IT System Maintenance in the age of AI

Introduction - The Maintenance Trap in IT

You don’t need to be in the IT industry for long to have witnessed this firsthand. Even non-IT users do. Those systems that haven’t been maintained for ages. From a user perspective, you “just” see a maybe aged user interface, non-evolving features, and old bugs or quirks become accepted by, possibly generations of, users. From a user perspective, you should have an eye on this. Often, this not only means that the system becomes cumbersome to use, but it also means that there are possibly no security updates being made. We will see just in a bit that it might even not be possible anymore. So think about which kind of data you want to put in there.

🎯 “How do we pick the RIGHT AI agent use case?

This is the question I hear most from customers exploring agentic AI.

Here’s the mechanism I run through together with the customer:

The 4-Quadrant Evaluation

When a customer brings me 5-10 agent ideas, we structure each one across four dimensions:

📊 Business Value & Strategic Fit → What pain does it solve? For whom? How often? → Can we quantify the impact? (Revenue, cost, time, quality) → Which KPI moves if this works for 6 months?

𝗣𝗲𝗿𝗳𝗲𝗰𝘁 𝘁𝗶𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝟮𝟬𝟮𝟲 𝗰𝗮𝗿𝗲𝗲𝗿 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴! 🎯

I just dived deep into the book “Solutions Architect Interview: Winning strategies and effective tactics for interview success” by Saurabh Shrivastava,  Neelanjali Srivastav, Dhiraj Thakur and Sanjeet Sahay and as someone who has led 200+ Solutions Architect interviews, I can say this is a genuinely valuable resource.

🎯 𝗖𝗼𝗿𝗲 𝗙𝗼𝗰𝘂𝘀: 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝗔 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄

This book is laser-focused on helping you ace Solutions Architect interviews—but it goes beyond typical interview prep by also explaining:

🤔 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆? Chasing single point solutions or exploring system-level AI solutions?

Yesterday I had the pleasure of listening to a great presentation by Chris Nosko. Among other important aspects, Chris touched on how AI technology represents a fundamental shift that requires system-level solutions.

Let’s dive into this, as it turns out to be key to defining your own AI strategy. 🧵 🎯 Single Point vs. System Level Solutions in Technology Adoption

🎯 𝗗𝗲𝗲𝗽 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗖𝗲𝗹𝗹-𝗕𝗮𝘀𝗲𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀

Last week I attended an outstanding presentation by my colleague Robert Himmelmann on “Cell-based Architectures” – one of the most insightful deep-dives I’ve experienced on advanced resilience patterns.

Rob’s talk abstract perfectly captured the essence: “Cell-based architectures are an advanced resilience pattern. Cells create a bulkhead pattern, limiting the impact of potential failures and using linear scale out at all layers of the architecture. In this presentation you will learn about the fundamentals of cell-based architectures and dive deep on one example. We will finish by discussing advanced patterns to cover diverse use cases.”

If you don’t have the data available, implementing an AI use case becomes a data gathering death march, often crossing organizational boundaries. Instead of spending 80% of the project time on building the use case, this time is spent on building the prerequisites. Overall project times increase and projects might remain unfinished.

If you don’t have the data available, imagination of what can be done can fall short. Good use cases might remain undiscovered. Instead of people experimenting with data to find possible use cases, this becomes a theoretical exercise.