The final sessions of the Gen AI Bootcamp focus on advanced AI workflows and real-world application deployment.

In RAG & Tools Integration, participants learn Retrieval-Augmented Generation fundamentals—including embeddings, vector databases, and retrieval workflows—while exploring tool calling and agentic patterns to make AI applications more context-aware and reliable.

During the Lab: Build & Deploy, attendees integrate their local LLM, RAG pipeline, and tools to complete a fully functional GenAI application, ready for deployment and validation.

The day concludes with the Live Demo Showcase, where selected participants present their applications, share insights, receive feedback, and celebrate achievements—fostering community, inspiration, and peer learning.

Deliverables: A deployed GenAI application, recognition for participants, and strengthened community connections.

Read more details at https://productledhub.com/gen-ai-unfold-summit/gen-ai-development-bootcamp/

The second section of the masterclass introduces participants to AI-enhanced DevOps practices, highlighting how AI can optimize the software development lifecycle. Attendees explore modern DevOps workflows, continuous integration fundamentals, and the testing pyramid, gaining a strategic understanding of where AI can accelerate and improve processes. The module also provides an overview of GitHub Actions architecture, showing how AI can assist in automating test generation and pipeline optimization.

Participants then dive into practical AI-assisted testing and CI/CD workflows, covering unit testing with Jest, end-to-end testing with Playwright, automated security and penetration testing, and full CI/CD pipeline creation with GitHub Actions. Throughout the module, attendees apply AI to generate tests, handle edge cases, identify vulnerabilities, and optimize workflows, gaining hands-on experience in building robust, maintainable, and secure applications while implementing fully automated DevOps pipelines.

Read more details at https://productledhub.com/gen-ai-unfold-summit/gen-ai-devops-mastery/

Unlock the true potential of AI by mastering the language it understands best—prompts. In this lab session, you’ll learn how to design clear, effective, and impactful prompts that drive accurate and creative responses from AI tools. We’ll explore the principles of prompt engineering, common pitfalls to avoid, and practical frameworks to structure your requests for maximum results. Whether you’re a beginner or looking to refine your skills, this session will equip you with actionable strategies to communicate with AI confidently and efficiently.

The afternoon sessions focus on building end-to-end GenAI applications by combining AI backends with modern frontend frameworks.

We start with TypeScript + Vercel AI SDK Fundamentals, where participants learn to design and implement GenAI backends. The session covers SDK architecture, API patterns, streaming responses, and prompt engineering, culminating in a working “Hello World” GenAI app.

Next, in Next.js + Shadcn UI Frontend Development, participants create a modern, reusable frontend connected to their AI backend. This session emphasizes project structure, state management, UI/UX best practices, and integration, delivering a functional frontend scaffold with chat UI ready for further development.

Read more details at https://productledhub.com/gen-ai-unfold-summit/gen-ai-development-bootcamp/

By 2026, talent scarcity is no longer a hiring problem, it is a structural risk. While 85 million roles may go unfilled by 2030, the deeper issue is not missing talent, but organizations’ inability to deploy skills quickly and flexibly under pressure.

This session reframes workforce optimization around team design. Drawing on applied work with technology teams and other constrained environments, it shows why narrowly specialized teams collapse under disruption and why multifunctionality and coverage are becoming strategic necessities.

Attendees will be introduced to an AI-driven workforce intelligence approach that models team reliability, flexibility, and resilience, simulates disruption scenarios (absences, role changes, skill gaps), and identifies where targeted skill development delivers the highest return. Organizations applying this model have improved team resilience by up to 10% while significantly reducing wasted training investment. The session equips product and technology leaders with a practical framework to redesign how work is structured before talent scarcity becomes operational failure.

The first section of the masterclass immerses participants in the modern AI development landscape and the role of AI-assisted engineering workflows. Attendees learn to set up and configure Claude Code and Cursor IDE, understand when and how to leverage each tool, and establish best practices for AI pair programming. This foundational module ensures participants have a fully optimized workspace and a strategic understanding of AI-assisted development in real-world projects.

The module then guides participants through AI-driven application development, documentation, and debugging. They explore full-stack code generation, prompt engineering, automated documentation, bug detection, and refactoring workflows. Finally, attendees gain hands-on insight into integrating Model Context Protocol (MCP) servers such as Microsoft Learn Context, Context7, and GitHub MCP, extending AI capabilities with external knowledge to build smarter, context-aware applications.

