Building AI-Ready Asset Management – with Leaders from FE fundinfo, Franklin Templeton, Ocorion, and Amundi

This interview analysis is sponsored by FE fundinfo and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

In the asset- and wealth-management (AWM) sector, firms face mounting pressure to reduce costs, improve margins and enhance client engagement even as assets under management soar.

According to the Thinking Ahead Institute and Pensions & Investments, the world’s 500 largest asset managers oversaw USD 139.9 trillion in Assets Under Management (AUM) at the end of 2024, a 9.4% increase from the prior year. McKinsey research further shows that global AUM reached approximately $147 trillion as of mid-2025.

AWM technology investment has been rapidly increasing, with financial services companies spending approximately $35 billion on AI implementation in 2023, according to Columbia Business School. Such investment is projected to more than double to $97 billion by 2027, making it the fastest-growing primary industry.

Major firms such as JPMorgan, Morgan Stanley, and Goldman Sachs are actively leveraging AI to streamline workflows and boost efficiency. Yet, the industry continues to face challenges in translating these investments into consistent cost efficiencies and productivity gains.

According to McKinsey, many asset managers allocate 60 to 80 percent of their technology budgets to maintaining legacy systems, leaving only 20 to 40 percent for transformation initiatives. The firm emphasizes that overcoming this imbalance is critical to unlocking AI’s potential to drive meaningful productivity gains and reshape the industry’s cost structure.

Herein lies the challenge: the market is growing, the pressure to accelerate is increasing, the value of AI is becoming increasingly apparent, yet the foundations for successful deployment aren’t yet properly in place.

In fact, results from an Ernst & Young survey published last month suggest that many firms note persistent barriers: more than half of AWM respondents report that their risk-governance frameworks for emerging AI remain inadequate.

In a recent series on the ‘AI in Business’ podcast, Emerj AI Research featured enterprise executives Paul Ronan, Chief Technology Officer at FE fundinfo; Deep Srivastav, Chief AI Officer at Franklin Templeton; Frank Hattann, Chief Commercial Officer at Ocorian; and Robert Kubin, Head of Sales for Central Europe at Amundi.

In conversations with Emerj Editorial Director Matthew DeMello and CEO and Head of Research Daniel Faggela, these leaders examined how wealth and asset management firms can implement AI through disciplined data foundations, outcome-driven process transformation, and governed scaling.

Their perspectives converge on three interdependent priorities: establishing data maturity as the precondition for AI success, redesigning workflows around measurable ROI, and aligning commercial innovation with regulatory and operational discipline.

The discussions underscore that effective AI adoption depends on integrating technology, governance, and culture — balancing build-versus-buy decisions, defining clear ownership, and tying every initiative to transparent, quantifiable business outcomes, with specific attention to:

• Building the data foundation for AI maturity: Treat clean, governed data as core infrastructure, establish a single source of truth, and ensure cross-functional ownership before scaling any AI initiative.
• Transforming workflows for measurable ROI: Replace isolated pilots with end-to-end process redesign that ties every AI initiative to clear business outcomes and accountable governance.
• Aligning data and revenue growth: Build a unified commercial data foundation where AI connects client insight to execution — turning fragmented records into measurable growth opportunities.
• Embedding AI for scalable efficiency: Treat AI as a core organizational capability — starting with governed, auditable processes, distributing ownership, and scaling through measurable, regulation-aligned transformation.

Building the Data Foundation for AI Maturity

Episode 1: How AI is Transforming Asset & Wealth Management from Data to Decisions – with Paul Ronan of FE fundinfo 

Guest: Paul Ronan, Chief Technology Officer, FE fundinfo 

Expertise: Financial Markets Technology, Derivatives Trading Systems, Architectural Solutions, Stakeholder Management 

Brief Recognition: With over 25 years of experience, Paul Ronan has worked as a technologist across capital markets, private banking, and brokerage. He has held senior roles in banking and fintech consulting, focusing on building technology organizations, reducing technical debt, and simplifying operating models.

