In this talk, Natella Krapivina presents an end-to-end case study on building a full AI production pipeline at a Latvia-based creative company serving global brands. The session is anchored by *The Translator* — an AI film produced with no traditional crew or budget, and selected by Google as one of the global top five out of 30,000+ submissions. Attendees will leave with a production checklist and a before-and-after breakdown of timelines, costs, and output quality.
Many people associate invention with groundbreaking discoveries, patents, or revolutionary products. In reality, invention happens every day. A new workflow that saves an hour each week, a system that eliminates a recurring problem, or a simple habit that improves consistency can be far more valuable than a world-changing idea that never leaves the drawing board.
In this talk, Mantas Damijonaitis shares practical techniques for developing an inventor’s mindset. Drawing from his experience as a software architect, entrepreneur, and continuous optimizer, he explores how curiosity leads to better solutions, how discipline turns ideas into reality, and why small inventions often have the greatest impact. Attendees will leave with actionable methods for identifying opportunities, designing their own systems, and continuously improving the way they work and live.
Most organisations invest heavily in AI tools and almost nothing in the human systems those tools depend on. In this session, Jamie Bykov-Brett, AI Transformation Strategist and founder of Bykov-Brett Enterprises, makes the case that the biggest barrier to AI adoption is not technical, but cultural. Drawing on hands-on consulting work across regulated industries and C-suite training programmes, Jamie presents a repeatable framework for diagnosing and closing the mindset gap that silently kills AI initiatives. Attendees will leave with a practical diagnostic tool, a model for phased cultural change, and concrete before-and-after examples from real enterprise deployments. This session is built for leaders who are tired of transformation roadmaps that look great on slides but stall in practice.
AI agents are no longer just prompts with tool calls. They are long-running, asynchronous systems that act across tools, queues, context, sandboxes, and teams. In this talk, Sam Komesarook shows why debugging agents is becoming less like reading logs and more like managing autonomous workers: tracing what happened, interpreting recurring behavior, catching failures live, and closing the loop from insight to intervention.
Most organizations already have access to AI tools, yet very few are achieving measurable business transformation.
After working with more than 150 companies on AI adoption and operational implementation, Peter Mesarec has identified a recurring pattern: AI initiatives rarely fail because of technology limitations. They fail because leadership teams approach AI without a structured execution model.
Companies often jump directly into tools, pilots, or automation before identifying where AI creates the highest operational value, how teams will actually adopt it, and how leadership should guide implementation across the organization.
In this session, Peter presents a practical leadership framework for turning AI from isolated experimentation into measurable operational impact.
The presentation is based on real implementation patterns observed across multiple industries and departments, including marketing, customer support, HR, finance, and operations.
Attendees will learns
Why most AI initiatives stall after the initial excitement phase
The leadership mistakes that create fragmented AI adoption
How to identify high-impact AI opportunities before investing in automation
How to prioritize AI initiatives based on operational ROI
Why employees use AI individually while organizations still see little measurable impact
How to reduce resistance, shadow AI, and tool chaos
What successful AI-first organizations consistently do differently
The session focuses on practical implementation, measurable outcomes, and organizational execution rather than AI hype or futuristic predictions.
Attendees will leave with a realistic framework they can immediately apply inside their own organizations to improve AI adoption, alignment, and business results.
In a remote-first software house, meetings and asynchronous chats quickly become the backbone of communication. As every new tool suddenly becomes “AI-powered”, instead of following the usual “AI everywhere” trend, Paweł Feliński and his team took a brain-first approach: first understanding how knowledge actually moved through the organisation, and only then deciding where AI could genuinely help.
That led to an unexpected discovery: many recurring agile “ceremonies” existed mainly to move information from one person to another.
Once they started treating organisational knowledge as an operational problem, not just a documentation problem, several things changed quickly. Post-meeting note-taking disappeared. Developers stopped joining estimation sessions by default. Specifications became asynchronous, reviewable, and easier to evolve. Some meetings became shorter. Others vanished entirely. What disappeared were not feedback loops or collaboration, but the manual overhead around them.
