, a product must offer services addressing key elements of cloud platforms that:

• Support a combination of public, private and hybrid cloud workloads;
• Include a general-purpose data platform, database, database management system, data warehouse, data lake or data lakehouse;
• Include data persistence, data management, data processing and data query functionality;
• Support database administrator, developer, data engineering and data architect functionality, and
• Support the AI-related capabilities of data preparation, AI/ML modeling, AutoML, GenAI, developer and data scientist tooling, MLOps/LLMOps, model deployment, model tuning and optimization.

A related report, the AI and Data Platform Buyers Guide, evaluates platforms that provide both AI and data capabilities. To be considered for inclusion in this Buyers Guide, a product must be marketed as a general-purpose data platform, database, database management system, data warehouse, data lake or data lakehouse. The primary use case is to support worker- and customer-facing operational applications and/or analytics workloads such as business intelligence or data science. The product should provide data persistence, data management, data processing and data query; database administrator functionality; developer functionality; data engineering functionality, and data architect functionality. It must also support AI-related capabilities, including data preparation, AI/ML modeling, AutoML, GenAI, developer and data scientist tooling, MLOps/LLMOps, model deployment, model tuning and optimization.

For its 2024 Buyers Guides, ISG assessed 12 providers: Alibaba Cloud, AWS, Cloudera, Databricks, Google Cloud, IBM, Microsoft, Oracle, Salesforce, SAP, Snowflake and Teradata.

ISG Software Research designates the top three software providers as Leaders in each category. For the 2024 studies, the leading providers in ranked order are:

Cloud-Native AI and Data Platforms: AWS, Microsoft and Google Cloud

AI and Data Platforms: Oracle, IBM and AWS

“The ability to meet enterprise needs for a unified AI and data platforms that can operate natively in or across cloud computing environments is now a key priority for assessing and selecting software products,” said Mark Smith, partner, ISG Software Research. “Our unique, groundbreaking research provides valuable insights to support that process.”

The ISG Buyers Guides™ for both Cloud-Native AI and Data Platforms and 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 Buyers Guides for Cloud-Native AI and Data Platforms and AI and Data Platforms and read executive summaries of each of the reports. 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 Software Research.

Organizations seeking guidance on Generative AI service providers can explore existing ISG Provider Lens™ research, upcoming studies and additional details in this digital brochure.

Source: BUSINESS WIRE