Have you ever wondered how an AI system, like a search engine or a chatbot, understands that the sentences “A dog chases the ball” and “A canine pursues the sphere” mean approximately the same thing? Of course, it’s not magic. It’s advanced mathematics, called semantic similarity, and the powerful concept of Sentence Embeddings.
At most conferences and events, we are seeing a growing number of examples of Synthetic Data, in its many forms and versions, being used for real projects. However, there still seems to be a body of thought that promotes unscientific criticism of this approach. This sort of criticism is likely to hold some people back from realizing the potential benefits and could cause real commercial damage to those who follow it blindly.
The 2025 ESOMAR Congress in Prague was dominated by one theme above all others:, and that theme was artificial intelligence. From the exhibition floor to the presentation sessions and corridor conversations, AI was the connective tissue running through the programme. What had been emerging in previous years became fully mainstream in 2025. Far from being a specialist topic, AI now underpins the methodologies, business models and strategic debates across the market research and insights industry.
For me the jury is out on the discussion about what synthetic data might be able to achieve in replicating human decisions and behaviour. I am worried by some of the overclaims and appalled by the number of people who reject the notions as being self-evidently wrong (without feeling the need to support their rejection with data). In this post, I want to explore one line of thinking relating to the broader debate, namely, what is the human brain doing and how might that help or hinder the creation of predictive models (AKA synthetic data).
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.
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.
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