Organizations are moving quickly from AI experimentation to enterprise-wide deployment of copilots, assistants, and autonomous agents. While many organizations focus their early efforts on governance and technology, they often struggle with a more fundamental question: who owns AI, how are decisions made, and what operating model will enable AI to scale across the business? The organizations seeing the greatest business value from AI are establishing Centers of Excellence that connect strategy, business priorities, technology teams, risk stakeholders, and end users. Rather than acting as a gatekeeper, a modern AI CoE serves as an accelerator by providing clear direction, repeatable processes, and a framework for turning ideas into measurable business outcomes. In this session, we'll explore how leading organizations are designing AI operating models that support sustainable adoption of copilots and autonomous agents. Through practical frameworks, real-world examples, and lessons learned from enterprise AI programs, we'll discuss how to establish ownership, prioritize use cases, manage demand, build organizational capabilities, measure value, and create the structures necessary to scale AI across the enterprise. Whether you're launching your first AI initiative or expanding into agentic AI, you'll leave with a blueprint for building an AI Center of Excellence that enables innovation, accelerates adoption, and helps organizations move from isolated pilots to enterprise-wide transformation. Attendees will leave with: - A practical blueprint for designing an AI Center of Excellence that aligns business strategy, technology teams, and operational stakeholders. - Frameworks for prioritizing AI investments, managing demand, and selecting high-value use cases for copilots and autonomous agents. - Strategies for defining ownership, funding models, success metrics, and value realization across AI initiatives. - An enterprise operating model that helps organizations scale from experimentation to sustainable AI-powered transformation.

Organizations are investing heavily in copilots, AI assistants, and autonomous agents, expecting transformative business outcomes. Yet many AI initiatives struggle to deliver value for a simple reason: the data behind them is incomplete, inaccessible, poorly governed, or disconnected from the systems where work actually happens. While organizations often focus on model selection, prompts, and agent design, successful agentic AI depends on something more fundamental: high-quality, trusted, and discoverable data. AI agents can only reason over the information available to them. When data is fragmented across repositories, trapped in legacy systems, duplicated across platforms, or lacking proper governance, even the most sophisticated agents produce limited results. In this session, we'll explore what data readiness means in the era of agentic AI and how organizations can prepare their information ecosystem to support copilots and autonomous agents at scale. Through real-world examples, architectural patterns, and practical frameworks, we'll examine common data readiness challenges and the strategies leading organizations are using to overcome them. You'll learn how to assess the quality, accessibility, security, and connectedness of your organization's data assets; determine whether information should be migrated, integrated, or surfaced through retrieval mechanisms; and establish the governance practices necessary to ensure agents can access the right information while protecting sensitive data. Whether you're preparing for your first AI initiative or scaling enterprise-wide agent deployments, you'll leave with a practical roadmap for transforming data from a barrier into a competitive advantage for AI. Attendees will leave with: - A framework for assessing organizational data readiness for copilots, AI assistants, and autonomous agents. - Strategies for identifying and addressing common data challenges, including silos, duplication, poor data quality, and inaccessible information. - Practical guidance for choosing between migration, integration, retrieval, and federation approaches when connecting enterprise data to AI experiences. - An understanding of how governance, security, permissions, and knowledge management influence AI outcomes. - A roadmap for creating a trusted data foundation that enables agents to deliver accurate, secure, and meaningful business value.

Many organizations have successfully introduced Microsoft 365 Copilot, but few have a clear roadmap for what comes next. After the initial excitement of AI-powered productivity, leaders often struggle to determine how to move from individual use cases to team transformation and ultimately to AI agents that can automate and execute business processes. This session introduces a practical Enterprise AI Maturity Model that helps organizations progress through four stages of AI adoption: personal productivity, team collaboration, process optimization, and autonomous agents. Attendees will learn how leading organizations in healthcare, manufacturing, and professional services are applying AI at each stage while balancing governance, risk, change management, and value realization. Through real-world examples, proven adoption patterns, and measurable success indicators, you'll leave with a framework for assessing your organization's current state and building a roadmap to achieve sustainable business outcomes with AI. Attendees will leave with: - A practical framework for assessing AI maturity, from individual Copilot usage to enterprise-scale AI agents. - An understanding of the governance, change management, and data readiness requirements needed at each stage of the maturity model. - An actionable roadmap with key metrics, success criteria, and investment priorities to accelerate AI adoption and business value.

Every organization is collecting AI ideas. The real challenge is deciding which ones deserve investment. AI workshops routinely generate dozens of opportunities, from copilots and agents to process automation and custom AI solutions. Yet many organizations struggle to move beyond experimentation because they lack a structured way to evaluate competing priorities. Should you invest in the high-impact initiative that may take months to deliver, or the quick win that generates immediate momentum? How do you compare a customer-facing AI agent against an internal productivity solution? And how do you ensure AI investments align to measurable business outcomes rather than excitement and hype? In this session, we'll explore a practical framework for evaluating and prioritizing AI opportunities across the enterprise. Drawing from real-world experiences helping organizations build AI roadmaps, we'll examine the factors that separate successful initiatives from stalled experiments, including business impact, value realization, urgency, implementation complexity, organizational readiness, risk, and adoption potential. Attendees will learn how to create a repeatable process for assessing AI opportunities, balancing short-term wins with long-term transformation, and building an AI portfolio that delivers meaningful business results. Whether you're supporting an AI Center of Excellence, leading innovation initiatives, or responsible for AI investment decisions, you'll leave with a framework that can be applied immediately within your organization. Attendees with leave with: - A practical framework for evaluating AI opportunities using business value, risk, readiness, complexity, and adoption criteria. - Techniques for comparing competing AI investments and prioritizing initiatives that align with strategic objectives. - An approach for building and managing an AI portfolio that balances quick wins, long-term transformation, and measurable business outcomes.

Organizations are investing heavily in copilots, assistants, and AI agents, but many are making a critical mistake: evaluating the technology before evaluating their readiness to adopt it. Successful AI programs depend on far more than model selection and platform capabilities. Data quality, governance, security, leadership alignment, workforce readiness, and change management often determine whether an AI initiative delivers meaningful business value or becomes another stalled pilot. Yet these organizational factors are rarely assessed before deployment begins. In this session, we'll introduce a practical executive readiness assessment designed to help organizations evaluate their preparedness for enterprise-scale AI adoption. Through real-world examples and lessons learned from AI transformation initiatives, we'll explore the six dimensions that consistently influence AI success: strategy and leadership alignment, data readiness, security and governance, operational capabilities, workforce enablement, and value measurement. Attendees will learn how to identify common warning signs that indicate an organization may not yet be prepared to deploy AI agents at scale, how to uncover readiness gaps before they become costly obstacles, and how to create a roadmap that improves the likelihood of successful adoption. Whether you're planning your first AI initiative or preparing to scale beyond copilots into agentic AI, you'll leave with a framework that helps leadership teams make more informed investment and deployment decisions. Attendees will leave with: -An executive assessment framework for evaluating organizational readiness across strategy, data, governance, security, operations, and workforce adoption. -The ability to identify common readiness gaps and warning signs that frequently derail AI initiatives. -A roadmap for improving organizational preparedness before investing in enterprise-scale AI agents.