Why AI efficiency is not translating into performance
Asset owners, asset managers and asset servicers report AI efficiency gains, but not significant investment performance or AUM growth. This is the AI proof gap, and governance is a critical missing link between AI adoption and measurable performance.
This report explains why this gap exists — and what firms need to do to move from isolated efficiency gains to enterprise-wide value creation.
Asset managers have crossed the AI adoption, not value, threshold
Asset managers are no strangers to measuring performance. Yet when it comes to AI, many of the firms that demand performance from every investment are struggling to demonstrate it from their own initiatives.
According to Grant Thornton’s 2026 AI Impact Survey of 950 business executives, asset management leaders are deploying AI broadly — but returns aren’t keeping pace. Sixty-one percent of asset managers say they are scaling AI across multiple functions or have fully integrated it into operations, reporting benefits in efficiency (73%), cost reduction (50%) and improved decision-making (48%). But only 41% report revenue growth. Asset managers are concentrating their AI use cases to back- and middle-office functions, not front-office use cases connected to revenue-generating activities — and that’s a problem.
The biggest barrier to greater revenue returns is untested governance, and it’s not just policy gaps. It’s fragmented evidence, weak traceability and limited continuous testing of controls.
AI is being implemented, but it is not improving ROI. Strong, tested governance enables asset management leaders to make AI investment decisions with confidence in order to apply AI in high-value workflows and drive measurable results.
Most asset managers cannot prove their AI governance works
The firms that will scale AI across functions from efficiency toward value creation are starting with tested governance that builds alignment across functions and connects AI investments to business goals. But only 22% of asset management leaders are very confident they could pass an independent audit of AI governance and controls within 90 days, and 53% cite governance and compliance barriers as active contributors to AI underperformance.
Firms that cannot centralize and defend their AI control evidence are limited in where they can extend or innovate AI use cases: into investment workflows, risk management-related middle office processes and front-office client-facing operations where both reputational and AUM growth stakes are high.
To ensure true governance readiness, asset management leaders should test controls before, during and after AI deployment, and clearly track and organize their AI system processes with documented evidence. This approach makes that expansion and innovation possible before SEC examinations, investor due diligence and board inquiries require firms to produce it under pressure.
“Many asset managers and asset servicers are limiting their revenue potential by keeping AI contained to the back office. Firms that want to scale AI to investor relations, sales and distribution functions will need to start with provable, centralized data and governance over their AI strategy. Once established, firms have the guidance and guardrails to deploy AI with more confidence across their workflows and systems to create greater value from the back office to the front office.”
Poor, fragmented data prevents AI scale
Data is essential to asset managers that, amid fee pressure, want to use AI to drive revenue. That AUM growth requires their data to provide insights for decision-making across product, technology and distribution channels, which must flow easily across the business and third parties.
While 53% of asset management leaders say AI is scaling across functions, only 8% say it is fully integrated into operations. Thirty-one percent cite data quality and integration as a primary barrier. Asset management executives recognize that data issues are a barrier to scaling AI more broadly, but their data fragmentation and legacy processes slow their efforts to change.
Asset managers instead need an integrated data foundation that standardizes and reconciles data from internal platforms with data from custodians and fund administrators through a configurable canonical model. Managers should focus first on reconciling high-value data domains such as portfolio data, customer relationship management data and transaction/accounting data. This approach allows firms to manage inconsistent definitions and manual reconciliations where they have the greatest impact on growth and provides a single source of truth for reporting and insights.
Firms should establish governance standards for data quality and build systematic remediation into their operating model. AI can be used to expedite this work. Asset management leaders who establish centralized AI governance early and redesign their workflows for AI-native execution are able to move toward client-facing and investment use cases faster and with much less regulatory friction.
Asset managers have AI efficiency, but not AI advantage
When asset owners, managers and servicers deploy AI, it’s often in back- and middle-office operations: risk monitoring, custodial services, fund administration and reconciliation and other efficiency-building activities. Firms that are setting themselves apart are putting AI to work in in front-office activities such as portfolio optimization, distribution strategy, due diligence questionnaires and investor reporting.
Firms that leverage AI only for efficiency gains face competitive consequences. They will be forced to reduce fees as large firms with large technology budgets and digitally-native competitors use AI to offer new products, services and client experiences that capture greater market share.
At the same time, 34% of asset management leaders say competitor moves are the biggest external factor driving their AI investments, including predictive analytics, forecasting and cybersecurity. Only 14% report accelerated innovation and just 25% report higher-quality outputs — both at significantly lower rates than the full survey sample.
Asset managers are taking cues from competitors when they should be thinking ahead about what their investors need and where AI can aid in value creation. The firms creating bottom-line value are moving AI into front-office workflows: what clients interact with and what drives AUM.
