Connecting SAP and Snowflake for Real Outcomes and Operationalized AI

Why This Webinar Matters

Retail organizations face unprecedented pressures, from evolving supply chains and changes in global trade policies to consumer expectations for personalization and efficiency. Traditional systems, siloed data approaches, and rigid processes make achieving agility and extracting AI insights challenging.  

This webinar introduced practical solutions that aim to break down these silos and integrate SAP ERP data with Snowflake’s cutting-edge platform. At its core, BDC Connect makes merging SAP and non-SAP data seamless while enabling bidirectional sharing, maintaining strong governance, and fueling scalable AI use cases. For businesses looking to innovate at speed, this is a game-changing move. 

Key Announcements and Themes

1. SAP BDC Connect & Its Transformative Potential

This release bridges the gap between two pivotal platforms, connecting SAP and Snowflake with bidirectional, zero-copy sharing capabilities. As Matt Florian explained, traditional data sharing methods with SAP were often “one-directional,” creating friction and bottlenecks for scalability and advanced analytics. 

Now, BDC Connect allows organizations to seamlessly share semantic definitions, technical infrastructure, and data bidirectionally between SAP and Snowflake, without needing to move data out of its original environment. This innovation eliminates hurdles in data access while maintaining SAP’s strong security model. 

Freddy Guard highlighted its business importance: “We truly believe that [BDC Connect] is going to make it much simpler for our customers to take advantage of all of their SAP data, merge it with non-SAP data, and really start driving deeper insights as you start activating your AI use cases.” 

2. Solving Challenges in AI Adoption

While AI promises transformative value for businesses, adopting and scaling AI initiatives across a complex enterprise remains challenging.

As Freddy pointed out, “It is relatively challenging to set up enterprise AI and scale enterprise AI successfully unless you start putting in the right building blocks in place.” 

Freddy and Matt outlined three key enablers for successful enterprise AI: 

• Data Accessibility: Harnessing relevant internal and external datasets is critical for building robust AI training models. SAP ERP systems house essential information for most retail organizations, and tools like Snowflake ensure seamless ecosystem integration.
• Data Platforms: Snowflake positions itself as the AI Data Cloud Company, offering easy-to-use, connected, and trusted architecture. Freddy elaborated: “We work across all three major public clouds, using an open architecture and enabling a comprehensive ecosystem of partners, including Hakkoda.”
• Governance & Use Cases: Identifying impactful use cases that influence business metrics (such as P&L) is vital. AI should not be a technical project—it needs to deliver measurable value in real-world contexts.

3. Democratizing Data Across Enterprises

One of the standout points was the emphasis on democratizing data access within organizations. Matt explained: 

“This will open up the entire landscape to take full advantage and get full value out of your SAP investments as well as the investments in the rest of your ecosystem.” 

Organizations often struggle to share insights across teams and departments. BDC Connect allows business users—not just analysts—to access actionable insights directly through integrated data flows. 

For example, merging SAP data with external datasets (like weather, supply chain trends, or third-party logistics) enables more proactive decision-making and better outcomes for predictive supply chain models.

Freddy summarized this shift, noting that “decision-making is no longer siloed by systems of record. You are able to drive deeper insights.” 

4. Use Case Prioritization: The AI ROI Formula

Both speakers emphasized starting small and focusing on ROI-driven use cases. Rather than “boiling the ocean” with multiyear AI implementations, Freddy advocated for pragmatism: 

“You’ve got to first start with use cases and really understand, is this question worth solving? Can it move your P&L?” 

Matt echoed the sentiment: 

“The use cases that we talked about aren’t trying to go for boil-the-ocean use cases…they’re looking at what is pragmatic, what is the right thing to do first.” 

5. Managing Automation and Human Trust

While AI brings automation closer to reality, human involvement remains critical for ensuring trust, governance, and addressing exceptions. Both Freddy and Matt agreed that humans will stay in the loop for most enterprise applications, at least until AI reaches full maturity in the industry. 

Freddy summarized: 

“We are probably still a ways away from pure agentic capabilities without human intervention…For most of the commercial applications today, there is a human in the lead.” 

Matt reinforced this point:  “Trust only comes over time—after you’ve proven that these AI-driven decisions are reliable.” 

6. Snowflake’s Competitive Edge

Freddy detailed Snowflake’s value proposition for enterprises aiming to streamline AI workflows: 

• Easy to Use: A comprehensive, managed platform capable of handling structured, semi-structured, and unstructured data.
• Connected Ecosystem: Open architecture enabling seamless integration across major public clouds and partner technologies like SAP.
• Trusted Security: Organizations can confidently run large models within Snowflake’s secure environment while maintaining full visibility and control.