Spoonful of AI podcast: Data – the real power behind AI

All about retail data readiness for AI, featuring Henrique Wakil Moyses, VP of Data Science at Crisp.

Is your data actually ready for AI? Can your teams leverage it in ways they understand and trust? Podcast host Carlos Rodela from leading foodtech publication The Spoon sat down with Crisp VP of Data Science Henrique Wakil Moyses to dig into what ‘AI-ready data’ really looks like for retail and CPG companies, and how Crisp is putting it to work.

The episode lands alongside Crisp’s 2025 AI Innovation Award and the launch of Crisp AI Agent Studio, a concierge-style platform where users leverage clean, harmonized retail data for everything from Monday-morning reporting to supply chain monitoring, promotions planning, and competitive research.

Below are a few highlights of the podcast. Listen to the episode for the full scoop. 🥄

1. Is your data ready for AI?

“Is your data ready for AI? Is your data clean and coming in on schedule with the accuracy you need?”

Referencing his extensive background leading data science at Anheuser-Busch, and before that, a physics PhD from NYU, Henrique shares that most consumer goods organizations don’t suffer from a lack of data, or data ingestion problems – more like data indigestion problems. Retailer feeds arrive with inconsistent naming and formats across products, stores, and channels, making basic analysis painstaking and unreliable. In the episode, Henrique offers an order of operations to automate, normalize, and harmonize retail data sets. 

Further, Crisp’s semantic layer purpose-built for the retail industry adds the business context teams need to interpret data instantly and feed it into models for reliable AI results.

2. How Crisp applies AI (inside and out)

Internally, Henrique shares that Crisp teams use AI to speed concept-to-prototype, bringing ideas to customers faster. Externally, Crisp focuses on practical wins that matter to retail and CPG. 

In addition to preparing customers with an optimal AI-ready data foundation, Crisp offers data science-ready AI Blueprints – open-source models that leverage retail data for insights and reporting around Demand Forecasting, Anomaly Detection, On-Shelf Availability (OSA), and more. 

Crisp aims to ‘demystify’ AI for retail-focused organizations, offering very practical use cases around supply chain optimization, and even master data management (MDM), where companies can more seamlessly organize product identifiers, attributes, and hierarchies – which is traditionally a highly manual and complex process.

3. Reducing waste with AI

Crisp was built on a foundational mission to reduce waste in the food supply chain. Henrique highlights grocery produce as a prime example: smarter forecasting and ordering (including a recent acquisition by Crisp of Shelf Engine, which focuses on ordering optimization) help keep shelves stocked without overbuying, cutting spoilage at the source. The same playbook applies beyond food – think consumer electronics and apparel – where demand sensing and inventory right-sizing reduce markdowns, returns, and carrying costs while lifting profits through more intelligent, responsive systems and better-informed teams.

4. The future of agentic commerce

Agentic AI can proactively surface issues, recommend actions, and trigger workflows, all grounded in the clean retail data teams trust. Crisp’s AI Agent Studio is a critical step toward that future.

Unlike other LLMs or chatbots, Crisp’s AI Agents are built specifically for the retail supply chain, drawing on deep industry expertise to uncover root causes, predict and prevent out-of-stocks, optimize promotions, and even prepare for line reviews. 

The vision is simple: go from data to done. In the Spoonful of AI podcast, Henrique shares why AI-ready retail data pushes teams past reporting and into real-time execution.

Ready to try it with your own data? Join our Open Beta waitlist for Crisp’s AI Agent Studio.