AI Insights & News

A deep dive into GenAI spend trap #5, exploring how organisations quietly accumulate overlapping AI tools across departments, pay for licences nobody uses, and let renewals roll over unchallenged, plus practical strategies for auditing, consolidating, and controlling AI tool spend.

A deep dive into GenAI spend trap #4, exploring how 'almost right' AI output creates expensive human rework cycles, erodes trust, and quietly becomes the largest hidden cost in your GenAI programme, plus practical strategies for measuring and fixing it.

A deep dive into GenAI spend trap #3, exploring how organisations waste time and money running dozens of pilots that go nowhere, and practical strategies for rationalising portfolios and building systems that can actually scale.

A deep dive into GenAI spend trap #2, exploring how organisations burn money using large language models for tasks that don't need them, and practical strategies for building hybrid pipelines that are faster, cheaper, and more reliable.

A deep dive into GenAI spend trap #1, exploring how defaulting to premium models can increase costs by up to 80% and practical strategies for implementing intelligent model routing to reduce LLM costs.

In 2026, the question is no longer whether AI can help your organisation, but how much work you are prepared to delegate to it. Discover why Agentic AI represents an operating model shift and what executives need to know to move from experimentation to real delegation of work.

GenAI spend has a habit of creeping up quietly before showing up as a serious line item. Learn about six common spend traps and practical ways to address them without degrading output quality or slowing teams down.

As Christmas 2025 approaches, a reflection on how AI matured this year - moving from flashy capabilities to calm, well-mannered systems that know when to talk, what to retrieve, and when to stop. The best AI doesn't replace people; it supports better thinking.

How InoGen AI built a high-quality Dutch-language RAG system using open-source embeddings and Llama 3.1, enhanced with feedback, session memory, and optimized retrieval to deliver accurate, transparent, and user-focused AI responses.