Scaling AI for Impact

Most organizations have moved past experimentation, yet scaling AI across the enterprise continues to expose gaps in data readiness, operating models, and ownership. Leaders are discovering that success depends less on algorithms and more on integration, governance, and execution discipline. Organizations are industrializing AI by connecting data, standardizing deployment, and maintaining performance over time. Attention is also given to where scale breaks down and what separates sustainable progress from stalled initiatives, putting an emphasis on repeatability, reliability, and long-term value creation.

Join this timely keynote presentation, which will address:

• What changes when AI moves from isolated use cases to enterprise-wide deployment
• How data infrastructure and deployment pipelines shape AI performance at scale
• Common execution pitfalls that slow or derail AI initiatives

Ritvik Shrivastava

Technical Lead - Multimodal AI

Meta

AI is increasingly operating beyond single tasks and functions, coordinating activities across finance, operations, IT, and every team. Leaders are seeing new efficiencies emerge as systems share context, trigger actions, and adapt workflows in real time. AI is becoming part of how workflows across the organization rather than living inside isolated tools, and examines where coordination breaks down and how architecture, ownership, and trust influence outcomes.

In this panel discussion, you will learn:

• How organizations connect AI across functions to reduce handoffs and delays
• Where agentic workflows create value and where they introduce risk
• What architectural and operating choices support adaptive, cross-functional work

Moderator:

Subhajit Ghosh

Senior Digital Transformation Leader

Lumen Technologies

Panelists:

Vishal Mirza

Vice President Software Engineering (Cloud Platform)

Mastercard

Jake Smith

Transformation Senior Manager

PepsiCo

Production environments are where AI ambitions meet reality. However, alert fatigue and constant firefighting pull engineering teams away from the work that actually moves the business forward. An alert fires, an engineer wakes up, triages, hunts dashboards, and acts while the outage clock runs.

The best engineers spend their time firefighting instead of building. In a world where AI is embedded in every workflow, that is a structural liability.

Modern organizations are adopting agentic production ops as a core pillar of AI maturity because it works alongside engineering teams to prevent issues before they cause impact, resolve incidents in minutes, and continuously optimize production systems with real-time context. Engineers stay focused on decisions that matter, not dashboards at 2am.

Join this session to leave with insights, including:

• A framework for integrating agentic systems into existing engineering workflows without displacing the humans who know the systems best
• Patterns for continuous evaluation and autonomous triage that reduce noise, surface only what needs human judgment, and keep teams ahead of issues across hybrid environments
• Governance considerations for human-in-the-loop incident response: auditability, trust boundaries, and escalation design

Venkat Ramakrishnan

President & COO

NeuBird AI

The race to scale AI has shifted from experimentation to execution. Organizations are no longer asking if they should implement AI, but how to do so across every function to drive measurable outcomes and competitive advantage. However, as AI becomes embedded in enterprise workflows through connected intelligence and agentic systems, it introduces unprecedented challenges in governance, data readiness, and operational alignment.

This session moves beyond the “black box” to explore the critical legal lens of AI adoption, including:

• Managing intellectual property risks, mitigating algorithmic bias, and ensuring cross-functional accountability
• How to modernize compliance frameworks to sustain trust and performance
• Transforming legal governance from a friction point into a driver of resilience and faster innovation cycles

Lindsey Williams

Associate General Counsel

LaBella Associates

While 59% of finance functions report using AI, only 11% have meaningfully implemented it. The gap is costly and most teams are stuck in fragmented pilots, scattered tools, and unclear ownership. As one CFO put it: “We have activity, but no capability.”

This session explores how a Finance AI Center of Excellence (CoE) can close that gap—transforming experimentation into structured, governed, and scalable capability. Through both strategic and practitioner perspectives, Larry Maisel and Anna Tiomina will outline how to embed AI into core finance operations, avoid common failure patterns, and build a foundation for trusted, decision-ready insights.

You will learn:

• How to structure a Finance AI CoE to move from fragmented activity to scalable capability
• Where AI initiatives break down in finance—and how to avoid common pitfalls
• What it takes to become truly AI-enabled, with governance, trust, and measurable value built in

Larry Maisel

Former Adjunct Professor

Columbia University, Graduate School of Business.

Anna Tiomina

Founder

Blend2Balance