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 revealed in alignment with our Emerj sponsored content guidelines. Learn extra about our thought management and content material creation providers on our Emerj Media Services page.
Global provide chains are fragile. Even minor disruptions can result in massive losses, impacting firm income and financial stability. When important hyperlinks break down, the cascading results incessantly surpass preliminary forecasts, compelling firms and policymakers to reassess their resilience methods.
A vivid instance comes from J.P. Morgan’s protection of Apple: throughout a single quarter in 2022, provide chain shortages led Apple to forecast $4-8 billion in misplaced income, underscoring how swiftly systemic interruptions translate into huge monetary impacts for even the world’s most subtle firms.
According to the OECD, efforts to make provide chains extra resilient — equivalent to bringing manufacturing nearer to house — might nonetheless scale back international commerce by over 18% and shave greater than 5% off international actual GDP.
Walmart’s international provide chain makes use of self-healing stock expertise powered by AI. The system routinely detects overstock and reroutes provide to shops that want it, stopping waste earlier than it occurs. According to Walmart’s personal executives, this expertise has already prevented over $55 million in extra stock losses, demonstrating how sensible automation delivers real-time enterprise worth.
Emerj lately hosted a particular sequence of the ‘AI in Business’ podcast with executives from throughout the CPG and retail industries, discussing how AI, information science, and agentic methods are remodeling operations, decision-making, and ROI in these sectors.
Executives featured within the sequence embody 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 methods are being adopted in CPG and retail, the challenges of integrating expertise with human-led processes, and the methods driving measurable enterprise influence and ROI.
This article examines numerous key insights from their conversations for leaders aiming to implement AI successfully, optimize information, and drive measurable enterprise influence:
- Driving ROI with AI and agentic methods: Investing in AI and agentic methods to maximise return on funding by streamlining provide chains, optimizing stock and assortment, and enhancing promotion effectiveness.
- Building alignment by mock simulations: Simulating end-to-end enterprise planning by mock classes helps groups determine gaps, align throughout capabilities, and construct the muscle for built-in, data-driven decision-making earlier than expertise enters the image.
- Optimizing CPG operations with brokers: Implementing agentic AI to handle provide chain and merchandising duties autonomously, the place brokers can reorder merchandise, alter planograms, and act on assortment alternatives, whereas studying from human suggestions.
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 knowledge science govt with over a decade of expertise main analytics and machine studying groups. Before becoming a member of Crisp as VP of Data Science, he held a number of management roles at Anheuser-Busch, together with VP of Data & Analytics. He holds a PhD in Physics from New York University.
Henrique begins his podcast look by explaining the sensible, high-impact methods AI and information science are being utilized in CPG and retail:
- Streamlining provide chains: AI improves demand forecasting, stock monitoring, and ordering methods, making certain merchandise are shipped on the proper time, avoiding stockouts and overstock, and saving cash. This is the first space the place organizations see direct ROI.
- Assortment and stock optimization at retail: AI ensures shops have the best merchandise in the best portions, monitoring stock virtually in actual time to stop stockouts and guarantee availability.
- Ultra-personalized e-commerce concentrating on: AI makes use of wealthy client information to serve the best merchandise to the best people on the proper time, enhancing engagement and gross sales.
- Promotion optimization in CPGs: Large CPG firms spend billions yearly on promotions — AI helps be certain that reductions and campaigns are utilized to the best merchandise and are literally delivering the meant ROI.
He additional explains the worth and potential of agentic AI in comparison with conventional AI in CPG and retail. Unlike typical AI that follows step-by-step automation, agentic AI, he says, can deal with resolution factors mid-process when new information or disturbances happen, serving to people pivot and make higher selections in actual time. One instance offered by Henrique: dynamically adjusting provide chain operations in response to sudden occasions.
“There are many use instances: on assortment, on detecting the reason why you got here out of inventory, or your stock is low, and [an agent can] suggest actions for you, even getting to a degree of truly performing these actions itself. It is perhaps coming into some form of information in a spreadsheet; it is perhaps sending alerts to a gaggle of individuals; there are various ways in which these brokers can all act. We’re going to start out seeing this extra and extra. So this discipline of agentic AI goes to maintain increasing.”
– 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 diploma in Computer Science from the Norwegian University of Science and Technology.
