The quiet momentum that changes everything

Every meaningful breakthrough in life begins with a long season of unnoticed work. At first nothing seems to move, but beneath the surface, discipline is building quiet momentum. Then one day, you cross the threshold, and what once felt impossible suddenly becomes inevitable.

This observation from Itayi Garande captures what many AI engineering teams in asset management are experiencing right now. After two years of pilots, proofs-of-concept, and scattered experiments, something has shifted. The question is no longer whether AI will transform investment management, it’s whether your organization will be among those who capture the value or be left explaining why transformation stalled.

2026 marks an inflection point. Oliver Wyman’s latest industry forecast identifies 2026 as a year of inflection for both the economy and the asset management industry. Microsoft’s Bill Borden emphasizes that success in 2026 won’t come from experimenting with AI, but from re-architecting core business processes to be human-led and AI-operated.

The data confirms we’ve reached a threshold. What happens next separates the firms building lasting competitive advantage from those still debating governance frameworks in committee.

The paradox of widespread adoption and elusive returns

The numbers tell a story of near-universal engagement masking a troubling gap. Grant Thornton’s 2025 survey of 500 asset management executives found that 73% consider AI critical to their organization’s future, with 77% claiming an effective AI strategy. An EY survey of 100 wealth and asset managers revealed that 95% have scaled generative AI to multiple use cases.

Yet beneath these impressive headlines lies a sobering reality. Carne Group’s research shows fewer than one in five firms have deployed AI for core operations. Two-thirds report only small or moderate returns on their AI investments, with 12% seeing no returns or negative results. Publicis Sapient’s analysis of firms managing $74.2 trillion in assets found only 19% report ROI greater than 7%.

“Asset managers are talking a big game on AI, but the sector is still waiting for true transformation,” observes Siobhan Noble, Chief Data and AI Officer at Carne Group. “The imperative is to first understand the problem statement, fix the process, and then apply technology.”

This gap between experimentation and production-grade deployment represents both the industry’s greatest challenge and its most significant opportunity. McKinsey estimates AI could eventually impact 25 to 40 percent of an average asset manager’s cost base, but only for those who move beyond pilots to enterprise-scale implementation.

What production-scale AI actually looks like

The firms achieving measurable business impact share a common pattern: they’ve moved from treating AI as a collection of point solutions to embedding it as operational infrastructure. Consider the contrast between industry averages and what leading firms have achieved.

Morgan Stanley’s AI Assistant, launched in September 2023, now achieves 98% adoption across financial advisor teams. The tool accesses over 100,000 research documents and has improved document retrieval efficiency from 20% to 80%. Critically, advisors report spending more time on client relationships because routine research tasks now take minutes instead of hours.

JPMorgan has deployed its LLM Suite to approximately 50,000 employees, representing 15% of its workforce. During the April 2025 market volatility, the firm’s Coach AI enabled rapid personalized client responses, contributing to a 20% year-over-year increase in asset and wealth management sales. The firm has also launched Proxy IQ, becoming the first major asset manager to replace third-party proxy voting advisors with AI-powered analysis.

UBS processes 8 million AI tool prompts per quarter across 52,000 employees, with over 300 active AI use cases. The firm has digitized 60,000+ documents into a queryable knowledge base and decommissioned 1,100 legacy applications as AI-powered alternatives replaced fragmented workflows.

In the hedge fund space, the transformation is equally striking. Research indicates that generative AI adoption among hedge funds grew from 6% before 2022 to 63% by 2024. Funds adopting these tools report 2-4% higher annualized abnormal returns compared to non-adopters. Point72’s AI-focused Turion Fund returned 14.2% in 2024 versus 6.2% for the Nasdaq Composite.

The applications gaining traction now

Several AI use cases have crossed from experimental to production-ready, with clear ROI profiles emerging.

Investment research automation represents the most mature application area. Platforms like AlphaSense can query 450 million+ documents, while competitors like Hebbia and Rogo have moved from curiosity to essential infrastructure. The productivity gains are substantial, with Deloitte estimating top global investment banks could see front-office productivity improvements of 27 to 35 percent by 2026 from AI research tools. Tasks that previously consumed days (synthesizing earnings calls, updating financial models, analyzing 10-K changes) now complete in minutes.

RFP and DDQ automation has emerged as an unexpected high-impact area. Asset managers responding to institutional consultant queries face punishing timelines with enormous competitive stakes, and new purpose-built solutions cut response times from weeks to hours. For firms completing hundreds of RFPs annually, the operational leverage is substantial.

Client engagement and distribution tools are accelerating rapidly. EY data shows 62 to 74 percent of asset managers now prioritize investments in personalized client outreach. One firm reported a 74% increase in win rates and 95% boost in quota attainment using AI-powered content management. The underlying shift: relationship managers spend 60 to 70 percent of their time on non-revenue-generating activities due to outdated systems. AI is reclaiming that time.