To support this, the team built a lightweight, LLM-agnostic internal knowledge layer designed around their actual delivery workflows rather than generic AI productivity demos.
Not every experiment worked. In several cases, removing a ceremony also removed hidden coordination value they had not noticed before.
This talk is a practical before/after case study of how a growing software company used a brain-first AI approach to reduce coordination overhead — and discovered that many agile “ceremonies” existed mainly to compensate for broken knowledge flow.
Three leading LLMs were tested on real email generation tasks, with 61% average quality and zero production-ready outputs. The problem isn’t the models — it’s what they’re working with. Prompt-to-HTML is structurally broken: no brand memory, no section roles, and no safe way to swap a layout without destroying content.
This talk introduces the Email Domain System — a semantic contract layer that turns AI from a generator into an orchestrator. Instead of improvising HTML, AI selects governed components, plans section sequences through compositional briefs, and fills content into defined slots. A variant swap changes the entire layout while preserving every heading, image, and CTA.
Dmytro Kudrenko will show, with real examples, how the full pipeline works and why this architecture generalizes beyond email: AI quality is bounded by the semantic contracts it operates within.
Service providers are suspicious of today’s AI agents because there is no practical way to verify that an agent is executing in line with the intent of a human who can be held responsible for the agent’s actions. This fundamental trust barrier is a problem of delegated authority. Current approaches using API keys or session tokens provide insufficient granularity, auditability, and security for real-world autonomous actions. As a result, manual human approval is required to complete transactions, and agents do not live up to their potential.
In this talk, Agne Caunt shows how to provide AI agents with secure, verifiable digital credentials that prove a user’s identity and permissions, allowing vendors to easily trust and approve transactions. She does this through a practical implementation of cryptographic delegation using verifiable credentials. The talk introduces a dual credential architecture combining user identity credentials (KYC) with agent authorization credentials (KYA) that prove bounded, time-limited delegation authority. The system integrates with the Model Context Protocol (MCP) and leverages zero-knowledge proofs for privacy-preserving verification.
Agne makes the conversation tangible with a demonstration illustrating how service providers verify the necessary KYC and KYA credentials. She then covers critical implementation decisions such as wallet UX to reduce adoption friction, RESTful API patterns for AI platform integration, credential lifecycle management, granular permission controls, and ecosystem challenges around standardization and service provider adoption. Attendees will leave with actionable insights for implementing delegated authority in their own AI agent systems.
Have you ever wished you had an equal who could play Counter-Strike just as well as you? Thanks to recent developments in Robot Foundation Models (RFMs), robots can learn how to play video games just like you, imitating your best and worst behaviours.
In this talk, Thomas Endres and Jonas Mayer show how to use imitation learning to teach an RFM to play arbitrary games. They draw first blood with an introduction to Robot Foundation Models. Attendees will learn how they work, what kind of data they are designed to process, and how that makes NVIDIA’s GR00T perfect for playing video games. After that, they quick-scope the concept of imitation learning, what kind of data they need to train their GR00T model, and how they can collect it from games. Finally, they tell the story of leveling up the model from total noob to global elite – one failure at a time – and share all the things they learned along the way.
Throughout the talk, Thomas and Jonas include live demos in their slides. To close the game, they challenge one member of the audience to beat GR00T live on stage in a 1v1.
AI is everywhere in business conversations today, yet many organizations ignore the elephant in the room: AI can only analyze what has already been documented. Some of the most valuable insights about emerging risks and opportunities never appear in systems, dashboards, or models. In this keynote, Maya Lievegoed, CEO of HUMINT Solutions Europe, reveals why artificial-intelligence-driven strategies miss these signals and how leaders can systematically capture and integrate this unrecognized human knowledge into their AI and intelligence workflows. Through real-world cases, she demonstrates how uncovering hidden insights has transformed organizational decisions and strategy. The result is smarter decisions, higher employee engagement, and a competitive advantage that data alone cannot deliver.