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If 2024 was the year when Artificial Intelligence (AI) came to the fore in Data Management, the next 12 months could see its spread ebb as Financial Institutions take a reality check and slow the frenetic pace of adoption.
That, at least, is one of the many predictions for the new year from of a coterie of Data experts who gazed into the crystal ball for Data Management Insight as we take our annual look at the likely trends for the coming year.
In the first of two parts, we begin with a deep look at the way AI will impact the Data Management industry and how organizations will incorporate it into their operations. This week we will also take in forecasts for Regulations and look at how Data skills gaps could help define 2025.
1. Artificial Intelligence (AI)
Justine Iverson, Head of Analytics for Capital IQ Solutions at S&P Global Market Intelligence.
“We believe 2025 key themes in Data Management will be underpinned by the continued investment in Generative AI (GenAI). For Data Providers, evolving their Data structure and delivery to be AI-ready will be necessary in driving additional value and relevancy for their users. As GenAI creates potential disruption, new AI-powered market entrants emerge, and the AI conversation evolves from Large Language Models (LLMs) to Agents, developing workflow solutions that move up the value chain and provide additional benefits to clients will be critical. Data Providers will need to continue to evolve their Data licensing models while also balancing IP protection. We also suspect to see increased partnerships between Data Providers and Technology Firms.”
“For Data consumers, we anticipate a continued focus on defining their AI strategy, organizing their own internal Data estate in conjunction with Third-Party Data, moving projects out of the Proof of Concept (POC) phase and adding incremental value for their organizations. Continued focus on identifying the right partners to support their needs while also meeting AI ethical, Compliance and transparency standards will be forefront.”
Peter Ku, VP and Chief Industry Strategist at Informatica.
“Many Banks seem convinced of the potential of AI, but they struggle with how best to scale, make it productive, and fit within existing budgets. At the same time, many institutions are grappling with “change fatigue”. They know they should modernise their technology and Data Infrastructure more quickly and invest more. But deploying AI more widely and successfully will not happen unless leaders more firmly address how, and to what extent, their Banks continue to rely on disjointed and antiquated legacy technology infrastructure, contributing to technical debt. While many Banks have been slowly but surely chipping away at their tech debt, it has been an albatross around Bank leaders’ necks for at least three decades. While many Banks are already well along the digital transformation journey, it may not be happening at the pace they would like.”
Nick Wood, AI product manager at Finbourne.
“Serious adoption [of AI] remains slow. This hold up is largely due to a lack of confidence in the incumbent Data Management processes, which need to be designed to support AI technologies. While AI can certainly act as a feature and capability in an overall workflow, Firms must be able to explain the models and trust the quality of the underlying Data to get there. With AI showing so much promise, prioritising modern Data infrastructures to address Data Quality concerns will be a priority for many asset managers next year.”
Cédric Cajet, Product Director at NeoXam.
“Firms are fast realising the tremendous benefits of AI, which can be harnessed to expedite coding and software development tasks, review investment Data, and even draft Investment Reports. And yet, many Financial Institutions lack a central view of their underlying Data making them ill-equipped to take advantage of the benefits of AI. As we enter a new year, Firms must resolve to ensure they can first normalise, validate and consolidate the full breadth of Data they interact with before rushing to integrate AI. There is little point in arming yourself with the very best AI weaponry only to see it back-fire.”
Angana Jacob, Global Head of Enterprise Research Data at Bloomberg.
“The widespread adoption of Retrieval-Augmented Generation (RAG) to augment GenAI with domain-specific Data and the increased use of knowledge graphs will drive demand for “AI-Ready Data”. Data must be actionable, clean and well modelled with deep, interconnected relationships to be useful for AI models. Depending on the application, AI-Ready Data requires different features, such as point-in-time historical Data for predictive AI models or metadata tags and annotation labels for unstructured text analysis. A Firm’s infrastructure also must be AI-Ready with centralised, scalable storage, dynamic compute and efficient Data retrieval for AI models.”
“There is an interesting self-reinforcing cycle at play, as the desire for AI-fuelled efficiency gains will drive the increased application of AI to optimize processes across the entire Data lifecycle – ingestion, pipeline management, transformations and monitoring. This Data Management optimisation will enable further adoption and scaling of end-to-end AI workflows, accelerating momentum in 2025.”
Julian Trotinsky, Global Director of Solutions Engineering at Gresham.
“AI and Machine Learning (ML) will solidify their role as transformative forces within Financial Services in 2025. Recent industry conferences have revealed a wave of enthusiasm and curiosity surrounding these technologies. Attendees have showcased innovative use cases while exploring new applications tailored to specific challenges.”
“Based on the last couple of conferences I have attended, we are already seeing Firms asking the question: “Have you considered this use case?” – a clear indication of the growing demand for AI-driven solutions. From predictive analytics to anomaly detection, Financial Institutions are set to leverage AI and ML to unlock unprecedented operational efficiency and customer insight.”
Gregor Stolz, Account Director at Gresham.
“While AI has dominated the Financial Services conversation in recent years, 2025 will mark the beginning of its decline, reminiscent of the Blockchain hype cycle. As initial excitement gives way to scrutiny, Firms may face challenges in scaling AI solutions beyond pilot projects, exposing gaps between lofty expectations and practical application.”
“While AI will remain a valuable tool in niche areas, its broader revolutionary impact could stall, prompting Firms to reassess investments and focus on tangible, ROI-driven outcomes. The industry may soon ask, “What happened to the AI revolution?” as attention shifts to emerging priorities.”
2. Regulations
Peter Ku, VP & Chief Industry Strategist at Informatica.
“With stricter capital requirements, ongoing and increasing concerns on commercial loan defaults, as well as ESG regulations, Financial Institutions will need modern Data Management, Data Catalogue, Data Governance and Data Quality solutions that provide “valid”, refined, fully governed and contextualised Data Assets that are trustworthy and fit-for-business use, to manage Risks and comply with industry Regulations and avoid unwanted audits and fines.”
3. Upskilling
Keith Viverito, General Manager of EMEA and APAC at Clearwater Analytics.
“Asset Managers are increasingly competing with technology companies in the battle for talent at the more junior level, and there is already a perception among graduates that outside of the Front Office investment roles, operations teams are not as glamorous as those that have more direct client-servicing responsibilities. But if Asset Managers can empower their Reporting staff with modern Data Management and Reporting systems, it puts them in a position where they can take a more proactive, analytical, and client-focused approach to Reporting. By shifting the mindset on what these roles can deliver to a client and how it can contribute to a wider strategy shift towards improved client servicing across the business, it puts the buy-side in a position where it can offer attractive career paths throughout the business, not just in Front-Office or relationship management roles. This is dependent on investing in the technology that is attractive to the talent that they are looking to hire.”
Peter Ku, VP & Chief Industry Strategist at Informatica.
“This is the time for the Financial Services industry to modernise how they manage and govern Data with solutions that also leverage Gen AI and Machine Learning to scale and automate complex Data Management and Data Governance tasks. Legacy infrastructure and outdated tools, accompanied by widespread, fragmented, siloed Data, hinder users from accessing and harnessing trusted insights. Developing AI and Data skills across the organisation, supported by metadata-driven intelligence, will ensure responsible Data use as trusted Data is democratised across the organisation for faster, successful outcomes.”
