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:

Workflow automation: Eliminating time-consuming, mundane work like data transformation and entry that used to require hours or intern labor
Strategic support: Brainstorming, strategic planning, code planning, and design patterns
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:

"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"
"We're implementing AI to help with knowledge retention and dissemination so the experts' answers reach more people's hands"
Focus on removing bottlenecks and freeing up people's time

2. Clear Long-Term Goals with Measurable Steps

Define what you're trying to achieve with AI long-term
Break it down into concrete steps with measurable ROI
Ensure each step has an actual, achievable outcome
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:

Record calls
Transcribe videos and podcasts
Store blogs and written content
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 and Formalization

This is especially critical in manufacturing, where Sebastian frequently sees companies with ambitious AI project ideas but:

Processes exist only in someone's head
The CRM system is "Bob's phone with all his contacts"
Sales approaches vary completely by individual with no standardization
No formal documentation of workflows or decision trees

The three-tiered AI implementation process:

Tier 1 - Data: Identify what data is needed and ensure it's being captured

Tier 2 - Process: Document, create, and formalize processes (healthy for business regardless, supporting legacy, growth, and consistency)

Tier 3 - AI Integration: Once processes are well-defined (ideally as flowcharts or UML diagrams showing logic trees), integrate the actual LLM components

Critical insight: Don't just throw AI everywhere. Use traditional automation for deterministic tasks and AI only where you need qualitative assessment. Using AI for math or deterministic systems can lead to significant issues.

Solving the Knowledge Retention Crisis

One of the most popular AI applications Sebastian sees in manufacturing is knowledge retention and dissemination. When you have people who've been with the organization for 20, 30, or 40 years with critical knowledge in their heads—or in "Mikey's CRM on his phone"—losing them creates devastating gaps.

Knowledge Capture Strategies

The right approach depends on where the data currently exists:

Email archives: One manufacturing client is leveraging 20-25 years of email conversations, using AI to analyze and create a knowledge springboard

Data synthesis: Use AI with structured inputs to generate data, then have subject matter experts review, correct, and essentially train the system on what's accurate

Existing documentation: Documentation may already contain answers but be inaccessible or unsearchable—AI can make it queryable and useful

Structured interviews:

Formal interviews recorded on calls
Self-guided prompts where the expert records video or voice responses on their own schedule
Adapt the method to how each person thinks and works best—make the process as smooth as possible

The key mindset shift: AI isn't here to replace jobs—it's here to ensure that the work someone has been doing for 30 years can continue for the next 30 years, preserving institutional knowledge and expertise.

Addressing AI Fear and Resistance

Sebastian has studied resistance extensively and emphasizes that education is the most important antidote to fear.

The less people understand about how AI works and what it does, the more they glorify it and see it as a threat. The more they understand it, the more they realize how to control and use it as a tool—just like a calculator helps a scientist do faster math equations.

The Self-Fulfilling Prophecy

The dangerous pattern: People who refuse to learn AI out of fear are actually at the biggest risk of replacement. If you didn't want to use computers when they were being invented because you feared job loss, but then jobs required computer skills, you couldn't get hired—and the thing you feared came t...