Read more details at https://productledhub.com/gen-ai-unfold-summit/gen-ai-devops-mastery/

You may have seen headlines about AI systems recommending that users eat rocks or put glue on pizza—classic examples of hallucinations. Retrieval-Augmented Generation (RAG) is an architectural approach designed to reduce these failures by grounding model outputs in reliable, external knowledge.

At its core, RAG combines two components: a retriever (typically a vector database) that fetches relevant context, and a generator (an LLM) that uses this context to produce the final response. While the concept is deceptively simple, building and deploying RAG systems in production introduces a range of real-world challenges—from data quality and retrieval accuracy to latency, evaluation, and operational complexity.

In this talk, you’ll explore the most common failure modes of production RAG systems and learn practical techniques to mitigate—or eliminate—them, enabling more reliable, trustworthy AI applications.

In modern product development, the ability to integrate Generative AI into everyday workflows is no longer optional — it is rapidly becoming a core product leadership capability. Forward-thinking organizations recognize that embedding GenAI into product practices is essential to staying competitive, accelerating innovation, and increasing team effectiveness.

This session presents a real-world transformation journey of a multi-thousand-person Product Management organization — moving from a high-level, organization-wide AI strategy to practical, agent-enabled workflows embedded directly into teams’ daily operations. Participants will gain a structured, step-by-step framework for analyzing existing product creation processes and systematically introducing GenAI across the entire product lifecycle, including discovery and user research, ideation, experimentation, roadmap planning, and go-to-market execution.

Designed to be highly actionable, the session equips leaders with a proven transformation approach, concrete use cases, and clear starting points to enhance productivity, decision quality, and organizational learning velocity within their product teams.

TBA

A One-Day Hands On Training Experience on Gen AI Applications

An immersive, hands-on technical bootcamp designed for engineers who want to dive into the AI era. Participants will learn LLM fundamentals, set up local models (Ollama, LM Studio), and build their first generative AI web application using TypeScript, Vercel AI SDK, Next.js, and Shadcn UI components. The session covers Retrieval-Augmented Generation (RAG) and tool integration, empowering attendees to code and deploy their own GenAI apps in one day—all running on their local machines.

By day’s end, every participant will have a working AI application running on their local machine—ready to extend and deploy to production.

The morning sessions of the Bootcamp provide participants with a hands-on foundation in Large Language Models (LLMs) and their practical applications.

We begin with a Unified Kickoff, introducing the agenda, learning objectives, and the transformative impact of generative AI on product and engineering workflows. From there, the LLM Fundamentals Deep Dive builds a strong conceptual understanding of how LLMs work, including tokens, context windows, temperature, and prompting strategies.

Finally, in Local LLM Setup & Configuration, participants get practical experience installing and running a local LLM using tools like Ollama or LM Studio, exploring performance considerations, privacy benefits, and model trade-offs. By the end of the morning, each participant will have a fully functional local LLM ready for experimentation and hands-on exercises.

Read more details at https://productledhub.com/gen-ai-unfold-summit/gen-ai-development-bootcamp/

Legacy systems aren’t the real obstacle — outdated thinking is. This session showcases how Natech leveraged AI to transform a traditional SQL database project into an AI-enhanced codebase, unlocking faster development cycles, stricter quality controls, and smarter, automated bug handling — all without rebuilding from scratch.

Wrap up Day 1 with key takeaways from the development-focused sessions, hands-on coding, and deployment labs. Celebrate standout projects, reinforce learning, and get ready for Day 2’s advanced workshops and deep dives.

Last year, LLMs helped developers write code faster. This year, Agentic AI and the Model Context Protocol (MCP) are transforming how applications are built end to end. In this session, you’ll see how Agentic AI accelerates the path from prototype to production by collaborating across design, frontend, backend, and cloud workflows. Through a live walkthrough, you’ll learn how developers can use agents and MCP to build, test, and deploy production-ready applications.

The final sessions of the Gen AI Product Mastery Bootcamp focus on showcasing, feedback, and reflection. Participants present their AI product concepts, receive expert guidance, and consolidate their learning from the day.

Teams deliver high-impact pitches covering the problem, user, solution, AI flow, feasibility, risks, metrics, and MVP. Judges and facilitators provide targeted feedback to refine reasoning and uncover blind spots. The bootcamp concludes with a reflection on key takeaways, celebration of accomplishments, and networking opportunities. Participants leave with validated, pitch-ready AI product concepts, actionable insights, and connections to a community of AI product innovators.

