Dear Readers,
Ever since the rise of consumer-facing chatbots, AI has been the word on everyone’s lips. But for our customers and their customers, the AI revolution started much earlier. Because this revolution is about the real-world impact of AI that delivers results, improves processes and drives productivity.
A prerequisite for maximizing the impact of AI within companies is to align internal operations with the excellence we aim to deliver externally. This requires a disciplined focus on standardizing processes and a commitment to systems thinking across all organizational borders.
Digitalization and automation are most valuable when we apply them to excellent processes.
That is why, to support our customers’ strategic ambitions, GBS is establishing process excellence as a foundation for effective digitalization and automation. In many cases, this means using our expertise to standardize and prioritize core processes to ensure consistency, scalability and efficiency. Our perspective allows us to ensure that our solutions for your business are integrated and future-ready. And, of course, we also aim to lead by example, creating a seamless experience when offering solutions to our customers and partners.
Welcome to the newest edition of our Inside magazine! It explores these topics in a fresh digital format and with new elements. We hope you enjoy the read and look forward to hearing from you.
Yours sincerely, Eckard Eberle, CEO Siemens Global Business Services
Creating the Synapses of a Digital Nervous System
In 2025, AI seems to be everywhere and doing everything. But for tech companies like Siemens, the AI revolution is also a data revolution. That’s because while common generative AI models offer general answers out of the box, proprietary data is key to driving business transformation.
We’re not just building the foundation for AI; we’re creating a connected ecosystem where teams collaborate through shared data to drive smarter decisions.
Sonia Sohanpall, Head of Advanced Analytics at Siemens GBS
Data freed from silos
Freeing this data from silos and getting it ready to do work is the aim of the Siemens-wide Data Access initiative. It aims to establish a central nervous system to unite data pathways within the Siemens network of companies into a group-wide data ecosystem. Built around a core called the Data Factory, it integrates, manages and publishes data in the form of reusable assets or data products. Via these digital nerve cords, teams can tap into many data signals within the group.
When data becomes a product
Treating data like a product means ensuring that it is FAIR (Findable, Accessible, Interoperable and Reusable). The Data Access initiative identifies and prepares the right data, along with enabling mechanisms such as compliance and export controls, so that businesses can focus on utilizing the data for powering productive use cases.
Data Access is how we connect Siemens’ digital landscape into one intelligent network. It accelerates the flow of trusted data into our Data & AI Cloud, powering strategic AI use cases across the company.
Moritz Heimpel, Head of Data Domains at Siemens IT
Access and transparency across Siemens
The Data Access initiative significantly contributes to creating a centralized data marketplace that empowers Siemens teams at every level, from the shop floor to senior management, with high-quality data, coupled with clearly defined ownership, allowing them to develop AI solutions in a more efficient and faster way.
The Data Access module is one of several company-wide initiatives that help build the core infrastructure, positioning Siemens as a leader in industrial AI.
Three questions for Sonia Sohanpall, Head of Advanced Analytics at Siemens GBS
What role does the Data Factory play at Siemens?
For enterprise use cases, contextual, proprietary data is key. This is where the Data Access initiative and our Data Factory come in. They comprise way more than just a technical process: the goal is to make sure each data set has a clear owner and a quality metric and is accessible for multiple business use cases. By doing this, we’re preparing our data to power the industrial AI solutions that will make Siemens a leader in this space.
Besides technology, what else is
needed to achieve this goal?
We are also introducing a cultural shift with the “share unless” principle. This is a fundamental shift from the “need to know” culture prevalent in many companies. “Share unless” means that all data is shared by default, unless there is a specific reason not to. While it doesn’t mean everyone has access to everything, it does ensure that people know where to find the data they need and can access it for their business purposes. The goal is to treat all of our data from factories, engineering and smart infrastructure as a single, shared asset.
Which AI solutions is the Data Factory
already enabling today?
For instance, the work we do at Data Factory enables automation engineers to quickly find answers to their questions, generate a basic visualization, and develop code for programmable logic controllers (PLCs). As a result, their workload is significantly reduced, routine tasks can be outsourced to Siemens Industrial Copilot, and the engineering of complex tasks is less prone to error. Development times are significantly reduced, while quality and productivity increase in the long term. And that’s just one of the first examples with many more to follow.
