Responding to RFPs used to feel like running a marathon (it’s just as painful as being on the RFP assessment team) – days of effort, multiple people, and thousands in costs. Recently, I asked myself: Could AI make this easier? What started as an experiment (we are always experimenting with the edge of this technology) with Microsoft’s Agent Framework on a local setup evolved into a multi-agent orchestration system that drafts RFP responses in under 15 minutes.
Category: AI In Business
GenAI Is a Powerful Hammer – Not Everything is a Nail
Generative AI is everywhere and it’s tempting to reach for it whenever something feels messy, slow, or frustrating.
But when a tool is this powerful – and this non-deterministic – the real question isn’t “Can we use GenAI?” It’s “Should we?”
Used well, GenAI boosts productivity. Used indiscriminately, it quietly introduces risk.
This is where GenAI stops being just a productivity tool and starts becoming a governance challenge.
GenAI Workflows – Sometimes Friction is Good (… and systems are fragile)
One of the challenges about GenAI adoption is simply getting started: picking tools, running pilots, training staff, and rolling out a plan. Another major challenge is where and how GenAI gets introduced into already fragile, tightly coupled organisational systems.
I was watching a Veritasium video (The Strange Math That Predicts (Almost) Anything) about complex systems and the moment they reach a “critical state.” A forest can look calm and stable right up until a single spark turns it into a massive wildfire. Not because the spark was special but because the system was already primed for runaway behaviour.
In a general sense, many organisations today look just like that forest, in a critical state.
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.
From Cells to Chat – Excel Agent vs. M365 Analyst Agent (Same Boston Crime Stats, Two Very Different Conversations)
Last week I explored the Boston Crime Statistics dataset (~260,000 rows) using Excel Agent Mode, which lives within Excel. This week I revisited the same dataset with the same question using Microsoft’s M365 Analyst Agent – it is a completely different experience.
Both tools analyze data and generate insights, but they differ in how you interact with them and how they talk, and show their work. One keeps you grounded in the familiar grid of Excel; the other lifts you into a conversational workspace (real Conversational Data Analytics) that feels more like working with a colleague than a formula bar.
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.
From Formulas to Conversations: How Excel’s Agent Mode Will Redefine Data Analytics
It used to be great to find others that ‘spoke Excel’ – understood the intricacies of the various lookup formulas or when to use index…match. I have spent some time working with Excel Labs Agent Mode and the ‘old’ Excel world is about to change dramatically.
Excel Agent Mode has arrived as part of Microsoft’s Frontier preview program, and after testing it myself to create survey data for my training courses, I can see this isn’t just another incremental update (spreadsheet link included in Further Reading section below). It might make those ribbon menus obsolete.
2025 Was Supposed to Be the Year of Agents – Is 2026 the Turning Point?
Back in 2024 (it seems so long ago now) I wrote about Agents (links below) and cautioned about how early we were in their evolution. Now, almost a year later we seem to be in a completely different place – brought back to my mind to revisit by the recent announcements from:
– Langflow – releasing v1.6
– Microsoft – consolidating AutoGen and Semantic Kernel into the Microsoft Agent Framework
– OpenAI – releasing AgentKit