James Curtis is a senior research analyst at S&P Global Market Intelligence, leading the database and data platforms vertical within the Data, AI & Analytics channel. Previously, he covered business intelligence, analytics and reporting, along with machine learning and data science. James’ current areas of concentration include database and related technology analysis, real-time analytics, cloud computing and cloud-native technologies for database as a service, database optimization tooling, generative AI and retrieval augment generation (RAG) environments, including vector stores and semantic search. James arrived at S&P Global Market Intelligence through its 2019 acquisition of 451 Research, which he joined in 2015. Prior to 451 Research, he held several senior roles in technology, marketing and communications. He served as VP of a large BPO firm where he oversaw marketing for analytic solutions. He held senior technical marketing roles at Netezza and later at IBM with responsibility for data warehousing, analytics and big-data products. He has managed global programs at HPE and worked as a case editor at Harvard Business School. James holds a bachelor's degree in English from Utah State University, a master's degree in writing from Northeastern University in Boston and an MBA from Texas A&M University.
This report highlights key topics for 2026 in data, AI and analytics. Generative AI continues to permeate all aspects of the modern IT stack, supporting functions that rely on high-quality data, user-friendly tools and robust infrastructure. While some organizations continue to experiment with GenAI, an increasing number are moving into full-scale adoption and considering the business impacts and outcomes of their GenAI investments.
The cloud sector continues to transform as hyperscalers, managed service providers, global systems integrators, hardware vendors and independent software vendors advance overlapping visions for IT infrastructure and application modernization enabled by automation and powered by the disruptive promise of generative AI. Developer services, integration, automation and business processes take on new importance as modern enterprise IT environments mature and we transition from the "era of cloud" to the "era of AI."
SAP is reengineering its enterprise applications around an AI-native architecture that links data, intelligence and process automation through a unified platform. The approach is designed to create a connected system of applications powered by domain-specific AI agents.
The Business Data Cloud aims to unify SAP's collection of cloud-native applications into a single, curated data layer. SAP continues to embrace an open ecosystem model that enables data interoperability between SAP-managed data products, customer-managed data and partner ecosystems.
The vendor has well-established strengths in both massively parallel and data processing, and is known to perform at scale for mission-critical workloads, where newer cloud-first providers falter or become too expensive. Yet it is undergoing an evolution as well, as demonstrated at the recent Teradata Possible event.
Big Blue is positioning itself as a hybrid cloud and AI specialist, with Db2 firmly integrated into that strategy. It continues to make hybrid cloud and AI investments in Db2, including a new AI-powered database management platform, as well as native support for vector store and similarity search.
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