The power of sharing data
Freeing data from silos: the Data Factory
Written by Calvin Major
How Agentic AI Is Transforming Work
What happens when AI becomes a true teammate, not just a tool? Meet Maya, the persona for whom GBS develops Agentic AI systems to help with processing exceptions in invoices. Her typical workday shows how Agentic AI is reshaping human-machine collaboration in the workplace.
8:00 a.m.
Maya opens her laptop and takes a sip of coffee. She works on the Accounts Payable team at GBS, responsible for ensuring invoices are processed accurately, timely and in line with internal controls. Invoice processing is already a hyperautomated process. However, even in these processes, there are exceptions to the automation rules, which need to be handled by people like Maya. But today, her workflow looks very different from just a year ago. Instead of diving into a backlog of exceptions and manual checks, Maya is greeted by a message – not from a colleague, but from her AI agent.
Unlike conventional chatbots, the AI agent doesn’t wait for Maya to initiate a conversation. It has already scanned incoming invoices, identified anomalies and started resolving them. As it goes through its tasks, the AI agent needs human judgment from time to time and reaches out to Maya via a chat interface, asking for guidance. This shift – from reactive problem-solving to proactive process monitoring – is redefining the roles of Maya and many of her colleagues.
Coaching the machine
As Agentic AI evolves, Maya’s responsibilities change too. She’s no longer just a processor of data; she’s becoming a coach to her AI agent. Around 12 o’clock, shortly before her lunch break, Maya explains a new business rule to the agent and corrects some of its proposed resolutions. The agent remembers and learns. In two to three years, Maya’s routine will likely almost completely revolve around guiding the agent through new scenarios, helping it adapt and improve.
Unlike conventional systems, Agentic AI is designed to take on responsibility. But for this to work, users like Maya must learn to trust the agent. Double-checking every action the agent takes throughout the day defeats the purpose. This requires a cultural shift. Employees must be onboarded not just into a new tool, but into a new way of working. At GBS, subject matter experts are part of the development process, shaping the AI’s behavior from day one.
Setting the stage for AI success
For Maya, it might mean that after a morning spent teaching the AI agent, she will return to her desk around 3 p.m. and spend the afternoon on higher value tasks like consulting and advising customers or suppliers or learning about AI. For Agentic AI to thrive, the company must prepare its foundations. Processes need to be structured and documented; data must be clean, accessible and integrated; and business rules must be codified. The agent is only as good as the information it can access. But if this groundwork is laid, GBS can realize an ambitious vision: a workplace where humans and machines learn together – and Maya’s story is just one example of many.
Scalable? YES!
The approach, currently in its pilot stage at GBS, can be replicated across other Siemens functions to enhance productivity. Exception-handling agents are best applied in processes with high transaction volumes and recurring deviations from the standard workflow. These agents only escalate complex cases that require human judgment. So it’s not just Maya’s workday that can be made more productive.
The agent framework can be scaled across key process areas and seamlessly integrated into existing systems to achieve measurable productivity gains. Potential areas of application for this Agentic AI solution are:
- Logistics and shipping: missing documents, address errors, customs clearance exceptions
- Inventory management: stock discrepancies, over/under deliveries
- Customer service and operations: ticketing and case management, claims processing (insurance/warranty), unclear complaint histories
Written by Calvin Major
Agentic Al prototype
- Faster, more accurate and compliant invoice processing
- Real-time visibility and transparency
- Manual work reduction potential of up to
Process AI: Solving the Enterprise Challenge of AI at Scale
Scaling AI across strategic use cases in one of Europe’s most successful tech companies is a vital part of building a strong technological backbone that powers both products and processes. And that’s exactly where Siemens’ Process AI initiative makes its contribution. How? We talked to one of the experts behind it.
It’s not the technology; that’s the easy part.
The coffee is still steaming as the early morning light filters through the windows of Siemens’ Global Business Services offices. It’s 7:30 a.m. in Munich, and Matthias Egelhaaf is already deep into his workday, preparing for a video call with teams across different locations to discuss everything from TenderGPT’s latest deployment to the complexities of scaling AI across an organization of more than 300,000 people.