How asset managers are using AI across the lifecycle
-
Front office
-
Middle office
- Back office
Product, distribution, trading, portfolios and investor experience
- Onboarding: Institutional and retail client onboarding and due diligence automation; fund onboarding/subscription document processing
- Investment strategy: Predictive modeling to assess competitive landscape; identifying market gaps using alternative data sets; macro regime classification
- Deal sourcing: Automated document review, data ingestion and red flag detection, and sentiment analysis on management calls
- Institutional due diligence and consultant engagement: AI‑assisted responses to institutional due diligence and consultant questionnaires
- Investor transparency and reporting: AI‑enabled generation of clearer, more timely, and more tailored investor communications — including portfolio explanations, risk narratives, performance drivers, and disclosures
Risk, compliance and oversight
- Portfolio monitoring: Intelligence performance dashboards to monitor performance, real-time AI valuation models for illiquid assets and automated exposure tracking against mandates
- Investment compliance: Accelerated onboarding of new guidelines, automated pre-trade compliance checks and regulatory change mapping to internal policies
- Risk management: Advanced risk scenario modeling and stress testing, anomaly detection for unusual performance patterns and adverse market condition simulation
Operations and infrastructure
- Administrative audit trail: 100% auditability with AI-driven documentation of all automated processes and decisions
- Finance operations: Invoice processing automation, predictive analytics for cash flow forecasting and optimization, expense report audit and fraud detection, and financial and regulatory reporting automation
- IT and systems development: Predictive maintenance for system failures, automated incident log analysis and response, and cybersecurity threat detection algorithms
- Human resources: AI recruitment tools for candidate screening, employee performance data and insights analysis, and personalized learning and development paths
Firms that move early and with precision in these areas will move from back-office efficiencies to performance-building use cases that improve investor experiences and increase AUM, deriving a higher-tier benefit from AI adoption.
Three actions to turn AI scale into AI success
These recommended actions are grounded in the capabilities Grant Thornton professionals help firms build.
The identified AI proof gap is the problem this research was designed to examine, and it is the problem Grant Thornton works with the asset management ecosystem — alternative investment managers, public investment managers, wealth managers and security service providers — to close: building a provable AI governance framework for asset management firms across front-, middle- and back-office workflows, providing AI compliance aligned with SEC rules and due diligence, standardizing data, and designing a responsible AI strategy and operating model that enables asset managers to leverage AI for scale and AUM growth.
In today’s asset management ecosystem, the AI proof gap is real. It is measurable. And it will not close itself without leaders taking action to establish provable governance, a trusted data foundation and confidence to innovate in client-facing functions.
Methodology
Between Feb. 23 and March 18, 2026, Grant Thornton surveyed 950 business leaders, a group restricted to CFOs, CIOs/CITOs, COOs, and VPs, department heads and directors who report directly to the C-suite. The asset management-specific subgroup comprises 100 respondents. Role-specific findings within the asset management subset of data are directional only.
Content disclaimer
This content provides information and comments on current issues and developments from Grant Thornton Advisors LLC and Grant Thornton LLP. It is not a comprehensive analysis of the subject matter covered. It is not, and should not be construed as, accounting, legal, tax, or professional advice provided by Grant Thornton Advisors LLC and Grant Thornton LLP. All relevant facts and circumstances, including the pertinent authoritative literature, need to be considered to arrive at conclusions that comply with matters addressed in this content.
For additional information on topics covered in this content, contact a Grant Thornton professional.
Grant Thornton LLP and Grant Thornton Advisors LLC (and their respective subsidiary entities) practice as an alternative practice structure in accordance with the AICPA Code of Professional Conduct and applicable law, regulations and professional standards. Grant Thornton LLP is a licensed independent CPA firm that provides attest services to its clients, and Grant Thornton Advisors LLC and its subsidiary entities provide tax and business consulting services to their clients. Grant Thornton Advisors LLC and its subsidiary entities are not licensed CPA firms.
Tax professional standards statement
This content supports Grant Thornton Advisors LLC’s marketing of professional services and is not written tax advice directed at the particular facts and circumstances of any person. It is not, and should not be construed as, accounting, legal, tax, or professional advice provided by Grant Thornton Advisors LLC. If you are interested in the topics presented herein, we encourage you to contact a Grant Thornton Advisors LLC tax professional. Nothing herein shall be construed as imposing a limitation on any person from disclosing the tax treatment or tax structure of any matter addressed herein.
The information contained herein is general in nature and is based on authorities that are subject to change. It is not, and should not be construed as, accounting, legal, tax, or professional advice provided by Grant Thornton Advisors LLC. This material may not be applicable to, or suitable for, the reader’s specific circumstances or needs and may require consideration of tax and nontax factors not described herein. Contact a Grant Thornton Advisors LLC tax professional prior to taking any action based upon this information.
Changes in tax laws or other factors could affect, on a prospective or retroactive basis, the information contained herein; Grant Thornton Advisors LLC assumes no obligation to inform the reader of any such changes. All references to “Section,” “Sec.,” or “§” refer to the Internal Revenue Code of 1986, as amended.
Grant Thornton Advisors LLC and its subsidiary entities are not licensed CPA firms.
Share with your network
Share