Market View
Navigating the AI transformation journey: How global service providers are reimagining value creation
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Yasuhiro Koide
AVP, Head of Innovation Management and IT Strategy at Genpact
About Yasuhiro Koide
Yasuhiro Koide serves as AVP, Head of Innovation Management and IT Strategy at Genpact, where he leads transformative AI initiatives and shapes cutting-edge technology strategies. With over 25 years of experience across logistics, supply chain, and operations industries—including tenure at Amazon and four US companies—he specialises in technology enhancement, AI implementation, and autonomous robotics solutions. His unique perspective spans from ground-level operations, having started as a DHL driver, to board-level strategic oversight, positioning him as a transformational expert who bridges operational execution with executive vision.
How do you see the service provider landscape evolving as AI capabilities mature, and what does this mean for client relationships?
The service provider landscape is undergoing fundamental change. We're shifting from traditional outsourcing models into strategic partnerships that enable end-to-end digital transformation. Clients increasingly expect service providers to deliver not just process efficiency, but proactive, insight-driven value creation.
Our Smart Command Centre in Japan, established through our alliance with Coca-Cola Bottlers, optimises service operations through intelligent automation and real-time visibility. This demonstrates how we're aligning with clients' evolving expectations—offering not only cost optimisation, but also greater agility, resilience and measurable business impact.
When I work with clients, their expectations around AI centre on reducing costs whilst maintaining or improving service levels. They want intelligent analytics to gain insights that improve operations. The relationship dynamic has shifted significantly; clients view us as partners who can deliver immediate productivity gains whilst building long-term capabilities.
AI technology is fundamentally a tool to solve problems, but the problem statement itself is the most critical component.
What practical challenges do you face when implementing AI solutions, and how do you approach problem identification?
The most important aspect is clearly defining the problem and understanding structural issues within operations. AI is fundamentally a tool to solve problems, but the problem statement itself is the most critical component. You need total optimisation perspective to improve overall efficiency and identify bottlenecks across entire supply chain flows.
What I'm doing currently involves going directly to sites and working with technicians. I literally ride along with them—getting in cars and spending entire days observing their work. This ground-level view is incredibly important because some problems might appear significant from a partial standpoint, but actually aren't when viewed from the entire system perspective.
My next step involves working with various departments, including customer service and other areas within the full value chain. This comprehensive approach helps us find the right problems and develop appropriate solutions. Only if AI is optimal do we implement it—sometimes the answer might be procedural changes rather than advanced technology.
From a corporate standpoint, I maintain both an executive-level view and what I call an "insect's view" from ground level. Having started as a DHL driver and later serving on a Board of Directors, I understand perspectives from both levels. This dual viewpoint is essential for effective problem identification and solution design.
How do you balance delivering quick AI wins with building scalable, long-term capabilities?
Balancing short-term results with long-term change is absolutely key to AI initiative success. Based on my experience in logistics, quick wins help secure early buy-in and demonstrate AI's tangible value to stakeholders who might initially doubt implementation.
In a previous project, I focused initially on AI-powered route optimisation for customer delivery, which showed immediate productivity improvement. Before implementation, technicians were experts at creating routes based on human knowledge and were sceptical about machine learning. However, after implementing the solution, we identified that machine learning could work much better than expected.
This success allowed me to expand AI use into other areas because we'd proven effectiveness. Simultaneously, we laid groundwork for long-term change by focusing on architecture, efficiency, and cross-functional collaboration. This dual approach ensures both immediate results and sustainable success.
The key is demonstrating value early whilst building robust foundations. Quick wins create momentum and stakeholder confidence, but without proper infrastructure and alignment, you can't achieve scalable change.
Quick wins help secure early buy-in and demonstrate AI's tangible value to stakeholders who might initially doubt technology implementation.
What organisational changes and new capabilities have you needed to develop to support AI implementation?
Effective AI implementation requires cross-functional orchestration among various stakeholders with different views and goals. We need roles focused on managing collaboration between IT, operations, strategy, and executive teams. The orchestration role is crucial for aligning diverse perspectives and ensuring successful implementation.
Specifically, we need specialists in analytics and business architects who can bridge requirements among stakeholders. These are essential new roles for AI initiatives, in addition to traditional roles like project management and system development.
However, securing people with these capabilities is extremely challenging. It's difficult to grow individuals with the necessary broad view because they need wide-ranging experience and the right mindset. What I've been doing is providing training environments and diverse opportunities that people need to drive by themselves.
The important point is how companies can provide challenging environments that grow people into what we need. We're also working with solution providers who can develop from scratch, and we collaborate closely with clients to adapt operations through various customisations.
How are you structuring architecture to support both current operations and future AI initiatives?
We've established real-time architecture that centralises streams into a unified lake, accessible through comprehensive dashboards. By leveraging this platform, we're optimising supply chain operations and enhancing decision-making with timely insights.
Currently, we're building dashboards to track KPIs daily or weekly, providing improvement recommendations to clients. Our next step involves automating these activities through AI. The framework allows us to extract and organise into master systems, enabling AI to calculate and produce optimal outputs.
Quality is fundamental—input quality directly defines output quality. We need comprehensive master information before AI can function effectively. The priority is creating frameworks to extract and organise properly, ensuring AI has the foundation for meaningful analysis and recommendations.
This approach encompasses not just logistics but comprehensive field service operations. Working with Coca-Cola on vending machine operations, we're managing hundreds of technicians providing repair services, spare parts supply chain management, and client interactions across locations. It's more complex than traditional logistics, requiring orchestration across multiple aspects.
Input quality directly defines output quality—we need comprehensive master data before AI can effectively function.
About Genpact
Genpact is a global professional services firm that delivers digital transformation solutions driving operational excellence for businesses worldwide. Born from GE with deep roots in Lean Six Sigma principles, the company seamlessly integrates operational rigour with advanced digital technologies including AI, machine learning, and cloud-based platforms. Genpact specialises in leveraging data-driven insights to optimise supply chains, enhance operational efficiency, and create sustainable growth across industries, helping organisations adapt to evolving business landscapes whilst creating long-term value through innovative solutions.
About Enate
Enate is the leading SaaS solution for business services. Enate orchestrates work from start to finish, giving clients the visibility and control needed to deliver better services. From email management and data analysis to intelligent document processing, Enate also offers a host of touch-button AI features designed to slash the time spent on manual work. Trusted by global service teams, Enate ensures smooth, consistent operations that help clients perform at their best.