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Trained on colossal amounts of data from articles, websites and social media posts gathered from across the internet, as well as transcribed interviews that capture the nuances of human speech, the GPT4 chatbot is undoubtedly a transformative step forward for generative AI.
By detecting linguistic patterns and familiar phrases, OpenAI’s Large Language Model (or LLM) has learned to infer what word is likely to follow from a sequence of words, thus providing something close to mind-reading capabilities for users. Indeed, generative AI is fast becoming a part of the common vernacular, thanks to ChatGPT and, more recently, its latest version, GPT4.
When the bot burst onto the scene in November 2022, there were few beyond (or even within) the labs of the San Francisco-based AI research company, OpenAI, who could have predicted the meteoric rise it would experience in just a few short months.
As part of its regular Hype Cycle for artificial intelligence, analyst firm Gartner predicts that generative AI will become a mature mainstream technology in non-supply chain applications in two to five years. While ChatGPT caught education and academia completely by surprise, it is unlikely to have much of an impact on how supply chain decisions are made, at least in the near-term future. Here’s why.
Gartner believes the application of generative AI in supply chains is some way off thanks to the way the application fundamentally learns. Trained with over 570GB of data and more than 300 billion words, GPT4 has a vast data lake from which to “learn”, but it won’t necessarily be able to apply that ability to individual supply chains.
Because supply chain models are so intricate and specific to each company, the expected arrival of generative AI into the mainstream is anticipated to be as much as a decade out – in a best-case scenario, that’s five times longer than non-supply chain applications.
Despite this potential lag time in the supply chain space, the responses from established tech brands such as Microsoft and Google to GPT4 have sparked something of a 21st century AI gold rush.
While a decade is a long time, particularly in the tech space, a burning question remains today: can supply chain leaders afford to sit back and wait it out to see if generative AI simply vanishes or tapers off? Much like EVs, cryptocurrencies and other disruptive technologies of the past 20 years, those that invest early tend to be the long-term winners.
If you ask GPT4 itself about how it could be applied to supply chain scenarios, it has some interesting and entirely plausible responses. “GPT4 can be a useful tool in the supply chain, helping to automate processes, provide insights and facilitate communication and collaboration between different stakeholders,” runs one such response.
While AI in the context of businesses and consumers isn’t new (just look at the proliferations of Alexa in homes or chatbots on websites), the unexpected media and consumer avalanche of interest, almost overnight, in GPT4 has transformed the AI conversation into a mainstream topic. It’s as likely to be discussed in boardrooms as around household dinner-tables, in much the same way the pandemic thrust supply chains into the limelight.
Besides the technical implementation of generative AI in supply chains, or any other industry, there are issues that need to be addressed – not least legal and ethical ones.
Many major tech companies have avoided introducing similar products because of such concerns. For example: can a company take credit for content generated by a chatbot? Or, how should we share the work that AI generates?
One of the world’s largest academic publishers, Springer Nature and the educational non-profit The International Baccalaureate recently commented that GPT4 could not be credited as an author of papers. However, both said they would allow scientists and students alike to use it to help with writing or generating ideas for research – the opacity of the argument put forward by academia is ironically clear.
Ethics aside, the question many CIOs and CTOs will be asking themselves is whether their companies should be investing in these types of technology, given they are unlikely to be ready for practical use any time before 2030.
Furthermore, at a time when billions of dollars in investment are being poured into this new space while tens of thousands of jobs are being lost in the technology sector, the question of whether to invest becomes even more difficult to navigate.
But what if this isn’t the right question to be asking at all. Maybe we need to rethink the whole narrative and ask something subtly different: can we afford not to explore the application of generative AI?
Neither an abstract theoretical technology that’s accessible to only coders or data scientists, nor a dystopian sci-fi plot, Open AI’s generative AI breakthrough has arguably democratised, and introduced both businesses and consumers to, a completely different category of tools that put the power and potential of AI on display for all to see.
Tech leaders who don’t have their app development team thinking about how to apply generative AI at some point in the near future are likely putting their companies at a long-term disadvantage.
And therein maybe lies the truly transformative quality of GPT4 to businesses everywhere, not simply supply chains. It’s not necessarily about the application of generative AI today, but rather how it is fundamentally changing our way of thinking about what could be possible tomorrow as we move away from menu-driven click interfaces to more natural conversational ones.
If you don’t want to wait until 2030 to deploy generative AI in your supply chain and would prefer to understand more about how AI and machine learning are powering smarter supply chain decision making today, you can find out more from the team at Manhattan Associates.