As the Chief Digitalization Officer for Global Business Services at Siemens, Matthias has played a key role in advancing Process AI, one of several complementary Siemens-wide initiatives that are specifically designed to build the technological backbone that enables Siemens to become a truly data- and AI-driven organization.
“People often ask me what the biggest challenge is in scaling AI across an organization like Siemens,” Matthias says, as we settle into our conversation. “It’s not the technology; that’s the easy part. It’s how we ensure that many different AI use cases across our business units don’t operate as isolated solutions but instead turn into building blocks for something much bigger.”
So how do you scale intelligent automation across a global tech company without falling into the trap of technological fragmentation? For the Siemens Process AI team, the answer lies in what they call “creating connections,” and the results are already showing.
We’re not just enabling individual use cases; we’re building a connected ecosystem.
From vision to reality: the Process AI revolution
Inside: Matthias, Process AI is part of Siemens’ transformation into becoming a data- and AI-driven company, with numerous use cases currently being assessed across different business units. How do you see Process AI fundamentally changing the way Siemens operates?
Matthias Egelhaaf: Think about it this way: traditionally, each Siemens unit would develop its own AI solutions. Decentralized experimentation has been important over the last years to gain an understanding of the capabilities of Gen AI and more recently of Agentic AI. But now is the time to assess what is already working within our organization and what has the potential to scale. In that regard, Process AI represents a paradigm shift. We’re not just enabling individual use cases; we’re building a connected ecosystem. We therefore identified clusters of use cases and created synergies between different solutions that are part of the same cluster. For example, when one functional team in a Siemens business unit develops an AI solution, we ensure it shares learnings and infrastructure with the rest of the same functions across the company. Or when different units develop a TenderGPT solution for tender management, we identify how those capabilities can be combined and made available to other business units’ sales processes. In the Tender Management cluster, we synchronized three separate use cases and created synergies between them to support bid managers in their daily work and bid processes. Success for us means moving from scattered AI solutions to a data- and AI-driven company where intelligence flows seamlessly across our entire value chain.
Overcoming challenges
Inside: Scaling AI comes with challenges like data quality, standardization, change management and prioritization across diverse businesses. Which challenge is most critical, and how is Process AI uniquely positioned to address these roadblocks?
Matthias: We are aware that, in order to successfully scale AI and realize its full potential, at Siemens, just like at other companies, we need to fulfil certain prerequisites. While data quality and availability are obvious challenges, I’d argue that one other critical issue is the diversity of processes and legacy systems at Siemens to fully utilize the benefit of AI. To overcome these challenges, Process AI is contributing to the overall strategy by enabling Siemens to scale high-impact use cases faster and generate synergies through centralized assessment. We are creating transparency about ongoing initiatives and are bringing people together to share experiences, data and lessons learned.
To name just a few key milestones, we have already prioritized 17 Process AI use cases and identified 13 potential Agentic AI cases. We have also driven 13 data as well as 6 technology support topics in collaboration with the other teams involved in building the core infrastructure, data and AI at Siemens.
Process AI is enabling Siemens to scale high-impact use cases faster and generate synergies through centralized assessment.
Programs such as SHERPA X – the SAP migration to S4 – also work towards harmonizing Siemens processes, which in turn will allow us to leverage and scale AI more easily and enable the business transformation at Siemens.
We make sure that AI projects are successfully implemented by having a codevelopment approach. It has been proven that if we involve people right from the beginning and include a hyperscaler as part of a hybrid project team, we are much faster, and the risk of failing is dramatically reduced.
From use cases to enterprise transformation
Inside: You’re taking both a top-down strategic approach and a bottom-up scaling approach by identifying productivity potential strategically as well as utilizing existing productivity cases in both Gen AI and Agentic AI. How do you ensure this approach contributes to reusable AI capabilities that can benefit the entire organization?
As Siemens is enabling the backbone of our economies, it is only natural that the AI solutions that we assessed with Process AI are covering areas like digital industries, smart infrastructure or mobility.
Matthias: This is where our combined approach brings clear benefits. Starting with our bottom-up approach, we are looking at strategic Siemens-wide use cases and identifying their main technical and organizational roadblocks and resolutions. Based on this and further use case-specific requirements, we can accelerate use cases by supporting them in overcoming their data and technology challenges, identifying scaling potential.
