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

This interview evaluation is sponsored by FE fundinfo and was written, edited, and revealed in alignment with our Emerj sponsored content guidelines. Learn extra about our thought management and content material creation companies on our Emerj Media Services page.

In the asset- and wealth-management (AWM) sector, companies face mounting strain to cut back prices, enhance margins and improve consumer engagement whilst property below administration soar; in response 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) on the finish of 2024, a 9.4% enhance from the prior yr. McKinsey analysis additional shows that international AUM reached roughly US$147 trillion as of mid-2025.

AWM know-how funding has been quickly rising, with monetary companies corporations spending roughly $35 billion on AI implementation in 2023, in response to Columbia Business School. This funding is projected to greater than double to $97 billion by 2027, making it the fastest-growing main business. Major companies resembling JPMorgan, Morgan Stanley, and Goldman Sachs are actively leveraging AI to streamline workflows and enhance effectivity. Yet, the business continues to face challenges in translating these investments into constant value efficiencies and productiveness positive factors.

According to McKinsey, many asset managers allocate 60 to 80 % of their know-how budgets to sustaining legacy programs, leaving solely 20 to 40 % for transformation initiatives. The agency emphasizes that overcoming this imbalance is crucial to unlocking AI’s potential to drive significant productiveness positive factors and reshape the business’s value construction.

Herein lies the problem: the market is rising, the strain to speed up is rising, the worth of AI is turning into more and more obvious, but the foundations for profitable deployment aren’t but correctly in place.

In reality, results from an Ernst & Young survey counsel that many companies notice persistent boundaries: greater than half of wealth- and asset-management respondents report that their risk-governance frameworks for rising AI stay insufficient.

In a latest collection on the ‘AI in Business’ podcast, Emerj 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, the leaders examined how wealth and asset administration companies can implement AI by means of disciplined information foundations, outcome-driven course of transformation, and ruled scaling.

Their views converge on three interdependent priorities: establishing information maturity because the precondition for AI success, redesigning workflows round measurable ROI, and aligning industrial innovation with regulatory and operational self-discipline.

The discussions underscore that efficient AI adoption is dependent upon integrating know-how, governance, and tradition — balancing build-versus-buy choices, defining clear possession, and tying each initiative to clear, quantifiable enterprise outcomes, with particular consideration to:

• Building the information basis for AI maturity: Treat clear, ruled information as core infrastructure, set up a single supply of fact, and guarantee cross-functional possession earlier than scaling any AI initiative.
• Transforming workflows for measurable ROI: Replace remoted pilots with end-to-end course of redesign that ties each AI initiative to clear enterprise outcomes and accountable governance.
• Aligning Data and income progress: Build a unified industrial information basis the place AI connects consumer perception to execution — turning fragmented information into measurable progress alternatives.
• Embedding AI for scalable effectivity: Treat AI as a core organizational functionality — beginning with ruled, auditable processes, distributing possession, and scaling by means of measurable, regulation-aligned transformation.

Building the Data Foundation for AI Maturity

Episode – 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 expertise, Paul Ronan has labored as a technologist throughout capital markets, personal banking, and brokerage. He has held senior roles in banking and fintech consulting, specializing in constructing know-how organizations, decreasing technical debt, and simplifying working fashions.

Ronan argues that the trail to AI worth begins not with fashions however with the construction and integrity of knowledge itself. In a sector the place product hierarchies, fund identifiers, and consumer info are saved throughout a number of legacy programs, poor information lineage stays the first impediment to scale.

Ronan describes a three-layer mannequin of AI maturity:

1. Data hygiene and governance: Consolidate information from a number of custodians, reconcile codecs, and implement stewardship. Firms should set up a “single supply of fact” earlier than introducing automation.
2. Operational readiness: Design workflows that eat structured, validated information and route it to choice factors routinely.
3. Transformation and scale: Use AI to create measurable efficiency uplift — shorter onboarding occasions, quicker compliance checks, improved advisor productiveness.

He notes that many companies deal with AI as a bolt-on functionality reasonably than a structural enabler. “If the information basis is weak,” he warns, “AI turns into a proof of idea that by no means reaches manufacturing.”

Ronan hyperlinks this to FE fundinfo’s broader emphasis on information as infrastructure reasonably than a by-product of reporting. Asset managers more and more search to personalize consumer portfolios or automate regulatory disclosures, however each targets rely upon constant metadata and model management. Clean information reduces model-training prices and shortens improvement cycles, making a compounding effectivity loop.

He additional emphasizes the cultural dimension: “Technology can not compensate for silos,” Ronan says. “AI adoption requires cross-functional possession — information homeowners, compliance officers, and client-service groups have to share a single vocabulary for information high quality.” Without that alignment, even subtle algorithms will underperform.

For wealth- and asset-management leaders, Ronan’s perspective underscores that information governance is the primary frontier of AI competitiveness. Investments in mannequin sophistication are wasted if information stewardship and course of integration stay fragmented.

Transforming Workflows for Measurable ROI

Episode – 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 worldwide chief with deep area experience, Deep Srivastav has pushed enterprise transformation leveraging AI and digital, together with creating award-winning AI-driven merchandise. He is a co-author in educational journals on leveraging AI for wealth administration personalization and a recipient of the celebrated Markowitz Award.

Srivastav cautions towards what he calls “AI tourism” — remoted experiments that produce demos however no operational influence. “The aim,” he says, “is to not construct extra proofs of idea, however to re-engineer workflows in order that people and algorithms repeatedly study from one another.”

