The Data Year Ahead: AI Comes of Age, Private Markets Become Less Opaque

2026 is set to be the year in which the evolutionary changes hinted in the past 12 months become established within the data landscape, according to expert predictions. Artificial intelligence will mature into the game-changing innovation it has promised for years and private markets, whose growth in importance in the past few years has been breathtaking, will be better served by the most sophisticated data processing.

AI Maturity

• The clearest impact of AI in 2026 won’t be in complex fund logic, it will be in how people use the systems that power those processes. We’re entering an era of intelligent assistance – AI-driven, contextual guidance built directly into workflows. It’s not a chatbot bolted on top but an operational layer that understands fund structures and recognises patterns, providing real-time support as people work. By embedding AI that coaches users inside the workflow, firms can reduce training effort, lower operational dependency and see faster ROI through fewer errors and quicker closes.
  
  The catalyst in 2026 will be the shift to modern, event-driven platforms that create unified, trustworthy, machine-readable data with auditability by default. That data foundation will be what finally enables practical AI that works reliably at scale.

– David O’Malley, President and Board Director, LemonEdge

• Financial services will demand AI accountability as the experimentation phase ends. The days of “AI for AI’s sake” spending are over. In 2026, financial institutions will pivot from proof-of-concept projects to demanding measurable business impact from every AI dollar spent. Banks and asset managers will no longer accept experimental chatbots or isolated AI tools—they’ll require clear evidence that AI is driving specific outcomes: customer retention rates, revenue growth percentages, operational scale improvements, and enhanced digital adoption metrics. This shift will force organisations to abandon siloed AI experiments in favour of integrated solutions built into core data architectures, with boards demanding comprehensive measurement frameworks that treat AI investments with the same rigour as any other strategic technology deployment.

– Rinesh Patel, Global Head of Industry, Financial Services at Snowflake

• The shift will accelerate from pilots to full workflow redesign: AI agents will handle end-to-end processes, while humans steer and intervene for exceptions. The upside is speed and freeing human capacity for higher-value work; the challenge is risk. Scaling this shift demands responsible AI and safe autonomy – with robust permissions, audit trails, and sandboxing as non-negotiable safeguards.

– Alexandra Mihailescu Cichon, Chief Commercial Officer, RepRisk

• We expect a surge in agentic AI used to automate suspect data detection and drastically reduce false positives. This shift effectively transforms data operations from a defensive cost centre into a streamlined value creator.

– Mark Hepsworth, Chief Executive, Gresham

• Delivering enterprise ROI with Agentic AI – AI usage will turn into a reality for enterprise use case applications, demonstrating productivity ROI through the use of AI agents. However due to regulatory regimes especially for financial services, we still expect to always have a “user in the loop”.

– Akber Jaffer, Chief Executive, SmartStream

Private Markets

• Limited Partners are done waiting weeks for data they can’t interrogate. They want Netflix-style reporting experiences: intuitive, self-service, and always current. In 2026, LP relations teams that can’t meet this demand risk losing out to platforms that can.

– David O’Malley, President and Board Director, LemonEdge

• The momentum is only going one way. With private markets on track to generate more than half of global asset management revenues, firms that hesitate risk being overwhelmed by the data. The next phase will focus on deeper automation, sharper forecasting, and a level of transparency once considered out of reach.

– Clément Miglietti, Chief Product Officer and Chief Technology Officer, NeoXam.

• For decades, the data industry prioritised scale over source, often with limited regard for who owned the underlying sources. AI is reshaping that equation. Many providers still sell data they do not own, leaving them with no control over the integrity or stability of their supply chains – a model that cannot survive in an environment defined by regulatory scrutiny and the threat of disintermediation. By 2026, clarity on origin, collection reliability, and crucially, permissible use will be non-negotiable. Without that transparency, organisations cannot be confident in the accuracy, suitability, or compliance of the data they rely on.

– Edgar Randall, Managing Director, Dun & Bradstreet UK and Ireland

Risk Management

• AI risk management will evolve beyond ethics to encompass operational survival. As AI agents and large language models become embedded in critical financial operations, the risk conversation will expand dramatically beyond bias and hallucinations. In 2026, financial institutions will grapple with AI risks that directly threaten operational resilience, from data residency challenges to systemic failures that could trigger regulatory sanctions. Firms without robust data foundations will find themselves unable to deploy AI at scale.

– Rinesh Patel, Global Head of Industry, Financial Services at Snowflake

• Expect a strong push toward orchestrating AI agents with human expertise to expand risk coverage – a shift that will be especially critical for private markets, where transparency remains limited and risks are harder to uncover. Banks, asset managers, and corporates will increasingly demand solutions that combine automation for scale with human oversight for accuracy, enabling tailored coverage across countries, sectors, companies, and projects. This hybrid approach, powered by due diligence–grade, human-labeled data, will become the standard for monitoring business conduct risks for performance and peace of mind.

– Alexandra Mihailescu Cichon, Chief Commercial Officer, RepRisk

Applications, Automation and Analytics

• Financial institutions will begin introducing AI agents into core business processes, from risk monitoring and surveillance to customer reviews and portfolio operations. As these systems take on multi-step work traditionally handled by teams of analysts, leaders will confront a dual challenge: how to measure productivity in a world where humans and AI collaborate, and how to govern agents that can make decisions and take action. Firms will move from measuring tasks handled by people to evaluating the performance of blended human–AI workflows based on speed of detection, accuracy of decisions, consistency with policy, and overall business impact.

