Experienced IT Manager with a strong background in DevOps, cloud computing, CI/CD, containerization, and software development. Proven track record in strategizing, planning, and executing IT initiatives while effectively managing resources and budgets. Skilled in strategic planning, problem-solving, and communication with good understanding of business principles, project management and team leadership. A results-driven leader with a broad spectrum of IT expertise, capable of driving innovation, efficiency, and security within an organization.

The emergence of Agentic AI represents a fundamental shift from traditional artificial intelligence to autonomous systems capable of independent reasoning, decision-making, and action execution. Unlike conventional AI that requires human oversight for each decision, Agentic AI operates as intelligent agents that can understand context, set goals, execute complex workflows, and adapt their behavior based on outcomes.This webinar provides a comprehensive introduction to Agentic AI, exploring what these systems are, how they differ from traditional AI, and why they represent the next evolution in enterprise technology.

The presentation will answer the following questions:

• What is Agentic AI more than "traditional" solutions?
• What can organizations use Agentic AI for?
• What are the biggest challenges in the implementation of Agentic AI?

In the presentation you will hear about the following technologies:

Agentic AI, LLM, AI Governance, DXP, Integration

Peter is an AI enthusiast and software engineer who loves exploring how AI can make a real difference. He has worked on building Retrieval-Augmented Generation (RAG) systems, red teaming AI models to spot weaknesses, and designing practical guardrails for large language models (LLMs). His focus is on making AI not just smarter, but also safer and easier to trust—using a mix of technical safeguards, bias checks, and clear accountability. He is excited to share some hands-on lessons and tips for building AI systems that are both robust and reliable, and to show how a thoughtful approach to AI safety can make all the difference.

This webinar explains why organizations must treat AI as a safety-critical technology by sharing real-world examples of AI failures, like hallucinations, bias, and unpredictable behavior. You’ll learn how these risks can cause real harm in fields such as healthcare, finance, and transportation. The session also covers practical ways to prevent problems—like red teaming, bias checks, and clear accountability—so you can build safer, more trustworthy AI systems.

The presentation will answer the following questions:

• What are the real-world risks when AI systems fail, and how do they impact sectors like healthcare, finance, and transportation?
• How do issues like AI hallucinations, bias, and unpredictable behavior arise—and what are their consequences?
• What practical steps and technical safeguards can organizations use to make AI safer and more reliable?
• How do new regulations and accountability frameworks help ensure AI systems are trustworthy and fair?

The presentation is for you, if:

• You work with, manage, or are considering deploying AI systems in your organization.
• You’re a software engineer, data scientist, product manager, or technology leader
  interested in AI safety and risk management.
• You want to understand the real-world impact of AI failures and how to prevent them.
• You’re responsible for compliance, ethics, or regulatory aspects of AI in your company.
• You’re curious about practical strategies and tools for building trustworthy, robust AI solutions.

IT teams chase orphaned accounts, duplicate identities, and systems that never sync. They fix one problem and three more appear. This is reactive work. It kills productivity and creates risk. AI agents change this. They hunt problems before problems hunt you. They clean data while you sleep. They prevent the fires instead of fighting them. Real deployments show 80% less manual cleanup. Better compliance. Happier users. This presentation shares five practical lessons from real-world deployments of AI Agents for identity data remediation, covering both technical approaches and organizational considerations for gaining stakeholder buy-in. Attendees will learn actionable strategies that align with Gartner's recommendations for modern identity governance.

This presentation will address the following questions:

• How can AI agents proactively identify and remediate identity data issues before they become security risks?
• What are the most common roadblocks to AI adoption in identity governance, and how can you overcome them?
• What results have real-world organizations seen after deploying AI agents for identity cleanup and lifecycle management?

The presentation is for you, if....

• You manage or secure user identities in enterprise IT environments.
• You’re frustrated with manual cleanup of orphaned or misconfigured accounts.
• You’re evaluating or deploying AI in identity governance or IAM workflows.
• You want to improve compliance, reduce risk, and align with modern identity strategies.

With over 10 years of experience in telecommunications and IT sales, I have worked with both Hungarian and global clients to help them achieve their business objectives through innovative technology solutions. I lead Oriana’s sales department as Chief Sales Officer, where I’m responsible for supporting our clients in digitalizing their business processes and preparing their organizations to fully leverage the potential of AI.

Data-driven decision-making is no longer optional, it’s the foundation for every competitive business today. Yet managing data has never been easy: multiple systems, scattered spreadsheets, and fragmented sources make it nearly impossible to rely on a single truth. A unified digitalization platform organizes all company data in one place, creating a solid foundation for insights and efficiency. On top of that, AI has become indispensable, without fully digitized, integrated systems, it simply has nothing to connect to, leaving organizations at a strategic disadvantage.

The presentation will answer the following questions:

• What happens when a leader loses control over the company?
• What is the first step on the path to order?
• How can you make confident, strategic decisions when your data is scattered across dozens of systems and spreadsheets?
• How can your business turn fragmented systems and spreadsheets into a single source of truth for smarter AI-driven decisions?

