Data and research

It’s clear that AI has implications for the more sophisticated analysis of data sets, be they respondent-level surveys and panels or first-party data sets. This is arguably one of the areas in which the use of AI is already the most established, with for example forms of AI and machine learning already in use to detect anomalous credit card purchases or predict the likely performance of stock markets. The perceived strength of using AI to analyze data is its ability to cope with large data sets, offering both speed and scale. Data analysis can be an extremely laborious task, so AI assistance in identifying patterns and increasing the ‘speed to insights’ is seen as exciting. It’s potentially a tool to better understand why things are happening.

“I hope that artificial intelligence and the future in this area will be able to create insights to have more deep learnings about the knowledge or the habits of the consumer. Human audiences will be complemented by algorithmic ones and AI can help communicate effectively in this sophisticated ecosystem”

Marco Robbiati, OMG

In some cases, this is about more than speed as AI can play a role in pattern recognition that would not be visible to the (human) naked eye. This is one of the reasons that those involved in healthcare data believe that AI could be a game-changer in preventative healthcare.Our experts gave examples of areas where they had already used AI for data analysis. In some cases, this was described as ‘dabbling’ to see what is possible (with ‘mixed results’), while others are using it more regularly. Areas cited include helping to write questionnaires, analyzing open-ended questions in survey research, analyzing audience data to model predicted ratings, lead generation to identify where new customers might be found and service tools used in customer relations.

“Internally, we've built our own large language model which we use in conjunction with our clients to essentially help do all the research that underpins a lot of the strategy that we do because like many of you, we produce an enormous amount of content and have to do a lot of research on behalf of our clients. So it's been extremely valuable from that perspective.”

Craig MacDonald, McKinsey