Tag: #GenAI

From RFx Documents to RFx Pipelines: Procurement in the AI Agent Era

GenAI (and especially agents) will reduce the time and effort involved in drafting RFx documents and responses. That said, the biggest constraints in procurement usually sit elsewhere – the bottleneck tends to shift rather than disappear.

Working on my own RFx response and RFx assessment agents started me thinking, that instead of a sequence of documents and meetings (Requirements → RFx → Response → Evaluation → Negotiation → Contract management → Close), procurement starts to behave more like a pipeline, a continuous workflow where structured outputs flow from one stage to the next, and get reused across cycles.

GenAI Adoption Opinions Seem Polarized – Pragmatism Will Win

If you follow the GenAI conversation closely, it can feel like whiplash – like there is no agreement. One day it’s “AI is rewriting the economy,” the next it’s “AI is all hype and risk.” It feels like we’re now in a “Great Divergence” – not just differing opinions, but two parallel realities shaped by incentives and where you sit in the organization (something I have seen in my own work – some organisations are embracing AI and realizing the benefits while other are flatly, not interested).

GenAI for Small Business: Why the Adoption Journey Looks Different

All organisations seem to be dealing with the same question – “Where do we even start with GenAI?”

But the context behind that question is very different. In large organisations, there are budgets, teams, governance committees, structured programs and projects. In small businesses, there’s you, a small team, and the pressure of everyday operations.

This article looks at why GenAI adoption isn’t just a scaled-down version of enterprise AI adoption – and why small businesses need a different, more streamlined approach.

Could Microsoft’s Researcher Agent Signal the End of My Copilot Studio M365 Research Agents?

In the ever changing world of enterprise GenAI, the new Researcher Agent functionality in Microsoft 365 Copilot started me questioning whether I should retire my own Copilot Studio developed M365 Research Agent. So, I tested it and really only found one minor flaw (that I couldn’t select sub-folders from SharePoint sites).

RFP Automation and Local AI: What Microsoft’s New Agent Framework (MAF) Means for Business

I’ve been experimenting with Microsoft’s new Agent Framework (MAF) – but instead of connecting to cloud systems, I’ve been running it entirely offline on an Amazon EC2, private cloud, instance. My goal was to see whether this new, unified framework could function offline, be used with offline LLM’s and process PDFs (of RFPs in this case), extract questions, and even draft answers – all without leaving a secure, private environment.

It worked remarkably well. But what’s even more interesting is what this means for organizations on multiple fronts: the ability to run sophisticated Agent workflows locally, maintain full control of data, and start automating complex knowledge tasks such as RFP responses, compliance checks, or policy reviews.

Boston Crime Stats Revisited – Excel Labs Agent Mode Does The Job.

Earlier this year I compared Google Colab and Excel Copilot for analyzing Boston Crime Statistics (Google Colab vs Excel Copilot). This time I tried the same data set with Excel Labs Agent Mode and it was a completely different experience in Excel.

With the same dataset – 260,000 records of Boston crime incidents – and the difference is night and day. Where Copilot stumbled and failed, while Agent Mode delivered a complete analysis with explanations and recommendations, all while staying comfortably within Excel.

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.

GenAI Skills Gap: Why Businesses Can’t Wait for Education Institutions

As educational establishments seemingly wrestle with how, or if, Generative AI (GenAI) should be formally integrated into their curricula, the conversation seems to circle around a familiar tension: education versus training (I’d love to hear from people embedded in the education space for their opinion).

Should STEM degrees remain focused on deep technical foundations, or adapt to include the practical AI skills employers well expect? One promising middle ground is adding humanities courses that sharpen critical thinking, ethics, and communication – capabilities essential for using AI responsibly. The challenge is finding the right balance so educational establishments can preserve their mission to educate while preparing graduates for the realities of an AI-enabled workplace.

Unlock Your Legacy Code: The GenAI Shortcut for BAs & Devs

It happened to me a quite a few years ago when I resurrected some of my C code from the 90’s and brought it up to date and if you’re a Business Analyst or Developer, you’ve been there as well: trying to decipher a legacy system with outdated documentation and only a handful of power users to guide you. Traditionally, we’ve relied on user interviews and painstaking manual testing to map out functionality. Using LLM’s, combined with the more traditional methods can give us extra insight.

Stay Focused: You’re Solving a Business Problem, Not Chasing the Next AI Trend

Being immersed in the world of AI can feel like being caught in a whirlwind, every week brings a new model, a fresh feature, or a must-try tool and the pace is not slowing down – it’s easy to get swept up in it all. That’s why I always value conversations with businesses that bring things back to what really matters, solving real problems. GenAI isn’t just the latest shiny object, it’s a powerful tool to unlock capacity and drive real value, when focused on a business problem.