Manual workflows and poor-quality data lead to countless issues for modern organizations. One error or untimely communication can mean the difference between turning a profit and a major catastrophe.
If you’re using SAP to run your business, there are dozens of practical ways to implement AI that can save you time and money while improving the accessibility and accuracy of your most valuable business data.
SAP Business AI is a popular suite of SAP AI solutions for enhancing various functions within an organization, but there are also plenty of powerful third-party options for improving SAP processes with AI.
In this guide, we’ll show you exactly how to implement AI in SAP efficiently while minimizing the risks of implementation.
What is AI in SAP?
Artificial intelligence (AI) in SAP refers to the integration of AI tools and processes with SAP systems. AI technology can greatly improve the accuracy, agility, and efficiency of various functions performed by SAP products.
Use cases include (but are not limited to) SAP’s own selection of AI tools, such as SAP Business AI, SAP AI Core, and SAP AI Launchpad. Other applications of AI in SAP include supporting software designed to integrate with your SAP system and improve functionality in one or more areas.
For example, you can optimize your procure-to-pay process in SAP and automate critical workflows—such as purchase requisitioning and invoice management—by integrating SAP with an AI-powered spend management platform like Vroozi.
Read More: How to Boost the ROI of Your SAP Investment
SAP AI solutions
There are a few SAP tools that can assist you in your implementation process. You may not need all of these to successfully implement AI, especially if you’re using intuitive software like Vroozi that handles much of the implementation for you, but it’s a good idea to cover these before we dive into the details.
Here’s a quick breakdown of the essential and nice-to-have SAP AI solutions:
- SAP S/4HANA
- SAP Business Technology Platform
- SAP Cloud Integration
- SAP AI Business Services
- SAP Master Data Governance
- SAP Data Intelligence
- SAP Solution Manager
- SAP Cloud ALM
SAP S/4HANA
This is SAP’s flagship ERP system. It manages and centralizes core business processes, and will function as the primary data source for the information required to train and run AI models related to those processes.
SAP S/4HANA comes with pre-built AI scenarios that can be used within the system. When you integrate other AI systems, activity within those systems will often trigger actions within S/4HANA.
SAP Business Technology Platform (BTP)
SAP BTP is a central hub for integrating systems with SAP applications and building new AI-powered applications. This is where you’ll build, deploy, and manage, custom AI or machine-learning models that interact with your SAP data without disrupting SAP S/4HANA.
SAP Cloud Integration
Cloud Integration is part of SAP BTP’s integration suite and is the platform that connects SAP and non-SAP systems. This is the tool that transfers data between systems and AI models.
SAP AI Business Services
Also a part of SAP BTP, AI Business Services is a suite of pre-built AI solutions that automate common business processes, such as extracting data from documents and recognizing business entities.
AI Business Services offers specific AI capabilities without requiring you to build new models from scratch.
SAP Master Data Governance (MDG)
SAP MDG is an application that centralizes governance and management of important master data, such as supplier information and financial data. Because AI models require consistent, accurate data, MDG is designed to ensure clean master data to accurately train AI models.
SAP Data Intelligence
Data Intelligence is an orchestration and management solution that connects and enriches data assets across complicated integration setups. It features data pipelining and machine-learning capabilities to tackle sophisticated tasks necessary for training machine-learning models.
SAP Solution Manager
Solution Manager is an on-premise platform for managing the lifecycles of SAP applications. It’s built to manage the implementation of AI solutions, particularly those that integrate with on-premise systems.
SAP Solution Manager can monitor the health and performance of systems like SAP S/4HANA and the AI applications that interact with them.
SAP Cloud ALM
Sap Cloud ALM is similar to Solution Manager, but it operates on the cloud rather than as an on-premise system. It’s designed to manage cloud-based SAP solutions such as SAP S/4HANA and SAP BTP.
Cloud ALM can help you manage and monitor your SAP AI implementation from testing and deployment to performance monitoring—making it essential for operating cloud-based SAP AI solutions.
Preparing for SAP AI implementation
Before you implement AI in SAP, you need to prepare your systems and your organization.
This means:
- Identifying AI use cases for SAP
- Defining clear objectives for implementation
- Assessing your capacity to implement AI systems
Identify AI use cases for SAP
The first step to implementing AI in SAP is identifying which business processes may benefit from AI. This means understanding the specific challenges or opportunities within your organization and then determining how AI can improve those processes.
Some examples of common AI use cases in SAP:
- Supply chain management
- Finance
- Procurement
- Human resources
- Sales and service
- Marketing and ecommerce
- Product development
Begin by engaging with stakeholders across different departments to identify problems you want to solve or opportunities you can capitalize on. Prioritize slow or inefficient processes, such as manual workflows or areas with poor data visibility.
Align these challenges with AI capabilities to determine which problems to tackle first. For example, you may discover that manual data entry in your supply chain forecasting process creates bottlenecks for procurement, or frequently results in compromised data.
AI automation and predictive analytics could speed up this process while improving data integrity for more accurate forecasting.
