ISG research highlights the growing need for unified AI and data strategies to enable scalable, accurate, and compliant enterprise AI deployments
Enterprises across industries are entering a new phase of artificial intelligence adoption—one defined not by experimentation, but by execution at scale. According to new research from Information Services Group (ISG), organizations are increasingly aligning their AI initiatives with data strategies, recognizing that sustainable, enterprise-grade AI deployments depend on tightly integrated platforms that unify both domains.
In earlier stages of AI adoption, many organizations treated AI and data as separate disciplines. Data teams focused on storage, pipelines, and governance, while AI teams concentrated on model development and experimentation. However, as AI use cases expand into mission-critical business functions, this separation is proving to be a structural limitation. AI models are only as effective as the data that feeds them, and without coordinated infrastructure, enterprises face significant challenges in scaling deployments reliably and efficiently.
ISG’s 2026 Buyers Guides™ for AI and Data Platforms provide a comprehensive view of this evolving landscape. The research evaluates 83 software providers, offering detailed rankings and assessments of platforms designed to manage both AI models and enterprise data. These guides span a wide range of categories, including AI platforms, data platforms, agentic AI systems, sovereign AI solutions, and emerging providers, reflecting the increasing complexity and specialization of the market.
A key finding from the research is that many organizations attempting to scale AI initiatives are hindered by fragmented data environments. Data is often siloed across departments, stored in incompatible formats, or governed by inconsistent policies. These issues create bottlenecks that limit the effectiveness of AI models and introduce risks related to accuracy, compliance, and trust. Before AI systems can deliver value, enterprises must invest in cleaning, standardizing, and governing their data assets.
Matt Aslett, director of research for analytics and data at ISG, emphasized that moving beyond experimental AI requires enterprise-grade software capable of supporting both model development and data management. He noted that while the number of available tools continues to grow, success depends on selecting and aligning platforms within a cohesive strategy. Organizations that fail to take a holistic approach risk creating fragmented ecosystems that are difficult to scale and maintain.
Modern enterprises are therefore prioritizing platforms that support the full AI lifecycle. This includes capabilities for data ingestion and preparation, model training and validation, deployment, monitoring, and continuous optimization. At the same time, these platforms must incorporate governance frameworks that ensure compliance with regulatory requirements and maintain data integrity. As AI becomes more deeply embedded in business operations, the need for transparency, accountability, and auditability is becoming increasingly critical.
The role of data platforms is equally important. Enterprises rely on these systems to ensure that data remains accurate, consistent, and trustworthy across the organization. As AI applications require ever-larger volumes of data, infrastructure costs can rise significantly. Coordinating AI and data platforms helps optimize resource utilization, reduce redundancy, and improve overall efficiency.
Another major trend identified in the ISG research is the growing demand for real-time, context-aware insights. Traditional analytics architectures, which rely on batch processing, are no longer sufficient for many modern use cases. Applications such as dynamic pricing, personalized recommendations, fraud detection, and predictive maintenance require continuous access to both data and analytical models. This has led organizations to adopt operational data platforms capable of performing AI inference in real time, enabling faster and more responsive decision-making.
The boundaries between AI and data platforms are therefore becoming increasingly blurred. Instead of treating them as separate layers, enterprises are moving toward unified architectures where data processing and AI inference occur seamlessly within the same environment. This shift not only improves performance but also simplifies system design and reduces operational complexity.
The rapid rise of generative AI and agentic AI is further accelerating this convergence. These technologies introduce new requirements for data platforms, particularly in handling unstructured data and enabling natural language interactions. One of the most important developments in this area is the use of vector embeddings, which allow systems to represent complex data types such as text and images in a form that can be efficiently processed by AI models. These embeddings are a critical component of retrieval-augmented generation (RAG), a technique that enhances the accuracy and relevance of generative AI outputs.
ISG expects that data platform providers will continue to invest heavily in hybrid processing architectures that combine operational and analytical capabilities. This approach is seen as essential for supporting next-generation AI applications, including generative and agentic systems, through at least 2028. By enabling real-time data access and advanced analytics within a single platform, these solutions provide the foundation for scalable, high-performance AI deployments.
The Buyers Guides highlight a diverse ecosystem of technology providers, ranging from established industry leaders to emerging innovators. Major cloud and enterprise technology companies such as Amazon Web Services, Google Cloud, Microsoft, IBM, and Oracle continue to play a dominant role, offering integrated platforms that combine data management, AI development, and cloud infrastructure.
At the same time, specialized providers such as Databricks, Hugging Face, and H2O.ai are driving innovation in areas such as machine learning, open-source AI, and advanced analytics. This combination of scale and specialization gives enterprises a wide range of options, but also increases the complexity of platform selection.
Among established providers, Oracle was recognized as the top Overall Leader across all evaluated AI and data platform categories. Other companies identified as Overall Leaders include Databricks, IBM, AWS, and InterSystems. These organizations have demonstrated strong performance across multiple evaluation criteria, including product capabilities, platform integration, and customer experience.
In the emerging provider category, companies such as Domino Data Lab, H2O.ai, and Hugging Face were named Overall Leaders for AI platforms, while MariaDB, Aerospike, and PingCAP led in data platforms. These emerging players are helping to shape the future of the industry by introducing new technologies and approaches that address evolving enterprise needs.
The research also identifies providers that have achieved Exemplary or Innovative status across various categories. Companies such as AWS, Databricks, Google Cloud, IBM, Oracle, SAP, and Teradata were rated Exemplary in multiple segments, reflecting their strong capabilities and market leadership. Meanwhile, providers like Snowflake, Microsoft, and Alibaba Cloud were recognized as Innovative, highlighting their contributions to advancing platform capabilities.
David Menninger, executive director of software research at ISG, underscored the importance of adopting a unified approach to AI and data. He noted that fragmented strategies are no longer viable in an environment where organizations depend on accurate, real-time insights to drive decision-making. Enterprises must develop comprehensive frameworks that integrate technology, governance, and processes to ensure that their AI investments deliver meaningful results.
The ISG Buyers Guides are based on more than a year of market and product research and are conducted independently of vendor influence. This ensures that the insights provided are objective and reliable, offering enterprises a trusted resource for evaluating technology options and making informed investment decisions.
Beyond technology selection, the shift toward integrated AI and data platforms has broader organizational implications. Enterprises must rethink how they structure teams, manage workflows, and define responsibilities. Collaboration between data engineers, data scientists, IT teams, and business stakeholders is essential for building systems that deliver value at scale. Additionally, organizations must invest in skills development to ensure that their workforce can effectively leverage these advanced platforms.
In conclusion, the research from Information Services Group highlights a critical turning point in enterprise AI adoption. By aligning AI and data platforms, organizations can overcome the limitations of siloed systems, improve accuracy and compliance, and unlock the full potential of their AI initiatives. As the demand for intelligent, real-time applications continues to grow, this integrated approach will be essential for driving innovation, efficiency, and competitive advantage in the digital economy.
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