A LinkedIn post from Clark University’s advancement team stopped me mid-scroll – not because “7 AI agents” is technically significant, but because it’s a new kind of organizational announcement. They describe software components the way you’d describe hires: clear roles, scopes, budgets, governance, and “human oversight”… plus an explicit boundary around relationship work.
It’s a glimpse of how automation can be socialized inside organizations.
Rolling out Generative AI in the workplace is more about people than platforms. Over the past year and half, I’ve helped a number of organisations launch GenAI initiatives – and nearly every one of them has surfaced questions, worries, or resistance from staff (with some common themes). These concerns are not signs of failure; they’re signs that people are paying attention. In this article, I want to share the most common concerns I’ve encountered – and how organisations can respond in ways that build trust, not tension.
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.
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.
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.
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
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.
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.
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.
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.
