Ensuring cost efficiency with enterprise-scale deployment of AI agents is a tough challenge for every organization. Did you know you already possess all the infrastructure right at your fingertips? Think distributed, managed bare-metal infrastructure offering 24/7 operational AI agents for under 100 EUR/month.

In the age of Cloud, k8s and VMs, an overlook is actual machines your company owns. Full governance and control at an unbeatable price for every business, regardless of their size.

Artificial intelligence promises unprecedented efficiency, yet many organizations still struggle to demonstrate meaningful business outcomes beyond faster content generation and lower costs per task. Too often, success is measured in tokens processed, prompts executed, or hours seemingly saved—metrics that can obscure whether AI is actually improving decisions, customer experiences, or employee effectiveness.

This talk challenges the assumption that efficiency alone is the goal. Instead, it presents a practical framework for designing AI pilots that are measurable, outcome-driven, and intentionally human-centered. Drawing on real-world implementation experience, the session explores how to define meaningful success metrics, identify where human judgment adds the greatest value, and avoid optimizing systems for activity instead of impact.

Many organizations have established AI principles, policies and governance structures. Yet employees often still struggle with practical questions: Which tools can I use? What data may I enter? Who is accountable for the result? And how can I innovate without creating unnecessary risk?

The challenge is no longer simply to define responsible AI; it is to translate governance into everyday decisions and scalable organizational practice.

This talk explores how companies can move from abstract principles to actionable guardrails, practical enablement and shared accountability. Drawing on experience from AI governance, compliance and large-scale employee enablement, Sindy Leffler-Krebs will show why governance should not be designed as a control layer added after innovation, but as an integral part of adoption.

Key takeaways:

• How to translate AI principles into clear and usable employee guidance

• Why AI literacy, leadership and culture are essential governance mechanisms

• How communities and practical learning formats accelerate responsible adoption

• How to balance innovation, accountability and human oversight

Participants will leave with a practical framework for making responsible AI understandable, actionable and scalable.

• Standardizing the adoption of foundation models and AI agents through a unified enterprise AI control plane that enables consistent governance while supporting decentralized innovation

• Establishing enterprise-wide AI governance by managing agent and model discovery, enforcing policies and providing operational transparency

• Monitoring AI performance, quality, resource utilization, and cost at scale to accelerate adoption while maintaining security, accountability, and business value

Most teams treat the model as the system. In production, the model is the easy part, the scaffolding around it is what determines whether your AI system is dependable, cost-effective, and correct. This session skips the introductions and goes straight into the hard practical problems: how to manage context windows under real-world constraints, when to retrieve versus when to keep things in memory, how compaction and caching decisions trade off latency against fidelity, and how prompt/instruction design shifts once you're optimizing for a system that runs thousands of times a day rather than a single chat interaction. I'll share concrete techniques and failure modes from building harnesses that turn a raw model into something you can actually trust in production; including where naive context management breaks down, how to think about memory as a design surface rather than an afterthought, and what "good" instruction design looks like once you're past the demo stage.