How to Make Your SAP Data Ready for Generative AI

Blog / How to Make Your SAP Data Ready for Generative AI

A retail chain in the US faced a sudden problem. They hadn’t anticipated how an unexpected cold snap would spike demand for winter gear in the Northeast. To tackle this, they needed deep, real-time visibility into the following areas:

• How much winter stock was available in Northeastern stores?
• How fast could they restock those stores with winter essentials?
• How should they adjust pricing in the Northeast to reflect the sudden spike in demand?

But they couldn’t find the answers. The reason? Data Silos.

It’s peak summer, and like every year, this CPG company had anticipated a high demand for its No-sugar beverages. A leading scientific journal ran a story advising caution against the aspartame content in the beverage. It caught a storm on social media, and two weeks later, stores are sitting on excess inventory. To solve this, the company needed deep real-time visibility into:

• Which locations were most affected by the excess inventory?
• How could they quickly adjust their marketing strategy to address consumer concerns?
• What alternative sales channels could they leverage to reduce the surplus?

They couldn’t find the answers. The reason? Data Silos.

Forrester Report: Employees are Losing 12 Hours a Week Chasing Data

Data Silos are a raging pandemic.

• A survey by Airtable and Forrester found that large organizations typically utilize 367 software apps and systems to manage their workflows.

According to a Google Cloud-IDC report:

• 27% of US manufacturers report a lack of necessary infrastructure to overcome data silos.
• 22.4% struggle with accessing siloed customer data. 

What Makes Extracting SAP Data So Tough?

Talking about data silos, it’s key to start with SAP data. Why? Because nearly 80% of companies worldwide use SAP to run their operations, and solving data silo issues in SAP alone can save millions. 

The challenge is that SAP has a very large and complicated setup. Within the SAP environment, data sharing is fast and seamless. But the second you step out of the SAP environment, you hit a roadblock. External applications do not naturally align with SAP’s data models or processing logic, leading to data exchange and synchronization challenges.

As this Cloudera article points out, transforming SAP data comes with its set of challenges: 

Too Many Tables: The SAP data landscape is complex, making it difficult first to locate the needed data among thousands of tables and then to extract it.

Customization Adds Complexity: SAP systems are highly customizable, allowing custom logic to be applied to meet specific business needs. The altered data structures become even harder to extract.

Every Extraction Is Unique: Getting the right data out of SAP involves technical hurdles. Whether pulling all necessary data at once or continuously updating it, the process demands specific approaches for every extraction.

How Do You Add Gen AI Capabilities to SAP Solving Supply Chain Challenges?

Integrating Gen AI into SAP requires careful planning. You need to first ensure that adding these capabilities doesn’t disrupt SAP’s operational integrity or go against its data exchange policies. 

Working With A SAP-Certified Partner 

As an SAP-certified partner, Pluto7 plays a pivotal role in this integration. With certified solutions that mesh seamlessly with SAP’s framework, Pluto7 ensures that adding Gen AI capabilities complements the existing SAP environment without causing disruptions. Our Decision Intelligence Platforms — Planning in a Box, CX Sense, and Konnect MDE — act as a bridge, connecting data from diverse sources, including SAP, Salesforce, and Oracle, into one centralized system.

The following section will show how you can go from a siloed data environment to Gen AI-powered decision-making in 4 weeks using Google Cloud and our decision intelligence platforms. We chose Google Cloud for this demonstration due to its comprehensive suite of tools — BigQuery for data analysis, Vertex AI for AI/ML model management, and LLM models like Gemini — optimally suited for refining SAP data. 

At the Heart of Gen AI is Data Foundation

Gen AI thrives on data quality. Better Quality Data = Output you can trust. We must understand that Gen AI is much more evolved than a vanilla ‘search’ function. It combines data from internal and external sources, factoring in real-time fluctuations to provide a result. And so, for this technology to live up to its potential, you need to provide a solid data foundation. 

