.NET Day Switzerland

Nick Gushchin

AI Transformation Manager at SFS Group,Conceptual architect & Co-founder of the Swiss AI Chatbot Factory, Advisor to AI Startup CloEE, Applied AI Lecture and Trainer at Digicomp Academy.

I am an AI Transformation Manager at SFS Group, a Swiss-based global industrial manufacturer operating in 50+ countries, with over 13,000 employees and CHF 3B+ in annual revenue. After 18 years in executive banking and technology leadership, I transitioned into applied AI by teaching myself Python and moving from managing large organizations to designing and scaling AI systems inside real enterprises. Today, I lead company-wide AI adoption — from strategic use case selection and prototyping to production deployment, governance, and measurable business impact. In parallel, I am an Applied AI Lecturer and Trainer at Digicomp Academy, where I teach professionals how to move from AI theory to practical, production-ready systems. This dual role — practitioner and educator — allows me to translate complex AI concepts into clear, actionable frameworks that resonate with both technical and leadership audiences. My work focuses on enterprise-grade AI: AI that survives compliance, legacy infrastructure, security constraints, organizational resistance, and economic reality. I design AI solutions embedded into core business and IT processes — not demos, not pilots, not hype. On stage, I speak about what happens after the hype: how leaders move from AI experimentation to operating capability, why most AI initiatives fail at scale, how to build resilient, auditable, and governable AI systems, and how to align AI strategy with execution, accountability, and ROI. I bridge technology and leadership, helping organizations turn AI potential into reliable, scalable, and teachable systems that work beyond the demo.

Sessions

How Not to Fail with AI: Lessons from Real-World AI Breakdowns

Intermediate English

Real-world AI failures and what they teach us about building systems that actually work. Everyone loves to showcase AI success stories, but the truth is, most AI projects fail. Quietly. Expensively. Repeatedly. Models that never reach production. Chatbots that frustrate users instead of helping them. Automation pipelines that break under real-world complexity. Over the past three years, as an AI builder working on more than 20 AI projects: from healthcare and chatbots to banking and industrial automation. I’ve seen firsthand how even well-funded, well-planned systems can fail in unexpected ways. In practice, most AI failures are not technical - they are leadership failures: unclear ownership, misaligned incentives, and the absence of decision-making accountability. In this talk, I’ll share real AI use cases that didn’t go as planned: what failed, why it failed, and what we learned in the process. From misaligned objectives and brittle prompt engineering to missing data context and weak orchestration between human and machine workflows. Every case reveals a deeper pattern behind why AI underperforms when it leaves the lab. This isn’t about pessimism! It’s about engineering realism. You’ll see how failure analysis can become a design tool, helping teams build AI systems that are reliable, auditable, and adaptable to change.