Predictions and Prescriptions for 2024

2024Ray Poynter, 3 January 2024

2024 will be an exciting ride for the world of insights and research. But before I jump into what I think will happen, let’s address the caveats. Pandemics, wars, economic crashes, political instability, and climate catastrophes could all derail the probable world. In this post, I set out my predictions for 2024 and my prescriptions for some good actions to take, assuming that no black swan events happen.


a) AI – positioning and land grabs
2023 was the year when interest in AI caught fire; in 24, we will see more impact from AI and more reaction to it. However, there will be more heat than light. We will see new products, services, and some early adopters, but the business scale will be modest. Regulators, agencies, and client insights departments will focus on initiating plans and rethinking their ways of doing business.

b) Synthetic Data – a battle for the heart of insights
So far, the emergence of synthetic data is the most disruptive aspect of AI. By synthetic data, I am referring to creating personas or synthetic participants from AI and using them to answer qualitative and/or quantitative questions. The downside is that synthetic data may be unreliable, but the upside is that it could be much faster and cheaper. I expect synthetic data to be quite widely adopted in 2024. We will see papers showing that it works and others demonstrating that it does not. My investigations lead me to suspect that it will be possible to identify when synthetic might be useful and when it would be unsound.

c) Qual at Scale – changing the paradigm
One key use of AI in 2024 will be analysing qual data (for example, text) at scale. By qual at scale, I am not referring to using AI to convert qual information into codes and numbers. I am talking about using AI to interpret meaning directly from the text and images, in a pale but interesting approximation to what humans do, opening the door to qual at scale.

d) Data quality – our last, best chance to fix it
One of the most exciting recent developments has been the Global Data Quality initiative, backed (by ESOMAR, MRS, IA, TRS and others). The upside is that this initiative will hopefully address the growing data quality concerns (for example, the prevalence of fraud). However, the downside is that time is running out to fix the data problem. The rise of alternative routes to insight (such as big data) and the possibility of synthetic data are all waiting to replace online panel surveys if we can’t clean up our act.

e) Smart DIY/Self-serve Platforms – democratizing research
There has been an enormous growth in the number and power of platforms to aid clients and non-researchers in conducting research. According to the Buyers and Users study, about 50% of all research projects are now conducted in-house. In 2024 we are going to see AI used to make these platforms smarter – with the aim of making them easier to use and making them safer for non-researchers to use. This will increase the use of evidence-based decision-making and the number of projects conducted internally by clients.

f) More training – upskilling for a new world
Recruitment of good talent has been a problem for the insights industry for a few years. The growth in AI is going to de-skill and remove some tasks and jobs, but it is going to increase the demand for storytellers, consultants, analysts, big thinkers, client success managers and others. AI will increase the use of evidence-based decision-making, and it will take care of the mundane. However, this automation of the simple extends the need for people to think about things like what needs researching, when it needs researching, what depth of answer is needed, and what the total picture is. I predict a significant increase in the demand for training, especially in the skills that are complementary to the rise in AI (and also in the use of AI).

My prescriptions
So, what do I recommend for agencies, organisations, clients, and individual insight professionals? Here are five key things to do in 2024.

1) Learn about AI
At the very least, get a ChatGPT account, learn about Prompt Engineering, and get a sense of what it can and can’t do. But remember, this is a fast-moving field. If you find it can’t do X, that may not be true in 12 months from now.

2) Get involved in evaluating/implementing AI
If your organisation is trying AI or bringing in new automation, try to get on the team. The very worst option is to be the person looking after the established option whilst others are looking at new alternatives.

3) Invest in your own training
A good employer will give you some opportunities, but to really move forward, you need to add more in your own time with your own resources. Join book clubs, attend courses, write articles and papers, watch videos, do online training etc.

4) Enhance your qualitative skills
Increasingly, research is going to be more about people and less about techniques (the AI will help with the techniques). Understanding people is essentially a qualitative skill. By qualitative skills, I include conversation analysis, semiotics, and ethnography as well as more conventional qualitative skills.

5) Develop your brand
Whether you are 21 or 61, there is a good case for developing your brand, whether you are a boutique agency or an international brand, there is a good case for developing your brand. Decide what you want to be known for, decide what you want to focus on, and then make it known to the world. Currently, the best platform for developing your personal brand is LinkedIn – make sure you utilise it.

Introduction to Story FindingWant to start accelerating your
story-finding skills?

Sign up for my Introduction to Story Finding course.

The course will take place online, 30 January 10am to midday New York time (3pm to 5pm UK time).