Unilever Delivering Customer Insight, Efficiency with AI Adoption
Arun Nandi, VP of Global Data and Analytics at Unilever, knows his way around data and how to apply it for positive business outcomes.
In my recent conversation with Arun in advance of this week’s AIHW and Edge AI Summit, Arun shared how his company is leveraging AI and data analytics to enhance business operations, drive sustainability, foster innovation and more.
Arun’s insights centered on the transformative potential of AI in improving corporate decision-making, optimizing supply chains, and refining product offerings. What’s better, Arun believes all of this can be delivered while maintaining a focus on environmental responsibility.
AI and Analytics Sit at the Core of Innovation
Unilever is one of the world's largest consumer goods companies. They handle vast amounts of data daily from across their business portfolio, ranging from information on supply chain logistics to culled insights on consumer preferences. Arun sees AI and advanced analytics as crucial in handling this data to extract meaningful insights that inform decisions at all levels of the organization, and that this extraction is critical for competitive advantage in today’s market.
From product development to marketing strategies, AI is being used to refine and predict consumer needs, ensuring that Unilever can keep ahead in an ever-changing market.
Arun shared some details about customer behavior modeling taken on by businesses like his. By analyzing consumer behavior data, organizations can personalize marketing and product recommendations, providing a more tailored and relevant experience for customers. This not only improves customer satisfaction, but also increases loyalty and retention, and this is widely deployed today utilizing existing analytics and AI capabilities. With new generative AI models, the ability to forecast consumer behavior will only grow more accurate and impactful.
Sustainability Through Data
Our interview also covered the challenge with efficient IT to fuel AI, and Arun highlighted that this is a core value at Unilever. In fact, AI is playing a pivotal role in helping the company achieve its environmental goals. Arun shared that Unilever is committed to reducing its carbon footprint and creating more eco-friendly products. Through data analytics, the company can identify areas where it can reduce waste, optimize resource use, and create sustainable products without compromising on quality or performance.
For example, AI helps Unilever track the environmental impact of its supply chain, ensuring that the company can source raw materials more responsibly and efficiently. AI also allows the company to forecast demand more accurately, reducing the chances of overproduction and excess waste. These efforts contribute to Unilever’s broader mission of achieving net-zero emissions and promoting a circular economy. And, they are part of a larger trend in corporate use of the powerful technology to enhance energy use and implement sustainable business practices.
The Future of Enterprise Data Architecture
Arun also provided his vision for the future of enterprise data architecture as one where AI and machine learning will become even more integral where continued investing in AI advancement is required to stay ahead of the curve. This includes building robust data infrastructures that can handle the increasing complexity and volume of data Unilever manages while ensuring privacy and security for consumers.
One of the challenges that Arun touched upon is the need for a cultural shift within organizations to embrace AI and data-driven decision-making fully. He stressed the importance of upskilling employees and creating a data-driven mindset across all departments to ensure the successful integration of AI technologies. This requires new avenues of collaboration, not just across Unilever business groups, but across the industry.
The solution for this collaboration is new partnerships between businesses, academia, and governments to drive innovation and tackle global challenges such as climate change and resource scarcity. By sharing data and working together, companies can amplify their positive impact on the world and achieve shared sustainability goals.
What’s the TechArena take? Our conversation with Arun was a fantastic reminder that AI has been implemented in IT organizations for years, automating critical functions in relation to advanced analytics. These powerful tools are opening new business opportunity and driving efficiency to corporate processes. While much of the chatter on AI is focused on advancement of large language models, the enterprise is happily deploying core AI-enabled applications across business functions with an eye to improve these functions with new model capability. As we look ahead to 2025 and expected deployments of gen AI in the enterprise, they likely will be built atop what’s already been done in many organizations.
Our second take? We see an important trendline on AI’s positive impact to overall corporate sustainability efforts and mindful use of energy and resources. Expect more stories from corporations in the months ahead on this theme as companies look to counter set the energy consumption utilized for deploying these models from data center to edge.
If you’re at AIHW Summit this week, be sure to check out Emerging Architectures for Applications Using LLMs, the Transition to LLM Agents, featuring Arun alongside experts from Stanford, Union.ai and Nava Ventures as well as his talk, Revolutionizing Language Models: Innovative Designs in Database Layers for Retrieval Augmented Generation, both within Tuesday’s lineup.
