Sometimes, managing multiple screens makes it hard to get information on picking delays. The challenges for many automation projects include the difficulty of connecting robots to a system that is capable of understanding and recognising the warehouse’s live priorities.
This is exactly what AI in SAP EWM in 2026 will address. The idea for warehouse AI is no longer a concept of the future. It’s now present in your SAP Extended Warehouse Management (EWM).
As a result of implementing 2025 FPS01 in SAP S/4HANA, organisations use AI technologies, including workload predictions and task execution, while responding to increasing SAP Module Demand, utilising SAP Joule for warehouse management. It is also used as an intelligent automated solution to enhance their performance.
According to Gartner, over 50% of new warehouses built in advanced economies will be robot-oriented, operated with AI, and will manage both individuals and robots working in the facility.
SAP CEO Christian Klein said that SAP will also use AI for its business solutions. Customers who utilise RISE with SAP can initiate three Joule Assistants in their first year of doing business with them. Customers who are growing with SAP GROW may have access to more than 20 different AI assistants at once.
In this blog post, we will examine how EWM in 2026 will use AI to enhance operations.
Why AI in SAP EWM Matters More Than Ever in 2026?
More warehouses are facing challenges, and some of the difficulties are worsening each day.
Due to a shortage of workers, warehouse positions are in high demand and difficult to fill. With increased demand, order volume is fluctuating, making it impossible for the staff to keep up.
Some warehouse fees cost too much, from rental fees, to utility bills, and employee wages, everything is increasing the operational cost. Customer expectations regarding delivery have increased. They want same-day delivery, and sometimes that seems impossible.
From Warehouse Management to Warehouse Intelligence
The main aim of Traditional EWM is to support warehouse processes such as confirming a pick, providing a layer for recording goods movements, or closing an order to a warehouse.
However, AI will add an extra element to EWM. AI will allow us to predict what will happen in the future and assess the best options available to us for action.
EWM is an execution based model. With the recent rise of AI, we can expect to move towards an AI-based model for use in warehouses.
The AI-based EWM system will not only provide recommendations on a course of action but also take action on its own or notify a supervisor that it needs their approval.
Digital ERP Solutions (Expert Insights)
The biggest barrier in our SAP EWM Projects is not the use of artificial intelligence. It is making sure that the warehouse-related data is correct and complete. If warehouse tasks are not confirmed correctly or RF transactions are missing, artificial intelligence cannot properly calculate labour resource requirements. So, we try to help customers improve their warehouse-related data before moving on to predictive labour demand planning.
Key AI Capabilities Now Available in SAP EWM
According to SAP EWM AI, there are various practical AI stacks inside EWM.
Forecasting through Predictive Analytics
By applying analytical techniques to historical warehouse data, SAP EWM can accurately forecast the number of workers needed to complete expected workloads.
Promoting Labour Efficiency Through Communication in Warehousing Operations
With Joule, a user can complete warehouse tasks, such as asking the system questions and performing warehouse functions, using natural language instead of navigating a complicated menu system.
SAP EWM enables a company’s robots and automated systems to work together by establishing connections and performing orchestration.
Human Error Reduction Through AI Technology
SAP EWM uses AI to identify potential system issues, such as material-receiving delays, supply chain bottlenecks, and service degradation. AI prevents them from becoming real problems for the business.
Expert Opinion:
Many companies have invested in warehouse robots and automation, but have not realised the expected benefits because they have planned manually. With the inclusion of AI technology, SAP EWM can help improve the quality of the planning process before a job starts. Faster robots are of no benefit if the warehouse job and the tasks they are to perform are not planned correctly before the job starts.
SAP's AI Strategy for Logistics and Warehousing (S/4HANA 2025 FPS01)
The latest version of SAP S/4HANA is now available as of October 2025 for on-premises or private cloud deployments, making SAP S/4HANA EWM Migration an important consideration for organizations planning AI-enabled warehouse operations.
At a high level, FPS01 enables warehouse supervisors to interact with SAP Extended Warehouse Management (EWM) via Joule SAP’s AI assistant using conversational input. They will now utilise Joule to execute their tasks in EWM without having to log into the classic EWM transaction screens.
As part of this process, supervisors would be able to use Joule to create transfer orders, confirm warehouse tasks and release delivery documents.
How SAP Is Embedding AI Across the Supply Chain Stack
SAP is implementing AI capabilities across the complete supply chain suite simultaneously.