As Siemens is enabling the backbone of our economies, it is only natural that the AI solutions we assessed with Process AI cover areas like digital industries, smart infrastructure and mobility. So let me give you some examples that we have identified in these areas.
When it comes to Siemens Mobility, for instance, we identified one use case that involves integrating data from various Siemens databases into a digital twin graph, providing convenient access to data through natural language queries. The system aims to enable further automation based on integrated data, using ontologies for data organization and an agent mesh for query management.
In our business enabling the digitalization of industries, we identified a unified platform that delivers 360° customer data and AI-driven next best actions. Whereas in the area of Smart Infrastructure, we identified the Sales Prediction Platform, which leverages AI-driven time series forecasting models to reduce manual effort and to increase the forecast accuracy of selected financial and sales KPIs, such as for new orders and revenue. This provides a reliable basis for decision-making and reducing manual effort while increasing the forecast accuracy.
Inside: You are also taking steps to further develop Agentic AI solutions. What is the potential there?
What excites me most is that sharing capabilities across the company doesn’t just make us more efficient.
Matthias: Yes, we are increasingly focusing on Agentic AI as the next big lever of productivity. One of the pilots developed is within the process of accounts payable. This pilot frees up human capacity by automating exceptions in processes with high transaction volumes and recurring deviations from the standard workflow. It can be replicated across other Siemens functions to drive productivity, for instance in logistics and shipping as well as customer service and operations. We are also looking at the potential we have not yet tackled based on data insights across the organization. That is why we introduced the AI Compass, a dashboard that helps us to identify the highest AI-based productivity improvement potential within Siemens along human capabilities. In addition, we developed a Process AI playbook that we call “10 steps to Process AI happiness” to make the adoption and implementation as easy as possible and ensure that learnings are shared across Siemens.
The morning sun has now filled the room completely, and Matthias is ready for his next meeting. Glancing through his glasses, he reflects enthusiastically: “What excites me most is that sharing capabilities across the company doesn’t just make us more efficient, it makes us more innovative, more responsive and ultimately more human in how we serve our customers.”
Interview by Flavia Coman
Smarter Search, Smarter Synergies
In large companies, teams may have different focus areas, different topics entirely even. The everyday work of an HR manager looks completely different than that of an engineer. Yet they face one identical challenge: finding exactly the right information they need for their work – even from various systems or large unstructured documents. Everyone knows the struggle and sometimes it’s like finding a needle in a haystack. How can this challenge be overcome despite different characteristics and requirements when it comes to information? Imagine a magnet that pulls out the specific needle you’re looking for – that’s what the Gen AI–powered assistants created by Siemens Global Business Services (GBS) do: retrieve information precisely for specific customer use cases.
Unlocking vast scaling potential begins with identifying similar use cases, both from a business challenge and technological approach.
Alina Engbert, Head of AI at Siemens GBS
Specialized solutions that augment human capabilities
When it comes to retrieving information, GBS has specialized AI solutions tailored to specific customer needs, each of which are nodes within a growing intelligence network. One solution that aids document analysis for engineering teams may become the foundation for sales support systems. Or maybe, an innovation in multilingual processing in one area could instantly benefit global teams facing similar challenges.
This strategic approach transforms productivity at scale: Every AI-assisted information retrieval strengthens the human-technology partnership’s ability to augment thinking and deliver insights faster and more effectively. The result? Enhanced intellectual productivity, optimized knowledge discovery, breaking down silos, and a connected intelligence network that empowers people to think, learn and act with greater capability.
Hi, we’re your AI workforce!
Written by Carla Mather and Laura Kamenicek
From Inbox Overload to Instant Action: Meet Bionic Agent
Hi, I’m Bionic Agent! Have we met before? No? Let me introduce myself. I’m a cloud-based solution created by Siemens Global Business Services (GBS) that uses natural language processing and generative AI to analyze data, improve productivity and increase efficiency. Pretty cool, right? But in times where AI is omnipresent, many bots can say that about themselves. So, what makes me special? It always comes down to what the customers say. Let’s hear about it from Siemens Smart Infrastructure (SI) and Digital Industries (DI), who I’ve been helping with everything from repair requests and order management to image data extraction.
