GenAI is an accelerant. It speeds up decisions, output creation, and information flow, often without strengthening the system underneath. And many organisations are already running “hot”: highly optimised, tightly interconnected, little slack, and dependent on tacit knowledge.
So the real question isn’t just “How much can we automate?” It’s also “Where does speed strengthen the system – and where does speed increase fragility?”
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.
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.
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.
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.
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.
I’ve spent the past few months experimenting and researching here and there with tiny and small language models, e.g. running log analysis on edge devices, processing audio in remote locations where connectivity is spotty, power is low and the environment harsh. They’re fast, efficient, and honestly? Pretty fun to work with and research. But lately, I’ve caught myself asking: Am I actually solving a problem here – or just doing something because it’s technically interesting? If you’re working with AI in any capacity, you’ve probably felt this tension too (and to be honest, sometimes because something is technically interesting, that can be a good enough reason for personal research).
Three critical components of the AI ecosystem are accelerating in different directions with no gravity to bind them. Technology development races ahead exponentially, legislation struggles to keep pace, and business adoption moves cautiously behind them, creating an unstable system that threatens our competitive positions.
Generative AI is becoming a staple in the modern workplace – but something’s not clicking. Despite the rollout of training programs and hands-on tools, it seems that some organisations still struggle to see meaningful impact. Why? Because knowing how to use GenAI isn’t the same as knowing how to work with it. I have been delivering training on GenAI for over a year now and the feature that stands out in the true adopters has been the Exploratory Mindset – it’s the mindset that really makes the difference.
You didn’t sign up for an AI platform but suddenly, your HR tool summarises resumes. Your file-sharing service suggests email replies and your CRM is auto-generating forecasts.
Welcome to the new world of silent AI rollouts, where vendors quietly add GenAI features to your software stack, often without clear notice, control, or consent. It’s not just a tech issue it’s a business, legal, and risk management issue.
