AI adoption is not just a technology journey. It is a business, people, data, governance, and ecosystem journey. That was the clear message from this panel, which explored how AI is being applied across consumer products, marketplaces, and industrial manufacturing. Venky Ramesh started the discussion by asking what business pressures pushed each organization to rethink their ways of working and look at AI to solve business problems.

Drawing from his experience, Terry Sapsalis from HEINEKEN recounted a capital and people-intensive challenge he faced in a new market. His team had to decide where to send thousands of trucks, which products to load, and how to avoid both missed sales and unnecessary delivery costs. For him, AI’s relevance comes from this kind of a real operational problem: improving the precision of daily decisions at scale.

On how companies can focus on initiatives that drive top-line or bottom-line value with AI, Terry said the starting point should always be the end state: what value is AI expected to bring? He tied this to productivity, effectiveness, and speed, noting that cost reduction and growth are easier to explain in P&L terms, while speed is harder to monetize because leaders may not immediately see the before-and-after impact.

Parag Shah of Just Eat Takeaway.com argued that many organizations approach AI adoption in the wrong order. They give people AI tools first, then provide skills training, and only later try to create the mindset to use them. In his experience, the reverse works better: build the mindset first, then the skill set, and then co-create the tool with users. That, he said, leads to faster adoption and stronger ROI from day one. He also spoke about how comparisons should not be made for the sake of it, as tech companies are far ahead in using AI agents in their operations because of a stronger shift in mindset.

Bringing the talent and change management lens, Himanshu Shah of Sudarshan Chemicals, said AI transformation is as much a people transformation journey as it is a technology transformation, and that organizations often underestimate the effort required to help the workforce adopt and adapt to AI. 

The panel then moved into AI maturity across the ecosystem. Venky asked the speakers to reflect on three levels: maturity within a company, across functions, and across ecosystem players beyond the enterprise. Terry said many organizations are still in the discovery phase. They may have strong use cases and early value, but have not yet fully captured AI’s potential across partners, retailers, distribution channels, consumers, and supply chains. For him, the barriers are not only readiness, but also trust and governance, especially when companies need to share data or collaborate across ecosystem boundaries.

Himanshu emphasized the need to build employee maturity. He said that beyond creating large-scale data stacks, empowering humans within functions to drive change, run small pilots, and build proofs of concept creates trust from within. This helps employees become more confident in using AI while keeping the transformation grounded in real operational needs.

When Venky asked the panellists to identify the bottlenecks slowing AI implementation and the enablers that could accelerate it, Terry pointed first to clean, good data, and then to faster decision-making. He noted that in a non-tech company, where growth has traditionally come from creativity and product quality, AI also requires a shift in organizational DNA. Himanshu chose a leadership mindset, saying stronger senior-level awareness and top-down endorsement would make adoption easier. Parag highlighted external dependencies, especially regulatory fragmentation.

The closing advice was practical. Himanshu urged the audience to start small, take smart risks, and remember that AI will not simply replace people; it will change how people operate. Parag reinforced the need to start with mindset, then skill set, then tools and training. Terry closed with a planning lens: define what you want to achieve in a year, set a clear milestone, identify what needs to change across process, data, and people, bring leadership along, prove the value, and then scale.