Articles on AI in operations

Perspectives on responsible AI implementation, governance, and the human dimension of technological change.

Modern office interior with workstations — symbolizing AI-driven transition in the labor market
AI and Labor Market

2026-03-05 · Emil Kanneworff

Will AI steal our jobs? Not in the way many imagine

AI replaces specific tasks, but also creates new roles in operations, security, verification, and advisory work. Here is why the likely future is transition, not mass unemployment.

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Developer in front of code on screen in an office setting — symbolizing operation and maintenance of AI automation
AI in Production

2026-03-05 · Emil Kanneworff

The hidden drawbacks of automating with AI

AI makes automation faster to build, but not necessarily cheaper to own. Here are the hidden drawbacks of AI automation — and how to avoid operational debt.

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European cityscape with modern architecture — symbolizing digital sovereignty and European technology
AI Strategy

2026-03-05 · Emil Kanneworff

European AI alternatives: Why digital sovereignty is not just politics — it is architecture

ChatGPT dominates the conversation. But European alternatives like Mistral, Proton Lumo, and Public AI offer something the American giants cannot: data sovereignty, transparency, and control. Here is what companies should know.

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Typewriter with 'AI Ethics' text — symbolizing ethical considerations in AI implementation
Responsible AI

2026-03-03 · Emil Kanneworff

5 ethical questions you should ask before implementing AI in your organization

AI can significantly improve efficiency and quality. But without ethical considerations, you risk resistance, distrust, and lower adoption. Here are five questions that should be asked before — not after — implementation.

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Person using AI technology on laptop — illustrating AI agents in practice
AI Tools

2025-12-16 · Emil Kanneworff

AI agents in practice: From personal assistant to multi-agent system

The market for AI agent platforms is exploding. But which tools make sense for your organization — and in what order? We walk through the five levels of AI agent maturity.

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Code on computer screen — illustrating the choice between no-code and code-based AI agents
AI Strategy

2025-11-18 · Emil Kanneworff

No-code or code? A decision framework for AI agents in regulated industries

Should you build AI agents with no-code platforms or custom code? For companies with compliance requirements, the answer is rarely black and white. Here is a framework based on complexity, control, and competence.

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Data center with servers and monitoring screen — symbolizing control over AI agents in production
AI in Production

2026-01-20 · Emil Kanneworff

AI agents are not apps — they are digital workers. And that changes everything.

Many companies treat AI agents as software products. But in practice, they behave like employees: they need clear mandates, supervision, and a kill switch. Here is what we have learned.

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Vintage key on keyboard — symbolizing security and access control for AI agents
Security

2026-02-10 · Emil Kanneworff

AI agents and system access: Three security risks — and how we solve them

When an AI agent gets access to your systems, three critical risks emerge. We walk through them and show how Vertex Solutions builds agents with security as the foundation.

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HTML code on computer screen — symbolizing digital presence and web development
AI Strategy

2026-03-04 · Emil Kanneworff

Make your digital presence AI-ready — before your competitors do

Google's WebMCP protocol signals a new era: websites must be built for AI agents, not just humans. Here is what it means for businesses — and how to get started.

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Lock and chain on computer — symbolizing security and vendor risk in the AI chain
Security

2026-02-24 · Emil Kanneworff

Your AI vendor is a security risk: What the Pentagon case teaches businesses

Pentagon threatens Anthropic for refusing to remove safety restrictions. OpenClaw agents are tricked into downloading malware. For businesses under NIS2, the message is clear: your AI vendor is part of your attack surface.

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Team of professionals in meeting in modern office — symbolizing collaboration and change management in AI adoption
AI Implementation

2026-01-06 · Emil Kanneworff

AI adoption does not fail on technology — it fails on people

95% of Danish business leaders plan to use AI. Half admit low competence levels. The problem is rarely the technology — it is change management, culture, and the human factor.

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Data center corridor with server racks — symbolizing data infrastructure and AI readiness
Data & AI

2025-12-02 · Emil Kanneworff

Your AI is only as good as your data — and your context

AI rarely fails on the model. It fails on data that is messy, unstructured, and context-free. The difference between AI that works and AI that hallucinates lies in the work that happens before the model even sees your data.

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Chess pieces balanced on a scale — symbolizing the balance of AI strengths and weaknesses
AI Strategy

2025-11-04 · Emil Kanneworff

What AI is good at — and where it fails: An honest guide for businesses

AI is neither magic nor hype. It is extraordinarily good at certain tasks and surprisingly bad at others. Here is an honest overview — without buzzwords — so you can invest wisely.

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Modern office with screens and technology — symbolizing AI agents in enterprise operations
AI in Production

2026-03-05 · Emil Kanneworff

AI agents for enterprises: What they can do, where they fail — and what actually works in production

AI agents promise automation of customer service, sales, and legal work. But most companies quickly discover that off-the-shelf solutions cannot do what they need. Here is what an AI agent can realistically do — and what it takes to put one into production.

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FAQ about insights

Are these articles generic or operations-based?

The content is written from practical implementation experience with governance and operations in focus.

Can we use these articles for internal decision memos?

Yes. The structure is designed for decision support with concrete reasoning and examples.

Where should we start if we are early in AI adoption?

Start with strategy, data readiness, and risk articles, then use cases and solution pages to scope execution.