Agentic AI (systems capable of autonomous reasoning, planning, and action) represents the next frontier. While only 7 to 10 percent of firms have deployed agentic capabilities today, 78% are actively exploring them. Gartner predicts 33% of enterprise software will include agentic AI by 2028, up from less than 1% in 2024. Franklin Templeton’s strategic partnership with Wand AI signals how leading firms are positioning for this transition.

The infrastructure imperative nobody wants to fund

The uncomfortable truth underlying the pilot-to-production gap: approximately half of all asset management firms lack basic processes to clean, normalize, and tag their internal data. This isn’t a technology problem, it’s a strategic failure masquerading as a technical challenge.

“Data quality” ranked as the second-most-cited barrier to AI success (51%), just behind cultural resistance (55%). Yet the firms achieving measurable returns have made infrastructure investment non-negotiable. Grant Thornton’s best practice guidance emphasizes building AI-ready IT and data platforms with modern, cloud-native systems and scalable data lakes as a prerequisite, not an afterthought.

The capital flowing into AI infrastructure reflects its strategic importance. Brookfield announced a $100 billion AI infrastructure program with NVIDIA and Kuwait Investment Authority. The Stargate Initiative represents $500 billion in commitments from OpenAI, SoftBank, Oracle, and Microsoft, while BlackRock’s consortium acquired Aligned Data Centers for $40 billion. These aren’t speculative bets, they’re strategic positioning for an AI-native future.

For individual asset managers, the lesson is clear: trying to bolt advanced AI onto legacy data architectures produces the modest returns that populate survey results. The 18% of firms with AI deployed in core operations share a common trait: they invested in data foundations before chasing use cases.

What building momentum actually requires

Geoffrey Moore’s “Crossing the Chasm” framework offers insight into the current moment. AI in asset management is transitioning from early adopters to the early majority, a passage that has consumed countless promising technologies. But as UX Tigers noted in their 2025 analysis, “AI is moving at unprecedented speed.” The window between competitive advantage and table stakes is compressing.

Building genuine momentum (the kind that produces compounding returns rather than one-off efficiency gains) requires several shifts in how AI engineering teams operate.

First, domain-based transformation rather than use-case proliferation. The firms reporting only modest returns typically have dozens of isolated pilots. The firms achieving impact have restructured entire domains (investment research, client service, operations) around AI-augmented workflows. This requires harder organizational work but produces multiplicative rather than additive gains.

Second, governance as an accelerant, not a brake. The Investment Association reports 75% of COOs need clearer guidance on scaling AI from experimentation to enterprise execution. But the leading firms have discovered that robust governance frameworks (clear accountability, transparent monitoring, compliant data handling) actually accelerate deployment by removing ambiguity and building stakeholder confidence.

Third, workforce preparation as a parallel track. EY’s research shows 97% of firms report minimal headcount changes currently, but 68% anticipate substantial workforce transformations within five years. Two Sigma’s Matt Greenwood captures the dynamic: “The future isn’t AI replacing humans. It’s humans who use AI well, replacing humans who don’t.” Teams that invest in AI literacy across roles (not just engineering) move faster because they face less resistance and more intelligent use-case identification.

Fourth, designing for the agentic future while delivering near-term value. The firms leading today are already architecting workflows where specialized AI agents can be introduced progressively. This means building modular systems, clear handoff protocols, and monitoring infrastructure that can accommodate increasing automation without wholesale redesign.

The 18-month window

McKinsey’s “600-day imperative” frames the competitive stakes: firms have roughly 18 to 24 months to move from proof-of-concept pilots to enterprise-scale AI deployment. Those who achieve this transition will capture cost and speed advantages that late adopters cannot match. Those who don’t risk finding themselves in a structurally disadvantaged position as AI-native competitors and well-resourced incumbents pull ahead.

The patterns of success are now visible. Leading firms align AI strategy to business outcomes rather than technology capabilities. They invest in foundational data infrastructure before scaling use cases. They build governance, oversight, and compliance from the start rather than retrofitting after incidents. They prepare their workforce and develop AI-literate talent across functions. And they design for an agentic future even while extracting value from current-generation tools.

VanEck’s portfolio managers frame it as a phase shift: AI is moving from phase 1 (build-out) to phase 2 (adoption), with phase 1 rewarding scale and storytelling while phase 2 requires a credible path to ROI.

Conclusion: from quiet momentum to inevitable transformation

The inflection point has arrived. The survey data, the deployment patterns at leading firms, and the magnitude of infrastructure investment all point to the same conclusion: 2026 will separate organizations that successfully transitioned from experimentation to production from those still searching for traction.

For AI engineering teams in asset management, the task ahead is clear. Stop treating AI as a collection of interesting experiments. Start treating it as operational infrastructure that requires the same rigor (in data quality, in governance, in organizational change management) as any mission-critical system.

The quiet momentum has been building for two years. The threshold is here. What once felt impossible (AI genuinely transforming how investment management works) is becoming inevitable. The only remaining question is which firms will be leading that transformation and which will be responding to it.

As Charles Givens observed: “Success requires first expending ten units of effort to produce one unit of results. Your momentum will then produce ten units of results with each unit of effort.” The firms that made those early investments are now entering the acceleration phase. For everyone else, the clock is ticking.