Learn more at https://productledhub.com/gen-ai-unfold-summit/product-mastery-bootcamp/

In this session, participants will explore how to unlock the full potential of Claude for building enterprise-grade AI applications. We’ll cover practical strategies for integrating Claude into real-world workflows, extending its functionality with custom tools, and implementing Model Context Protocol (MCP) servers to enable scalable, secure, and production-ready AI systems. Designed for technical leaders, product teams, and AI practitioners, this talk provides actionable insights for deploying Claude-powered solutions at scale.

The next wave of ecommerce won't be driven by better interfaces — it will be driven by AI agents that shop, compare, negotiate, and pay on behalf of consumers and businesses. This shift from "click-to-buy" to "intent-to-done" will fundamentally rewire how merchants sell, how payments flow, and how trust is established in digital commerce. In this session, Maria will explore how agentic commerce moves beyond chatbots and recommendation engines into a world where  AI agents become the primary counterparty in transactions — and what that means for merchants, payment providers, and the infrastructure that connects them.

This section helps participants translate design thinking into actionable AI product concepts, combining user experience, metrics, risk management, and MVP planning.

Participants start by transforming problem framing into a concrete user experience, mapping the user journey, defining AI touchpoints, planning for ambiguity and failure modes, and designing both the “happy path” and critical edge cases. They then learn to measure success in AI products, aligning user outcomes with business impact, distinguishing product vs. model metrics, and managing data and operational risks. Finally, participants consolidate insights into a structured, pitch-ready AI product concept, finalizing problem statements, user journeys, AI interactions, data needs, measurement approach, and MVP definition.

By the end of this block, participants will have a fully defined, realistic, and ambitious AI product concept, ready for prototyping and presentation.

Learn more at https://productledhub.com/gen-ai-unfold-summit/product-mastery-bootcamp/

The PM role is undergoing its most significant transformation since Agile. As AI agents absorb execution, the value of product leadership shifts dramatically: from managing backlogs and writing tickets to designing systems, engineering intent, and making the judgment calls no model can make.

This talk draws on real experience building AI-native products and directing agent systems in production to deliver six hard-won lessons from the shift. No theory, just patterns that only become visible once you've operated on the other side of the transition.

Attendees will leave with a clear mental model of what the Orchestrator PM looks like in practice, an honest picture of the traps teams fall into when they accelerate without governance, and concrete steps to begin the transition before it's made for them.

AI is writing more code than ever before—but most of it never reaches production. The real challenge isn’t capability; it’s trust.

Modern applications are inherently distributed: mobile apps communicate with cloud services, which interact with other services, queues, and databases. This complexity creates edge cases that AI systems struggle to anticipate and traditional testing approaches often fail to uncover. These issues arise when critical system contracts remain implicit rather than explicitly defined and verifiable.

In this talk, we explore what it takes to trust AI-generated code in production environments. Through the development of a real-world distributed application using Generative AI, we will examine the patterns, tools, and infrastructure practices that enable AI-assisted development to scale safely and reliably. Rather than simply coding faster, developers must evolve their approach to building systems that remain understandable, testable, and dependable—even when AI writes much of the code.

Finally, we’ll look ahead at emerging research and frameworks that make entire classes of distributed system failures impossible to express. These innovations point toward a future where AI doesn’t just generate code more quickly—but produces distributed systems that are correct by design and provably reliable.

Most AI initiatives fail not from lack of innovation, but from building solutions before defining success. This session introduces the Reverse Strategy Framework - a proven evaluation-first methodology that flips traditional AI development on its head. Instead of selecting tools first, the aduience will learn to define measurable outcomes, establish evaluation criteria, and architect autonomous systems that actually deliver business value at scale. Drawing from enterprise deployments across global organizations, we'll dissect why AI demos rarely translate to production and reveal the hidden debts - data, decision, and evaluation- that sabotage implementations. The audience leave with a concrete playbook for moving beyond proof-of-concepts to autonomous systems that transform operations, complete with frameworks for multi-agent orchestration, production observability, and ROI measurement that satisfies both technical teams and executive boards.

The morning sessions lay the foundation for designing AI-powered products. Participants begin by aligning on teams, roles, and project prompts, then dive into the fundamentals of AI product management and problem discovery.

