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.
Zürich, Switzerland
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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.
Area of Expertise
Topics
From Ticket Chaos to Strategic Efficiency: How AI Is Transforming Internal IT Support
In this session, I’ll show how Artificial Intelligence can take internal IT support to the next level — by automating ticket triage, routine workflows, and even physical asset management. You’ll see real examples of AI in action: chatbots resolving first-line issues 24/7, smart routing that cuts resolution time in half, and AI co-pilots helping developers fix problems before they escalate. I’ll walk you through a practical, step-by-step roadmap for implementation, highlight key KPIs to measure success, and share lessons learned from real-world adoption — including mistakes to avoid. This talk is ideal for IT leaders, support managers, and anyone looking to transform their internal support from a cost center into a strategic advantage.
From AI Chaos to Client Value: Building a Client-Centric AI Operating Model
Most AI initiatives fail not because the technology is immature, but because organizations treat AI as a set of tools, not as an operating capability.
Enterprises launch pilots, dashboards, and proofs of concept, yet struggle to scale them into measurable client and business value. The root cause is rarely data or models - it’s the absence of a clear, client-centric AI operating model.
Drawing on my work leading enterprise-wide AI transformation inside a global industrial organization, I will show how companies can move from fragmented AI experiments to a Client-Centric AI Factory - an operating model where AI, automation, and agentic systems are aligned around real client and business outcomes.
We will explore:
- why most AI initiatives stall after the pilot phase,
- how to structure AI governance, ownership, and accountability,
- and how to design AI systems that integrate into core processes instead of sitting on top of them.
The session focuses on practical leadership decisions, architectural patterns, and real-world lessons that help organizations turn AI from a cost center into a scalable, value-generating capability.
The AI Financial Bubble: When Intelligence Meets Speculation
Why the smartest tech boom might end like every bubble before it — and what real builders can do differently.
The world is experiencing an AI gold rush — and just like every rush before it, most of what glitters isn’t gold. Startups with no real models are raising millions. Enterprises are “going AI” without solving a single business problem. Valuations are skyrocketing, while actual productivity gains remain stubbornly flat.
Drawing on 18 years of executive experience in banking and financial strategy, I’ve seen this pattern before — from fintech bubbles to digital transformation hype cycles. Today’s AI boom follows the same speculative logic: capital chases trends faster than value creation can keep up.
In this talk, I’ll dissect the anatomy of the AI financial bubble — how speculative capital, media hype, and short-term incentives created a self-reinforcing illusion of exponential progress. But this isn’t a doomsday talk — it’s a reality check for serious builders. I’ll share a clear framework for identifying genuine value in the AI market, separating signal from noise, and building products that can outlive the hype cycle.
You’ll see what happens when “intelligence meets speculation” — and learn how to position yourself on the right side of the coming correction.
Architecting Robust AI Systems for Small Teams and Founders
How to build startup-level AI architectures that actually scale — without a data science army.
In 2025, building AI systems isn’t just for Big Tech anymore. With today’s tools and frameworks, small teams — or even solo founders — can create production-grade AI systems. The real challenge is not building something that works, but building something that keeps working.
Drawing from my experience leading enterprise-scale operations and now building AI-first infrastructures at the Swiss AI Chatbot Factory, I’ll share a practical blueprint for creating reliable, maintainable AI architectures that scale with minimal human overhead.
This talk focuses on the architectural thinking behind sustainable AI startups: modular design, automation chains, agent orchestration, and fallback logic. You’ll see how to avoid the common traps of “prompt spaghetti,” vendor lock-in, and untraceable LLM behavior — and instead design systems that are transparent, testable, and business-aligned.
How Not to Fail with AI: Lessons from Real-World AI Breakdowns
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.
Why AI Is Useless for Compliance
...and Why You Still Need It
A strategic view on the current limitations of AI in legal compliance — and how to use it wisely.
While AI systems are rapidly transforming business operations, their application in legal compliance remains deeply constrained. From the inability to interpret legal nuance to the lack of transparency in decision-making, today's AI is often misaligned with the core demands of regulatory work: accountability, context, and traceability.
This session offers a clear, expert-driven perspective on the state of AI in compliance — beyond the hype. Drawing on real-world cases and strategic frameworks, I outline the current technological boundaries, discuss key risks for organizations and regulators, and propose actionable approaches to using AI safely and effectively in complex legal environments.
Attendees will gain:
- A grounded understanding of what today’s AI can and cannot do in compliance
- Strategic considerations for implementing AI tools responsibly
- Case-based insights into where AI has failed — and where it adds value
- A framework for human-centered, auditable, and compliant AI integration
This talk is designed for: compliance leaders, general counsel, regulatory professionals, and executives seeking clarity — and confidence — in how AI can support (but not replace) human judgment in high-stakes domains.
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.
Zürich, Switzerland
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