The current program

The central question is: Why are many companies still unable to generate real added value from AI despite high investments – and what do they need to do differently now?

With Agentic AI, AI doesn't just trade – it actively engages. But the leap from pilot projects to scaled deployment is only possible with new governance, sovereignty, and a transformed operating model. In this exclusive session, we'll discuss how to scale AI agents without losing control – with clear governance, full sovereignty, and an operating model that productively unites humans and AI. Using best-case examples from real-world practice, you'll learn how to create genuine business value and how to measure it. For decision-makers who want to move beyond experimentation and scale their operations.

Agenda

09:00 Welcome
Larissa Holzki, Editorial Head of TECH – Handelsblatt

09:05 Launch Handelsblatt x Infosys AI Trend Report
Stefan Hezel, Head of Marketing EMEA – Infosys
Dr. Jan Kleibrink, Chief Growth Officer & Managing Director – Handelsblatt Media Group
Dr. Frank Säuberlich, Senior Principal Data & AI – Infosys Consulting

09:20 Discussion:
Why are many companies still unable to generate real added value from AI despite high investments – and what do they need to do differently now?

10:00
Business breakfast

Agentic AI promises to transform organizations by enabling autonomous systems that can plan, decide and act with minimal human intervention. But successful adoption depends on far more than deploying the technology, it requires robust governance, resilient architectures and a culture that empowers people to work alongside non-human decision-makers. The opportunities are great, but the road ahead won't be simple. Let's explore how organizations can move quickly without sacrificing trust, control or long-term organizational resilience.

Most companies react to customer problems like this: the customer reports it, the system responds. Real change happens one step earlier: the system recognizes the problem before the customer even notices it.

Gordian Braun demonstrates live on stage how this works. In 15 minutes, he builds a voice agent (live!) that acts proactively: It makes contact, understands the situation in the conversation, and integrates the solution directly into existing processes. A glimpse into the future of customer service—not theoretical, but live and tangible.

• What distinguishes a market-ready innovation from an experiment, and how can the difference be recognized?
• Where does the transition from research to industrial practice most often fail, and who bears the responsibility for this?
• Which innovations will be standard in five years, and what needs to happen to achieve that?

The Handelsblatt editorial team in discussion with:

How can Europe transform excellent research into faster, scalable products, companies, and industrial applications? The panel brings together the startup ecosystem, industry, and policymakers.

• Which AI innovations will shape the next five to ten years – and who should develop them?
• How must startups, established companies, research and politics collaborate to turn ideas into scalable applications?
• What framework conditions does Europe need to combine speed, investment power and technological sovereignty?

Fewer skilled workers, higher expectations, increasing cost pressure: BARMER is industrializing AI agents to measurably relieve the burden on key service and administrative processes in the statutory health insurance system. Together with Capgemini, the company is demonstrating in the A³ project how four use cases can be transformed into a scalable transformation program: with reusable components, clear governance, and a robust data/platform foundation. The focus is on moving from isolated initiatives to end-to-end automation – with the prospect of agentizing up to 200 core processes within three years.

Pilot projects demonstrate the potential of AI agents – but how do you transform them into a stable, enterprise-wide system? In this panel, experienced leaders discuss key questions: What mistakes occur during scaling – and how can they be avoided? When does a pilot project become an "island" – and how do you recognize when it's time to scale? What data and platform foundations are truly needed to ensure scaling doesn't fail due to interface issues? And how do you maintain cost control for token and usage fees? A discussion about the leap from isolated successes to a networked infrastructure.

• When does an AI agent become a true alternative to humans, and when is it dangerous rather than helpful?
• Which industries and business models will be radically transformed by autonomous AI systems, and which are not yet ready for this?
• Who bears the responsibility when something goes wrong with an agent – the developer, the company, or the AI itself?

What conditions must companies create in the next twelve months to ensure that AI does not remain stuck in individual projects, but measurably contributes to future viability?

• What three decisions must the board of directors and management make themselves now?
• What technical, organizational, and cultural prerequisites need to be established simultaneously?
• How can you tell after twelve months whether a company is truly AI-ready?

The figures are impressive: Gartner forecasts $64 billion in AI spending by 2026 – an increase of 63 percent. But the reality is uncomfortable: Over 80 percent of companies miss their cost forecasts by more than a quarter. Over 40 percent of their AI projects could fail by 2027.

Token costs are exploding. Employees are often working more, not less. And the real efficiency gains remain enigmatic. How do companies practically control their AI budgets? Why do studies show that intensive AI use leads to more work – due to monitoring, complexity, and mental exhaustion? And how can AI become an investment rather than a cost driver?

A discussion about the costs, the hidden pitfalls, and the crucial question: How can AI expenditures generate real added value?

By 2027, competitive advantage will no longer be determined by the choice of model, but by mastery of the value chain.

When AI capabilities become a commodity: Where do real competitive advantages arise: in model access or in control over your intelligence architecture? Who controls the AI's decisions—the company or the model provider? Is a proprietary foundation model really the solution?