Arun Nandi, VP of Global Data and Analytics at Unilever, knows his way around data and how to apply it for positive business outcomes.
In my recent conversation with Arun in advance of this week’s AIHW and Edge AI Summit, Arun shared how his company is leveraging AI and data analytics to enhance business operations, drive sustainability, foster innovation and more.
Arun’s insights centered on the transformative potential of AI in improving corporate decision-making, optimizing supply chains, and refining product offerings. What’s better, Arun believes all of this can be delivered while maintaining a focus on environmental responsibility.
AI and Analytics Sit at the Core of Innovation
Unilever is one of the world's largest consumer goods companies. They handle vast amounts of data daily from across their business portfolio, ranging from information on supply chain logistics to culled insights on consumer preferences. Arun sees AI and advanced analytics as crucial in handling this data to extract meaningful insights that inform decisions at all levels of the organization, and that this extraction is critical for competitive advantage in today’s market.
From product development to marketing strategies, AI is being used to refine and predict consumer needs, ensuring that Unilever can keep ahead in an ever-changing market.
Arun shared some details about customer behavior modeling taken on by businesses like his. By analyzing consumer behavior data, organizations can personalize marketing and product recommendations, providing a more tailored and relevant experience for customers. This not only improves customer satisfaction, but also increases loyalty and retention, and this is widely deployed today utilizing existing analytics and AI capabilities. With new generative AI models, the ability to forecast consumer behavior will only grow more accurate and impactful.
Sustainability Through Data
Our interview also covered the challenge with efficient IT to fuel AI, and Arun highlighted that this is a core value at Unilever. In fact, AI is playing a pivotal role in helping the company achieve its environmental goals. Arun shared that Unilever is committed to reducing its carbon footprint and creating more eco-friendly products. Through data analytics, the company can identify areas where it can reduce waste, optimize resource use, and create sustainable products without compromising on quality or performance.
For example, AI helps Unilever track the environmental impact of its supply chain, ensuring that the company can source raw materials more responsibly and efficiently. AI also allows the company to forecast demand more accurately, reducing the chances of overproduction and excess waste. These efforts contribute to Unilever’s broader mission of achieving net-zero emissions and promoting a circular economy. And, they are part of a larger trend in corporate use of the powerful technology to enhance energy use and implement sustainable business practices.
The Future of Enterprise Data Architecture
Arun also provided his vision for the future of enterprise data architecture as one where AI and machine learning will become even more integral where continued investing in AI advancement is required to stay ahead of the curve. This includes building robust data infrastructures that can handle the increasing complexity and volume of data Unilever manages while ensuring privacy and security for consumers.
One of the challenges that Arun touched upon is the need for a cultural shift within organizations to embrace AI and data-driven decision-making fully. He stressed the importance of upskilling employees and creating a data-driven mindset across all departments to ensure the successful integration of AI technologies. This requires new avenues of collaboration, not just across Unilever business groups, but across the industry.
The solution for this collaboration is new partnerships between businesses, academia, and governments to drive innovation and tackle global challenges such as climate change and resource scarcity. By sharing data and working together, companies can amplify their positive impact on the world and achieve shared sustainability goals.
What’s the TechArena take? Our conversation with Arun was a fantastic reminder that AI has been implemented in IT organizations for years, automating critical functions in relation to advanced analytics. These powerful tools are opening new business opportunity and driving efficiency to corporate processes. While much of the chatter on AI is focused on advancement of large language models, the enterprise is happily deploying core AI-enabled applications across business functions with an eye to improve these functions with new model capability. As we look ahead to 2025 and expected deployments of gen AI in the enterprise, they likely will be built atop what’s already been done in many organizations.
Our second take? We see an important trendline on AI’s positive impact to overall corporate sustainability efforts and mindful use of energy and resources. Expect more stories from corporations in the months ahead on this theme as companies look to counter set the energy consumption utilized for deploying these models from data center to edge.
If you’re at AIHW Summit this week, be sure to check out Emerging Architectures for Applications Using LLMs, the Transition to LLM Agents, featuring Arun alongside experts from Stanford, Union.ai and Nava Ventures as well as his talk, Revolutionizing Language Models: Innovative Designs in Database Layers for Retrieval Augmented Generation, both within Tuesday’s lineup.