The day starts with a unified kickoff to welcome attendees, provide orientation, form teams, and frame problems for creative, focused collaboration. Next, participants explore AI product management fundamentals, mastering core principles such as reasoning, pattern recognition, summarization, and generation. They then define users, articulate pain points, and identify where AI can deliver real value, while uncovering technical, data, behavioral, and ethical risks. Finally, attendees focus on designing AI-driven experiences, learning user-centric design, human-in-the-loop flows, fallback states, and how to scope a feasible, valuable MVP.

By the end of the morning, teams are aligned, a clear problem statement is established, and participants have a first blueprint for AI-driven product ideas.

Learn more at https://productledhub.com/gen-ai-unfold-summit/product-mastery-bootcamp/

George Nikolaropoulos sets the stage for the next two days, highlighting how Generative AI is transforming product development, engineering, and leadership.

We will spotlight the most pressing opportunities and challenges in AI today—from unlocking user insights and leveraging predictive analytics to driving automation, personalization, and responsible AI practices. It’s your first glimpse into the conversations, hands-on workshops, and real-world case studies that will shape the event.

In a time when Generative AI is reshaping markets, workflows, and even leadership expectations, senior decision-makers face a pressing challenge: how to cut through the hype and act with clarity. This 30-minute session creates space for critical reflection—not on what AI can do, but on what it should do for your organization.

We’ll unpack how foundational concepts like large language models, vector search, and knowledge augmentation are being applied in strategic contexts across industries. More importantly, we’ll address what true readiness looks like—organizationally, technically, and culturally. Through real-world examples and a pragmatic lens, you’ll be invited to assess both your company’s AI maturity and your own role in guiding its evolution.

If you’re navigating transformation or simply asking “what now?”, this session is designed to inform, challenge, and engage—not to sell, but to spark a more meaningful conversation.

Executive Learning Opportunity:
This discussion also introduces our executive-level Generative AI course: a focused 20-hour program built for C-suite leaders. The course provides the frameworks, language, and actionable roadmap needed to lead AI adoption with confidence. By linking strategic insights to hands-on decision-making, it ensures leaders don’t just understand AI—they can act on it.

The Networking Dinner of this year’s forum will serve as the culminating highlight of the conference — a sophisticated evening designed to celebrate innovation, leadership, and collaboration in the AI era.

Hosted in an elegant setting at the Benaki Museum, the dinner will gather speakers, distinguished guests, corporate leaders, policy-makers, and partners for an exclusive networking experience in a relaxed yet prestigious atmosphere.

Purpose & Experience:
- Executive AI Course Launch – The evening will feature the exclusive unveiling of our AI Executive Education Program for C-level Leaders. This initiative is designed to:
1. Equip executives with a strategic understanding of AI’s transformative potential across industries.
2. Provide actionable frameworks for AI-driven growth, governance, and ROI-focused decision-making.
3. Address leadership challenges in scaling AI adoption, building AI-ready cultures, and aligning innovation with business objectives.
4. Offer a capstone module tailored to each executive’s strategic priorities, ensuring immediate applicability within their organizations.
- Foster Connections – Provide a unique space for meaningful conversations among top-level executives, innovators, and thought leaders.
- Cultural & Culinary Experience – Guests will enjoy a curated dining experience

The Gala Dinner will embody the Forum’s vision: where leadership meets AI innovation, and relationships are forged to define the future.

AI is reshaping the very foundation of how businesses operate, demanding a new kind of leadership at the top. For C-level executives, keeping pace with this transformation means more than adapting—it requires a fundamental shift in how they think, lead, and make decisions. The stakes are high, and the speed of change is relentless. In this panel, we explore what it means to lead with intelligence—and how senior executives can develop the mindset, fluency, and capabilities needed to drive AI impact at scale. From understanding agentic systems and responsible AI governance to collaborating with technical teams and rethinking decision-making processes, this conversation unpacks the critical competencies required to lead in an AI-powered world.

As artificial intelligence continues to reshape industries, Mergers & Acquisitions (M&A) involving AI-driven companies are accelerating. For C-suite executives, AI M&A presents unique challenges and opportunities—whether acquiring AI startups to enhance innovation, integrating AI capabilities into legacy enterprises, or positioning their own organizations for acquisition. This session will provide C-level executives with a strategic framework for navigating AI-related M&A, covering valuation strategies, due diligence, integration challenges, and regulatory considerations. With AI playing a critical role in business transformation, leaders must be equipped to assess AI-driven opportunities and make informed acquisition decisions that drive long-term value.

