By Elaine Burke
This time last year, discussions about generative artificial intelligence (AI) were largely limited to a small section of the sci-tech world.
Then, in November, ChatGPT was released to the public and quickly became the subject of myriad conversations on the future of work and productivity across all sectors.
In the intervening months, many more chatbots and even the large language models that underpin them have been unleashed for businesses to trial, test and tweak.
AI high performers
A recent global survey on the current state of AI from consulting firm McKinsey tells us that a cohort of “AI high performers” is leading in the adoption of generative AI.
These are organisations that were early adopters of traditional AI and have seen the most value from its application. They already have AI embedded in their operations and can attribute at least 20 per cent of their earnings (before interest and taxes) to AI adoption.
Unsurprisingly, these AI high performers are outpacing other organisations in their adoption of generative AI.
This means they are using generative AI tools in more business functions, and they are more likely than others to use AI in product and service development, whether that means adding new features to existing products with AI or creating all-new AI-based products.
These organisations are also seen to be using AI more often in risk modelling and for HR tasks such as performance management and organisation design.
Another differentiator among the AI high performers, as noted by McKinsey, is that they are less focused on deploying generative AI for cost reduction than on creating new business and revenue opportunities.
Industry leaders set the standard
McKinsey doesn’t mention any particular AI high performers by name, but we can see the qualities identified reflected in the movers and shakers of the AI sector – those at the forefront of developing new AI-centric products and those that have been using more traditional AI tools most effectively up to now.
In its examination of AI adoption, McKinsey also noted that just over one-fifth (21 per cent) of survey respondents said their organisation had established policies governing the use of generative AI.
And while even high performers are struggling to establish the best practices in AI adoption, they are much more likely than other organisations to be making efforts to do so.
“Companies that are approaching generative AI most constructively are experimenting with and using it while having a structured process in place,” Alexander Sukharevsky, a senior partner at McKinsey London, advised.
And so, the early movers on generative AI could well set standards to be replicated across industries.
Google CEO Sundar Pichai has been setting out the organisation’s AI principles since its early days with the technology. Microsoft, another forerunner due to its investment in ChatGPT creator OpenAI, has outlined its approach to using AI responsibly.
“Being deliberate, structured and holistic about understanding the nature of the new risks – and opportunities – emerging is crucial to the responsible and productive growth of generative AI,” added Sukharevsky.
Data that can disrupt and displace
Earlier this year, The Economist’s Intelligence Unit examined how companies far beyond the tech world are making use of AI technologies, highlighting its application across a wide range of industries.
Be it agriculture, car manufacturing, retail, fast-moving consumer goods or fashion, AI is being put to work.
The financial services industry was an early mover in bringing automation, algorithms and AI into the fold.
For example, machine learning has found widespread use in fraud detection, where long-term players in payments technology – such as PayPal, Mastercard and Visa – can leverage decades of data on user behaviour to train their algorithms and improve predictions.
When it comes to generative AI, again, it will be those organisations with the capability of training large language models on their own datasets that will unlock its greatest potential. They will not just be adopters of these technologies but shapers of it.
And it’s not just the finance sector that has this kind of valuable user data to work from.
Pushback is likely
Netflix successfully disrupted the entertainment sector with an effective distribution model and a powerful recommendations engine driven by user data.
AI is deeply embedded across the organisation and the technology has been deployed to select thumbnails based on user behaviour, or even help Netflix’s content production team decide where to film.
How Netflix will now move forward with generative AI as an industry leader is already causing consternation among actors’ and writers’ unions in the US, reminding us that early adopters will also be on the frontline for pushback.
Additionally, while McKinsey notes that generative AI’s first movers are more likely to focus their efforts on new developments than cost-cutting, the products and services they release may well offer the latter to other organisations, at the expense of creative workers.
For example, WPP, the world’s biggest ad agency, has partnered with none other than Nvidia – the chipmaker primarily responsible for the hardware that powers generative AI – on a generative AI engine that can produce tailored advertising content rapidly and at scale.
This could generate new business for WPP, but could also put some creatives in the wider industry out of work.
Perhaps in response to this potential disruption, McKinsey’s report notes that AI high performers are expected to engage in higher levels of reskilling than other organisations.
Generally speaking, the survey’s respondents expect a significant level of reskilling over the next three years as a result of AI adoption.
Just as these industry leaders will set the standard for best practices in generative AI’s application, they have the opportunity to lead the way in ensuring that the disruption it causes does not lead to widespread displacement.