Ronan argues that the path to AI value begins not with models but with the structure and integrity of data itself. In a sector where product hierarchies, fund identifiers, and client information are stored across multiple legacy systems, poor data lineage remains the primary obstacle to scale.

Ronan describes a three-layer model of AI maturity:

1. Data hygiene and governance: Consolidate data from multiple custodians, reconcile formats, and enforce stewardship. Firms must establish a “single source of truth” before introducing automation.
2. Operational readiness: Design workflows that consume structured, validated data and route it to decision points automatically.
3. Transformation and scale: Use AI to create measurable performance uplift — shorter onboarding times, faster compliance checks, improved advisor productivity.

He notes that many firms treat AI as a bolt-on capability rather than a structural enabler. “If the data foundation is weak,” he warns, “AI becomes a proof of concept that never reaches production.”

Ronan links this to FE fundinfo’s broader emphasis on data as infrastructure rather than a by-product of reporting. Asset managers increasingly seek to personalize client portfolios or automate regulatory disclosures, but both goals depend on consistent metadata and version control. Clean data reduces model-training costs and shortens development cycles, creating a compounding efficiency loop.

He further emphasizes the cultural dimension: “Technology cannot compensate for silos,” Ronan says. “AI adoption requires cross-functional ownership — data owners, compliance officers, and client-service teams need to share a single vocabulary for data quality.”

Without that alignment, even sophisticated algorithms will underperform. For AWM, Ronan’s perspective underscores that data governance is the first frontier of AI competitiveness. Investments in model sophistication are wasted if data stewardship and process integration remain fragmented.

Transforming Workflows for Measurable ROI

Episode 2: Driving AI Adoption in Wealth and Asset Management – with Deep Srivastav of Franklin Templeton

Guest: Deep Srivastav, Chief AI Officer, Franklin Templeton

Expertise: Data & Digital in Financial Services, Enterprise Transformation, Digital Investment Solutions

Brief Recognition: A global leader with deep domain expertise, Deep Srivastav has driven enterprise transformation leveraging AI and digital, including creating award-winning AI-driven products. He is a co-author in academic journals on leveraging AI for wealth management personalization and a recipient of the prestigious Markowitz Award.

Srivastav cautions against what he calls “AI tourism” — isolated experiments that produce demos but no operational impact. “The goal,” he says, “is not to build more proofs of concept, but to re-engineer workflows so that humans and algorithms continuously learn from each other.”

Deep frames his approach as business-first design. Before modeling begins, leaders should define the decision they want to accelerate or de-risk — whether that’s trade execution, compliance triage, or portfolio rebalancing — and then trace the data dependencies backward. These steps prevent the common pitfall of launching pilots disconnected from ROI metrics.

At Franklin Templeton, Srivastav’s team applies a layered ROI framework:

• Operational ROI: Time saved in data preparation and document review
• Commercial ROI: Improved client-interaction quality and retention rates
• Strategic ROI: Insights that inform new product design or distribution strategy

He notes that achieving these outcomes often requires retraining employees rather than replacing them. “Advisors and analysts need to see AI as augmentation, not automation,” he explains. “The moment they perceive it as displacement, adoption stops.”

To maintain engagement, Srivastav recommends measuring “cost per decision” and “advisor productivity uplift” instead of abstract accuracy scores. This anchors model performance to business outcomes that leadership can monitor.

Srivastav also highlights governance. Many firms underestimate how data fragmentation and role ambiguity impede ROI measurement. He advises creating AI councils — cross-departmental groups combining risk, compliance, and data science — to vet projects and define acceptable use. “Governance is the scaffolding for scale,” he says. “Without it, you end up with isolated experiments that never integrate.”

Aligning Data and Revenue Growth

Episode 3: Scaling Growth in Asset Management with AI-Ready Data – with Frank Hattann of Ocorian

Guest: Frank Hattann, Chief Commercial Officer, Ocorian

Expertise: Revenue Operations, Sales Leadership and Management, Scaling Sales Organizations

Brief Recognition: A former leader at PayPal, LinkedIn, and Microsoft, Frank Hattann has over 20 years of experience building and scaling sales organizations. He has a proven track record of delivering significant year-over-year revenue growth and transforming businesses through innovative sales strategies.

