Agenda – 14 May 2026

The AI in Professional Education Summit opens with a direct conversation about why this moment matters more than any other in the history of enterprise learning. Paul Siegel, Founder of Starweaver, sets the context for the day — sharing what brought this summit into existence, what the audience will take away, and why the organizations in this room are already ahead of the curve by showing up.

— Understand the mission behind the AI in Professional Education Summit and what makes this gathering different from every other AI conference on the circuit.

— Get a clear picture of the day ahead — the sessions, the speakers, and the single most important question every enterprise in the room needs to answer before they leave.

— Hear directly from Starweaver's Founder on what the data, the market and the last 18 months of building AI learning content at scale has taught him about where professional education is going next.

The world of professional learning has changed faster in the last two years than in the previous two decades. This keynote cuts through the noise to examine what AI truly means for organizations that train, upskill and develop talent at scale. Attendees will leave with a clear picture of where enterprise learning is headed and what separates organizations that will lead from those that will scramble to catch up.

— Understand why the pace of AI adoption in professional education is accelerating beyond what most organizations have planned for, and what that means for your L&D strategy today.

— Explore how leading enterprises are already using AI to transform not just how learning is delivered, but how it is designed, personalized and measured at scale.

— Walk away with a framework for assessing your organization's current AI readiness and the three most important steps to take in the next 90 days.

For decades, enterprise learning has relied on standardized programs built for the average employee. AI is making that model obsolete. This session explores how organizations are deploying AI to create genuinely individualized learning experiences — adapting in real time to each learner's role, skill gaps, pace and goals. Attendees will hear directly from practitioners who have moved beyond the pilot stage and are seeing measurable results at scale.

— Discover how hyper-personalization works in practice inside large enterprises, including the data inputs, AI models and LMS integrations required to make it real.

— Explore how leading organizations are using AI to map individual learning journeys to business outcomes, reducing time-to-competency while improving employee engagement and retention.

— Learn how to make the internal case for personalized AI learning to skeptical stakeholders, with real examples of productivity gains and cost savings achieved by early adopters.

Agentic AI represents a fundamental leap beyond chatbots and content recommendation engines. These systems do not just respond — they plan, act and execute across complex workflows without constant human instruction. This session examines what that shift means specifically for enterprise learning and development, where AI agents are beginning to autonomously design curricula, track progress, flag skill gaps and trigger interventions. The implications for L&D teams, budgets and organizational capability are profound.

— Understand the critical difference between the generative AI tools your team is already using and agentic AI systems that operate with autonomy across multi-step learning workflows.

— Explore real enterprise deployments where agentic AI is already handling onboarding, compliance training, coaching nudges and upskilling pathways — without a human in the loop for every step.

— Learn how to evaluate whether your organization's current infrastructure is ready to support agentic AI, and what investment decisions you need to make now before the window of competitive advantage closes.

Most organizations deploying AI in learning are getting generic results because they are feeding their systems generic inputs. Context engineering — the discipline of structuring the information, constraints and objectives you give an AI system — is what separates powerful, business-specific AI from a tool that sounds impressive but delivers little. This session goes beyond prompt engineering to explore how L&D teams can build AI systems that truly understand their industry, their organization and their learners.

— Learn what context engineering is, why it is rapidly replacing basic prompt engineering as the core skill for AI-powered learning design, and how to start building this capability inside your team today.

— Explore how leading organizations are using retrieval-augmented generation, custom knowledge bases and domain-specific inference models to create AI learning tools that reflect their unique business context and compliance requirements.

— Walk away with a practical framework for auditing your current AI learning tools against a context engineering standard — identifying where generic outputs are costing you quality, relevance and learner engagement.

The traditional content development cycle — scoping, scripting, recording, editing, reviewing, publishing — is being compressed beyond recognition. Organizations that once measured content production in months are now operating in days. This session examines the AI-powered instructional design workflows behind that transformation, exploring what this means for the quality, consistency and scalability of enterprise learning content, and what the role of the human expert looks like in a world where AI does the heavy lifting.

— Understand the end-to-end AI-assisted content production workflow that leading organizations are using today, from topic identification and script generation through to video production and multi-platform distribution.

— Explore how to maintain pedagogical quality and expert credibility at speed, including the human review checkpoints and quality assurance processes that prevent AI content from becoming generic or inaccurate.

— Learn how to build a business case for AI content production investment, including benchmarking your current cost-per-course against what AI-enabled production now makes possible.

Everything discussed today becomes tangible in this live demonstration of Journeybuilder — Starweaver's AI-powered platform for building personalized enterprise learning journeys at scale. This is not a slide deck walkthrough. This is a live build, showing exactly how organizations are creating bespoke, career-mapped, AI-curated learning experiences for their workforce in real time.

— See how Journeybuilder integrates with existing LMS libraries, YouTube content and internal knowledge bases to create a unified, personalized learning environment without replacing your current infrastructure.

— Watch a live enterprise use case — a learning journey built from scratch for a specific role, industry and skill gap — demonstrating the speed, quality and personalization that Journeybuilder delivers.

— Get a first look at many other education AI tools that aree learning coaches in every employee's pocket.

Paul Siegel closes the May Summit with a synthesis of the day's most important insights, a direct challenge to every organization in the room, and a first look at what is coming on September 17th. This is not a passive wrap-up — it is a call to action.

— Leave with the three most important things to do in your organization in the next 30 days based on everything covered today.

— Get a first look at the September 17th AI in Professional Education Summit: The Implementation Playbook, and why you need to register before you leave today.

— Connect with speakers, fellow attendees and the Starweaver team in the virtual networking room that opens immediately after closing remarks.