Summary
Asset owners cannot scale AI on fragmented, inconsistent investment data. A unified data foundation brings together records across portfolios, managers, asset classes, and systems, while data quality controls validate, normalize, and govern the information. The result is a trusted, AI-ready layer for analytics, risk, reporting, and investment oversight.
AI transformation has reached an inflection point, turning the page from chatbots and text summarization to autonomous agents that perform multi-step operations like P&L attribution, exception management, and trade capture. Pension funds, insurers, endowments, and sovereign wealth funds are rushing to make hype real. AI's capabilities will help them manage incredibly complex portfolios and multi-asset strategies.
The next phase is about scaling AI workflows and realizing return on investment, while also ensuring compliant audit trails, explainability, and mitigating AI risks. There is a secret formula to ensuring AI agents reason accurately with trusted, accessible data underneath. The asset owners who implement a unified data foundation that produces an auditable, secure system of record through shared models and logic, with applied governance and lineage at the source, will leap ahead of the pack in terms of complying with rigorous transparency standards in reporting, mitigating operational risk, and driving returns.
Why do asset owners need platoons of AI agents? Because today’s complexity is beyond human capabilities, that is, unless you hire platoons of humans. If asset allocators can get their agentic AI initiatives right, they can adapt to and even thrive in myriad disruptions. Institutional asset owners have spent the past decade moving away from straightforward portfolios of public equities and bonds. But they didn’t move away from their traditional technology platforms. It makes automating investment lifecycle operations not unlike trying to drive on a highway that's under construction with lanes closed, lane shifts, and uneven pavement. Institutional investors' diversification into private equity, private credit, infrastructure, real estate, and direct investments is designed to capture uncorrelated returns outside of volatile public markets. But older tech stacks make it manually onerous if not impossible to accurately manage risk, execute analytics, and optimize allocation strategies with a total portfolio view.
In 2017, the top issue on asset owners' minds was low market returns, tied for second was finding alpha opportunities. In 2025, the number two issue became managing complexity and associated workload growth.i Technical debt from legacy systems can be a crippling force, squeezing margins and thwarting AI adoption progress.
AI tools can manage colossal datasets, pulling insights from disparate data living in different systems (private markets, public equities, digital assets) and standardizing unstructured data that old platforms cannot comprehend. However, AI is only as good as the data foundation on which it is built. The modern asset owner data platform must be able to ingest and normalize investment data from these fragmented systems, and it must consolidate it into a central source of truth for the entire investment department to use. Only 17% of institutional investors feel their systems are ready to leverage AI and other future technologies. Just 31% of investors have their public and private market data integrated and accessible from the same system.ii
An AI-ready data foundation can orchestrate, normalize, and centralize all security master and reference data. It produces a unified repository of security data, terms and conditions, and asset-level attributes, ensuring validity, integrity, and trustworthiness. This is the only way to effectively deploy agentic AI workflows that can deal with complicated cross-asset portfolio operations.
"AI agents automate portfolio design, rebalancing and tax optimization, enabling advisors to focus on strategic decisions while improving client outcomes. The automation of portfolio management processes can reduce operational costs by 40 percent to 50 percent by minimizing manual interventions." - Agentic AI is changing wealth management, KPMGiii
When it comes to increasing alternatives allocations, asset owners remain most zealous about asset-based finance (ABF) strategies.iv An AI-ready data foundation will enable an asset allocator dealing in ABF to cleanly process loan tapes by ingesting and normalizing data from incoming unstructured data, from scores of private market funds, each sending ad hoc valuation data via PDF. ABF requires tracking hundreds of attributes, including original balances, prepayment provisions, FICO scores, and repayment terms. Additionally, it is hard to run Total Portfolio Approach (TPA) strategies without the ability to see the total portfolio exposure, positions, and performance.v
AI, layered onto a unified and standardized data infrastructure, helps institutional investors achieve comprehensive portfolio visibility and automate cross-asset portfolio operations:
Boards and oversight committees are no longer happy with a retrospective review of summarized reports that are outdated once they hit their inboxes. They now demand granular portfolio intelligence that allows informed governance decisions.
For LPs and private markets investments, the Institutional Limited Partners Association (ILPA) Reporting Template increased the required expense tracking categories from nine to 22. For insurers, the National Association of Insurance Commissioners (NAIC) task force has expanded risk formulas from only six designations to 20 different risk ratings for fixed-income securities. Beneficiaries of pension plans and sovereign funds expect detailed, timely, and digitally accessible reporting on fund performance, fee transparency, and risk exposures.
Asset owners' core mandate is not only driving returns; it is governance, compliance, and on-time reporting. Data presented to the internal board should match data presented to external parties. Because of the increased allocations to alternatives and stringent oversight expectations, compliance, exposure, fees, and performance reporting have become much more data intensive. AI agents using consolidated data models can help institutional investors move from batch processing to real-time reporting, from a reactive monthly PDF into a live, transparent window into the portfolio, one that matches the team's internal view.
Manual or fragmented operational processes cannot consistently meet today's transparency mandates without significant error risk and resource cost. The capability to deliver on-time, accurate reports to demanding stakeholders — federal and state regulators, rating agencies, boards of directors, policyholders, plan participants — requires unified data platforms that provide accurate security masters.
Did your firm begin the AI transformation journey by integrating a unified data foundation or does it still rely on manual reconciliation, manual data aggregation, and manually assembled reports? An AI P&L agent can autonomously analyze a portfolio's performance by comparing actuals to outcomes, breaking P&L into various drivers, and analyze which asset classes, managers, or securities contributed to losses or gains, for example.
Northern Trust's 2026 study revealed that 70% of global asset owners cite “harnessing the power of AI” as a top operational challenge, while 57% cite data integration and accuracy as key obstacles.vi AI adoption is easy, operationalizing AI is harder, optimizing AI is harder still. But there is a path to these types of governed, explainable multi-step agents, and that path runs on a newly paved data superhighway.
Rochelle Glazman
Rochelle is responsible for enabling go-to-market and growth strategies across sales, marketing, product, and client engagement. Before taking on this role, Rochelle was a Senior Pre-Sales Consultant, engaging with clients and prospects across the financial services industry. Prior to joining Arcesium, Rochelle spent over five years at BlackRock Aladdin servicing institutional asset managers and leading several implementation projects across North and South America. She graduated from Vanderbilt University with a degree in economics.
Sources:
i Thinking Ahead Institute, March 2025. https://www.thinkingaheadinstitute.org/content/uploads/2025/04/FF-TAI_AOPS24_ClosingShortInfoReport_v3x.pdf
ii McKinsey, November 2025. https://www.mckinsey.com/industries/private-capital/our-insights/mckinsey-on-investing
iii KPMG, 2026. https://kpmg.com/us/en/articles/2025/agentic-ai-changing-wealth-mgmt.html
iv P&I, August 18, 2026. https://www.pionline.com/alternative-investments/pi-private-credit-asset-based-finance-growth-pension-funds
v CFA Institute, July 28, 2026. https://rpc.cfainstitute.org/research/reports/2026/total-portfolio-approach
vi Northern Trust, May 19, 2026. https://www.northerntrust.com/japan/pr/2026/nt-peer-study-asset-owners-focus-on-data-and-operating-model-resilience-amid-digital-disruption
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