Dag says CPG firms will undertake AI in phases, beginning with foundational use instances equivalent to class administration, promotion administration, and provide chain operations. Over time, they’ll transfer to extra superior analytics, together with:
- Price elasticity estimation
- Assortment optimization
- Spotting distribution alternatives
- Sophisticated demand forecasting
A key level Dag stresses is that AI drastically reduces the hassle required to investigate and act on information. Currently, people give attention to a small subset of top-performing merchandise as a result of analyzing the total portfolio is just too time consuming.
With AI, these insights might be generated extra incessantly (each day vs. weekly/month-to-month) and throughout all the product portfolio, not simply the highest 5–10 merchandise, permitting firms to unlock extra worth and make sooner, extra knowledgeable selections.
Here, Dag highlights two details about how AI can remodel work in CPGs:
- Reducing toil and releasing time for strategic work: Currently, a lot analytical and strategic work is undermined as a result of workers are tied up with repetitive duties equivalent to working Monday morning stories or each day provide chain checks. These handbook duties devour time that would in any other case be spent on higher-value decision-making.
- Enabling clever, adaptive agentic functions: AI brokers can transcend easy alerts or dashboards. For instance, in provide chain monitoring, whether or not an out-of-stock alert is important is dependent upon context — historic tendencies, product lifecycle, forecast variations, and many others. Traditional interfaces can’t simply seize all these nuances.
AI brokers, nevertheless, study from human suggestions, together with qualitative insights, and adapt future alerts to scale back false positives and negatives, enabling smarter, context-aware decision-making.
“We’re going to see quite a lot of agent-to-agent communications. For instance, in a promotional marketing campaign the place inventory is working low, brokers will have the ability to take corrective motion and reorder merchandise instantly. Similarly, for assortment modifications, if an agent identifies a possibility for a product that isn’t on the shelf however must be — as a result of it’s performing nicely in comparable areas — it may possibly replace the planograms and submit them to the retailer. The agent can both route the replace for human approval or, in some instances, submit it instantly 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 in depth expertise main international, matrixed groups of digital and expertise specialists to drive measurable enterprise transformation and progress. In earlier roles, Hari has labored with main organizations, together with Reckitt, Revlon, and Bloomingdale’s. She holds a Master of Science in Business Analytics from NYU Stern.
In her dialog, Padma emphasizes that expertise alone doesn’t drive transformation; folks and processes do.
She explains that organizations typically mistakenly assume that adopting new expertise will routinely deliver change. In actuality, expertise is simply an enabler; it’s the people, those making selections and managing operations, who decide whether or not change really occurs.
To construct efficient end-to-end or built-in enterprise planning, she suggests firms ought to first map their processes and resolution factors — for instance, how gross sales targets (equivalent to rising gross sales by 10%) join to provide, manufacturing, and planning groups. Currently, these capabilities typically work in silos, every pursuing its personal goals.
Hari argues that earlier than bringing in expertise, organizations should:
- Redesign processes round shared enterprise objectives (like worthwhile progress).
- Align folks by defining roles (personas) and how they’ll work within the new, built-in setup.
- Test the brand new course of in a “mock world” — a simulation that permits groups to follow, discover gaps, and construct new habits earlier than implementing expertise.
Only after this groundwork ought to expertise and information be layered on, as they act like rocket gas — accelerating a course of that’s already well-structured and aligned.
Hari continues, saying that by simulating the built-in enterprise planning course of by dashboards and alignment conferences utilizing actual information, groups study to assume when it comes to enterprise drivers and detractors, and what’s serving to or hurting efficiency. This, she argues, allows them to start out making systemic, related selections slightly than remoted ones.
The mock classes create a shared studying atmosphere the place capabilities like gross sales, provide, and manufacturing can see how their actions have an effect on each other and follow cross-functional alignment.
Padma believes agentic AI will likely be transformative for CPG firms. She emphasizes that firms that customise AI to suit their distinctive “organizational DNA” will achieve probably the most vital aggressive benefit.
“We’re shifting towards a world with each a digital and a bodily workforce — and that’s the place CPGs can leapfrog. Unlike closely automated sectors, CPGs nonetheless depend on human-led operations, so agentic AI can remodel how they work. The expertise received’t change their DNA; it is going to amplify it, turning scattered, handbook selections into structured, real-time intelligence.”
– Padma Hari, Chief Digital Officer at Nestlé Purina
