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The terms “artificial intelligence” and “machine learning” get thrown around a lot in the modern business world, where both of these technological advancements are helping organizations automate processes, take the pressure off their human workforces and do more with less.
The new kid on the advanced technology block is known as generative artificial intelligence (AI), which IBM defines as “deep-learning models that can generate high-quality text, images and other content based on the data they were trained on.” Generative AI effectively picks up where “regular” AI leaves off by taking raw data and “learning” to generate statistically-probable outputs when prompted to do so.
“Whereas traditional AI algorithms may be used to identify patterns within a training data set and make predictions, generative AI uses machine learning algorithms to create outputs based on a training data set,” Investopedia explains. Some of recent examples of generative AI interfaces include:
- ChatGPT—an AI-powered chatbot trained to interact with users via natural language dialogue. Users can ask ChatGPT questions, engage in back-and-forth conversation and prompt it to compose text in different styles or genres, such as poems, essays, stories or recipes, among others
- DALL-E—it uses a neural network that was trained on images with accompanying text descriptions. Users can input descriptive text, and DALL-E will generate photorealistic imagery based on the prompt.
- Bard—a chatbot powered by AI technology that can answer questions or generate text based on user-given prompts.
“At a high level, generative models encode a simplified representation of their training data and draw from it to create a new work that’s similar, but not identical, to the original data,” IBM explains, noting that generative models have been used for “years” in statistics to analyze numerical data. “The rise of deep learning, however, made it possible to extend them to images, speech and other complex data types.”
Using Generative AI in Procurement
While still “new” by technology standards, generative AI is already being touted as a good addition to any procurement team’s toolbox. For example, GEP says generative AI can help improve efficiency, productivity and decision-making.
“These large language models can be used to improve search capacity, automate tasks, identify patterns and make predictions that would otherwise be time-consuming,” GEP says. “The result? Significant savings in time and money, as well as improved decision-making.”
Here are three current and potential applications of generative AI in procurement:
- More organized, interactive searches. Generative AI is different from web search keywords (e.g., traditional web search, web crawling, ranking, bids in paid search) or reports, which often require updates on a regular basis. “It can answer questions about what, where, when and why, and it can be customized to individual needs,” GEP explains. “This means that users can get more relevant information with more targeted prompts.”
- Less time spent preparing RFPs, reports and presentations. Generative AI can assist in generating the initial draft and quickly getting things started. “In short, AI can improve productivity at the individual level,” GEP points out. “In the long term, AI could become more integrated and be offered as an embedded or add-on service.”
- Use historical data for better outcomes. Generative AI can be used for decision support with potential trends and forecasts. “This means that buying organizations do not have to rely on the intuition of managers, market intelligence reports or close-loop model calculations,” GEP says. “AI can perform the computations based on historical data and potential scenarios and outcomes.”
Weighing Out the Risks
In “How Generative AI Is Transforming Supply Chain and Procurement Roles,” Arkestro’s Edmund Zagorin writes about how procurement leaders have watched the launch of ChatGPT and other generative AI bots like Bard and Claude with “intense excitement and curiosity.” He also warns procurement professionals about the dangers of jumping right into using generative AI without knowing the related implications and risks.
Zagorin tells companies to establish responsible use of generative AI outside of the workplace and set boundaries with suppliers. “Whether your team members and suppliers know it or not, any information put into ChatGPT may be accessible to competitors and other third parties,” Zagorin writes. “IT teams are already racing to establish rules, but they tend to be internally focused.”
As technology advances and business practices change, Zagorin expects the risks and legal restrictions surrounding AI will continue to evolve. “There is a lot of AI hype, and it's nothing new for many in procurement and yet these models are only getting better, and the speed of improvement has never been faster,” he points out. “If we don't have internal discussions and form clear boundaries and specific intentions ahead of this technology's acceleration, we may be stuck with the ideas and recommendations that AI itself generates.”
