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Human, only better. How do we keep up?

by Manoj Chiba: Associate professor at GIBS, management professional, senior consultant and data scientist.
Would you trust ChatGPT - or the latest evolution in language software, GPT-4 - to pick out your new fridge? I did.

ChatGPT - or Chat, as the artificial intelligence (AI) chatbot likes to call itself - burst onto the world stage on 30 November 2022. Within five days of its launch, ChatGPT had reached a million users, surpassing 100 million active users in January 2023. In the process, the 3.5 version unsettled industries and professionals from academia and education, healthcare, marketing and the media. On 14 March 2023, OpenAI released its GPT-4 version, an even slicker, more creative and nuanced version of Chat.

Developed by OpenAI (which was originally co-founded by Elon Musk), and backed by Microsoft, Chat was not the first iteration of this technology, nor will it be the last. It comes from a line of AI siblings, such as InstructGPT, which collectively we refer to as large language models (LLMs). LLMs are sophisticated learning AIs capable of being trained to understand huge amounts of text and data, so much so that they can summarise information and even respond to questions using natural, human-like language.

That is the essence of Chat. It’s a chatbot with which you can interact.

We interact with chatbots all the time, in fact they’ve already become commonplace. If you buy data using Absa’s ChatBanking on WhatsApp or Facebook, that’s a chatbot. Secured an e-boarding pass for FlySafair? Another bot. Or used Ask Discovery to get your latest tax certificate? Again, your query was answered by a machine.

What, then, makes ChatGPT so important in the evolution of this technology?

First, Chat’s user interface is easy, making it a comfortable fit for the public at large. Second is the model itself, which is nothing short of impressive. Any AI has to be trained on a set of variables, and Chat has included just over 170 billion parameters. Its ability to interact with more than 170 billion factors all at once makes Chat a powerful tool and one that is still learning. Just like a human being, Chat will stumble and fall and arrive at some strange conclusions along the way, but this is simply because it is mimicking human behaviour.

Reactions to the tool have been mixed because of this potential unpredictability, with even Musk commenting to CNBC that Chat could be both positive and a potential “risk to society” if left unregulated. Either way, it has the power to transform how we, as humans, interact, learn and navigate the world; so much so that during a presentation at the RageX conference on 9 December 2022 I was already prepared to nail my colours to the mast and dub Chat a true game-changer.

What does this mean for you and me?

Actually, quite a lot. Already we are seeing the potential for Chat to transform how we interact with brands, how we shop and make decisions. This has huge implications for an industry we are all intimately acquainted with - the world of retail. 

Retail marketers beware

There are a few big use cases for LLMs which we should consider in the context of the retail sector. While you probably won’t find a Chat robot running around your local store anytime soon, the tool is likely to hold value in the world of ecommerce and, for consumers, as an online buying tool which they can use to compare products across brands. For retailers and brands, this will necessitate up-to-date and accurate information which Chat can draw on to help guide consumers.

As an example, I had to buy a fridge-freezer in January. Since I know nothing about appliances, I decided to take Chat out for a spin. I logged on and asked Chat to compare different fridge brands in South Africa based on specific features and prices. I got a good output, then I did a quick price check to confirm Chat’s findings, and I made a decision. Other than nipping to the shop to see the unit and assess its robustness, I was able to save time and effort.

The ability to compare products and prices is the first impact we can expect to see in the retail space. The second concerns hyper-personalisation. This is not a new arrow in the quiver of advertising and marketing gurus. Just consider how brands like Nutella and Coca-Cola offer personalised labels or cans bearing your name. However, AI can give marketers the personal insights and real-time data they need to create adverts or product specials targeted specifically to consumers and their needs. The potential this opens up for marketers in the retail space is staggering.

Also on the marketing side, we can expect to see AI output influencing how brands engage with customers in a more personal and interactive way. This could take the form of getting instant answers online, or as a support and customer service mechanism which answers questions and provides instant feedback. The likes of Carrefour, Shopify, Chinese giant JD.com and Amazon are already moving into this space, highlighting the fact that companies that choose not to go this route will, in the future, be found wanting - and not just in the marketing space.

LLMs will likely prove beneficial in ensuring that intellectual property and accumulated institutional knowledge can be more easily accessed and retained by retail companies in the future, even as key staff members leave. However, it’s not all positive, and there are a few warning bells already going off for an industry like retail.

Two key questions to ask involve the possible shift from stores as sales channels to showrooms, and what this means for jobs and the physical buying experience. Then there is the issue of brand positioning and differentiation in a crowded retail space. Companies guard this sort of information jealously, but trade secrets would become a veritable open book to an LLM like Chat. This could be construed as a threat to retailers in their current form, although I would argue that the bigger threat will emerge when larger numbers of people begin to blindly trust the output from LLMs, even if the technology gets it wrong from time to time. If you thought fake news was a problem, wait until the technology moves from the sort of narrow AI that powers self-driving cars and morphs into general intelligence, which can do anything a person can - including influence human perceptions.

AIs like Chat will not replace humans, but the people who use these tools will rise to the top. So, my advice to FMCG executives is to use LLMs as an accompaniment to what you do. Don’t panic, but do recognise that of the top emerging technologies in 2023, AI headlined the list. Serious money is being poured into these technologies, which means that in a short space of time LLMs will be ubiquitous and, yes, they will disrupt your business.

The good news is that while you can’t outsmart AI, you can embrace the potential and run with it.

How retailers (and companies in general) should respond to AI

If your organisation is feeling overwhelmed by the arrival of ChatGPT - and now GPT-4 - then follow these simple steps to create a strategy for engagement.
  • Figure out where Chat is getting its info and then optimise those channels. Keep information updated and pay special attention to search engine optimisation.
  • Not sure how to figure out where Chat is finding its sources? Simply ask Chat where it is getting its info.
  • Check if you are missing a channel that Chat is using to pick up information or chatter about your brand. If you don’t have a presence then establish one - quickly.
  • Use Chat to pick up insights and gauge sentiment about your brand, service levels and offerings.
  • Fiddle. The secret to understanding how to use large language models (LLMs) in your business is to play with it.

Useful resources:
Gordon Institute of Business Science
Making an impact to significantly improve the competitive performance of individuals and organisation through business education to build our national competitiveness. GIBS is a leading business school in the heart of Sandton’s business hub, offering a wide range of executive and academic programmes.
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