How Opella's Gaurav Shah Is Preparing for AI’s Data Volume Explosion
Opella capped April with some significant news: The manufacturer of such over-the-counter brands as Allegra, Dulcolax and Icyhot officially completed its planned separation from Sanofi on April 30.
Under the deal, Sanofi sold a 50% controlling stake of Opella to CD&R while retaining a 48.2% stake. Bpifrance holds the remaining 1.8% stake.
It was somewhat fortuitous timing for CGT’s Analytics Unite attendees, as Gaurav Shah, Opella’s global head of data, analytics and AI, served as the event's closing keynoter that day and was admittedly able to share his viewpoints a bit more easily.
See also: Learn how CPGs like Kellanova and PepsiCo are proving the value of AI
In a fireside chat with CGT editorial director Lisa Johnston, Shah discussed how organizations can embrace innovation, foster a data-driven culture and scale AI initiatives to drive the future of retail and consumer groups.
“The next big transformation that will change the world is going to happen through AI or data. This is going to be a change that is going to impact all of our lives,” Shah said. The question becomes, “How are we preparing ourselves to get to that transformative way of working?”
Key Focus Areas
Enabling AI to handle the imminent “data volume and velocity explosion” in ways that will drive growth, operational efficiency and customer engagement is the challenge. He discussed key areas to focus on to meet that challenge:
Value streams: Shah cited four areas with AI potential: demand/consumer engagement/customer centricity; managing/allocating spend; fulfillment/supply chain resilience; and operations.
“The underlying connection point is data and AI,” Shah said.
Operational models: The continual evolution of existing roles and cross-functional teams, as well as resulting changes in processes and workflows, will require adjusting operational models to that new way of working, including how you integrate those capabilities within your processes.
Counterpart interactions: Limited interactions impede progress. Shah noted that teams on the business side and the digital side often don’t understand one another, and offered this suggestion on bridging that divide:
“Look at your phone, at your calendar in your laptops and see in the next week how many meetings or interaction points you have with your business counterparts or stakeholders — not your own technical teams, not your own business teams, but your counterparts in business — because that's going to be your make or break … If that number is less than 60%, there is a problem.”
Cross-industry collaboration: Industries at various points in their AI journeys should come together.
“Interact with different industries, different verticals around data AI capabilities,” Shah said. “We need to make sure that we partner with organizations that have built these capabilities and packages for us to onboard our teams and people.”
Building internal learning models and capabilities to make sure literacy is spread across all functions and is properly, collaboratively managed is key, he noted.
