For almost two years now, the business world has been under the spell of genAI. As groundbreaking and exciting as the prospect of this new, disruptive technology might be, there are just as many questions about what it might mean in the longer-term for industries, especially supply chains. Nitin Dsouza from Capgemini provides some answers to these myriad questions.

With the rise of artificial intelligence (AI), especially generative AI (genAI), we are at the dawn of a new era. We are already seeing that we can unlock more value in supply chains with genAI than with traditional AI. The deployment of large scale AI models with more than 100 billion parameters, fed by high-quality training data, is the prelude to the next disruption. The seed for disruption of the companies that represent the digital age has now been planted.

Strengthening competences

The supply chain has always been a big user of machine learning (ML) and more recently AI. For decades, we have been using traditional forms of ML and AI (tradAI) for prediction, optimisation and orchestration. That has enabled companies to create more value with their supply chain. With genAI, we are adding to that. GenAI enables the generation of text, images, sound and code. We can translate data to text, as well as use text to analyse data and visualise the insights.

GenAI will strengthen the competencies of supply chain professionals. Once they discover the value of genAI, they don’t want to live without it. Capgemini has developed applications where a buyer deploys genAI to analyse and compare supplier quotes, leading to faster and better decisions. Another example concerns a demand planner who uses genAI to generate python code to analyse a given dataset and then deploys genAI again to visualise the resulting insights. And all without having years of coding experience.

The seed for the next wave of digital disruption has been planted.

Challenges and tips

On the road to large-scale deployment of genAI, we encounter the necessary challenges. The first concerns trust in this new technology. Everyone knows by now that genAI can present new content with great alacrity, which then turns out to be wrong. Cost is another concern.
Using genAI requires a relatively large amount of computing power and energy and therefore money. The last challenge concerns the skills needed. There is a huge shortage of AI talent. Moreover, there is such a thing as the automation paradox: the more we automate, the more competences people need to be able to work with that automation.

For companies looking to stand out in genAI, Capgemini has three tips: first, set an agenda including objectives, frameworks, and guidelines; next, give your employees a toolkit that allows them to experiment freely and safely. And finally, build prototypes, deploy them and learn from the experiences.

Any company working on genAI knows there are use cases that produce amazing results, but also use cases that have less impact than expected beforehand. The trick is to learn lessons from them.

Manhattan and Capgemini shared their ideas about genAI in a recent webinar. Scan the QR-code to view the recording.

 Nitin Dsouza, Vice President Digital Engineering & Supply Chain at Capgemini

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