Hattann brings a revenue-focused perspective to AI enablement, with a background in both technology and commercial operations, and he observes that the next frontier for AI in asset management lies at the intersection of client engagement and data architecture.

Many financial services providers still rely on manual workflows and siloed systems. AI, in his view, offers a way to close that gap by making outreach, analytics, and client onboarding more intelligent and timely.

Hattann identifies three levers that AI unlocks:

1. Faster market analysis and opportunity detection: AI agents can monitor data sources continuously, identifying emerging investment themes or client-segment shifts before traditional research cycles do.
2. Top-of-funnel engagement: Machine learning enables firms to reach prospects “at the right time with the right message,” improving conversion and retention.
3. Client-service optimization: AI-based summarization and automated document handling free relationship managers from administrative tasks, enabling more time for strategic discussions.

He warns that manual, legacy processes still dominate much of the sector. Up until the present, he notes, there was a general sense of not wanting to miss out on AI gains without having to be the first to take the substantial risk. That risk aversion, he argues, is dangerous in a period of technological inflection.

Hattann also draws attention to the disruption of traditional fee models. Many professional-service environments, including trust and fund-administration firms, still bill by the hour. AI threatens that structure by reducing the time required for routine tasks. The challenge for leaders is to redefine value in terms of outcomes delivered, not hours logged.

Internally, Hattann recommends that commercial teams use AI-ready data to identify client opportunities previously hidden by fragmented records. Consolidating CRM, product, and regulatory data has produced what Hattann calls a “single commercial spine” — an architecture in which AI models can generate alerts, prioritize leads, and support cross-selling.

He frames AI adoption not as an IT initiative, but as a joint commercial-technology transformation. The CCO, COO, and CTO must jointly sponsor data modernization and agree on how insights translate into revenue growth. 

Embedding AI for Scalable Efficiency

Episode 4: Strategic AI Adoption for Asset Managers and Enterprise Decision Makers – with Robert Kubin at Amundi

Guest: Robert Kubin, Head of Sales for Central Europe, Amundi

Expertise: Investments, Traditional Asset Management, Private Equity, Venture Capital

Brief Recognition: Robert Kubin, a senior executive with over 20 years of international experience, has served as the CEO of an asset manager, the CIO of an insurance company, and a management consultant. He brings detailed knowledge of investments and asset management matters to the conversation.

Kubin views AI adoption through the lens of scale and regulation. Asset management, he notes, is a service industry with limited pricing power and rising operational complexity. “You can’t raise fees,” he explains, “Regulators won’t allow it, and competition keeps them flat. Growth must therefore come from scale and efficiency.”

Kubin observes that many firms have invested heavily in technology without achieving proportional productivity gains. “These investments often plug holes,” he says, “but they don’t transform the system.” AI, in his view, offers a structural remedy if integrated correctly.

He outlines three principles for embedding AI sustainably:

1. Strategic commitment: Executives must frame AI as a core capability, not a side project. The first step is cultural — accepting AI as a long-term enabler rather than a temporary trend.
2. Distributed ownership: Kubin recommends appointing “AI champions” across business lines and geographies who identify opportunities and maintain feedback loops with the central AI function.
3. Build vs. buy balance: Larger firms may develop models internally, but smaller asset managers should selectively acquire proven solutions. The goal is a hybrid model that combines proprietary data with scalable tools.

He also stresses that regulation requires explainability and auditability. Every AI output influencing investment or compliance decisions must be traceable. “The best model,” he says, “is useless if you can’t explain it to your regulator.”

Kubin adds that cultural transformation and governance are inseparable. To make AI part of “organizational DNA,” leadership must tie innovation incentives to concrete use-case outcomes — such as faster client-onboarding or improved KYC accuracy. With metrics visible across functions, shared accountability is easier to achieve.

For smaller firms, Kubin suggests starting with process-level automation — for example, AI-assisted KYC or automated marketing material generation — then scaling toward analytics and decision support. “You start small, test, measure, and scale,” he explains. “That’s how AI moves from novelty to necessity.”