Area | What AI Adds |
|---|---|
Transportation Management with AI | Automatically processes shipping documents and freight papers. Identifies risk and potential problems before they impact business operations with the carrier. |
Integrated Business Planning with AI | Allows planners to collaborate on plans through natural language (chat-based). Links long- and short-term goals by enabling teams to create a single vision of success. |
AI in Manufacturing & Shop Floor | Automatically releases a production order when its conditions are fulfilled. Uses to identify exceptions or problems and plan for resolution. |
Experience insights:
When engaging in SAP EWM optimisation, we have found that our customers are less focused on AI taking over for warehouse workers. They want tools that help them support their supervisors in making decisions quickly. Warehouse Managers spend a great deal of their time identifying bottlenecks and not executing transactions. AI enables changes to how that time is divided.
SAP Joule for EWM: Natural Language Navigation and Outbound Process Assistance
What is SAP Joule?
SAP Joule is the AI assistant developed by SAP and integrated into SAP applications. Now, without going through transaction codes or searching across various screens, users can simply ask their questions in natural language and receive answers or take action.
How Joule Assists in SAP EWM
It simply helps in accessing warehouse information. Instead of running reports, warehouse supervisors can query Joule to get a list of all overdue warehouse tasks. It also shows the picking workload list for shift B.
Joule provides answers to these types of questions based on data it collects from employee workloads, warehouse tasks, and deadlines. Additionally, Joule can also provide recommendations for actions that warehouse supervisors can take based upon the same data.
Rather than relying on traditional RF devices and screens to confirm or cancel warehouse tasks, warehouse employees can now do so simply by chatting with Joule in natural language.
Outbound Process Support
Joule is designed to assist in many outbound processes. It helps identify urgent tasks that threaten to compromise service levels, while minimising disruption. It also identifies problems before the risk of delayed deliveries arises. It guides the new or less experienced staff through warehouse processes.
Predictive Labour Demand Planning in SAP EWM: From Reactive Staffing to AI Forecasting
What Is Predictive Labour Demand Planning?
The SAP S/4HANA Cloud, Private Edition, includes Predictive Labour Demand Planning EWM. This tool helps warehouses to estimate the future workload by analysing how much work is done across activities such as picking, packing, and staging.
According to research conducted by McKinsey & Company, applying both AI and advanced analytics to supply chain operations results in significant increases in forecast accuracy and operational efficiency. The predictive workforce planning will become an increasingly critical competency for contemporary warehouses.
Features of AI-assisted Predictive Labour Demand Planning
This is the fundamental backend that was developed for for pLDP. This allows users to estimate their future workload using the actual volume of goods picked and packed for their outbound operations. And this is done via RF-based picking and packing.
This was introduced as a machine-learning model within the EWM. This analyses past and present data to forecast future workload.
With this, the PLDP functionality was enhanced with new updates, such as incorporating RF-based staging into the workload forecast. Users can utilise API-based picking and historical data to assist forecasting.
How PLDP is Configured
For an organisation to generate demand for employees, it will need to activate the EWM Scenario EWM_LDP_FORECAST2_00 in the system. To accurately estimate future demand, LDP requires historical data on activities that have taken place within the warehouse.
It also focuses on tasks that have already been completed. If more historical data is available, the forecasting process will be better.
As a result, dashboards will display workload estimates for the warehouse manager. It will tell the manager whether additional employees are required.
Robotics and MFS Integration Patterns in AI-Driven Warehouses
The Role of Robotics in Warehouse Operations Is Growing
Robots are becoming an increasingly popular option for companies. It increases productivity, safety, and efficiency in the warehouses.
Gartner predicts that half of all new warehouse facilities in developed nations will be built specifically to accommodate robotic operations.
The warehouse robotics EWM integration marketplace offers so many robots today that companies will often need multiple types working together. Therefore, a central system must be used to control and coordinate all of the robots.
MFS (Material Flow System) of SAP EWM
As the MFS, or Mobile Fulfilment System, is a form of automated order fulfilment technology for “parts to picker” that utilises a fleet of mobile robots to raise and carry movable storage pods to their respective human workers at designated picking locations in logistics and warehouse operations. The SAP’s Material Flow System is a function that allows EWM to interact with and directly control automated equipment in the warehouse.
MFS provides:
- PLC Communication
EWM connects directly to the controller of the warehouse equipment.
- Conveyor Integration:
Moves product between warehouse areas via automated conveyor systems.
- ASRS Integration:
Connects EWM to Automated Storage and Retrieval Systems (ASRS), which automatically store and retrieve products from an ASRS storage location.
Expert Insights:
The most successful warehouse automation AI does not start with robots. It starts with process improvements, data maintenance, and understanding of operational roles. While robots and AI have the potential to improve overall performance, they also expose the issues.
