Beyond the Hype: Making AI Work in Manufacturing with Sebastian Chedal

In this insightful and practical episode, Lisa Ryan welcomes Sebastian Chedal, founder of Fountain City and co-founder of TestFox.ai. Sebastian helps executives implement AI strategies that actually work, focusing on one critical question: How do you join the 20% of AI initiatives that succeed instead of the 80% that fail? With 60% of his work in manufacturing and industrial sectors, Sebastian brings a grounded, practical perspective where implementation matters more than hype.

A Journey Through Digital Transformation

Sebastian's journey began in 1998 when he started Fountain City in the Netherlands. Over more than two decades, his work has evolved through network security, website and app development, creative projects, and ultimately into digital transformation with a focus on AI implementation—predominantly in manufacturing.

As a self-described generalist at heart with diverse interests, Sebastian has founded five businesses total (two non-profits that didn't make it), giving him an entrepreneurial track record that includes both successes and failures. This real-world experience informs his practical, results-oriented approach to AI implementation. Fountain City has been the anchor and core of his professional life, adapting and evolving as technology has transformed over the past 26 years.

The Catalytic Moment: Why AI Is Different Now

Sebastian draws a powerful parallel between today's AI landscape and the mid-1990s internet era, when people would ask, "What's a website? I don't need a website. Why would I need a website?" People didn't understand the benefits, how it worked, or how much effort it would take to implement.

Like many technological innovations, AI has finally reached a threshold catalytic point where it becomes truly useful, effective, and mainstream. The real breakthrough with large language models (LLMs)—what most people refer to when discussing AI today—is the ability to create qualitative automations, not just deterministic ones.

The Fundamental Difference

Deterministic automation (traditional): If this number is above this number, do this thing—straightforward logic gates we've had for decades.

Qualitative automation (AI-powered): Integration of nuanced, context-dependent decisions into automation processes, opening entirely new categories of automation.

This capability works at multiple levels:

1. Workflow automation: Eliminating time-consuming, mundane work like data transformation and entry that used to require hours or intern labor
2. Strategic support: Brainstorming, strategic planning, code planning, and design patterns
3. Knowledge work: Tasks requiring judgment, context, and understanding rather than simple calculations

The last year in particular has brought proposals and curiosity from people wanting to understand what it actually takes to put these systems in place—but the hype also leads to overestimation of capabilities and underestimation of implementation effort.

Becoming AI-Ready: The Foundation for Success

Sebastian outlines several critical dimensions of AI readiness that organizations must address:

1. Management and Strategic Vision

The wrong approach: "We need to make sure 30% of our processes are run by AI by the end of the year."

This mandate isn't inspiring and doesn't give teams something meaningful to rally behind, even if it's the directive from stakeholders or management.

The right approach: Transform mandates into meaningful vision:

1. "We're bringing in AI to help you do less of the time-consuming work that distracts you from the real work you want to be doing"
2. "We're implementing AI to help with knowledge retention and dissemination so the experts' answers reach more people's hands"
3. Focus on removing bottlenecks and freeing up people's time

2. Clear Long-Term Goals with Measurable Steps

1. Define what you're trying to achieve with AI long-term
2. Break it down into concrete steps with measurable ROI
3. Ensure each step has an actual, achievable outcome
4. Keep the focus as narrow as possible—projects that try to do too many things with AI often fail

3. Data Infrastructure

Start before you even have a project: Capture as much data as possible everywhere you can:

1. Record calls
2. Transcribe videos and podcasts
3. Store blogs and written content
4. Organize existing documentation

Without good data, you end up with generic inputs for AI systems. With rich data, you can plug services directly into it and create genuinely useful, customized solutions. Data can be organized, synthesized, or analyzed—but you must have it first.

4. Process Documentation