Businesses worldwide spend USD 31.7 bn on translation every year, whether that's fully AI-driven or reviewed by professionals. While AI tools keep getting better, a fundamental issue remains: no non-human system will ever be able to certify correctness. Professional translators, on the other hand, are often simply too slow and expensive. The result for most businesses: outsourced processes with 17 different vendors, uncertain data protection, manual corrections and no continuous adaptation of the underlying language models to their use case.

Launched in February 2025 at supertext.com, our AI translator is based on enterprise-grade, sovereign LLMs hosted on our Swiss servers or on infrastructure of the customer's choice. We seamlessly integrate more than 3,000 experienced human linguists: our Verification feature lets users have important translations reviewed by professionals within 8 minutes while tailoring their custom translation model to their use case in the process. This makes the system more precise every day, reducing the need for professional oversight over time.

Our enterprise clients see an up to 90% increase in ready-to-publish AI output, 93% faster turnaround for professionally verified translations and a 64% reduction in overall translation spend. These gains aren't theoretical, but are in production today at 1,500 businesses including Swiss Life, AXA and SBB – even for Swiss German and Romansh.

For many financial institutions, Quality Assurance (QA) has ballooned into a massive liability, costing millions and stalling deployment. This session exposes the playbook leading banks use to turn this cost center into a competitive advantage. We explore the shift from manual grunt work to human-supervised AI agents. This approach delivers the speed of automation with the safety of expert oversight. We will analyze real-world cases showing how this specific AI implementation slashes overhead and dramatically.

Most companies are still experimenting with GenAI, but very few manage to scale beyond pilots—especially in highly regulated industries like insurance. In my talk, I will share how we moved from isolated GenAI use cases to designing and delivering the first Agentic AI–native claims-handling platform across three companies and five countries within a global insurance group. I will explain how Agentic AI changes process design, product architecture, and governance, and what it takes in reality to orchestrate multiple specialized AI agents while ensuring safety, reliability, and compliance with the AI Act, GDPR, and DORA. The talk focuses on practical learning: how to design agent roles and orchestration patterns, how to build trust and non-functional safeguards into autonomous flows, how to prepare the workforce for AI-centric operations, and how to balance speed with regulatory requirements. Attendees will leave with a concrete playbook for evolving from GenAI experiments to an AI-native operating model and with insights into the measurable impact on productivity, quality, and customer experience in claims handling.

GenAI promises to transform how organizations work — but turning that promise into secure, scalable, real‑world solutions is often harder than it seems. In this talk, discover how Migros built a flexible, enterprise‑grade chatbot platform that enables teams to create powerful assistants in just minutes, without compromising on security, governance, or quality.
Two years ago, we created our first chatbot — and quickly learned that technical innovation was only one part of the challenge. Alongside building early prototypes, we had to navigate aligning stakeholders and define governance models that satisfied both business needs and strict security requirements. This journey laid the foundation for what would eventually become our Chatbot Creator Platform.
In this session, we’ll show you that platform in action, share the lessons we learned while creating it, and explain how we built a system that now empowers teams across Migros to ship AI assistants in record time. You’ll walk away with practical insights for scaling GenAI in a complex enterprise environment — beyond the hype and toward real, sustainable impact.

"The model isn't sure", "The AI assistant wants to be helpful"…. We use these phrases daily as shorthand, but are they undermining your risk strategy? This talk argues that anthropomorphic language is a big sleeper risk in GenAI governance today. When we assign human verbs—thinking, deciding, lying—to probabilistic systems, we create an "Illusion of Intent." This linguistic drift isn't just a semantic annoyance; it is a governance hazard. It hacks human empathy, creates false trust, and obscures liability by treating system failures as character flaws. In this session, we will dismantle the habit of humanizing the machine. We will explore how precise, de-anthropomorphized language acts as a firewall for ethical safety and improves collaboration between Legal, Tech, and Product teams. Join us to learn why the most critical update to your governance framework isn't new code—it's a new vocabulary. Let’s stop governing the ghost and start governing the tool.