In the fast-moving world of AI, organisations often struggle to decide which initiatives to prioritise. The opportunities seem endless, yet resources are limited, and different parts of the business may disagree on what matters most.
To cut through this, I use a framework I call The AI Territory. It adapts classic strategy tools (such as the Eisenhower Box and Value–Effort matrix) to the specific challenges of AI adoption.
As somebody who was a pioneer during the uptake of the Internet by the research ecosystem, I have been struck by how different I think the AI impact will be.
My feeling is that, compared with the Internet, the AI changes will be:
Faster
Driven by the big players
Take senior as well as junior jobs
There is widespread agreement amongst seasoned consultants that if an organisation is going to realise the benefits of AI fully, it needs to be adopted throughout the organisation. I firmly believe this to be true, and I am working with a number of organisations to help them achieve this. However, there are good ways and bad ways of going about this that I will illustrate with three case studies. Norges Bank, Shopify, and Klarna.
Here is an example of using a simple agent to make your life slightly easier. I will show the example using Copilot, but you could just as easily use ChatGPT, Gemini, Claude etc. You can use the same Instructions that I use for the Copilot.
Every so often, a new term captures the imagination of the tech world. “Agent” is one such word. From boardrooms to blogs, organisations are talking about how agents will transform work. Industry surveys suggest this is more than hype. KPMG’s Q2 2025 AI Quarterly Pulse Survey reports that agents are moving beyond experiments: a third of organisations already use AI agents in production[1] and nearly nine in ten leaders expect agents to necessitate fundamental organisational change[1]. Gartner goes further, predicting that by 2028, one-third of enterprise software applications will include agentic AI (up from less than 1% in 2024) and at least 15% of business decisions will be made autonomously via agents[2]. Deloitte anticipates that 25% of companies using generative AI will launch agentic pilots in 2025, rising to 50% by 2027[3]. When market leaders and consultancies align on the direction of travel, it is worth paying attention.
This post is not a discussion of tech; it is a comment on strategy. Your organisation’s decision about the sort of AI companies it will depend on could have enormous consequences. This is the story of a company that, like Icarus, flew high but fell to earth, and the many customers whose AI plans were disrupted.
Microsoft have just published a study that predicts the 40 jobs most likely to be disrupted by AI – and Market Research Analysts and Data Scientists are both on their list (see more at Fortune). The Microsoft study is based on analysing 200,000 conversations with Bing. Microsoft examined the questions AI was asked and the instances where it was successful.
ChatGPT’s new Agent Mode is another major leap forward in how AI is advancing, especially in the field of Agentic computing. It also represents a significantly increased risk level. In this post, I outline what Agent Mode is, the associated risks, and provide a simple example.
This is another post looking at the hot topic of Agents, building on the post from yesterday that created a Data Checking Agent. Today, I am looking at the combination of Instructions and Knowledge to create a more powerful Agent. Specifically, I will upload an Online Consumer Questionnaire House Style document and create a set of instructions to accompany it. For this example, I am going to use Copilot, but any LLM that allows agents to use Instructions and Knowledge would be suitable.
Agents are one of the key AI developments that is re-shaping the way we conduct business and processes. An agent is a software tool capable of tackling a task without user supervision. In this post, I will illustrate agents with an example that takes data (for example, from a quantitative survey).
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