How AI Solutions Are Created 🏗️

AI is making its way into your daily work. But how do AI tools actually arrive at a company? There are three paths — and understanding them helps you contribute to discussions and choose the right tools for your tasks. The Build vs. Buy vs. Partner Framework provides a clear overview.

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🎯 What You'll Learn

• The three paths to AI implementation and their trade-offs
• A clear cost estimate for each option
• The 5-question checklist before every AI decision
• Common mistakes to avoid

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The Build-Buy-Partner Framework 🔀

🔨 Build (In-house Development)

💡 Tip: Build only makes sense when AI becomes your core competency. For support processes, Buy is almost always the better choice.

🛒 Buy (Off-the-shelf Solution)

🤝 Partner (Consulting + Implementation)

📖 Definition: Total Cost of Ownership (TCO) includes not just acquisition costs but also ongoing costs for maintenance, updates, training, and operations over the entire usage period.

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The 5-Question Checklist ✅

Answer these questions before every AI decision:

1. Is the problem clearly defined? — No clear problem, no good solution. "We want to use AI" is not a problem. "We spend 40 hours/week on manual data entry" is.

2. Is there enough data? — Some AI solutions need proprietary data (custom models), others work immediately (LLM-based tools). Clarify what your approach requires.

3. Is ROI measurable? — Define concrete KPIs before starting: time saved in hours, error reduction in percent, cost savings in euros.

4. Who maintains the system? — AI is not set-and-forget. Who monitors quality? Who adjusts prompts? Who responds to errors?

5. Is compliance ensured? — EU AI Act, GDPR, industry-specific regulations. Check before starting, not after.

⚠️ Caution: Don't skip this checklist. Most failed AI projects don't fail because of technology — they fail due to unclear goals, missing data, or insufficient change management.

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Avoiding Common Mistakes 🚫

• ❌ Seeing AI as a silver bullet — AI solves specific problems, not all of them
• ❌ Starting too big — Better to begin with a quick win than to overhaul everything at once
• ❌ Starting without training — The best tools are useless if you don't know how to use them effectively
• ❌ Underestimating costs — Ongoing costs (API, maintenance, support) often exceed the initial investment
• ❌ Scaling without a pilot phase — Prove it first, then roll it out

🏢 Real-world example: A mechanical engineering company wanted to train its own LLM (Build) for technical documentation. Estimated cost: €400,000. After consulting (Partner), they discovered that a customized RAG system with Claude API (Buy) at €800/month met 90% of requirements.

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📋 Summary

• Build for core competencies and unique competitive advantages
• Buy for standard problems and quick starts (recommended for most SMEs)
• Partner for complex strategic projects with missing expertise
• Always run through the 5-question checklist before investing
• Start small, measure, and only scale with proven success

🎯 Exercise: Think of a concrete task in your area of work and evaluate it with the 5-question checklist. Which approach (Build/Buy/Partner) would fit best?

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Next lesson: Risks and Limitations of AI — what you absolutely need to know.