Jacqueline Karlin

AI initiatives don't fail because of models. They fail because of weak data foundations. For AI leaders, the real challenge is ensuring their organization has the data literacy, governance, and operating discipline required to move from experimentation to production. This session focuses on how to assess and upgrade your organization's data readiness to support reliable, scalable AI systems.

• Assess and benchmark your organization's data & AI readiness

Identify gaps in data quality, accessibility, ownership, and literacy that are blocking AI deployment 

• Design a practical data literacy strategy for AI adoption

Define what different roles (execs, operators, engineers) need to understand, and how to scale that capability across the business 

• Build governance frameworks that enable (not slow) AI

Develop aligned data and AI governance models covering quality, access, risk, and compliance, without creating bottlenecks 

• Close the gap between data strategy and execution

Identify cultural, structural, and process barriers, and create an action plan to embed data-driven ways of working

As agentic AI introduces systems that can autonomously plan and execute tasks, organizations face growing complexity in choosing the right mix of tools, platforms, and frameworks. The challenge is balancing speed, customization, and control while avoiding long-term tech debt. This session explores how to make smarter build vs. buy decisions in a rapidly evolving AI landscape.

• Achieve clarity on needs by mapping AI and agent capabilities to concrete business outcomes and workflows

• Optimize technology portfolios by balancing in-house development, vendor platforms, and agent orchestration layers

• Minimize tech debt by consolidating overlapping tools, standardizing interfaces, and prioritizing interoperability

AI isn't just changing how work gets done, it's changing who (or what) does the buying, selling, and deciding. Most companies are still optimizing for human workflows while agents begin to transact, orchestrate, and choose on their behalf. The real challenge isn't efficiency, it's opportunity. This presentation unpacks the structural gaps, data, systems, and business models, and shows how to redesign your operating model to be discoverable, invocable, and competitive in an agent-driven economy.

• Differentiate short-term efficiency gains from long-term growth by identifying where agentic AI creates new value

• Modernize operating models by shifting from static processes to real-time, orchestrated systems

• Unlock growth opportunities by designing agent-driven customer journeys instead of optimizing existing ones