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.

First public delivery