The advent of generative AI and openly accessible LLMs since the latter half of 2022 certainly feels revolutionary. In an increasing number of use cases, it looks, sounds and acts revolutionary too and, as an industry, we are all embracing and celebrating each and every win that positively impacts our ways of working.

We should certainly recognise and credit those taking the first steps, whether it be the digitalisation of insights or the enterprise-wide embedding of AI platforms. We are all going to learn so much from them and their experiences.

In the chatter, discussion and debate currently, the insights industry is once again displaying the behaviours that make us a stand-out amongst all commercial professional functions. Simply, that the predominant and prevailing driver is one of seeking to collaborate, to share and find solutions that work well for us all. For sure, there is competitive advantage to be gained in places but prescient amongst all debate is a desire to grow trust, accuracy and reliability in what we all do and deliver to our clients, our own businesses and, most importantly of all, put respondents, consumers and the general public at the heart of decision-making.

But if there is a potential risk worth thinking about, it is that a considerable amount of the ‘insights’ discussion becomes centred on increasing the speed and reducing the cost of generating knowledge and insight.

In conversations with various peers over the last year, I have heard stories about marketing directors believing they don’t need insights anymore because “ChatGPT has all the answers”. Another peer shared the number of times she’s been asked by stakeholders, “can’t you just AI it?”.

One observation is that the first discussion around ‘insights AI’ is whether it will make everything faster and cheaper. And perhaps the risk in evidence is to be too willing to make only those factors our priority.

Yes, there will be beneficial outcomes that meet the speed and cost challenges posed. They are imperative, and no commercial function would ignore any opportunity to find greater efficiencies. But is there also a danger that by primarily focusing on speed and cost as our goal, we collectively risk commoditising our industry’s principal unique selling points (USPs) and our primary tools of competitive and intellectual advantage?

Can you think of any other industry where the principal players have conspired and collaborated to commoditise its primary products’ value, even if indirectly, by independently choosing to follow exactly the same strategic pathway as everyone else?

The answer is ‘Yes, we can’ – perhaps airlines, fast-fashion, certain household technologies and some consumer goods. Did you know that airports now deliver greater relative return to shareholders (by weighted average cost of capital) than airlines (McKinsey, December 2022 )? The drivers of that phenomena should be obvious.

Of course, this is not to say the entire insights industry becomes commoditised or is yet in danger of becoming so. But if we allow intelligence strategy discussions in our organisations to become increasingly focused on speed and cost alone and we incentivise those parameters as the metrics by which we are judged, we do potentially put our higher value USPs, and even perhaps ourselves, at future risk.

If we are to maintain a thriving, ever-growing industry, attractive to all great talent and valued as a vital strategic asset by our organisations, insights AI can absolutely create faster, cheaper and, possibly, equally good quality data, but ‘Insights AI plc’ should continue to hero the enhanced human understanding that we have and ensure ‘fast’ and ‘cheap’ are enabling parameters to achieving this.

Our high-value discussions, among many angles, should centre on the enlightened understanding of category dynamics that we have always had and that new ML tools now enhance. It should spotlight what kinds of growth are possible and impossible to achieve to our clients and that such forecasts are now super-charged with data-science-led predictions. These discussions can maintain the vital presence of the customer voice that technology now enables. The key is to directly engage on these many ‘high-value’ conversations that centre on business strategy and action.

There is one common theme from all of those genuine AI experts who have views on the latest technological disruption paradigm share – and that is, as a starting point, to focus on real-world business issues, challenges and opportunities and work out how these technologies can help you.

Learning from what we’ve seen from big data hype in the past – when the rush to invest in the technologies of data acquisition decoupled us from the fundamental question of what we were actually going to use them for – focusing on the high-value business challenges seems like a sensible way to go and is the best use of our USPs.

When those same AI professionals are now writing and blogging about AI model collapse due to lack of newly available training data, I am immediately struck by the huge competitive advantage that sits entirely within our gift.

If the AI paradigm currently sees data as a depleting resource, the insights paradigm sees data not as a depleting resource but as an accumulating one. Be that the case, there opens up huge opportunity for new high-value capabilities to be created, new areas of expertise to stand, new differentiated knowledge to be extracted and for us to own, and with it, many more opportunities to occupy seats at the right tables leading on high-value strategy discussions.

The outcome of that is a growing, dynamic, valued sector where our talent thrives and flourishes, and insights and analytics is genuinely seen as a vital strategic asset to any business.

Nick Rich is an insights consultant and adviser, and was previously vice-president of insights and analytics at The Carlsberg Group