Email overload? Let me handle it
The SI Customer Services Electrification & Automation team is primarily responsible for managing and resolving customer queries and delivering comprehensive service support across the lifecycle of energy automation systems. Consequently, they receive hundreds of repair requests each year. Due to the volume and the diverse nature of the requests, it takes several weeks for the team to respond to each query, which impacts both response time and customer satisfaction. And that’s exactly where I step in. Through automatic email classification with over 90% accuracy, automation of repair requests and guaranteed scalability for other similar processes, I have
- improved the team’s efficiency
- reduced the manual effort significantly
- enhanced productivity and customer experience as a result
Bionic Agent streamlines our workflow and significantly accelerates our response times to customers.
Theresa Niegel, Business Excellence Professional at SI Customer Services Electrification & Automation
What took weeks before, now gets done in a couple of minutes. Theresa Niegel, Business Excellence Professional at SI Customer Services Electrification & Automation, says: “Bionic Agent streamlines our workflow and significantly accelerates our response times to customers. This leads to increased customer satisfaction and enables us to focus on more complex tasks.” While saving our customers time is a simple operation for me, it means a huge workload reduction for them.
Goodbye manual data entry, hello automated excellence!
Another team at SI, the one responsible for the Buildings division in the UK, had a different issue for me to tackle. They are responsible for handling Service Quoted Works such as Labour & Materials and Moves and Changes, ensuring accurate order processing and coordination across service operations. The orders they receive must be manually reviewed, categorized and registered into an SDI template. An SDI template is a structured Excel-based format used to convert customer quotes into order entries, ensuring that all required details like site location and equipment numbers are captured for processing.
Next, the team needs to perform validation checks to ensure compliance with business rules and correct data in SAP. This process involves multiple manual steps, creating inefficiencies and increasing the risk of errors. To avoid this, I now automatically classify the requests received. I extract the relevant data from emails and attachments, populate the SDI template and perform necessary validation checks. Furthermore, AI helps me to precisely extract and validate the data, which ultimately reduces errors in the order entry. In addition to that, I also provide real-time data tracking and automated reporting to improve insights into process performance and compliance. That’s what automated excellence is about. By significantly reducing manual data entry and errors and accelerating order processing, I enable the team to improve operational efficiency and ensure faster order fulfillment.
Maximizing accuracy
For the same team at SI, I was able to improve a second process: the closing, invoicing and tick sheet review, and percentage completion. All these tasks involve multiple steps and often require manual entries. This process is not only time-consuming and inefficient, but it is also prone to errors. To solve this, I have taken the following measures:
We’ve truly seen an improvement in how we manage customer requests, driven by a newly streamlined process that integrates intelligent automation through a Gen AI solution.
Sharon Wallace, Head of Branch Functions at SI Buildings UK
- Automating the closing processes for faults and planned maintenance by monitoring SAP tasks and updating server site folders
- Automating the invoicing process for Labour & Materials and planned maintenance activities by extracting relevant information from work reports and generating invoices in SAP
- Streamlining percentage completion tracking and data presentation for KPIs by automating tick sheet review and integration with KPI dashboards
- Standardizing the structure of data extracted from tick sheets, ensuring consistent and accurate recording of completion percentages
- Providing real-time data tracking and automated reporting to improve visibility into process performance and compliance
Sharon Wallace, Head of Branch Functions at SI Buildings UK, confirms that I have added significant value to their daily operations: “We’ve truly seen an improvement in how we manage customer requests, driven by a newly streamlined process that integrates intelligent automation through a Gen AI solution. Thanks to close collaboration with Roopa Satish, Regional Process Office Head and Account Manager, Irem Polat, Digital Solution Architect, and their teams, we’ve successfully implemented Bionic Agent to enhance our data-handling capabilities – leveraging automated entity extraction, PO validation and real-time data checking via Snowflake solution.” Helen Chappel, Business Excellence Project Manager at SI Buildings UK adds: “This transformation marks a significant milestone on our journey toward operational excellence, enabling faster, more accurate and efficient processing.” And there’s even more: phase two is being prepared for development, which will further elevate the team’s capabilities and outcomes.
