Turning Fragmented Retail Data into Unified Insights for CPG Brands – with Leaders from Crisp and Nestlé Purina

This article is sponsored by Crisp and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

Global supply chains are fragile. Even minor disruptions can lead to large losses, impacting company revenue and economic stability. When critical links break down, the cascading effects frequently surpass initial forecasts, compelling companies and policymakers to reassess their resilience strategies.

A vivid example comes from J.P. Morgan’s coverage of Apple: during a single quarter in 2022, supply chain shortages led Apple to forecast $4-8 billion in lost revenue, underscoring how swiftly systemic interruptions translate into massive financial impacts for even the world’s most sophisticated companies.

According to the OECD, efforts to make supply chains more resilient — such as bringing production closer to home — could still reduce global trade by over 18% and shave more than 5% off global real GDP.

Walmart’s global supply chain uses self-healing inventory technology powered by AI. The system automatically detects overstock and reroutes supply to stores that need it, preventing waste before it happens. According to Walmart’s own executives, this technology has already prevented over $55 million in excess inventory losses, demonstrating how smart automation delivers real-time business value.

Emerj recently hosted a special series of the ‘AI in Business’ podcast with executives from across the CPG and retail industries, discussing how AI, data science, and agentic systems are transforming operations, decision-making, and ROI in these sectors.

Executives featured in the series include Henrique Wakil Moyses, Vice President of Data Science at Crisp; Dag Liodden, Chief Product Officer and Co-founder at Crisp, and Padma Hari, Chief Digital Officer at Nestlé Purina.

During these conversations with Emerj Editorial Director Matthew DeMello, leaders dived deep into how AI and agentic systems are being adopted in CPG and retail, the challenges of integrating technology with human-led processes, and the strategies driving measurable business impact and ROI.

This article examines various key insights from their conversations for leaders aiming to implement AI effectively, optimize data, and drive measurable business impact:

• Driving ROI with AI and agentic systems: Investing in AI and agentic systems to maximize return on investment by streamlining supply chains, optimizing inventory and assortment, and improving promotion effectiveness.
• Building alignment through mock simulations: Simulating end-to-end business planning through mock sessions helps teams identify gaps, align across functions, and build the muscle for integrated, data-driven decision-making before technology enters the picture.
• Optimizing CPG operations with agents: Implementing agentic AI to manage supply chain and merchandising tasks autonomously, where agents can reorder products, adjust planograms, and act on assortment opportunities, while learning from human feedback.

Driving ROI with AI and Agentic Systems

Episode: Turning CPG Complexity into Real-Time Decisions with AI – with Henrique Wakil Moyses of Crisp

Guest: Henrique Wakil Moyses, Vice President of Data Science, Crisp

Expertise: Data Analysis, Mathematical Modelling, Machine Learning

Brief Recognition: Henrique is a data science executive with over a decade of experience leading analytics and machine learning teams. Before joining Crisp as VP of Data Science, he held multiple leadership roles at Anheuser-Busch, including VP of Data & Analytics. He holds a PhD in Physics from New York University.

Henrique begins his podcast appearance by explaining the practical, high-impact ways AI and data science are being used in CPG and retail:

1. Streamlining supply chains: AI improves demand forecasting, inventory tracking, and ordering systems, ensuring products are shipped at the right time, avoiding stockouts and overstock, and saving money. This is the primary area where organizations see direct ROI.
2. Assortment and inventory optimization at retail: AI ensures stores have the right products in the right quantities, monitoring inventory almost in real time to prevent stockouts and ensure availability.
3. Ultra-personalized e-commerce targeting: AI uses rich consumer data to serve the right products to the right individuals at the right time, improving engagement and sales.
4. Promotion optimization in CPGs: Large CPG companies spend billions annually on promotions — AI helps ensure that discounts and campaigns are applied to the right products and are actually delivering the intended ROI.

He further explains the value and potential of agentic AI compared to traditional AI in CPG and retail. Unlike conventional AI that follows step-by-step automation, agentic AI, he says, can handle decision points mid-process when new data or disturbances occur, helping humans pivot and make better decisions in real time. One example provided by Henrique: dynamically adjusting supply chain operations in response to unexpected events.

“There are many use cases: on assortment, on detecting reasons why you came out of stock, or your inventory is low, and [an agent can] recommend actions for you, even getting to a point of actually performing those actions itself. It might be entering some sort of data in a spreadsheet; it might be sending alerts to a group of people; there are many ways that these agents can all act. We’re going to start seeing this more and more. So this field of agentic AI is going to keep expanding.”