As AI systems evolve from assistive to autonomous, CTOs are at the forefront of an enterprise redesign. This panel explores how technology leaders are architecting intelligent, adaptive platforms that go beyond automation—creating infrastructure that supports continuous learning, decision-making, and scalable autonomy. We’ll dive into the real technical and organizational challenges of deploying agentic models across complex environments, and how forward-thinking CTOs are aligning architecture, data, and ops to deliver enterprise-grade intelligence.

In today’s enterprise landscape, bottlenecks in application development, legacy complexity, and limited capacity for software development talent are common roadblocks to innovation. In this live session, Comsys CEO Vassilis Galakos demonstrates how enterprises can design, build and deploy secure, cloud-optimized business applications at speed using Jaggle, an AI-powered application generation platform engineered for organizations that demand software systems that are functionally rich, secure, and with unlimited integration capabilities. Designed for Innovation Leaders, this session will demonstrate the development of a real-world application using low-code visual development and AI assistance. Attendees will see how Jaggle accelerates delivery time and bridges the gap between business demands and application development latency without compromising best-in-class standards.

In a world of rapidly evolving regulations, compliance has become one of the most resource-intensive challenges for businesses. This session will explore how AI can help legal departments and compliance teams manage complexity, reduce risk, and dramatically cut the cost of research and contract review. Through a live demonstration, we’ll show how AI turns dense regulation into actionable insight — saving time, lowering external counsel spend, and empowering leaders to make faster, safer decisions at scale.

Every CEO knows that growth depends on talent. But in a world where skills expire quickly, traditional training no longer keeps up. This session explores how AI-enabled learning platforms like TalentLMS transform training into a growth engine. Learn how AI personalizes learning paths, reduces training time, and provides actionable insights that drive performance. Walk away with a clear understanding of how TalentLMS helps businesses turn learning into a competitive advantage.

Τhe next wave of AI brings a shift from supportive tools to self-directed agents, business leaders are entering a new era of opportunity—and accountability. This panel unites trailblazing CEOs and strategic thinkers to explore how Agentic AI is revolutionizing enterprise value, competitive positioning, and scalable growth. From integrating autonomous agents into key business functions to rethinking leadership models for an AI-first future, the conversation will provide strategic insights and real-life examples of how top executives are embedding AI deep within their organizations—not just as a feature, but as a foundation.

The EU AI Act is ushering in a new regulatory paradigm—classifying AI systems by risk level, imposing specific obligations on high-risk applications, and introducing penalties for non-compliance. Meanwhile, other jurisdictions are shaping their own approaches, creating a fragmented landscape that global companies must manage across markets and teams. In this panel, top legal experts, compliance officers, tech executives, and policymakers will unpack the practical implications of current and upcoming AI regulations. We’ll dive into how forward-looking enterprises are designing governance structures that align with evolving standards while preserving innovation. From legal audits and algorithmic impact assessments to cross-border harmonization strategies, we’ll outline what it means to be “AI-regulation ready” in 2025—and beyond.

As we open the second day of the Forum, George Nikolaropoulos and Despina Kitsou will set the stage for a program focused on moving from vision to execution. Building on the strategic insights shared the previous sessions, the 2nd day’s sessions will emphasize practical applications, leadership imperatives, and the pathways for organizations to translate AI potential into measurable impact

Many enterprises face significant challenges when attempting to integrate AI into legacy IT environments. This session will explore practical strategies for modernizing outdated infrastructure, transitioning to AI-powered cloud architectures, and strengthening cybersecurity resilience. From system interoperability to risk mitigation, we’ll unpack the technical and organizational levers needed to transform legacy systems into AI-ready foundations.

As organizations embed AI into critical systems, a fundamental question arises: Who owns AI accountability? This panel addresses the shift from ad-hoc or siloed oversight models to a deliberate, organization-wide governance framework, where responsibility for ethical, safe, and transparent AI is clearly distributed across business functions.

Participants will explore how leading enterprises are designing cross-functional AI governance structures that scale with complexity and risk. From legal and technical teams to product and ethics advisors, the session highlights what true accountability looks like at the enterprise level—and how to operationalize it.