The presentation is for you, if:

• You are a senior leader or executive responsible for making high-stakes decisions on a daily basis, where the accuracy and reliability of data is critical.
• You oversee large teams and complex operations, and you don’t have the bandwidth to monitor every task or workflow individually.
• You need a system that consolidates processes, provides a single source of truth, and alerts you only when your intervention is required, allowing you to focus on strategic priorities instead of operational minutiae.
• You are looking to leverage digitalization and AI to streamline decision-making, reduce risk, and gain real-time insights into your organization’s performance, this presentation is designed specifically for you.

Oresztész is a chief software engineer and solution architect with 18 years of professional experience, spanning small start-ups to large international enterprises. He has over a decade of experience leading engineering teams on a wide range of projects, from R&D and product development to software maintenance.

His recent work focuses on the frontier of Generative AI, designing agentic architectures, RAG pipelines, and text-to-SQL solutions. His broad expertise also covers solution architecture, cloud-native systems, IoT, and DevOps/SRE principles. He is a strong advocate for Agile and XP methodologies, including TDD, BDD, and Continuous Delivery.

As a frequent conference speaker and technical writer for platforms like DZone, Oresztész shares his passion for technical excellence. At EPAM, he primarily focuses on solution architecture, mentoring and coaching other developers, building and forming software development teams, and implementing agile and lean methodologies.

So you're looking forward for leveraging LLMs as a BI tool, but you're unsure how to start and what are the initial challenges? How far you can get with your own in-house solution if you don't want to go with a ready-made product? What are the most complex initial problems you need to solve? Good news is that however generative AI is quite novel technology, some software methodologies and best practices are still applicable. This presentation would like to aid technical people in their upcoming journey on introducing generative AI into a quite complex territory, from an architect's perspective.

The presentation will answer the following questions:

• How can LLMs be leveraged effectively for Business Intelligence workflows?
• What are the key architectural trade-offs when building in-house generative AI solutions?
• How can performance, scalability, and query optimization be achieved with existing tools and methodologies?
• What are the practical strategies for ensuring data quality, governance, and system-wide adoption?

The presentation is for you, if:

• You’re a technical professional exploring LLM-driven solutions for BI.
• You’re tasked with architecting scalable, compliant, and efficient generative AI systems.
• You want to understand trade-offs in technology choices and optimize workflows.
• You’re seeking actionable insights on performance, data governance, and evaluation in AI-powered systems.

Balazs brings over 25 years of leadership experience in enterprise transformation and AI strategy across global markets. As a seasoned educator, advisor, and keynote speaker, he specializes in equipping senior leaders to drive real-world AI adoption with confidence and clarity.
Highlights of his experience include:

• 25+ years of leadership in enterprise transformation across Europe, the US, and China
• Recognized expert in AI strategy, digital transformation, and change leadership
• Creator of the top-rated Business AI for Leaders course (NPS: 9.6 | CSAT: 100%)
• Lecturer at multiple universities, keynote speaker at industry conferences on AI adoption and transformation
• Founder of AI Acceleration Program: guiding leaders from vision to strategy to action
• Led large-scale transformations across banking, finance, insurance, telecom, ICT, FMCG, healthcare, logistics, education, NGO; from multinationals to SMEs
• Directed global training and communication programs reaching 25,000+ employees for enterprise platforms (e.g., SAP, ServiceNow)

Trusted by major institutions including KBC Bank, Aegon Global Technology, Konica Minolta, Hungarian Development Bank, Deutsche Telekom IT Solutions, Telenor, SPAR, American Express, Citibank, Provident Financial, Accenture

Many companies are promoting, developing, or selling AI tools and solutions. However, not every solution is the best fit for a company’s strategy and business context. In this presentation, you will learn how to prepare for AI implementation in a technology- and vendor-independent way, gaining insights that are relevant for both business leaders and developers.

We will discuss how organizations can take practical steps today (even if they are not yet ready to implement AI) to align AI initiatives with their business strategy, increase personal and organizational efficiency, and prepare for successful adoption.

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The presentation is for you if:

• You want to prepare your organization for successful AI adoption.
• You are an AI developer or service provider and need to understand business-side challenges to better support your clients.
• You want a technology-independent approach to AI strategy and practical guidance for early implementation steps.

Key takeaways:

• Practical strategies to identify AI opportunities that fit your business objectives and organizational context.
• Steps to increase efficiency and readiness both on personal and organizational levels.
• Understanding of the leadership challenges and key “ingredients” for successful AI deployment, including human, data, and technical aspects.

Dávid has a background in software development, and security has always played a key role in his work. He has been involved in both successful and unsuccessful ventures. The development of OpenShield began at the end of 2024, providing a security layer for LLM models.

In addition to LLM models, MCP servers have also emerged. Their proliferation introduces new security risks, as LLMs are just as vulnerable to attacks as other systems. The main challenge lies in the diversity of attack types. Traditional security solutions alone are no longer sufficient. Moreover, MCP systems can provide direct access to corporate data, further increasing the risk.

This presentation will address the following questions:

• Why is it important to protect LLM models?
• What is prompt injection?
• What challenges must be faced?

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