AI news ·
SEI names chief AI strategist and two other senior leaders to build out artificial intelligence capabilities
SEI, which manages roughly $1.9 trillion in assets, named three executives to lead AI: a Chief AI Strategist, Head of AI Orchestration, and Chief Data Officer. The move treats AI as core infrastructure, not a pilot project.
SEI Establishes Dedicated AI Leadership to Treat Technology as Core Business Function
SEI, the Pennsylvania-based financial technology firm managing roughly $1.9 trillion in assets, has appointed three executives to lead its artificial intelligence efforts: Sneha Shah as Chief AI Strategist, Michael Tryniszewski as Head of AI Orchestration, and William Coffey as Chief Data Officer.
The appointments reflect a deliberate structure. Shah sets AI direction across the enterprise. Tryniszewski operationalizes strategy across business lines. Coffey builds the data infrastructure that enables both.
The move signals how financial services firms are treating AI as essential infrastructure rather than an experimental initiative. SEI CEO Ryan Hicke said the company needed "clarity, accountability, and long-term vision" to make AI work at scale.
Strategic Positioning Over Experimentation
Shah, who spent more than 25 years at London Stock Exchange Group, Refinitiv, and Thomson Reuters, joined SEI in 2023 and built out SEI Next, the firm's innovation unit. Her new role focuses on enterprise AI strategy, build-versus-partner decisions, and converting emerging technology into business results.
She emphasized the pace of AI development outstrips most technology shifts. "That demands both intention and adaptability," Shah said. "Our focus is on applying AI in ways that enhance human decision-making, strengthen trust, and deliver measurable outcomes."
The company takes a disciplined approach to where it builds proprietary capabilities versus where it partners, invests, or acquires technology.
What This Means for Management
For managers overseeing AI initiatives, SEI's structure offers a practical model: separate the strategist from the operator, and ensure data infrastructure gets dedicated leadership. This prevents AI from drifting into departmental silos or becoming a perpetual pilot program.
The three-role structure also clarifies accountability. When something fails or succeeds, the organization knows who owns the outcome.
For more on building AI strategy into your organization's operations, see AI for Management and AI for Executives & Strategy.