The rise of agentic AI marks a turning point in science and engineering, moving beyond passive data analysis to active hypothesis generation, real-time experimentation, and adaptive problem-solving. This new paradigm enables AI systems to work hand-in-hand with human experts, accelerating breakthroughs in materials science, drug discovery, and sustainable engineering.
Future workflows will harness self-directed AI agents capable of navigating vast datasets, uncovering novel insights, and optimizing complex simulations—dramatically shortening the path from concept to application. By drawing on principles such as neuroplasticity-inspired frameworks and reversible computing, agentic AI will enhance efficiency, reduce computational waste, and remain ethically aligned.
This keynote ny Simon See, Global Head Nvidia AI Technology Centre, NVIDIA will explore how agentic AI is reshaping discovery itself—unlocking new frontiers in scientific reasoning while ensuring innovation remains sustainable, responsible, and human-centered.

AI is no longer optional — it’s a strategic imperative. Yet most organizations struggle to go beyond experimentation. In this session, we’ll move past the hype and focus on how to build a real-world, actionable AI adoption roadmap tailored to business needs, resources, and risk profile. Whether you’re in manufacturing, finance, healthcare, or services, the key to AI success lies not in chasing tools — but in aligning technology with your core processes, data maturity, and human workflows. We’ll explore the lifecycle of AI adoption through practical phases: business alignment, capability assessment, data infrastructure readiness, model integration, and continuous improvement. You’ll see how to transition from “isolated use cases” to full-fledged AI-enabled workflows that deliver measurable ROI. We’ll also discuss the most common traps companies fall into — from overinvesting in infrastructure to neglecting user trust and model feedback loops. Attendees will leave with a visual framework for decision-making, a maturity checklist, and key metrics of how to track progress. If you’re planning to adopt AI — or struggling to scale it — this session will give you the clarity and structure to move forward with confidence.

AI has moved from the realm of experimentation to becoming a core enabler of business transformation. This session will explore how Greek enterprises are translating AI strategies into tangible outcomes—reshaping operations, customer experience, and decision-making. Leaders who are driving AI adoption within their organizations will share insights on bridging the gap between ambition and implementation, overcoming internal resistance, and building sustainable, data-driven cultures.

Artificial Intelligence is transforming the financial services landscape, and NBG is actively investing in building strong AI capabilities to stay ahead. In this session, we will unpack the bank’s strategic vision for AI and the factors driving this transformation. Participants will gain insights into the specific areas where NBG is applying AI, from enhancing customer experience and operational efficiency to strengthening risk management and innovation capacity.

 Manufacturing plants, offshore platforms, and vessels generate critical operational data but often lack reliable connectivity for cloud-based AI solutions. This session explores how deploying intelligent systems directly at remote locations enables immediate decision support, reduces operational costs, and maintains business continuity regardless of network availability. Learn how industrial operators and maritime crews are using on-site AI to diagnose equipment issues, optimize processes, and access expert knowledge instantly—transforming isolated assets into autonomous, intelligent operations. We'll examine real-world deployments where local AI capabilities have prevented production losses, reduced satellite communication costs, and enabled 24/7 operational intelligence without cloud dependency.

In the era of AI-driven cyberattacks, organizations must evolve beyond traditional defense. ClearSkies™ TDIR (Threat Detection, Investigation & Response) platform empowers enterprises to detect earlier, respond faster, and adapt continuously by combining holistic telemetry, Generative AI augmentation, and Agentic AI autonomy. This session will showcase how ClearSkies™ transforms Security Operations into an AI-orchestrated resilience engine, enabling smarter workflows, reduced analyst fatigue and automated response at machine speed—helping teams stay secure, compliant, and resilient in the age of AI threats.

Too often, AI enters product roadmaps as a checkbox for innovation rather than a driver of meaningful value. This session reframes AI adoption through the lens of product management strategy: starting from business cases, grounding in discovery, and measuring outcomes. We’ll look at the risks of hype-driven implementation, and instead propose methods to identify AI use cases that strengthen your competitive edge and deliver tangible results. Participants will gain a toolkit to critically evaluate AI’s role in their strategy and to lead AI discussions with clarity and confidence.

AI is no longer just a tool—it’s a strategic force that’s redefining how organizations create value. This session brings together leading Greek board members, CEOs, and AI strategists to explore how corporate governance must evolve in the face of rapid AI transformation. The discussion will focus on how boards can lead responsibly, align AI with long-term goals, and ensure their companies remain competitive and ethical in a fast-changing world.

Welcome to the Disrupt Lead AI Forum 2025! In this energizing opening session, George Nikolaropoulos and Ioannis Papikas set the stage for an exciting journey—where leaders, innovators, and changemakers unite to shape how AI will redefine the future of business.