Agent in the Loop – Responsibility and Control in the Age of Autonomous Agents

The "new" colleague works around the clock, conducts independent research, prioritizes tasks, initiates processes, and accesses company systems. For many companies in the Handelsblatt AI Circle, this is already everyday life.

AI agents are increasingly leaving the protected demo environment and assuming real responsibility – within defined boundaries, but with growing scope for action. In doing so, they are not only putting productivity and efficiency to the test, but also established roles, leadership models, and responsibilities.

We are discussing:

• Who gives tasks to an AI agent?
• Who decides what they are allowed to do independently?
• Who controls their results and decisions?
• And who takes responsibility if something goes wrong?

In this AI Circle session, we won't discuss whether companies should use agents, but rather how their use works in practice. In a confidential setting, we'll share experiences with AI agents, OpenClaw, and autonomous systems, and discuss the necessary rules, control mechanisms, and new forms of collaboration.

From AI Governance to AI Factory: How Governance Enables Orchestration, Adoption, and Return on AI

Using a practical case study from a global industrial company, this masterclass demonstrates how an established AI governance system can be further developed into the control architecture of an AI factory. The focus is on the continuous cycle from clear guidelines, through portfolio and delivery orchestration, to qualified adoption and measurable results in business processes. Participants learn how, based on this foundation, steering can make sound decisions about scaling, redesigning, or halting AI initiatives.

AI in Action!

No transferring. No waiting. Case closed. How real-time AI voice agents resolve issues independently.

„"Please hold the line..." Customer inquiries today often end in waiting loops, transfers, and media breaks. It doesn't have to be this way. In this masterclass, Jens Heilmann and Holger Hornik from msg.group demonstrate how modern AI voice agents understand calls in real time, respond contextually, and resolve issues independently – all without human intervention. Using concrete practical examples, they illustrate the underlying technological components and the limitations of current systems. This masterclass is for anyone who wants to take the next step in automating customer contact.

AI agents are fundamentally changing the operating model of companies. Productivity increases, automation levels grow, ROI is measured – but one crucial question often remains unanswered: How successful is the collaboration between humans and agents?

Traditional key performance indicators (KPIs) are no longer sufficient. In her keynote address, Dagmar Schuller demonstrates why new KPIs for trust, acceptance, and human-agent collaboration will determine the future success of AI initiatives. To successfully scale AI agents, companies must measure not only their performance but also the quality of their interaction with humans.

The BMW Group's AI transformation is already having an impact across all business units, changing processes, roles, and the way the company is managed. To scale AI agents securely and efficiently, an AI-native operating model with a standardized technical layer and robust governance structures is needed. Clear guidelines for employees, consistent rights management, and continuous monitoring of token consumption create transparency, security, and centralized control, forming the basis for sustainable value creation.

AI agents should act – but not unchecked. How do companies define governance models that enable autonomy while simultaneously limiting risks? In this panel, experienced executives discuss key questions: Where are approvals and control points needed – and where does control become a hindrance? How is accountability ensured when systems act independently? And who bears the responsibility when an AI agent makes a decision? Learn what effective guardrails look like.

Further speakers are being coordinated.

• What AI skills will managers, departments, and technology teams need in the future?
• How can further education be transformed from a voluntary offering into an integral part of daily work?
• How can companies prevent different approaches to AI from exacerbating existing inequalities and resistance to transformation?

• Which skills and capabilities do employees need today — and which will matter tomorrow?
• How can HR strategies position employees as active shapers of the transformation — not just recipients of change?
• How can companies build AI-ready teams and enable employees to use AI confidently, responsibly and productively?

Larissa Holzki in an interview with:

• What actually makes a database „AI-ready“ – beyond cleansing and central storage?
• How can companies overcome fragmented data landscapes without waiting for years of transformation programs?
• What responsibility do departments, IT, and management each bear for data quality and usability?

The Handelsblatt editorial team in discussion with:

Every company is currently experimenting with copilots and language models – but despite enormous investments, a real ROI often fails to materialize. The reason: Consumer AI knows everything about world history, but nothing about how your business actually operates. Without deep operational context, AI models search for answers in countless IT silos – leading to inaccurate results and causing token costs to skyrocket.

FRITZ! has stood for digital sovereignty and "Made in Europe" for decades. The rapid development of AI now presents Europe with structural questions: How can AI be implemented within the tension between international competitive pressure, dependencies, and regulation? What framework conditions does Europe need to avoid falling behind in AI value creation – and which mistakes of the Big Data era must be avoided this time? Jan Oetjen shares practical perspectives and discusses what companies and policymakers must do now so that European firms can proactively shape, rather than reactively influence, the AI landscape.

• Which platform architecture enables rapid experimentation without creating new shadow IT?
• How much standardization is needed for scaling? Where must there be room for decentralized innovation?
• How can companies avoid new dependencies on individual cloud, model, or platform providers?

Companies are rolling out AI, relying on models whose access, rules, and pricing they don't control. At the same time, attackers have long been using the same technology without any restrictions. Abdelkader Cornelius draws on practical experience to show what this means for every company that relies on AI today: which dependencies are becoming real, where agents have become attack surfaces, and what secure AI deployment actually looks like.

