Robot helping growth

AI making a difference to real people

I think too much of the discussion about AI is focused on what it might deliver in the future. I believe it is important that we look at what has already been achieved to get a sense of where we are and where it might go soon. This post presents a set of examples from around the world and across various fields.

The Territory Map

The AI Territory – A Tool for Strategic Planning

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.

Good, bad and ugly aspects of mandating universal adoption of AI in organisations.

AI Needs to be Compulsory

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.

Image conveying agents

The Rise of Agents – A Key Trend

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.

the 40 jobs most at risk

Microsoft List the 40 Jobs Most at Risk of AI Disruption

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 Agent Mode is another Leap Forward – with risks

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.

Questionnaire Checking Agent

Creating an Agent to Check or Create Questionnaires

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.