Agentic AI and copilots are quickly becoming part of everyday workflows—but what do they actually enable, and how should we think about their role in the way we work, decide, and build?

This session takes a clear-eyed look at the current state of agent-based AI systems: what they are, how they function, and where they’re genuinely creating value. Through real-world examples and use cases, we’ll move past the buzz and explore the practical, evolving relationship between humans and intelligent tools.

Expect insights on:
1.The capabilities (and limitations) of agentic AI today
2.What “copilot” really means in different work contexts
3.How to evaluate value, not just novelty, when deploying AI tools

Designed for those who want to engage critically—and constructively—with AI’s next frontier.

As enterprises increasingly integrate Large Language Models (LLMs) and Generative AI (GenAI) into mission-critical applications, ensuring their accuracy, efficiency, and reliability has become a top priority. Traditional testing methodologies fall short in capturing the dynamic nature of AI models, necessitating a new paradigm in automated testing and benchmarking.

This session will introduce an innovative AI testing framework designed specifically for LLM and GenAI applications. It will explore the latest advancements in automated evaluation tools, real-world performance metrics, and domain-specific benchmarks that measure AI effectiveness beyond traditional accuracy scores. Attendees will gain actionable insights into testing methodologies that ensure AI models are robust, unbiased, and scalable across various industries and datasets.

Prompt Engineering is a new field that focuses on creating and improving prompts to get the best results from language models (LMs), diffusion models, and other advanced technologies. The Crafting AI Prompts Framework is split into two parts: IPE and IVPE. This framework helps users effectively use Generative AI for writing and creating visual content, while also promoting ethical and responsible use. Through this deep dive the participants will be able to understand basic concepts around the framework and effectively apply it to use GenAI effectively and responsibly in a daily manner!

As AI-powered copilots become standard across industries, their presence is no longer a differentiator—it’s the baseline. The real opportunity now lies in how companies reshape their product strategies to lead in an AI-native world.

This session challenges product and strategy leaders to look beyond surface-level integrations and rethink how AI informs their core value proposition, team structures, and roadmap decisions. It’s not just about adding intelligence to existing products—it’s about building products and organizations that are fundamentally more adaptive, data-driven, and aligned with emerging user behaviors.

We’ll cover:
1.Why the “copilot phase” is just the beginning—and what comes next
2.The mindset shift required to move from reactive to strategic AI integration
3.The emerging role of the AI Product Manager as a cross-functional catalyst
4.How to structure experimentation and product discovery around fast-moving technology
5.Approaches for gathering meaningful product requirements in a landscape where user needs are still forming

This is a conversation for those ready to evolve—beyond features, beyond hype—and shape what the next generation of intelligent products can be.

With a solid understanding of Generative AI, it’s time to explore how to effectively interact with and optimize AI assistants—without writing a single line of code. In this hands-on session, participants will engage with a custom-built Generative AI assistant powered by Llama, Llama, and DeepSeek-R1. Through guided, no-code exercises, attendees will experience firsthand how to refine AI outputs and maximize performance.

We’ll begin by experimenting with prompt engineering, where participants will explore different ways to structure inputs to guide AI responses. By adjusting tone, specificity, and constraints, they will see how small modifications can significantly improve relevance, coherence, and accuracy.

Next, we will dive into response optimization techniques, demonstrating how settings like temperature, response length, and system instructions impact AI-generated content. Attendees will practice adjusting these parameters through interactive exercises, gaining intuition on how to tailor AI behavior for different use cases.

Finally, we’ll discuss efficiency and best practices, showing how to make interactions smoother, reduce hallucinations, and achieve more reliable outputs. By the end of this session, participants will have a hands-on understanding of how to effectively work with GenAI models, empowering them to leverage AI assistants like a pro.

Generative AI is reshaping industries by enabling powerful applications in content generation, automation, and decision-making. However, to truly unlock its potential, one must understand not just how it works, but also how to optimize it for efficiency and accuracy. This session provides a structured introduction to Generative AI, making it accessible for novice students and AI enthusiasts.

We will start with the fundamental principles of Generative AI, explaining how models like Llama and DeepSeek-R1 generate human-like responses. Participants will gain insights into key architectural components, including transformers, attention mechanisms, and tokenization, as well as the differences between training and inference processes.

Moving beyond the basics, we will explore essential optimization techniques used to enhance model performance. This includes strategies for improving response accuracy, reducing latency, and balancing computational efficiency. Attendees will also learn about the latest advancements in fine-tuning methods, parameter-efficient training, and the trade-offs between different deployment strategies.