Agentic AI opens up new opportunities for businesses, while simultaneously increasing the power of cybercriminals. AI is increasingly becoming a tool for attackers, fundamentally changing the threat landscape. Carsten Meywirth provides an exclusive look at current developments and attack patterns, showing how cybercrime is industrializing and what strategies companies should develop to keep their organizations resilient and operational.

Robotics and autonomous systems are becoming the means of execution in the real world – meaning the legal framework determines scalability, time-to-market, and investment security. Dr. Alexander Baghal-Schmid's presentation clarifies which regulations apply simultaneously to Physical AI and which deadlines for 2026–2028 now need to be included in roadmaps and product decisions. Specifically: What prerequisites must companies and manufacturers create now to ensure that Physical AI moves from pilot projects to secure, compliant, and regular operation?

How can a company with 600 employees compete against tech giants that are ten to twenty times larger? Instead of scaling the number of employees, it needs a fundamental rethink of how talent uses AI to achieve exponential impact.

In this keynote, trivago CEO Johannes Thomas goes beyond the AI hype and asks the more crucial question: What does it really take for genuine organizational transformation? trivago's shift from AI-supported work to workflows fundamentally restructured by AI forms the framework of a strategic case study – one that goes beyond simply introducing AI tools and rethinks how an organization works and what it takes for every individual to operate at a fundamentally higher level.

Efficiency is only one part of the added value that an AI transformation enables. The crucial lever behind 10x impact lies in enabling each individual to become significantly better at the work that truly matters: building systems, managing quality at scale, deepening the relationships that drive value creation, and making better decisions faster and with greater confidence.

While companies invest billions in artificial intelligence, another form of intelligence is being overlooked: human intelligence (MI).

AI can analyze, formulate, and prepare decisions. But it encounters organizations where human judgment, imagination, and experience are often given surprisingly little space. The paradoxical danger: We are investing in increasingly intelligent machines – and simultaneously removing human intelligence from the workplace.

How can organizations bring AI and MI together in a new way? The future belongs neither to humans nor to machines alone. It belongs to organizations that understand when machines calculate better, when humans judge better, and how artificial and human intelligence combine to create real performance.

The crucial question is therefore no longer just: How intelligent will AI be? But rather: How revolutionary would it be to unleash AI on the world of work now?

Today, AI agents are primarily efficiency tools: customer service bots, shopping assistants in e-commerce, automated workflows. But the next wave will be fundamentally different: The agentic economy describes an ecosystem in which autonomous agents no longer operate within platform logics, but rather break through them, thereby creating new markets, reorganizing value chains, and fundamentally changing business models.

In this discussion, we will explore what this transformation means:

• Technological: Where are the limits of agents today – and when will they be overcome?
• Strategically: How must companies rethink their business models to avoid becoming mere suppliers in an agentic economy?
• Geopolitical: Could Europe gain a new competitive advantage through decentralized, sovereign agent architectures – instead of falling back into platform dependencies?

Drawing on perspectives from research, startup innovation and industry, we examine how companies must set the course today to remain competitive tomorrow.

The Handelsblatt editorial team in discussion with:

Further speakers are being coordinated.

How companies can manage the uncontrolled use of AI without stifling innovation.

Shadow AI isn't just a security issue. It's a symptom. It reveals that the official organization isn't AI-ready. That processes are too slow. That employees think faster than their companies can act. Those who merely suppress shadow AI also suppress the innovation that employees are already driving. Those who ignore it ignore the risks that are growing daily. In this panel, we'll talk to leaders managing this complex situation – about the reality behind the policies, the hidden risks, and the question: How do you build a culture where AI innovation is possible – but controlled?

Further speakers are being coordinated.

AI agents are not just changing processes – they require a fundamental redesign of organizations, roles, and decision-making logics. In this panel, leaders from strategy, technology, and human resources development discuss how they are repositioning their companies for the age of agent-based AI. They share concrete experiences: Which implementation models work? How are decision-making rights between humans and machines being redefined? And how can organizational change be achieved successfully? Those who don't rethink their approach will be overtaken by faster competitors.

• Physical AI connects machine learning models with machines that operate in the real world. The crucial question is not whether impressive demos are possible, but when systems can be used reliably, safely, and economically in production and logistics.
• What advances in robotics basic models, sensor technology, and learning methods are changing industrial applications?
• Where does the greatest economic leverage lie: flexibility, productivity, skills shortage, or new products?
• What is missing between successful demonstration and scalable operation – data, standards, security, hardware, or business models?

The Handelsblatt editorial team in discussion with:

How would we organize work if we were starting over today? AI can do far more than just write texts or summarize information. With agent-based workflows, it's beginning to actually take over tasks. This changes the rules of the game for companies and for us as individuals. But many are missing out on a large part of this potential if they simply apply AI to existing processes instead of rethinking them from the ground up.

Christoph Magnussen shows how companies can now make the leap from generative to agentic AI and why we need a new understanding of work to do so. Human expertise and craftsmanship will not become less important, but rather a prerequisite for using AI effectively.