Switzerland gave the world precision watches, iconic typography and some of the cleanest design ever made, and its quiet lesson is that people trust things that are beautifully made. Most enterprise AI is not. It often arrives as a chat box bolted onto an old process, functional but forgettable, and people quietly drift back to the way they worked before.

This opening keynote invites leaders to treat AI as a design challenge as much as a technical one. How to bring designers, writers and the people who do the work into the room early, how small moments of delight decide whether a tool is adopted or abandoned, and why the AI experiences people return to are the ones built with the same care as any product they love.

Here is a simple test for any AI program. If your main model provider doubled its price, changed its terms or could no longer keep your data where your clients expect it to stay, how long would it take to move? For many organizations the honest answer is months, because prompts, evaluations, fine tuning and workflows were all built around one vendor without anyone deciding that on purpose. For Swiss organizations the question runs deeper still, as clients and supervisors ask where data is processed and which jurisdiction a provider answers to, and as sovereign Swiss AI platforms offer a new place to land.

This keynote panel treats portability as a discipline rather than a hope. Our panelists share how they run switching drills, which parts of the stack they insist on owning, the contract clauses that protect their exit, and how they balance the convenience of one provider against the freedom to leave.

The race for ever larger models makes the headlines, but inside many Swiss banks, insurers and reinsurers a quieter story is unfolding. Smaller models trained for a single domain are proving faster, cheaper and easier to govern, especially for numerical work like forecasting, pricing and risk, where a confident guess is worse than no answer at all.

In fifteen focused minutes, this session shares where the giant was swapped for the specialist, what it did to accuracy and cost, and how to decide which problems truly need the biggest brain in the room.

For Swiss companies that serve customers across Europe, responsible AI is fast becoming part of everyday business. The EU AI Act applies to AI systems offered or used in the EU, with many of its requirements for higher risk systems arriving over the next few years, while Switzerland is preparing its own approach built on targeted updates to existing laws. Either way, the practical question for leaders is the same. How do you build AI that clients, supervisors and your own people can trust?

This panel looks at what that means day to day. How teams classify their systems, document data and build human oversight once and use it everywhere, which systems they are prioritizing, and how responsible AI practice is becoming a reason clients choose them rather than a burden they carry.

The phone call never died. It is still the channel customers reach for when something truly matters, and in Switzerland that call might arrive in German, French, Italian or Romansh, often in a regional dialect, and sometimes switch languages mid conversation. For years automation there meant endless menus and the familiar plea to speak to a human. A new generation of voice AI reasons directly on speech, following context, tone and intent across an entire conversation.

This session explores what that means for customer service, sales and internal support. Where it already works, how it copes with dialects like Swiss German, how to design it with dignity, and how to keep trust when the voice on the line is not a person.

AI systems learn from what they are fed and act on what they are told, and attackers have noticed. A few poisoned documents in a knowledge base, a hidden instruction inside an email or a tampered model download can quietly change what an assistant says and does. For companies whose reputation rests on discretion and reliability, an AI that can be manipulated is a risk to the brand itself.

This disruptor session shows how leading organizations test their AI the way an attacker would, watch what goes into it and what comes out, and keep a clear record of every decision their agents make.

Most of the AI conversation has lived on screens, in chat windows, documents and dashboards. The next chapter steps into pharmaceutical plants, precision manufacturing, rail networks, warehouses and hospitals, and Zurich, home to some of Europe's leading robotics labs and startups, is helping to write it. World models help machines understand space, time, cause and effect, so robots and autonomous systems can rehearse an action before they take it.

Our panel explores where physical AI is already creating value across Switzerland, what it demands from data and infrastructure, and how leaders prepare their operations, safety teams and people for machines that move.

Some of the most valuable data in the enterprise is the data you are not allowed to touch. Patient records, client conversations, fraud cases, the rare failure that happens once a decade. Synthetic data offers a way through, realistic records that carry the patterns without the people, which matters all the more under Switzerland's revised data protection law and its long tradition of client confidentiality.

In fifteen focused minutes, this session separates the promise from the pitfalls. Where synthetic data is already speeding up projects in regulated industries, how to prove it is both useful and truly private, and what happens to models that learn too much from machine made examples and too little from the real world.

Every organization has a few people who simply know. The master machinist who can feel when a part is a fraction off, the reinsurance underwriter who senses a bad risk before the numbers show it, the plant engineer who can hear when a machine is about to fail, the operations lead who remembers why a rule was written twenty years ago. Much of what they know was never written down, and many of them are approaching retirement.

This closing conversation explores how leaders are using AI to listen to, record and preserve that expertise while those experts are still in the building. How to invite people to share what they know rather than feel replaced by it, how to turn stories and judgment into something a new colleague or an agent can learn from, and how to honor the people whose wisdom built the company.