By the end of this session, participants will have a strong conceptual understanding of Generative AI, equipping them with the knowledge to build and optimize AI applications in the next hands-on session.

Agentic AI systems are emerging as powerful tools that can anticipate needs and make autonomous decisions. This session explores the practical applications and implications of these proactive AI agents across industries.

In this session we'll examine:

1.Core principles of agentic AI and how it differs from traditional reactive systems
2.Real-world case studies in healthcare, finance, and customer service
3.Potential benefits, including improved efficiency and decision-making accuracy
4.Challenges in implementation, including integration with existing systems and processes
5.Ethical considerations and the need for human oversight
6.Concrete examples and critical analysis, which will assess whether agentic AI represents a genuine leap forward in decision-making or just an overhyped trend. Attendees will gain insights into the current state of agentic AI and its potential impact on their industries.

Securing LLMs is critical for GenAI success, yet it remains one of its toughest challenges. From mitigating prompt injection and adversarial vulnerabilities to ensuring compliance, the complexities can feel overwhelming. This session introduces an actionable checklist, grounded in real-world scenarios, to help attendees simplify and structure their approach to LLM security.

AI goes far beyond chatbots—its real impact lies in Highly Autonomous and Agentic AI-driven operations, where Networks are managed with Intents, and AI translates Data Engineering high-level objectives into machine-executable actions. This session explores how Generative AI is shaping the future of telecoms enabling self-optimizing networks, and how GenAI-driven data platforms simplify operations for engineers across industries. We’ll also discuss how AI is driving sustainability, optimizing data center energy use and network efficiency to reduce costs and carbon footprints. This is not just about short-term automation but long-term innovation!

Join us for an exclusive workshop where you’ll learn how to build your own Generative AI (GenAI) application using Retrieval-Augmented Generation (RAG). In this hands-on session, we will guide you through creating an AI chatbot that retrieves relevant information from contextualized data. You’ll gain practical experience in storing vector embeddings in a vector database, a crucial component for efficient similarity searches in AI applications. Additionally, we’ll delve into the RAG architecture, exploring how integrating retrieval mechanisms with generative models enhances the accuracy and relevance of AI-generated responses.

This workshop is designed for anyone looking to dive deeper into building practical and impactful GenAI solutions. Don’t miss this opportunity to enhance your skills and bring your ideas to life!

Prompt Engineering is a new field that focuses on creating and improving prompts to get the best results from language models (LMs), diffusion models, and other advanced technologies. The Crafting AI Prompts Framework is split into two parts: IPE and IVPE. This framework helps users effectively use Generative AI for writing and creating visual content, while also promoting ethical and responsible use.

Prompt Engineering is a new field that focuses on creating and improving prompts to get the best results from language models (LMs), diffusion models, and other advanced technologies. The Crafting AI Prompts Framework is split into two parts: IPE and IVPE. This framework helps users effectively use Generative AI for writing and creating visual content, while also promoting ethical and responsible use.

Join us for an exclusive workshop where you’ll learn how to build your own Generative AI (GenAI) application using Retrieval-Augmented Generation (RAG). In this hands-on session, we will guide you through creating an AI chatbot that retrieves relevant information from contextualized data. You’ll gain practical experience in storing vector embeddings in a vector database, a crucial component for efficient similarity searches in AI applications. Additionally, we’ll delve into the RAG architecture, exploring how integrating retrieval mechanisms with generative models enhances the accuracy and relevance of AI-generated responses.

This workshop is designed for anyone looking to dive deeper into building practical and impactful GenAI solutions. Don’t miss this opportunity to enhance your skills and bring your ideas to life!

Thanks to the general availability and widespread use of large language models such as ChatGPT, Gemini & Co., we have become aware of the disruptive importance of AI for our discipline. It is obvious that when testing AI systems, we must deal with new technologies, test techniques and therefore new requirements and skillsets for testers. Above all, AI-based systems will have a massive impact on the work of testers; large language models and other AI systems can already partially take over the work of testers.

What does this mean for the role of testing in the next few years? What changes will we have to adapt to and what will the role of testing look like in ten years? Will software testing, as we know it today, still play a role in the future? And if so, do we still need humans for this? In my talk, I would like to shed light on the current status of testing with AI, show possible future scenarios and try to find plausible answers to these questions.