Where enterprise data is headed in 2026

KEY TAKEAWAYS

What will define enterprise data in 2026? This article looks at insights from Bloomberg’s Enterprise Data & Tech Summit in London that include the rise of agentic AI, interoperable cloud infrastructure, and governed data frameworks.  

As generative AI investment accelerates, with global spending projected to reach $1.3 trillion by 2032, and data volumes continue to expand, financial institutions are reengineering how information moves across their organizations, from research and trading to compliance and client reporting. 

With AI becoming more deeply embedded in data analysis and broader market forces reshape how firms operate, leaders are rethinking their data infrastructure and governance models. The shift mirrors a turning point in adoption, where AI is moving beyond efficiency gains to become a catalyst for growth and differentiation supported by strong underlying data. Against this backdrop, what trends are defining these changes now and how are they expected to evolve as firms look ahead to 2026? 

This article looks at key trends, based on conversations at Bloomberg Enterprise Data & Tech & Summit in London in November, where industry experts, including asset managers and banks, discussed how interoperable systems, high-quality data, and explainable AI are becoming the foundation of competitive advantage. 

AI at a pivotal moment 

As financial institutions move from experimentation to enterprise-wide adoption, AI is shifting from supporting technology to a driver of innovation and competitive differentiation. According to Tony McManus, Global Head of Enterprise Data and Indices at Bloomberg, the implementation of AI is at a turning point, where companies will move beyond using the technology solely to cut costs and start harnessing it to drive new ideas and growth.  

“You see initially a trend towards efficiency, but as people really understand how to use the technology, what you will then see is a trend towards innovation. The companies that really win won’t be the ones that reduce headcount; they’ll be the companies that do the most innovation,” says McManus. Access to vast quantities of high-quality data will be key to unlocking innovation. “We all know that AI is very good, but the output and the impact of AI are only as good as the data.” 

The rise of interoperable and multi-cloud data infrastructure 

The move to the cloud has been one of the key trends in in the financial services space over the last decade, but companies are now finding that optimal solutions require integration of multiple cloud platforms, says Neill Clark, Managing Director and Head of State Street Associates EMEA. “Five years ago, I would have said the key is to get all of your data on a cloud – ideally a single cloud – and once you’ve done that, you’ve got a set of cloud-native tools and you’re good to go,” says Clark.  

“Now the notion of a multi-cloud strategy plus on-prem makes more sense. The reality is everyone ended up multi-cloud by default because they couldn’t get to one cloud. Now it’s the right choice to make, because new tools are coming out all the time and computing costs vary.” 

Colette Garcia, Global Head of Enterprise Data Real Time Content at Bloomberg, confirms that this approach aligns with how firms are striving to balance flexibility and precision in their data strategies. “That resonates with us – getting you the data wherever you need it, cloud-agnostic, on-prem-agnostic. It’s the quality of the data and being able to deliver it wherever you need it,” she says. 

Integration of AI into investment and research workflows 

Another growing trend experts point to is the integration of agentic AI into core areas of business, including investment research and portfolio management. The goal is not to eliminate human insight and judgment, but to enhance it through automated data retrieval. 

According to Grégoire Dooms, Head of Data Research & Development at Systematica, AI enriches the information sets that analysts and portfolio managers can incorporate into their decision-making. “AI has completely lowered the bar for access and scale of processing unstructured data – text data,” says Dooms. “It is enabling us to build feature-extraction pipelines that are sector-specific or asset-specific and scale them tremendously.”  

State Street’s Clark adds that broader data plus natural language processing tools have significantly enhanced his organization’s research product. “We’ve seen significant metric improvements: 60% better measurement of some macro criteria, 20–30% better prediction outcomes in certain use cases,” he comments. “It’s a meaningful revision in how you can access unstructured data.”

Dawn of a new era for explainable and real-time data access 

AI capabilities have revolutionized the relationship between financial services organizations and their clients, allowing firms to share data and insights with clients in real-time.  “We’re experimenting with open-architecture data sharing with our clients—with no barriers at all,” said State Street’s Clark.  

“You’ve still got to assemble the control around it, with real-time access to data at a moment of your choosing, in a format of your choosing, integrated and delivered in a way that you can integrate with your other data sets. That feels like the way we’ll be exchanging information with our clients in the future, somehow.”  

Notably, as AI’s capabilities grow, companies have to continually upgrade the guardrails that ensure regulatory compliance and ethical practices, turning strong data strategy and governance into a competitive advantage. And in a fast-approaching future where AI-enabled models may make investment decisions without human input, the need for these controls becomes even more critical. 

Keeping up with a fast-changing environment 

Technological capabilities are changing so fast that it’s hard to predict what AI will look like in a month, let alone a year or five years out. However, experts agree that technology already spreading into many aspects of the financial services industry would only become more ubiquitous.  

“It’s not really about who’s going to use AI and who’s not, and who’s going to get left behind,” says Bloomberg’s McManus. The question really becomes: Who’s going to use it in the most intelligent way? Who’s going to be thoughtful, measured, and really understand how to derive real value from the AI they develop. 

Explore how Bloomberg is using AI to deliver actionable insights that empower you to move faster, work smarter and achieve better results here. Learn more about Bloomberg Enterprise Tech & Data solutions here. 

Insights in this article are based on panels and fireside discussions at the Enterprise Tech & Data Summit held in London in November 2025.