Video MyBionic Click here
An image is worth a thousand words – and lots of data
Speaking of automation and efficiency, I have been helping another team transform its processes. The Digital Industries Information Technology Customer Services team, which serves as the IT organization for DI’s Customer Services business, has been working with me on an exciting AI-driven transformation project. The challenge? Extracting product data from various images and documents – a task that traditionally requires significant manual effort. That’s where I come in. Through my image data extraction feature in “MyBionic,” our secure customer portal, I automatically recognize, classify and extract key attributes such as product names, codes and specifications from various file formats including PDF, JPEG and PNG. This not only reduces manual data entry significantly but also improves accuracy – a win-win situation.
And we’re not done yet – the team is planning an integration with the SiePortal system. This is where I can help the team receive data and attachments directly via API and return processed license file data automatically. This integration will further streamline the workflow and enhance efficiency.
Let’s transform together!
As these success stories demonstrate, I’m more than simply another AI solution. By combining natural language processing with a deep understanding of business processes, I’m helping teams transform their daily operations – one email, one order, one image at a time. And this is just the beginning. With continuous improvements and new capabilities being developed, I’m ready to take on more challenges and support even more teams across Siemens – and beyond. Want to explore how I could help streamline your processes? Let’s connect and discover the possibilities together.
Written by Laura Donnemiller
Smart Support, Seamless Experience
We’ve all had to file service requests or search for information on different platforms, sometimes making inquiry after inquiry until we finally get to what we need. Now imagine your service request or inquiry could be processed as easily as ordering your favorite book online. Siemens Global Business Services (GBS) is enabling its customers to do just that: With My Services – a powerful platform built on ServiceNow – we are transforming how users access support. No more wondering who to contact or where to go; every GBS service request, big or small, now begins on a single, intuitive portal.
My Services is designed to create a unique user journey, reducing complexity and creating smooth user interactions. It offers an “Amazon-like” ease of use and ensures a streamlined process from start to finish.
At the heart of My Services lies a sophisticated delivery model, carefully engineered to ensure that every customer interaction gets the attention it deserves. “This isn’t just a simple ticketing system; it’s a comprehensive orchestrator that handles everything from knowledge sharing, process control, case tracking and performance reporting,” says Catarina Simões, Head of GBS Delivery Management. “These pillars work seamlessly behind the scenes, setting clear standards for consistent quality while adapting flexibly to the unique needs of each business,” emphasizes Catarina.
The platform treats every case as more than just an issue to be solved – it’s an invaluable opportunity to learn and improve. In the future, when a customer logs a case, the system will not only help to find a resolution but also diligently analyze the event for recurring trends and underlying causes. This crucial insight is directly linked into the platform’s continuous improvement mechanism, leading to smart, lasting changes in how services are delivered, technologies are managed, and teams are trained. Over time, the system will become increasingly efficient, proactively eliminating common pain points and adding new enhancements. It’s a truly dynamic and evolving support ecosystem.
AI-powered productivity
While many of the AI features operate behind the scenes, their impact is tangible. For end users, AI search helps surface the most relevant articles and solutions. This feature is already live in some countries like the Netherlands, Philippines, United Arab Emirates, Egypt, Saudi Arabia, Qatar, Kuwait, Oman, Pakistan and South Africa, with a global rollout underway.
For GBS human agents, AI accelerates resolution times with automated summaries, response generation and intent recognition. “Service agents get a lot of questions, and there are different ways to phrase these questions,” notes Philip Hechtl, Head of Artificial Intelligence and Digital Service Management at GBS: “AI understands the intent behind them and thus helps us find the right information faster.”
Built for scale – and speed!
Since its launch, My Services has posted some truly impressive figures, showcasing its robust capabilities and widespread adoption:
What’s even more remarkable is that these results have been achieved with only minimal customization – a key requirement and a major reason why the team chose to partner with ServiceNow. Thanks to ServiceNow’s robust capabilities, the system is inherently designed to scale, featuring modular connectors that allow for incredible flexibility across phone systems, SAP integrations and future Agentic AI capabilities.
Elevating the user experience
For our customers, the result is simple yet incredibly powerful: faster solutions, clearer communication and the reassuring knowledge that GBS is continually investing in their experience. Beyond convenience and transparency, My Services embodies a culture of continuous learning – transforming every challenge into an opportunity for progress.
With My Services, GBS is actively reshaping how support is delivered, making every user interaction smart and efficient. It’s an exciting step forward in our commitment to excellence!
Written by Flavia Coman
Experts
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