Recently I had the opportunity to test the new Google AI-Video generator powered by Veo 3(.1). The demo was truley impressive and scary at the same time! And then we were able to test it ourselves …
(more…)Tag: GenAI
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o365 “control plane” for AI Agents coming
I just read an article on InfoWorld, that Microsoft rolls out Agent 365 ‘control plane’ for AI agents. The description sounds quite well what an enterprise needs in terms of compliance and security:
Microsoft said that Agent 365 unlocks five capabilities intended to make enterprise-scale AI possible:
- Registry, to view all agents in an organization, including agents with agent ID, agents registered by the user, and shadow agents.
- Access control, to bring agents under management and limit access only to needed resources.
- Visualization, to explore connections between agents, people, and data, and monitor agent performance.
- Interoperability, by equipping agents with applications and data to simplify human-agent workflows. They would be connected to Work IQ to provide context of work to onboard into business processes.
- Security, to protect agents from threats and vulnerabilities and remediate attacks that target agents.
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What I am Missing in Most GenAI Conversations
When people talk about Generative AI, the focus is usually on:
- Prompting
- LLMs
- Chatbots
- Proofs of Concept (POCs)
But what I am missing a lot in those conversations are:
- Try classic automation first
- Process integration: Can I add it into a process so that it fixes a problem?
- Data privacy
- Security
- Works council/employee representation (if applicable)
- Observability (not just the usual observability but also prompts and responses)
- Robust data pipelines (a.k.a ETL)
- Model Selection
- Model decay & re-evaluation (How often will you need to update? Currently about ~1x / year)
- Regulatory Compliance AI Act (EU)
- Costs (Tokens, maintenance, scaling — over years, not demo days)
- Scalability
- Latency & Performance:
- Testing (“it works in demo” ≠ “it works in production with real users”)
- Human-in-the-Loop (HITL):
- The other 95% of the app (The “boring” software stack around the AI)
- APIs (If it’s meant to automate, it needs to talk to other systems)
If there’s a user interface:
- Interface design & UX (no one uses what they can’t understand)
And the elephant in the room:
- How do you address the fear—justified or not—that you might be innovating people out of their jobs?
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Why Your Favorite AI Tool Might Be Isolating You
AI chat tools are a remarkable invention. Their rapid adoption speaks for itself: instant access to information, tailored feedback, and the ability to explore ideas or discuss one own thoughts or questions without friction – never before did we have such opportunities. But this power can come with a risk.
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How to guide your teams to use GenAI effectively while avoiding the pitfalls
We often see and hear a lot of hype and – unfortunately – enshittification when it comes to GenAI. Despite knowing that there are indeed some valuable use cases for the application of AI to solve some issues, I rarely read about a employee friendly adoption of GenAI.
The article AI for Network Managers: Leading Teams in the Age of Intelligent Automation was a quite refreshing read in that regard!
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Github Copilot is the Coach I always Wanted
We hear a lot about the bad side of AI Code Generation etc. But there are also quite some good sides that should not be ignored.
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Mistral – the European ChatGPT
OK, I admit that I’m sometimes too focused on technology to realise that not everyone is aware of all the different AI services out there. — This week, I mentioned Mistral.ai to a colleague and was surprised that he hadn’t heard of it. But, as I said, you can’t know everything.
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Those Who Forget the Dotcom Crash Are Doomed to Repeat It — with GenAI
A friend recently recommended Craig McCaskill’s article “The Bubble That Knows It’s a Bubble” with a disclaimer “it’s quite a read”. After forgetting it and coming back to it later I can say: it’s so worth reading it!
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Is the GenAI Revolution over already?
Just recently I saw the article (that probably most of us already noticed with a gentle smile), that AI coding tools can slow down seasoned developers by 19% (on InfoWorld). And just now I came to another article on Futurism, that “AI Use Is Now Declining at Large Companies”.
Heise.de picked up the article as well (Ernüchterung statt Euphorie: KI-Nutzung in den USA geht zurück) and outlines that the big hope of additional revenues has not come true (well … as if a technology would print money) and that the negative effects start to be more visible.
Yeah well — I’m wondering where exactly we might be in the Gartner Hype Cycle. I guess somewhere near the “Trough of disillusionment”? I don’t think that there is any doubt that GenAI has some really really beneficial use cases. But to me it did sound a lot like the time when the use of big data was totally overhyped.
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AI vs. the Legacy Black Box Codebase
If you want to see a cool example of how Generative AI can be used to tackle one of the nastiest problems in enterprise IT, you might want to invest some minutes in reading “From Black Box to Blueprint.”
What it’s about: Large organizations often have to rely on systems that are both business-critical and poorly understood due to lots of legacy. The article describes how Thoughtworks approached such a case: combining a multi-lens strategy (UI reconstruction, logic inference, change data capture, etc.) with AI-assisted “binary archaeology.”
I really like the idea as it’s not about replacing humans, not hype but solving a real problem: complex — legacy — codebases. Something no one of us likes to lay their hands on.
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