Define clear objectives for AI implementation
Once you’ve determined the areas that will benefit most from AI implementation, set clear objectives that reflect a solution to your problem. This helps you stay on track throughout the implementation process and provides a measuring stick for success post-implementation.
The more specific you are with your objectives, the better positioned you’ll be to determine whether you’re on the right path. Set success criteria early and develop a detailed roadmap to ensure maximum efficiency throughout the implementation process.
Assess your capacity to implement AI systems
Before you commit to implementing new technologies it’s important to check your existing systems’ compatibility with SAP AI tools.
If they’re not currently compatible, consider the following steps:
- Upgrade to the SAP S/4HANA system. This is the most well-suited SAP ERP system for implementing AI.
- Get the SAP Business Technology Platform (BTP). This platform provides the necessary services for integrating AI and machine learning capabilities with SAP systems.
- Connect SAP S/4HANA with BTP using SAP Cloud Integration (formerly known as SAP CPI). This is an integration platform that supports the connection of SAP and non-SAP cloud and on-premise applications.
- Investigate SAP AI Business Services. Depending on your AI implementation goals, there may be a viable solution within SAP’s suite of AI business tools.
- Research non-SAP AI integrations. Prioritize AI-driven solutions that go beyond standard SAP offerings. Vroozi’s AI-powered procure-to-pay platform stands out as a leading alternative, delivering intelligent automation, enhanced compliance, and superior user experience. For organizations seeking advanced AI capabilities in their procurement processes, Vroozi should be your go-to solution.
Pro Tip: If you’re unsure where to start researching third-party SAP AI tools, check out software review platforms like G2 and Capterra and filter your search by service type.
How to implement AI in SAP in 5 simple steps
It’s essential you follow the correct steps to ensure effective AI implementation. Botched implementations can cost organizations thousands—sometimes millions—of dollars, and often leave you with more problems than you began with.
To implement AI in SAP, follow these five steps:
- Prepare your data and configure systems
- Integrate AI services with SAP
- Test, adjust, and optimize your integration
- Deploy your AI implementation and monitor performance
- Establish documentation and train users
Let’s get started.
1. Prepare your data and configure systems
Your data needs to be clean, organized, and meet the accessibility requirements of your SAP AI solution before you can begin the implementation.
First, identify which data sources from your SAP system and external systems are needed. Then conduct a data quality assessment to identify gaps, inconsistencies, duplicate data, and missing values.
Pro Tip: Consider enriching your data with external sources—such as market conditions, social media insights, and expert opinions—to enhance AI predictions.
Next, leverage SAP Master Data Governance (MDG) to establish data governance policies. These will aid in maintaining your data integrity. Sync your data between SAP and non-SAP systems using SAP BTP or SAP Data Intelligence to prepare it for integration with your AI solution.
2. Integrate AI services with SAP
Once your data is clean and ready to go, set up a SAP BTP global account, subaccounts, and spaces for development, testing, and production.
Enable the services or apps you need (this may mean purchasing new software or upgrading your SAP subscription model).
If you’re using on-premise software, set up SAP Cloud Connector to securely connect your AI solution with the SAP system. Then configure destinations in SAP BTP. This will involve setting up a URL, connection details, and authentication. If you’ve chosen SAP AI Business Services, this will also require configuration from the SAP BTP cockpit.
To prepare SAP S/4HANA for AI integration, create a link between S/4HANA and your AI services in SAP BTP by implementing communication arrangements. Configure your communication systems by creating users, assigning roles, and setting up user permissions.
3. Test, adjust, and optimize
If applicable, you may need to develop and train machine-learning models with historical data. This can be done using external frameworks imported into SAP or directly through the SAP AI Foundation tool.
Now that you’ve implemented AI in SAP, it’s vital you test and validate your implementation before going live to prevent overcommitting to something that doesn’t work and reduce the risk to your business.
Test various AI scenarios to validate the accuracy, data processing efficiency, and quality of your new integration workflow. It can pay to run lots of small tests for different workflows or groups of data before testing the implementation as a whole.
Adjust the integration configurations based on your results. You may need to tweak specific models and processes to ensure optimal performance.
4. Deploy your AI implementation and monitor performance
Once you’re happy with the validation process, it’s time to go live with your new workflow.
Deploy your AI solution in the production environment and use tools like SAP Solution Manager or SAP Cloud ALM to monitor the performance of your implementation. Refer to your roadmap and objectives to assess whether the service is achieving the desired results.
Pro Tip: Establish a feedback loop and periodically retrain AI models with new data to ensure you’re getting the most out of your AI integration.
5. Establish documentation and train users
It’s important to maintain documentation detailing how to set up, configure, and use your new AI services. This will help future users learn the system quickly and prevent misuse.
Provide training sessions and dedicate time to ensuring users and administrators fully understand what the tool can do and how to use it efficiently. Stay up to date with the AI solution provider’s system updates to be sure you’re making the most out of any new enhancements that are introduced after your implementation.
