Category: Enterprise AI

Unlock the Power of Small: Fine-Tuning Gemma 3 270M (A Business Perspective)

We’re all familiar with the massive, powerful language models that run on vast server farms. What if the next big breakthrough in AI isn’t about being bigger, but smaller?

Over the weekend I fine-tuned Gemma 3 (270M) end-to-end—LoRA → merge → GGUF → Ollama and ran it locally. It wasn’t perfect (tbh, it was more of a learning exerciser to understand the process), but it was fast, inexpensive, and genuinely useful for narrow, domain-specific tasks. Here’s what tiny models are, why they matter to business, and how to get started without boiling the ocean.

Tiny Language Models, Big Impact: Why Google’s Gemma3 270M Matters for Business

Big AI models often steal the spotlight, but sometimes the smartest move is going smaller. Google’s new Gemma3 270M shows just how powerful a compact, efficient language model can be – especially when it runs offline, on low-power devices, or in remote locations. For businesses, this isn’t just a technical breakthrough; it’s a new frontier of opportunity.

The Ground Keeps Shifting: Why GenAI Feels So Unsettling Right Now

If you’ve been using GenAI tools like Microsoft Copilot or ChatGPT in your day-to-day work, you’ve probably had this experience: something that used to work, like a prompt you carefully refined, is suddenly behaving differently. Maybe it’s not as helpful. Maybe it’s giving unexpected results (that’s what happened to me this week). Maybe it just… stopped working entirely.

Fine-Tuning for the Rest of Us (sort of): How Microsoft Copilot Studio Just Made AI Customisation a Whole Lot Easier

Microsoft’s recent Build 2025 announcements have brought some massive updates to the whole GenAI Copilot platform. One of the most exciting features for me (and perhaps a revealing feature in terms of Microsoft’s GenAI strategy) is the introduction of Microsoft 365 Copilot Tuning. This new feature is set to revolutionise how organisations tailor AI to their specific needs, moving powerful model fine-tuning capabilities from the often complex of data scientists and technical staff to the more accessible world of business power users.

Unlocking the Power of Generative AI: Why Your Organisation Needs a Maturity and Readiness Assessment

Generative AI (GenAI) is revolutionising industries by automating content creation, enhancing decision-making, and sparking innovation. But before jumping in, it’s essential to pause and do the groundwork. In today’s fast-moving, hype-driven tech landscape, that can feel a bit old-school, but understanding your organisation’s readiness isn’t just prudent, it’s foundational. A GenAI Maturity and Readiness Assessment is a crucial step to ensure that the excitement of innovation is backed by the stability of preparation.

Problems with Business Continuity Planning? GenAI can help…

Business Continuity (BC) planning is all about staying resilient when things go sideways. But in an age of continuous change and increasingly complex systems, traditional planning approaches can fall short. That’s where Generative AI (GenAI) can help – not as a replacement, but as an intelligent partner helping us think faster, deeper, and more creatively.

Why You Need a Portfolio of GenAI Projects (With Business Cases to Match!)

Generative AI (GenAI) has the ability to transform industries, unlock efficiencies, and drive innovation – no doubt. Are organisations struggling to move beyond scattered experiments or uncoordinated deployments? The key is to treat GenAI projects like any other strategic investment – by building a portfolio and backing each initiative with a solid business case, while appreciating the difference that GenAI brings and the rapid evolution of the GenAI market.

The Rise of Shadow GenAI and the Risk of Technical Debt

I have written previously about technical debt and GenAI choices and when you add on Shadow GenAI the situation becomes even more complex. The accessibility of Generative AI (GenAI) tools means that everyone can innovate like never before. However, this surge in un-monitored GenAI usage, often referred to as “Shadow AI”, combined with GenAI only now starting to come down from the ‘Peak of Inflated Expectations’ can lead to significant challenges, including the accumulation of technical debt within organisations.

Generative AI and Enterprise Architecture: The GenAI Impact

In preparing last weeks article I came across a series of blog posts from Ardoq (thanks to Ed Granger) that deserves more attention (catch it here). There are quite a few posts (maybe too many) on how GenAI and Agentic AI can be used in organisations but not much on how we conceptualise and plan for this from a Enterprise Architecture (EA) perspective. Ardoq’s insightful blog series delves into GenAI’s impact on EA and more – it’s really worth a read.