Deep frames his strategy as business-first design. Before modeling begins, leaders ought to outline the choice they wish to speed up or de-risk — whether or not that’s commerce execution, compliance triage, or portfolio rebalancing — and then hint the information dependencies backward. These steps forestall the frequent pitfall of launching pilots disconnected from ROI metrics.

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

• Operational ROI: Time saved in information preparation and doc overview
• Commercial ROI: Improved client-interaction high quality and retention charges
• Strategic ROI: Insights that inform new product design or distribution technique

He notes that attaining these outcomes usually requires retraining workers reasonably than changing them. “Advisors and analysts have to see AI as augmentation, not automation,” he explains. “The second they understand it as displacement, adoption stops.”

To preserve engagement, Srivastav recommends measuring “value per choice” and “advisor productiveness uplift” as a substitute of summary accuracy scores. This anchors mannequin efficiency to enterprise outcomes that management can monitor.

Srivastav additionally highlights governance. Many companies underestimate how information fragmentation and position ambiguity impede ROI measurement. He advises creating AI councils — cross-departmental teams combining threat, compliance, and information science — to vet tasks and outline acceptable use. “Governance is the scaffolding for scale,” he says. “Without it, you find yourself with remoted experiments that by no means combine.”

Aligning Data and Revenue Growth

Episode – 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 chief at PayPal, LinkedIn, and Microsoft, Frank Hattann has over 20 years of expertise constructing and scaling gross sales organizations. He has a confirmed monitor file of delivering vital year-over-year income progress and remodeling companies by means of modern gross sales methods.

Hattann brings a revenue-focused perspective to AI enablement, with a background in each know-how and industrial operations, and he observes that the subsequent frontier for AI in asset administration lies on the intersection of consumer engagement and information structure.

“When I left Microsoft for monetary companies,” Hattann says, “it felt like touring again in time.” Many monetary companies suppliers nonetheless depend on handbook workflows and siloed programs. AI, in his view, provides a option to shut that hole by making outreach, analytics, and consumer onboarding extra clever and well timed.

Hattann identifies three levers that AI unlocks:

1. Faster market evaluation and alternative detection: AI brokers can monitor information sources repeatedly, figuring out rising funding themes or client-segment shifts earlier than conventional analysis cycles do.
2. Top-of-funnel engagement: Machine studying permits companies to achieve prospects “on the proper time with the appropriate message,” bettering conversion and retention.
3. Client-service optimization: AI-based summarization and automated doc dealing with free relationship managers from administrative duties, enabling extra time for strategic discussions.

He warns that handbook, legacy processes nonetheless dominate a lot of the sector. “Up till now,” he notes, “no person needed to be first — however no person needed to be final both.” That threat aversion, he argues, is harmful in a interval of technological inflection.

Hattann additionally attracts consideration to the disruption of conventional payment fashions. Many professional-service environments, together with belief and fund-administration companies, nonetheless invoice by the hour. AI threatens that construction by decreasing the time required for routine duties. The problem for leaders is to redefine worth when it comes to outcomes delivered, not hours logged.

Internally, Hattann recommends that industrial groups use AI-ready information to establish consumer alternatives beforehand hidden by fragmented information. Consolidating CRM, product, and regulatory information has produced what Hattann calls a “single industrial backbone” — an structure during which AI fashions can generate alerts, prioritize leads, and assist cross-selling.

He frames AI adoption not as an IT initiative, however as a joint commercial-technology transformation. The CCO, COO, and CTO should collectively sponsor information modernization and agree on how insights translate into income progress. 

Embedding AI for Scalable Efficiency

Episode – 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 government with over 20 years of worldwide expertise, has served because the CEO of an asset supervisor, the CIO of an insurance coverage firm, and a administration advisor. He brings detailed information of investments and asset administration issues to the dialog.

Kubin views AI adoption by means of the lens of scale and regulation. Asset administration, he notes, is a service business with restricted pricing energy and rising operational complexity. “You can’t increase charges,” he explains, “Regulators received’t enable it, and competitors retains them flat. Growth should subsequently come from scale and effectivity.”

Kubin observes that many companies have invested closely in know-how with out attaining proportional productiveness positive factors. “These investments usually plug holes,” he says, “however they don’t remodel the system.” AI, in his view, provides a structural treatment if built-in accurately.

He outlines three rules for embedding AI sustainably:

1. Strategic dedication: Executives should body AI as a core functionality, not a facet mission. The first step is cultural — accepting AI as a long-term enabler reasonably than a brief pattern.
2. Distributed possession: Kubin recommends appointing “AI champions” throughout enterprise traces and geographies who establish alternatives and preserve suggestions loops with the central AI operate.
3. Build vs. purchase steadiness: Larger companies could develop fashions internally, however smaller asset managers ought to selectively purchase confirmed options. The aim is a hybrid mannequin that mixes proprietary information with scalable instruments.

He additionally stresses that regulation requires explainability and auditability. Every AI output influencing funding or compliance choices should be traceable. “The finest mannequin,” he says, “is ineffective in case you can’t clarify it to your regulator.”

Kubin provides that cultural transformation and governance are inseparable. To make AI a part of “organizational DNA,” management should tie innovation incentives to concrete use-case outcomes — resembling quicker client-onboarding or improved KYC accuracy. With metrics seen throughout features, shared accountability is less complicated to realize.

For smaller companies, Kubin suggests beginning with process-level automation — for instance, AI-assisted KYC or automated advertising and marketing materials era — then scaling towards analytics and choice assist. “You begin small, check, measure, and scale,” he explains. “That’s how AI strikes from novelty to necessity.”