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?”
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date and if you’re a Business Analyst or Developer, you’ve been there as well: trying to decipher a legacy system with outdated documentation and only a handful of power users to guide you.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.
As artificial intelligence continues to reshape industries globally, Canada finds itself at a critical point in AI regulation. With the recent appointment of Evan Solomon as Canada’s first-ever Minister of Artificial Intelligence and Digital Innovation, we are seem to be charting a new course that prioritises economic growth while maintaining responsible oversight. Where does this leave organisations that are eager for clarity and direction in an increasingly complex global and asymmetrical regulatory environment?
Buying a Generative AI solution, whether it is a discrete or embedded GenAI solution, isn’t like buying a CRM or ERP system. It’s a whole new ballgame, one where you can’t always see the rules, and the players (the models) can sometimes make up their own. GenAI procurement requires a fresh playbook. Let’s break down what’s changing, why it matters, and how you can stay ahead.
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.
Agentic AI and LLM tools enable remarkable capabilities, from automating workflows to generating content and insights. As I spend more time in Langflow and really start to appreciate the power of the systems that can be developed I started wondering about how they could be monitored, how do we implement Observability – then a note about Datadog LLM Observability came across my feed and got me thinking that this is worth looking at more deeply.
A week or two ago I came across an article from Business Standard that summarised a Gartner report suggesting that over 30% of GenAI projects won’t survive beyond proof of concept (PoC) and will be dropped by the end of 2025. Having run a large project portfolio I’m always interested in stats like this so I decided to pick at this a little and see whether this is indeed an issue, or just a headline.
The upcoming rise of Agentic AI – AI that operates more like an independent worker than a traditional tool will fundamentally transform the landscape of work. This evolution prompts an important question: what role do unions play in a world where AI acts as a virtual employee?
The world of AI is evolving rapidly, moving from passive tools to dynamic, “agentic” AI – technology that can operate autonomously, making decisions, interacting with employees, and handling tasks like a true virtual team member. While this shift brings exciting opportunities for efficiency it also brings new challenges for oversight, ethics, and integration into workplace culture. HR stands at the heart of this, ensuring that these “virtual employees” align with company values, policies, and workforce goals.
Am I living in a GenAI echo chamber? While my LinkedIn feed overflows with the latest AI breakthroughs and ‘must-try’ features, my experience in the trenches tells a different story. As a volunteer leading GenAI projects, delivering prompt engineering training and talking about GenAI in the non-profit sector, I’ve witnessed a gulf between the breathless pace of AI innovation and how most people actually use these tools day-to-day. (I have to say that I have not noticed resistance, concern – yes, but not resistance and in all cases I see the ‘wow’ moment happen when people realise the possibilities and practical applications.)
