Across the investment industry, the firms replacing fragmented systems and manual workflows with a unified data foundation are turning operational complexity into a competitive advantage.
The investment industry is at an inflection point. Capital is flowing across asset classes at unprecedented speed, strategies are converging, and the operational demands of managing public and private assets side by side are testing infrastructure that was never built for this world. Whether a hedge fund, an institutional asset management firm, a sell-side desk, or a private markets operation, the challenge is the same: The data and operational backbone that supports the business is pushed beyond its limits.
Firms positioned to pull ahead in 2026 are not the ones with the most AI capabilities. They are the ones that have invested in getting their data foundation right. The competitive edge is in the architecture.
The conversation around AI in investment operations has shifted quickly. Two years ago, the question was whether AI could be trusted with operational workflows. Today, the question is whether your data infrastructure is good enough to make AI useful at all. The firms seeing real returns from agentic AI (automated reconciliation, intelligent exception management, self-service analytics) are the ones that already invested in a unified, governed data layer. The ones still running on disparate point solutions are finding that AI amplifies chaos rather than eliminating it.
This is not a theoretical concern. Nearly three-fourths (73%) of asset management industry executives say AI is critical to their organization’s future, yet Gartner warns that 30% of GenAI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. The gap between ambition and execution is a data infrastructure gap.
This edition of ArVision pulls together our latest thinking on where investment operations are heading, from the architectural foundations needed to scale agentic AI and the shift from SaaS to Service-as-a-Software to operating model modernization and the talent crunch reshaping how firms build operations. The throughline is clear: The firms that treat data architecture as a strategic priority, not a back-office afterthought, are the ones positioned to compound their advantage as AI capabilities mature.
Investment firms have spent years modernizing individual components but many still cannot scale AI because their operating model remains fragmented. The journey from component modernization to integrated, AI-ready operating models is not about buying more technology. It is about connecting what you already have into a unified foundation.
The cornerstone piece that frames this journey identifies five dimensions of integration maturity. Firms that progress across all five dimensions (data, workflows, analytics, AI, and operating model) move from isolated efficiency gains to compounding operational advantage. Those that remain stuck at the component level keep solving the same problems over and over, never building the momentum that turns technology investment into strategic differentiation.
The whitepaper explores practical AI use cases across investment operations and makes the case that fragmented operating models, not technology gaps, are the real constraint on AI readiness. The firms that recognize this are pulling ahead. The ones that do not watch their competitors compound their advantage. Dive into the evolution of investment operations and what it takes to build an AI-ready operating model.
See the Total Portfolio View in action with this short clip: $12.4B AUM unified across public and private markets, with TWR and IRR calculated side by side, drawing on 12 data sources with LLM-powered GP report extraction. The clip showcases how a total portfolio view becomes possible when built on a governed data foundation. Connect with us to see more.
88%
of organizations report regular AI use, but fewer than 10% achieve scale (McKinsey, 2025)
13%
of investment/asset management firms have fully completed data modernization, while 37% aren’t even in active initiatives (BetaNXT, 2025)
The gap between ambition and execution is not about AI capabilities. It is about the data foundation underneath them. Firms that invest in unified, governed data infrastructure before scaling AI are the ones turning ambition into measurable operational advantage. Citigroup reports that some firms are already seeing up to 50% workflow improvement from AI in investment operations. Those that skip the foundation work keep abandoning projects and watching their investment compound for someone else.
Agentic AI Is Moving from Pilots to Production
Firms across every segment are scaling agentic AI past experimentation into production workflows. But the ones seeing real returns invested in structured data, clean access layers, and encoded domain skills before deploying agents. Scaling past the easy wins requires front-to-back discipline. Read more on what it takes to scale agentic AI in investment management.
Operating Model Modernization Is the Critical Differentiator
Component-level modernization is not enough. Firms need integrated operating models with unified data foundations to unlock AI’s full potential, and the gap between firms that have done this and those that have not is compounding. Flexible, modular infrastructure is becoming the backbone of modern investment firms, not a nice-to-have. See why flexible, modular infrastructure is becoming the backbone of modern investment firms.
The Talent Crunch Is Reshaping How Firms Build Operations
With experienced operations professionals in short supply, firms are turning to technology and managed services to scale without proportional headcount growth. More than 80% of managers are considering outsourcing data management, and operating model design has become a strategic priority rather than a procurement decision. Learn how firms are building modern operating models amid the talent crisis.
Efficiently managing data can be the difference between success and failure for an investment firm. Complex pools of data are being aggregated and managed manually within spreadsheets, creating more work and increasing the likelihood of errors. Arcesium’s Aquata data platform provides seamless front-to-back integration, processing, and harmonization of investment lifecycle data. Streamlined workflows optimize efficiency and empower teams with analytics and insights capabilities to inform growth strategies, while unified data supports more robust investment analyses.
Outcomes
Modernizing technology infrastructure is integral to supporting new business strategies and resolving data management challenges. Yet many firms face a common dilemma: whether to build in-house or purchase off-the-shelf software to address their growing and disparate data volumes. Arcesium’s Aquata data platform is designed to optimize data management for the investment industry, with preconfigured data models, sophisticated data transformation, control mechanisms for entitlements, and powerful analytics and reporting tools.
Outcomes:
Arcesium Intelligence Is Gaining Momentum
Following its May 2026 launch, Arcesium Intelligence is accelerating rapidly. By September, over 1,500 agents, skills, and applications had been built, at which point Arcesium released six new agent capabilities including reusable skills and version control. Designed as a production-grade agent harness, these capabilities enable operations teams with AI they can trust, control, and scale. The same month, Arcesium Intelligence won "Best AI Solution for Investment Intelligence" at the 2026 Hedge Fund Services Awards. Embedded in Arcesium's front-to-back operations platform, Opterra, and the Aquata enterprise data platform, Arcesium Intelligence is helping financial institutions turn complex data into faster, more actionable intelligence.
Arcesium Appoints Brian Rosenberg as President, Revenue and Commercial
Brian Rosenberg joined Arcesium in September 2026 and will shepherd all aspects of client and partner development, as well as go-to-market strategy and execution. Rosenberg brings two decades of experience leading global commercial teams at prominent institutions including Wilshire Indexes, Qontigo, FTSE Russell, LSEG, SunGard, MSCI, and RiskMetrics Group.
Arcesium's Front-to-Back Platform Vision Is Taking Shape
Following its February 2026 acquisition of Stockholm-based portfolio and order management (P/OMS) systems provider Limina, Arcesium’s unified front-to-back platform is coming together. The integration of Limina’s cloud-native P/OMS with Arcesium’s middle- and back-office solutions is eliminating legacy fragmentation and connecting siloed data, giving investment managers the speed and insight they need to operate intelligently across asset classes and global markets. The acquisition also deepens Arcesium’s European presence, following the opening of its Hong Kong office in January 2026.
Arcesium will be at the following events over the coming months. If you're attending any of these, we'd love to connect. Reach out to your Arcesium contact or get in touch to arrange a meeting.
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