Creating a data foundation means combining data from various sources—like SAP, Salesforce, and NetSuite—into one clean, enriched, and unified view. With this foundation in place, you are ready to solve multiple use cases across Sales, Marketing, Supply Chain, Finance, and more.

The Cortex framework accelerates this process. Developed by Google Cloud, this framework is built for migrating SAP data to BigQuery in the quickest and most cost-effective way. It has reference architectures and pre-trained models to speed up this integration. Common migration hurdles, such as identifying SAP tables, reconciling data formats, and ensuring data quality, are significantly reduced. What normally takes months, Cortex accomplishes in days.

Data Silos to Generative AI in 4 Weeks 

Our decision intelligence platforms are designed to sit on top of the data foundation and provide enterprise-ready solutions for structured and unstructured data. They have an LLM-agnostic architecture, can deliver deterministic responses with source references, and feature enterprise access controls. 

These platforms are built specifically for SAP, highly customizable, and can be deployed within 60 minutes. They are trained with vast domain knowledge and utilize Google Cloud’s advanced analytics tools to deliver tangible results in just four weeks.

Data On Demand: Ask, Analyze, Achieve

Once you have deployed the decision intelligence platform, gaining insights becomes intuitive. You can prompt the platform with questions or scenarios using natural language via voice or text commands. The platform then translates your question into an SQL query, blending internal and external data sources to find the information you need. With Google’s Gemini powering the engine, you’ll receive prompt and context-rich responses.

Building on this foundation, our platform offers a suite of features designed to further enhance your insight discovery process:

• Adding New Data Sources: Integrate new data sources on demand, from structured internal databases to unstructured external feeds.
• Scalable As Data Volume Grows: Automatically adjusts to accommodate increased data volumes from internal and external sources without manual intervention.
• Supports Multiple Data Formats: From CSV files in your cloud storage to JSON data from web services and binary data from IoT devices, the platform can ingest and process a diverse set of data formats.
• Automated Data Mapping: Utilizes AI to recognize patterns and structures in new data sources, making it simpler to add this data to your existing models.
• Real-Time Data Processing: Capable of ingesting and analyzing data streams in real-time, enabling immediate insights from sources like social media feeds, website activity, and sensor outputs.
• Expertise and Support: Access to specialists for assistance with complex data integration challenges, ensuring you can leverage the full potential of your data assets.

What Challenges in Supply Chain Can You Solve With Gen AI? 

The right way to begin leveraging Gen AI within your operations is to focus on a singular case to quickly recognize its business value, and then progressively incorporate more applications. To give you a glimpse into how others in your field are using Gen AI, here’s a list of top use cases to think about:

• Enterprise-wide Visibility: Consolidates operational, manufacturing, and planning data to offer a comprehensive overview of business performance and opportunities.
• Inventory Visibility: Aggregates data across storage locations to provide a clear, current view of stock levels.
• Demand Sensing: Integrates market signals and sales data to adjust demand forecasts, aligning inventory planning with real-world conditions.
• Inventory Aging Management: Reviews inventory age data to flag aging stock, prompting actions to reduce holding times and prevent obsolescence.
• Last Mile Delivery: Coordinates transportation schedules, monitors delivery progress, and tracks resource allocation for optimized logistics operations.
• Risk Management: Accesses financial records, compliance guidelines, and audit trails to identify and mitigate potential risks in financial and operational activities.
• Preventing Out-of-Stock: Examines sales history and current inventory levels to identify items likely to be depleted and recommends reorder timings.

Disrupt or Be Disrupted: Gen AI Awaits Your Move

As you explore these transformative use cases, you’ll realize you can’t do this alone. Doing all this data work is cost-intensive and takes effort away from the core problems you’re here to solve. 

You need a partner with deep expertise in the SAP landscape and data engineering. That’s where we come in. In 4 weeks, we can take your biggest supply chain challenge and turn it into an opportunity with Gen AI. 

If you’re still not convinced, we invite you to join us in a workshop, where we will break down every single step for you, showing you exactly how it’s done and why we are the ones who should be doing it for you.

Take Me to the Workshop