Baltic Summit 2026

24/09/2026, Pomorski Park Naukowo-Technologiczny Gdynia
Gdynia, Poland

Copilot Studio Agents: Marketing Demos vs. Real Enterprise Practice

Marketing demos show that building AI agents in Copilot Studio is fast and simple. However, real-world deployment tells a very different story. Research shows that over 80% of AI projects fail to reach production, 95% fail to produce clear financial returns, and Gartner predicts that 40% of agentic AI projects will be canceled by 2027 due to high operational costs and unreliable outputs. - AI agents do not behave like standard, predictable Power Platform applications - Treating an autonomous agent like a simple IT project creates major hidden risks Drawing from real enterprise projects, this session looks past the keynote hype to examine real operational problems: - Unexpected costs: Pay-as-you-go billing and multi-step reasoning loops consuming Copilot Credits unexpectedly - Data and knowledge delays: SharePoint syncs taking 2 to 3 days instead of a few hours to update knowledge - User interface friction: Confusing Dataverse connection popups that look like system errors to end users - Human risk ("Cognitive Surrender"): Studies showing that users accept incorrect AI logic up to 73.2% of the time without double-checking - Attendees will get a clear, practical playbook to control AI costs, assign risk ownership to business leaders, and properly monitor agent performance Key Takeaways - Understand the Real Cost Model: Learn how models, runtime, context, and tools consume Copilot Credits —and how to set spending caps to prevent budget shocks - Fix Data Foundations First: Avoid slow or poor SharePoint search by setting up clean data quality standards and proper search indexing - Assign Clear Risk Ownership: Shift risk responsibility back to business leaders and governance boards instead of leaving it all on developers - Monitor What Matters: Look beyond basic server uptime to track reasoning steps, error chains, and user acceptance rates Target Audience Power Platform Developers, Enterprise and Solution Architects, AI Leads, and IT Decision-Makers who manage risk, budget, and governance for company AI projects.