The Data Integrity Imperative in Modern Market Research
Nina Schertel of Liga Pesquisa argues that the quality of data interpretation outweighs model complexity in driving actionable business intelligence.
By LNGFRM Team
Published June 23, 2026
Yatin Garg, a senior leader at Amazon with experience spanning BCG, Kellogg, and IIT Delhi, has built a playbook for scaling AI across global retail. By aligning cross-functional teams around a shared roadmap, securing multi-year investment through phased milestones, and balancing rapid AI innovation with legacy modernization, he shows how strategy and structure can turn prototypes into enterprise-wide impact—improving customer journeys, partner ecosystems, and long-term business performance.
The integration of artificial intelligence into global retail is no longer a futuristic concept but a present-day imperative for competitive advantage. As companies move from isolated experiments to enterprise-wide scaling, they face significant organizational, technical, and financial hurdles.
Yatin Garg, a senior leader at Amazon with over 12 years of experience across technology and e-commerce, offers a perspective grounded in leading large-scale AI and machine learning initiatives. With a background that includes advising Fortune 500 CXOs at Boston Consulting Group and holding degrees from Kellogg School of Management and the Indian Institute of Technology Delhi, his experience highlights the critical link between organizational strategy and tangible business outcomes.
Garg’s journey offers a valuable perspective on the complexities of scaling AI in a global retail context. His experience highlights the strategic choices necessary to align large, cross-functional teams, secure sustained investment, and modernize legacy infrastructure—while ensuring that each initiative delivers measurable value to customers, partners, and the bottom line.
The primary driver for scaling AI in retail is the pursuit of a superior customer experience, a goal that requires deep technical innovation. For an online marketplace, this means creating a seamless journey from product discovery to delivery, a challenge that AI is uniquely positioned to solve.
Garg explains that the initial vision was rooted in fundamental customer needs. He states, “Amazon aspires to offer a best-in-class experience for its customers. Unlike physical stores, Amazon’s online store offers a virtually unlimited variety of products for customers on the webpage that do not require physical shelf space.”
Fulfilling this promise necessitates sophisticated solutions across the entire value chain, from supply chain logistics to the digital storefront. The decision to invest heavily in AI was a direct response to this complexity.
Garg notes, “Delighting customers and offering the best value drove our early decisions to invest in scaling AI and machine learning solutions across our retail business operations.” This focus on customer-centric value is critical, as studies show that enhancing B2B customer experience can lower churn by 10-15% and increase win rates. Success requires a balanced set of metrics beyond single scores, incorporating measures like Customer Effort Score (CES) and Customer Lifetime Value (CLV).
One of the greatest challenges in scaling AI is not technological but organizational: aligning disparate teams like engineering, product, finance, and program management around a unified vision. Without a clear, top-down strategy, AI initiatives can remain fragmented and fail to deliver broad-based value.
Garg emphasizes the need for a cohesive approach, stating, “Driving broad-based value through AI across the company requires a coordinated effort and top-down vision. It needs to be integrated into the company’s vision so all parts of the organization can work backwards.”
This alignment is crucial for moving from experimentation to scaled implementation. The central challenge, according to Garg, was to create a common framework for execution.
He adds, “Our biggest challenge towards having an AI roadmap was to build a shared understanding of how we envisioned the customer experience to look like and how the whole organization was going to work backwards. Early on, we created AI ‘guilds’ that had direct access and visibility with the leadership team to drive faster decision-making.”
This approach mirrors the hub-and-spoke model, which combines a central AI hub for governance with decentralized spokes in business units. Research suggests this structure is effective, with one review finding that 70% of financial institutions with highly centralized GenAI models advanced use cases to production, compared to only 30% with decentralized approaches, underscoring the importance of a strong central strategy.
AI-driven transformations are often multi-year endeavors that require significant, sustained investment, making executive buy-in a critical prerequisite. To secure this commitment, leaders must present a clear, compelling business case that balances short-term wins with long-term strategic value.
Garg explains that the process begins with establishing clear principles for prioritization. He says, “The most important part in gaining executive buy-in is to lay out clear tenets and a mental model for prioritizing projects. Then we work backwards from that mental model to lay out short-term and long-term deliverables, clearly stating their customer value over time.”
Building confidence among stakeholders for long-term projects involves breaking them down into manageable phases with clear success metrics. Garg notes, “We dissected long-term projects into granular milestones with clear success metrics that helped build confidence among senior stakeholders to commit to multi-year investments.”
This strategy aligns with the shift toward an outcome-based funding model, where investment is tied to business results rather than predefined outputs. A comprehensive enterprise AI investment plan typically allocates significant budget not just to technology but also to talent, training, and process transformation, reinforcing the need for a holistic, long-term financial commitment.
The ultimate test of any AI strategy is its ability to deliver measurable performance improvements, whether through revenue growth or operational cost reduction. Garg points to the implementation of conversational agents as a concrete example. For consumers, these tools streamline the shopping experience.
