November 24-27

Traditional web applications send raw, sensitive user data to the server, relying on downstream **back-end** processes for redaction. This workshop challenges that paradigm by moving the privacy boundary directly into the browser.

In this hands-on session, Rasmus Longva Haugland shows participants how to build a privacy-first **front-end** architecture using Next.js. Participants will learn how to implement a local AI model that detects and masks personally identifiable information (PII) in real time, ensuring sensitive data is intercepted and sanitized before a network request is ever made.
Key takeaways:
How to integrate local, WebGPU-accelerated AI models into a real-world Next.js application.
Techniques for offloading heavy inference tasks to Web Workers to maintain a seamless user experience.
A concrete, privacy-by-design architecture that participants can adapt for their own production environments.
A pragmatic breakdown of where client-side AI meaningfully reduces compliance exposure and where it does not.

This full-day workshop, led by AI Transformation Strategist Jamie Bykov-Brett, takes participants from AI curiosity to confident implementation through the hands-on application of proven frameworks. Jamie draws directly on the curriculum he has designed and delivered for enterprise clients, including organisations in regulated sectors, combining strategic thinking with practical execution. The day is structured in four modules: mapping the AI opportunity landscape, designing human-centric workflows, building a measurement and accountability framework, and navigating stakeholder resistance. Participants work through live exercises using their own organisational context and leave with a completed AI opportunity map, a stakeholder communication template, and a 90-day implementation checklist. No prior technical background is required. This workshop is designed for cross-functional teams, including HR, operations, marketing, and leadership, who need to move from strategy to action.

Traditional web applications send raw, sensitive user data to the server, relying on downstream backend processes for redaction. This workshop challenges that paradigm by moving the privacy boundary directly into the browser.

In this hands-on session, Rasmus Longva Haugland shows participants how to build a privacy-first frontend architecture using Next.js. Participants will learn how to implement a local AI model that detects and masks personally identifiable information (PII) in real time, ensuring sensitive data is intercepted and sanitized before a network request is ever made.

Key takeaways:

* How to integrate local, WebGPU-accelerated AI models into a real-world Next.js application.
* Techniques for offloading heavy inference tasks to Web Workers to maintain a seamless user experience.
* A concrete, privacy-by-design architecture that participants can adapt for their own production environments.
* A pragmatic breakdown of where client-side AI meaningfully reduces compliance exposure and where it does not.

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.