AI Transformation

My Thoughts on AI Transformation

Sebastian Stöhr

Turning Enterprise AI from pilots into practice | Sr. Advisor AI Transformation

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  Sebastian Stöhr

  Turning Enterprise AI from pilots into practice | Sr. Advisor AI Transformation

  2mo ·

  Most enterprise AI initiatives are stuck in a loop. One impressive PoC after another gets built, applauded, and then quietly forgotten.

  Why? Because we confuse technical feasibility with business readiness.

  Building a prompt that works once in a controlled showcase is easy. Building a reliable workflow that survives the daily mess of complex edge cases, compliance constraints, and time pressure requires real product ownership.

  Before you fund or start your next AI prototype, run it through this 5-Question Gatekeeper:

  • The Task: What specific, painful task is this solving, and how are users doing it today?
  • The User: Who exactly is the target user, and what established habit must they change to adopt this tool?
  • The Quality: What defines “good enough” for the output, and what is the exact tolerable error rate?
  • The Workflow: What is the human’s exact role? Do they review, edit, approve, or just monitor?
  • The Lifecycle: Who owns the maintenance of prompts, context data, and automated tests three months after the demo is over?

  If nobody takes ownership of these five questions before development begins, you are building a demo, not a product.

  Stop optimizing for demo applause. Start optimizing for users who return to the tool week after week.

  #EnterpriseAI#AIStrategy#AITransformation

  

  sebastianstoehr.de

  Escape the AI PoC Trap

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  Sebastian Stöhr

  Turning Enterprise AI from pilots into practice | Sr. Advisor AI Transformation

  3mo ·

  Ever feel like modifying AI prompts is just an endless cycle of fixing one thing and breaking another?

  You tweak a sentence to improve a specific use case, only to find that you’ve completely broken the use case you added last week. In the AI world, prompts, system instructions, and agent workflows define software behavior — and behavior can regress. Tracking these side effects with manual reviews alone is virtually impossible.

  If you want to transition from experimenting with AI to using it professionally, reliability is essential.

  🎯 The solution? We need to shift our mindset and start treating our AI setups like traditional software.

  In this article, I share the concept behind building an automated AI test suite and introduce tools like #promptfoo. It’s the first step to relying more on facts than your gut feeling alone.

  How are you currently managing and testing your AI workflows?

  #AIStrategy#EnterpriseAI

  

  sebastianstoehr.de

  Your AI setup needs tests, not just intuition

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  Sebastian Stöhr

  Turning Enterprise AI from pilots into practice | Sr. Advisor AI Transformation

  4mo ·

  We’ve all been there: Information buried in a chat thread from 2022, an outdated Confluence page, and three different JIRA tickets.

  I’m currently working on a pipeline to turn this “raw data swamp” into a Domain-Specific Knowledge Graph. Here is the 2-step approach:

  Step 1: The Archaeologist (Extraction)

  Using MCP to connect to our systems, this skill:

  • Merges historical data into a “current truth” markdown file.
  • Maintains a changelog of how a topic evolved.
  • Links every claim back to the original source.

  Step 2: The Librarian (Interlinking)

  A folder of files is just a library without an index. The Librarian:

  • Identifies relationships (e.g., “Product A is an add-on to Product B”).
  • Creates a navigable “Markdown Wiki” for AI.
  • Enables complex reasoning (e.g., explaining sales trends based on product dependencies).

  How are you ensuring your AI has the “current truth” and not just some (maybe outdated) data?

  

  sebastianstoehr.de

  How to Turn Scattered Data into AI Intelligence

• I post on AI transformation and engineering leadership a few times a week. If this is your kind of thing, let's connect over there.

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  Sebastian Stöhr

  Turning Enterprise AI from pilots into practice | Sr. Advisor AI Transformation

  4mo ·

  24 Hours. One Production-Ready iOS App. 🚀

  By leveraging modern tools, I integrated AI meal scanning, Apple Watch support, and Home Screen widgets into a diet tracker in record time.

  This experiment proves that the barrier between “idea” and “execution” has vanished. We need to stop optimizing old workflows and start reinventing how product teams actually work together.

  

  sebastianstoehr.de

  Building an iOS app in just a day