How AI Changes the SAP EWM Consultant Role in 2026?
SAP EWM consultants in 2026 have spent a significant amount of their time configuring storage types, process types and wave templates. But many changes with AI in SAP EMW in 2026 have been made. Professionals exploring SAP EWM Jobs in 2026 will find that companies increasingly expect consultants who understand AI capabilities alongside traditional EWM configuration.
Companies expect a consultant who understands the capabilities of different AI tools, such as Joule and pLDP. You can make recommendations. And then you can use that to monitor and control.
As Gartner’s data predicts that by 2030, 80% of people will have daily contact with smart robotic systems. SAP EWM consultants need skills in artificial intelligence governance and business process automation, supported by a must Have SAP certification that validates expertise in modern SAP technologies.
Many organisations mistakenly believe that implementing AI will immediately reduce labour costs in their warehouses. But achieving labour cost reductions in a warehouse requires improved visibility into the work being done, rather than simply reducing headcount.
As shown in the World Economic Forum’s Future of Jobs 2025 Report, the most rapidly developing skills for today’s employees are Analytical Thinking, AI Literacy, Technology Management, and Systems Thinking. For SAP EWM consultants, this implies combining traditional warehousing experience with knowledge of AI governance, data quality, and automation.
Case Study: Building an AI-Enabled Warehouse with SAP EWM
A recent SAP EWM optimisation at a retail distributor in Bangalore revealed the challenges they face. Labour shortages were causing order backlogs during peak promotional periods.
After facing the challenges, they decided to implement SAP EWM at the facility to oversee warehouse operations. With assistance from Joule, the company were to validate the tasks and resolve potential issues with minimal discussion. The supervisor’s planning time got reduced from 90 minutes to 40 minutes. And 18% reduction was seen in the warehouse backlog.
These initiatives helped reduce overtime labour expenses by 14%. The improvement in peak-demand workforce planning helped them improve the data quality by 11%. It also helped them during peak-line activity periods and prepared them for quarterly seasonal sales, offering valuable learning opportunities for professionals exploring entry level SAP positions.
Expert Insights:
Our experience working with agentic AI warehouses shows that agentic AI offers a much better process. In warehouses, we used AI to identify poorly organised data and processes. This allowed us to rapidly locate problems and areas of concern. Using agentic AI helped the company determine the order of priority for its resolution activity. It also helped companies to focus on resolving the largest deficiencies first.
Future Trends: SAP EWM AI in 2026
The SAP EWM artificial intelligence will be an independent entity. Warehouse AI will be able to carry out a variety of tasks simultaneously, with limited reliance on human input.
A virtual version of the warehouse will be continuously updated and adjusted using real-time data. Therefore, many businesses will use digital twins not only for construction but also to test and improve how things are done in warehouses every day.
AI will perform resource allocation (determining which worker or Robot should perform which task) in “real time”, thus allowing warehouses to respond to sudden increases or decreases in workload rather than relying on static schedules.
Conclusion
The use of technologies such as SAP Joule warehouse management to enable NLP and PLDP for better resource allocation, and advances in Robotics Integration via MFS, help companies increase productivity and lower operational costs while also responding to rapidly changing customer demands.
As consultants at Digital ERP Solutions, we find that AI is more effective when companies have well-defined, standardised warehouse processes. They collect accurate and reliable data to implement AI as soon as possible. For SAP EWM professionals and businesses, the year 2026 will provide an excellent opportunity to utilise AI-driven warehouse management and stay ahead of the curve in an increasingly Automated Logistics Industry.
At Digital ERP Solutions, we provide consulting services to help manufacturers, distributors, and logistics companies enhance their warehouse operations through SAP Extended Warehouse Management (EWM).
FAQs
- Is SAP Joule Supported within SAP EWM?
SAP Joule Warehouse Management is designed to help users work more efficiently by allowing them to use natural language input to complete tasks.
- What is Predictive Labour Demand Planning in SAP EWM?
Predictive Labour Demand Planning is an artificial-intelligence-driven application that predicts future workload in a warehouse in areas such as picking, packing and staging..
- Does Predictive Labour Demand Planning Require Engineered Labour Standards (ELS)?
No, there is no requirement for using Engineered Labour Standards to utilise Predictive Labour Demand Planning. It uses historical warehouse data to forecast the future. It can be used independently.
- Is SAP EWM Possible to Use with Robots in a Warehouse?
SAP EWM can be used with warehouse robots via a connection to MFS and newer Machine-to-Machine technologies, such as SAP Event Mesh EWM and robotic management platforms. It helps with easier automation management.
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