AssetOps Chicago schedule

Setting the tone for a day of action-oriented discussion on data, automation and operational alpha in the US buy-side.

During this 5-minute networking session, the aim of the game is to go and meet two people you don't already know.   

• AI and automation reshaping investment workflows
• Integrating private markets into traditional portfolios
• Operating model implications for large and mid-sized asset managers

Reserved for Alpha FMC

• Building trusted, scalable data foundations that support operations, analytics, and AI initiatives
• Breaking down organizational and technology silos to create a unified data ecosystem
• Moving from experimentation to enterprise-wide AI by aligning data strategy, governance, and business objectives

• Where are operations leaders focusing time and budget for the next 6-12 months?
• Upskilling teams for data literacy, automation and AI enablement
• Peer exchange on priorities across public and private market operations

Moderator: Stacy Luchin, Managing Director - Information & Business Solutions, Diamond Hill

Speakers:

Cassie Hrtanek, Chief Operating Officer, The Inland Real Estate Group

Chris H Kuehne, SVP Global Head of Investment Operations, Northern Trust Asset Management

Jack Lynch, Chief Strategy Officer, Ridgeline

• Operational implications of distressed credit cycles
• Risk monitoring across credit portfolios
• Collaboration between risk, investment, and operations teams
• Data and reporting challenges in private credit

Moderator: Medy Agami, Senior Partner, Ben-Roz

Speakers:

Bhavna Goyal, Senior Director, ORIX Corporation USA

Sharif Arikat, Global FX Head of Operational Change/Capability, Northern Trust

Adam Ness, Chief Operating Officer, Meeder Investment Management

• Preparing operational data for AI workflows
• Breaking down data silos across front, middle, and back office
• Data governance and investment data architecture under pressure

• How COOs are balancing automation with empathy — managing fear, change fatigue, and reskilling across diverse teams
• Embedding a culture of experimentation, agility, and data literacy across the organization
• What defines an effective operational leader in 2026: communication, adaptability, and psychological safety

Moderator: Luis Pelaez, Head of Internal Sales & Service, Nuveen

Speakers:

Chandra Halverson, Senior Vice President Operations, Brookstone Capital Management

Stacy Luchin, Managing Director - Information & Business Solutions, Diamond Hill

Laura Peck, Chief Operating Officer, Riverwater Partners

• Scaling AI safely across front-to-back operations without introducing operational or regulatory risk
• Establishing guardrails for security, compliance, transparency, and auditability as AI becomes embedded in investment workflows
• Integrating AI, automation, and human oversight to create controlled, end-to-end operational processes that drive efficiency while maintaining control

Speakers:

Jon Emswiler, VP of Financial Services, North America, SS&C Blue Prism

Tony Chavez, Director of Solutions Consulting, SS&C Blue Prism

• How are firms building strategic partnerships that support global operating models and client service excellence?
• Designing oversight frameworks that satisfy regulatory expectations while enabling operational agility
• Ensuring data integrity, service continuity, and consistent client outcomes across outsourced functions

Moderator: Chris Johnson, Head of Back Office, Teza Technologies

Speakers:

Jen Burka, Head of Investment Operations, Harris | Oakmark

Rochelle Levy, Chief Compliance Officer, Bluespring Wealth Partners

AI agents do not pause and second-guess your data’s trustworthiness. If a human sees a stale price or a NAV input that drifted out of range, their muscle memory tells them this is not reasonable, before they move to a decision. Agentic workflows are designed to act at machine speed without this crucial step to ensure data is fit for purpose. As asset managers hand more to copilots and agents, the real risk isn't the AI itself but the data feeding it, and that risk has enormous potential impact.

This session breaks down what it actually takes to build data that is fit for use and action.

• Why AI adoption in asset management is quietly raising the stakes on data quality as AI removes and automates workflows

• How to augment your data quality efforts: a shared responsibility across ops, data, and investment teams with AI giving them deterministic superpowers

• How to spot the data blind spots AI won't tell you about, the kind that show up as a mispriced NAV or a stale valuation, that an agent has no reason to question

• Operational implications of multi-asset portfolios
• Data integration across public and private investments
• Portfolio transparency and reporting challenges
• Technology platforms supporting hybrid portfolios

Moderator: Susan Barreto, Editor, Alternatives Watch

Speaker:

Zane Alajou, Chief Financial Officer, Hawk Ridge Capital Management

• Where AI and automation are delivering real returns in US operations (workflow orchestration, reconciliation, reporting)

• Overcoming the execution gap: model governance, training data, and control frameworks in regulated environments
• Rethinking talent and change management for a hybrid human-AI workforce

Moderator: Mary-Patricia Harrison, Global Securities Data Lead - Senior Vice President, Northern Trust

Speakers:

Jordan Elkind, VP of AI Strategy and Transformation, Meeder Investment Management

Jim Seng, Director, Financial Operations, Longview Asset Management

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.