Generative AI for Supply Chain Management, Part I: Laying a Foundation
Five days. That’s how long it took ChatGPT — the generative artificial intelligence (AI) language model created by OpenAI — to capture one million users, according to global consulting firm McKinsey & Company.
For context, the firm says reaching the same level of adoption took:
Over two months for Apple’s iPhone
Ten months for Facebook
More than three years for Netflix
ChatGPT certainly isn’t the only generative AI player in town, but its blazing-fast adoption demonstrates the surging excitement about the potential of generative AI to transform the business landscape across industries.
In fact, according to the recently-released results of a Gartner poll that surveyed over 2,500 executive leaders:
45% — indicated that “the publicity of ChatGPT has prompted them to increase artificial intelligence (AI) investments”
70% — indicated that their organization is “in investigation and exploration mode” with generative AI
19% — indicated they are “in pilot or production mode” with generative AI
“The generative AI frenzy shows no signs of abating,” said Frances Karamouzis, Distinguished VP Analyst at Gartner in a statement. “Organizations are scrambling to determine how much cash to pour into generative AI solutions, which products are worth the investment, when to get started and how to mitigate the risks that come with this emerging technology.”
But in the midst of all the hype, many are trying to understand exactly what this new technology is — and why it’s different from traditional AI models.
As McKinsey & Company puts it, “The breakneck pace at which generative AI technology is evolving and new use cases are coming to market has left investors and business leaders scrambling to understand the generative AI ecosystem.”
In this first article in our series about the potential for this advanced AI capability to transform supply chain management, we’ll tap into two McKinsey & Company resources to help lay a generative AI foundation:
Here, we’ll weave together brief and basic descriptions informed by both resources and encourage you to check them out on the McKinsey & Company website for more detailed information.
How is generative AI different from traditional AI?
McKinsey & Company says what’s creating so much buzz is generative AI’s unique ability to create new content based on the data it ingests — rather than just making predictions and recommendations, as is the case with traditional AI.
“This content can be delivered in multiple modalities, including text (such as articles or answers to questions), images that look like photos or paintings, videos, and 3-D representations (such as scenes and landscapes for video games),” the firm says. “Even in these early days of the technology’s development, generative AI outputs have been jaw-droppingly impressive, winning digital-art awards and scoring among or close to the top 10 percent of test takers in numerous tests, including the US bar exam for lawyers and the math, reading, and writing portions of the SATs, a college entrance exam used in the United States.”
Although generative AI models that produce content in a single format are the most common, McKinsey & Company says multimodal models are emerging that make it possible to create different types of content from the same generative AI offering.
“All of this is made possible by training neural networks (a type of deep learning algorithm) on enormous volumes of data and applying ‘attention mechanisms,’ a technique that helps AI models understand what to focus on,” the firm says. “With these mechanisms, a generative AI system can identify word patterns, relationships, and the context of a user’s prompt…”
Although traditional AI models may use the same tools, the firm says they aren’t designed with the ability to generate new content. Instead, they use content that already exists to do the work they do.
The role of foundation models in generative AI
McKinsey & Company says generative AI makes use of “foundation models” — which represent another key differentiator from traditional AI.
“The underlying technology that enables generative AI to work is a class of artificial neural networks called foundation models,” the firm says. “Artificial neural networks are inspired by the billions of neurons that are connected in the human brain. They are trained using deep learning, a term that alludes to the many (deep) layers within neural networks. Deep learning has powered many of the recent advances in AI.”
Although generative AI and traditional AI both make use of deep learning capabilities, McKinsey & Company says certain characteristics make foundation models different from prior generations of deep learning models.
“Given the versatility of a foundation model, companies can use the same one to implement multiple business use cases, something rarely achieved using earlier deep learning models,” the firm explains. “A foundation model that has incorporated information about a company’s products could potentially be used both for answering customers’ questions and for supporting engineers in developing updated versions of the products. As a result, companies can stand up applications and realize their benefits much faster.”
Describing a foundation model as similar to a “Swiss Army knife,” McKinsey & Company says that with adaptations, it can support many different types of tasks and be used for multiple purposes.
“Once the foundation model is developed, anyone can build an application on top of it to leverage its content-creation capabilities,” the firm says. “Consider OpenAI’s GPT-3 and GPT-4, foundation models that can produce human-quality text. They power dozens of applications, from the much-talked-about chatbot ChatGPT to software-as-a-service (SaaS) content generators Jasper and Copy.ai.”
But current iterations of generative AI also come with potential risks (which we’ll cover later in this series), which is why the firm also notes that current foundation models aren’t “naturally suited to all applications.”
Beyond the “brain” of generative AI
In addition to the foundation model, which McKinsey & Company describes as the “brain” of generative AI, the firm says there’s an emerging value chain that is needed to support generative AI training and use — which includes:
Services — which provide the expert support needed to develop and deploy generative AI offerings
Specialized hardware — which provides the massive computing power needed for training the models
Cloud platforms — which make it possible to tap into the specialized hardware
Applications — which are built on top of the foundation model to enable the completion of specific tasks
Model hubs and MLOps (machine learning operations) — which offer “the tools, technologies, and practices an organization needs to adapt a foundation model and deploy it within its end-user applications”
For more detail about the basics of generative AI, please check out these two McKinsey & Company resources:
And please join us next week for Part II in our series, when we’ll take a look at potential impacts of generative AI in supply chain management.