– Rinesh Patel, Global Head of Industry, Financial Services at Snowflake

• Geospatial intelligence is poised to move from a niche input to a standard layer in risk management, underwriting, and supply-chain decisions – especially among early adopters. The upside? Earlier risk detection and sharper transition insights. The challenge? A familiar bottleneck: integrating geospatial data seamlessly into enterprise platforms and linking it with other datasets to unlock full value.

– Alexandra Mihailescu Cichon, Chief Commercial Officer, RepRisk

• We will see a step change in interconnectivity of enterprise workflows through new protocols. Building open API based interconnectivity and the emergence of MCP (Model Context Protocol) we expect enterprises to enhance dynamic workflow connectivity Unlocking the dynamic data insights – enterprises will increasingly use technology to supplement current static management information to more real-time dynamic data insights, through natural language interface.

– Akber Jaffer, Chief Executive, SmartStream

Data Governance, Lineage and Quality

• Firms will continue to modernise their data operations in the age of AI. Rich metadata, transparent lineage, and well-modelled, interconnected data are more important than ever for firms to scale, innovate, and power their resilience. Demand for differentiated datasets such as dividend projections, fund flows, credit, liquidity, and transition risk, and foundational private markets data will also continue to rise as investors, traders, and risk teams seek an edge.Ultimately, success in the next phase of data management will depend on pairing differentiated, high-quality content with best-in-class delivery technology. This creates a data foundation that enables firms to scale, adapt, and thrive in an increasingly intelligent marketplace.

– Leila Sadiq, Global Head of Enterprise Data Content at Bloomberg

• One of the key trends we are likely to see next year is the emergence of growing numbers of data strategy teams. Businesses are increasingly realising they cannot get good results from AI, or even agree on basic numbers without high-quality, easily accessible data. Private markets firms have been slower to use modern technology than sectors like consumer goods, which have had large volumes of data for many years.What were considered innovations in 2025 – automated waterfall calculations, integrated compliance workflows, and auditable data lineage – will become standard requirements in 2026. The leaders are not just automating processes – they are designing systems around governance intelligence, where every decision and data point is connected.

– David O’Malley, President and Board Director, LemonEdge

• Organizations will double down on harmonisation, interoperability, and governance to close the gap between AI ambition and data reality, laying the foundation for scalable, responsible AI adoption.

– Alexandra Mihailescu Cichon, Chief Commercial Officer, RepRisk

• The priority shouldn’t be spending more on technology but rather spending more strategically. Asset managers must confront an uncomfortable truth: recent McKinsey research shows the majority of technology spending – often 60-80% – goes toward maintaining legacy systems rather than genuine transformation. This “complexity tax” isn’t just expensive; it actively prevents firms from capturing the substantial efficiency gains – potentially 25-40% of the cost base – that AI and modern platforms can deliver.
  
  Firms need to shift investment away from keeping fragile systems alive and toward building modern, unified data platforms that can actually support AI-driven workflows.

– Gareth Evans, Chief Product Officer, FINBOURNE

• A growing best practice in AI deployment is moving away from monolithic models and instead delegating specific tasks to specialised models. This modular approach ensures outputs remain traceable, dependable and significantly less prone to AI hallucinations. Traceability is non-negotiable – particularly in financial services, where every decision, from loan approvals to credit scoring and fraud detection, must leave a verifiable audit trail. As regulatory scrutiny intensifies, financial institutions will prioritise AI systems that combine accuracy with accountability, ensuring that even automated decisions can be audited with confidence.

– Alexandra Mihailescu Cichon, Chief Commercial Officer, RepRisk

• We expect investment in holistic data platforms will accelerate as AI is only as smart as the data that feeds it. While cloud migrations and advanced analytics have driven progress, many investment managers still struggle with fragmented data scattered across various data lakes or silos. Without proper governance, AI can amplify problems rather than improve human decision-making and deliver better results for their customers.

– Ulrik Modigh, Head of Managed Business Service, SimCorp

• The industry will move definitively past the phase of cloud migration toward true cloud optimisation. The era of “lift and shift” is over; merely hosting monolithic legacy architectures in the cloud is no longer a viable strategy for firms facing increasing data volumes and data management requirements. We anticipate a market correction where the focus shifts entirely to cloud-native microservices that deliver genuine, infinite elasticity. The competitive edge will belong to firms that adopt architectures designed to ingest and process real-time data at scale, rather than those simply struggling to maintain legacy systems in a new environment. This infrastructure maturity will support a more pragmatic phase of AI adoption, moving away from generative hype toward specific, high-value operational use cases. Firms will increasingly replicate the efficiency models of major tech players, deploying AI primarily for cost optimisation rather than speculative alpha generation.

– Mark Hepsworth, Chief Executive, Gresham

Sustainability

• While the language around sustainability has evolved – less about morals and more about materiality; less about compliance and more about competitiveness; less about harm reduction and more about redesigning how value is created – the underlying drivers have barely changed. That’s because the core issues remain the same: biodiversity, human and labor rights, governance, and supply chain resilience.
  
  The fundamentals haven’t shifted. The rhetoric may change, but the risks don’t. Business conduct risks continue to translate into operational, reputational, and financial exposure. At the end of the day – regardless of the terminology – capital deployment is about managing risk and capturing opportunity. Sustainability plays a role both.

– Alexandra Mihailescu Cichon, Chief Commercial Officer, RepRisk