Executive summary
As explored in Grant Thornton’s 2026 AI Impact Survey, asset managers have largely cracked back-office AI, but efficiency gains alone won't win allocators or grow AUM. To evaluate whether an AI investment is ready for front-office scale, CFOs and COOs should follow a concrete five-question decision-making framework: Does it move AUM? Is it embedded in a live workflow? Can it survive regulatory scrutiny? Does it have a named business owner? And are the exit criteria defined? Firms that can answer yes to all five are positioned to turn AI from an operational tool into a competitive differentiator.
A five-question framework for front-office AI in asset management
Asset managers are well on their way to building AI into middle- and back-office workflows.
“Across the industry, everyone is starting from the back office,” said Thanga Ramalingam, Grant Thornton Transformation Partner. “For most asset managers, efficiency is the first thing that comes to mind — it’s low-hanging fruit. Get a quick win, show efficiency, and then get investment to innovate."
But efficiency is a short-lived benefit. It funds the next round of AI investment, but it doesn’t provide the client-facing value that wins the next investor. The firms pulling ahead are redeploying their savings into front-office use cases that provide value to the client and the portfolio.
But that’s where many asset managers reach an AI scale ceiling, and the data shows it. Grant Thornton’s 2026 AI Impact Survey reveals that 73% of asset managers are building efficiency from AI — 10 percentage points above the cross-industry average. Yet only 25% report higher-quality outputs, compared to 43% across all industries, and just 14% report accelerated innovation, compared to 31% across all respondents.
Asset managers can’t build competitive differentiation with efficiency alone. Firms that concentrate AI capital in back-office operations may protect existing margin, but that doesn’t demonstrate to potential clients why they should invest with one firm over another. The firms standing out are moving AI into the front- office workflows: institutional and retail client onboarding, institutional due diligence and investor reporting.
To capture that front-office value, CFOs and COOs need to stop approving AI investments based on efficiency potential alone and start evaluating them on whether they drive AUM growth, strengthen client relationships or improve investment outcomes. But most firms are scaling AI without a shared, consistent framework for deciding what gets funded, what scales and what gets shut down.
AI use cases in front-office asset management
Asset managers generating competitive returns from AI are not necessarily spending more. According to “The AI-powered investment firm,” another AI study Grant Thornton conducted with research firm ThoughtLab, AI leaders in asset management get higher returns despite investing at similar levels to their peers.
The difference isn’t how much capital they’re spending, but where they’re directing it. Leaders are concentrating AI in high-value use cases in the front office that directly affect revenue, client outcomes and investment performance, often in distribution and investor relations functions. That includes client onboarding, investment strategy, deal sourcing, due diligence and investor reporting. In the middle office, leading firms are prioritizing risk and fraud protection as well as data security and privacy.
Other high-leverage front-office AI plays that leading firms are deploying include:
- AI-assisted due diligence questionnaire (DDQ) and request for proposal (RFP) response generation
- Allocator-specific portfolio commentary
- Revenue management and client -360 intelligence
- Personalized investor reporting at scale
- AI-enabled deal sourcing and screening for private markets
- Explainable investment-thesis support tooling for portfolio managers and analysts
- Next-best-action customer relationship management (CRM) workflows
- Proactive retention triggers
- Digital engagement tailored to client behavior
“These are use cases where AI changes what a firm can do differently for clients, not just how fast it does what it already does,” said Viral Chawda, Grant Thornton Transformation Partner.
“We saw one private equity client materially increase AI adoption and grow assets under management from $5 billion to $9 billion in under a year after embedding AI with unified data into the daily workflow of its market-facing teams,” Ramalingam added. “It’s a reminder that real value comes from making AI actionable in the revenue engine, not just AI that produces insights.”
AI leaders also measure success differently. Rather than tracking cost savings and efficiency metrics alone, they monitor revenue growth, employee productivity, client satisfaction and model performance — holding AI investments accountable to the outcomes that actually move the business.
| Top seven metrics for firms | Metrics that leaders use more than others |
|---|---|
| Strategy: Revenue growth, reduced risks, new business models People: Employee productivity, customer satisfaction, customer retention Innovation: New products and services, time to market and time to value AI-specific: Model accuracy, client user adoption and frequency of use of AI models |
Source: The AI-Powered Investment Firm, ThoughtLab in partnership with Grant Thornton Advisors, 2025
The gap between AI leaders and laggards isn’t just about technology. It’s undetermined prioritization, and it shows up most visibly in how firms decide which AI investments get approved.
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Asset management AI investment decision framework
Asset managers get the most traction from front-office AI when use cases are tied to enterprise priorities such as growth, client retention, product uptake and speed to market, rather than being pursued as standalone technology pilots. In practice, that means aligning investment, distribution, product, compliance and technology teams around a defined business outcome, then embedding AI directly into the workflow that drives it.
The firms using AI to differentiate from competitors evaluate every AI investment against the same set of standards before capital is approved. Not just cost and timeline — but also value creation, defensibility, accountability and risk containment.
For asset management CFOs and COOs, who are driving and evaluating AI investment decisions, approval should start with five questions.
In this AI investment decision framework, each question is designed to distinguish AI that earns front-office investment from AI that belongs in a separate efficiency bucket — or doesn’t belong at all.
5-question AI investment framework for asset management leaders
Finance and operations leaders should revisit this decision gate regularly: at funding approval, then again at the 90-day proof checkpoint and again at every annual budget cycle. AI use cases that pass at pilot but fail at scale should be down-shifted or retired as necessary.
Contacts:
Partner, Transformation
Grant Thornton Advisors LLC
Thanga Ramalingam is a Partner in Grant Thornton’s Transformation Advisory practice, specializing in AI-enabled transformation across Financial Services, including banking, asset management, private equity, and insurance.
Edison, New Jersey
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