He explains, “Amazon implemented conversational style agents on the website (Rufus) to help customers find products, ask specific questions, and read summarized user reviews. This helped Amazon appeal to more customers and increase conversion through its purchase funnel.”
This same technology was extended to the company’s vast network of business partners, demonstrating AI’s capacity to improve ecosystem-wide efficiency. Garg adds, “Similarly, we launched conversational style agents for our 100,000+ selling partners that do business with Amazon. Such a solution helps Amazon drive efficiency and increase customer value by offering a wider assortment of products.”
This focus on partner experience is critical, as research shows 80% of partners report that ‘ease of doing business’ is their top criterion for vendor selection. To achieve this, companies are encouraged to adopt flexible models based on partner value motions rather than rigid segmentation.
Accelerating global product launches powered by AI and ML requires more than just technology; it demands fundamental cultural and structural changes within an organization. According to Garg, leadership alignment is the foundational element. He states, “The most important aspect was leadership alignment in building a shared understanding that the use of AI across Retail operations is going to be a key differentiator in the future to offer better customer experiences.”
Beyond alignment, success depends on creating structures that foster specialized expertise and rapid innovation. Garg elaborates, “Structurally, Amazon created separate groups that specialize in building foundational AI capabilities with direct visibility to leadership.”
This approach is consistent with establishing an AI Center of Excellence (CoE), which can operate on a hub-and-spoke model to balance central governance with business unit autonomy. For large enterprises, this can evolve into a federated model where a central ‘GenAI factory’ develops foundational capabilities that are then leveraged by divisional CoEs, blending centralized efficiency with domain-specific expertise.
A significant hurdle in any large-scale technology initiative is managing the operational complexity of legacy systems while pushing for rapid innovation. Modern AI-based solutions must be integrated with older infrastructure without disrupting core business functions.
Garg points to advanced technologies as a key enabler. He says, “We use the latest technologies such as agentic AI, Model Context Protocol, and Retrieval-Augmented Generation to ensure we keep innovating fast on the technology layer that provides new experiences to customers while maintaining legacy systems.”
This modernization effort is often aided by AI itself, which can improve developer productivity. Garg adds, “AI is helping us innovate fast by augmenting developer productivity to modernize legacy systems, creating synergistic outcomes.”
A common strategy for this is the Strangler Fig pattern, where new services are gradually built around a legacy system, eventually replacing it. This approach can be enhanced with a data streaming backbone like Apache Kafka to decouple applications, as seen in Allianz’s modernization efforts. Furthermore, AI-powered tools can create a risk heat-map to identify and remediate technical debt in critical code modules.
Measuring the full ROI of AI initiatives
Assessing the true business value of AI requires looking beyond conventional financial metrics like Net Present Value (NPV) or Free Cash Flow (FCF). While these are important, they often fail to capture the intangible benefits that drive long-term growth, such as improved customer satisfaction and partner relationships. Garg emphasizes a more holistic approach to measuring ROI.
He explains, “While conventional financial frameworks continue to be the go-to mechanisms to calculate financial ROI, we also use intangibles such as end customer delight to simplify the purchase journey, vendor and seller experience to reduce cost of doing business, and employee satisfaction to ensure we retain the best talent.”
The key is to translate these intangible goals into concrete input metrics that can guide decision-making. Garg adds, “While there’s no single metric that tracks all such intangibles, we bring input metrics for each of these dimensions directly into our prioritization and value assessment framework to identify trade-offs and drive stakeholder decision-making.”
This aligns with best practices in B2B customer experience, where firms use predictive models to identify at-risk customers and quantify potential revenue impact. To secure executive support, it is crucial to frame these initiatives in terms of financial outcomes, as strong B2B CX can significantly increase win rates.
For leaders seeking to build organizational capabilities for scaling AI, the journey must begin with strategy, not just technology. A common pitfall is focusing on bottom-up technical development without first securing top-down strategic alignment, causing promising prototypes to stall.
Garg’s primary advice is to start at the top. He says, “A key piece of advice I’d give other leaders is to drive the change top-down before thinking about building bottom-up talent and technical capabilities.”
Without this alignment, organizations struggle to realize the full potential of their AI investments. Garg warns, “Companies get stuck at the prototype stage and don’t realize the full value of AI at a large scale when there’s misalignment between top-down strategy and bottom-up organizational capabilities.”
This perspective is supported by strategic frameworks like BCG’s recommendation to dedicate 70% of AI effort to people and processes, 20% to infrastructure, and only 10% to algorithms, highlighting the importance of the organizational component. This strategic direction must be supported by robust data governance models that ensure consistency and control as AI scales across the enterprise.
Ultimately, the successful scaling of AI in global retail is a testament to organizational maturity as much as technological prowess. It requires a clear vision that places customer value at its core, a deliberate strategy for aligning cross-functional teams, and a disciplined approach to securing investment and measuring returns.
As leaders like Garg have demonstrated, building these capabilities is the true foundation for turning the promise of AI into sustainable, enterprise-wide performance.
Nina Schertel of Liga Pesquisa argues that the quality of data interpretation outweighs model complexity in driving actionable business intelligence.
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