– Henrique Wakil Moyses, Vice President of Data Science at Crisp

Optimizing CPG Operations with Agents

Episode: Building an AI-Ready Data Foundation for CPG Success – with Dag Liodden of Crisp

Guest: Dag Liodden, Chief Product Officer and Co-founder, Crisp

Expertise: Entrepreneurship, Product, Business Strategy

Brief Recognition: Dag is a seasoned entrepreneur who co-founded Giant Leap Technologies and then Tapad, which was later acquired by Experian Marketing Services. He holds a Master’s degree in Computer Science from the Norwegian University of Science and Technology.

Dag says CPG companies will adopt AI in stages, starting with foundational use cases such as category management, promotion management, and supply chain operations. Over time, they’ll move to more advanced analytics, including:

• Price elasticity estimation
• Assortment optimization
• Spotting distribution opportunities
• Sophisticated demand forecasting

A key point Dag stresses is that AI drastically reduces the effort required to analyze and act on data. Currently, humans focus on a small subset of top-performing products because analyzing the full portfolio is too time consuming.

With AI, these insights can be generated more frequently (daily vs. weekly/monthly) and across the entire product portfolio, not just the top 5–10 products, allowing companies to unlock more value and make faster, more informed decisions.

Here, Dag highlights two main points about how AI can transform work in CPGs:

1. Reducing toil and freeing time for strategic work: Currently, much analytical and strategic work is undermined because employees are tied up with repetitive tasks such as running Monday morning reports or daily supply chain checks. These manual tasks consume time that could otherwise be spent on higher-value decision-making.
2. Enabling intelligent, adaptive agentic applications: AI agents can go beyond simple alerts or dashboards. For example, in supply chain monitoring, whether an out-of-stock alert is critical depends on context — historical trends, product lifecycle, forecast variations, etc. Traditional interfaces can’t easily capture all these nuances.

   AI agents, however, learn from human feedback, including qualitative insights, and adapt future alerts to reduce false positives and negatives, enabling smarter, context-aware decision-making.

“We’re going to see a lot of agent-to-agent communications. For example, in a promotional campaign where stock is running low, agents will be able to take corrective action and reorder products immediately. Similarly, for assortment changes, if an agent identifies an opportunity for a product that isn’t on the shelf but should be — because it’s performing well in comparable regions — it can update the planograms and submit them to the retailer. The agent can either route the update for human approval or, in some cases, submit it directly into the planogram system.”

– Dag Liodden, Chief Product Officer and Co-founder at Crisp

Building Alignment Through Mock Simulations

Episode: The Future of AI Agents in Consumer Goods Operations – with Padma Hari of Nestlé Purina

Guest: Padma Hari, Chief Digital Officer, Nestlé Purina

Expertise: Blockchain, Artificial Intelligence, Data Science

Brief Recognition: Padma has extensive experience leading global, matrixed teams of digital and technology experts to drive measurable business transformation and growth. In previous roles, Hari has worked with leading organizations, including Reckitt, Revlon, and Bloomingdale’s. She holds a Master of Science in Business Analytics from NYU Stern.

In her conversation, Padma emphasizes that technology alone doesn’t drive transformation; people and processes do.

She explains that organizations often mistakenly assume that adopting new technology will automatically bring change. In reality, technology is just an enabler; it’s the humans, the ones making decisions and managing operations, who determine whether change actually happens.

To build effective end-to-end or integrated business planning, she suggests companies should first map their processes and decision points — for example, how sales targets (such as increasing sales by 10%) connect to supply, manufacturing, and planning teams. Currently, these functions often work in silos, each pursuing its own objectives.

Hari argues that before bringing in technology, organizations must:

1. Redesign processes around shared business goals (like profitable growth).
2. Align people by defining roles (personas) and how they’ll work in the new, integrated setup.
3. Test the new process in a “mock world” — a simulation that allows teams to practice, find gaps, and build new habits before implementing technology.

Only after this groundwork should technology and data be layered on, as they act like rocket fuel — accelerating a process that’s already well-structured and aligned.

Hari continues, saying that by simulating the integrated business planning process through dashboards and alignment meetings using real data, teams learn to think in terms of business drivers and detractors, and what’s helping or hurting performance. This, she argues, enables them to start making systemic, connected decisions rather than isolated ones.

The mock sessions create a shared learning environment where functions like sales, supply, and manufacturing can see how their actions affect one another and practice cross-functional alignment.

Padma believes agentic AI will be transformative for CPG companies. She emphasizes that companies that customize AI to fit their unique “organizational DNA” will gain the most significant competitive advantage.

“We’re moving toward a world with both a digital and a physical workforce — and that’s where CPGs can leapfrog. Unlike heavily automated sectors, CPGs still rely on human-led operations, so agentic AI can transform how they work. The technology won’t replace their DNA; it will amplify it, turning scattered, manual decisions into structured, real-time intelligence.”

​– Padma Hari, Chief Digital Officer at Nestlé Purina