“Reoassunto provides dedicated support for reintegration,� Proietti Pantosti said. “Our goal is to significantly reduce the rate of reoffending among first-time convicts.� As processes for reintegration are complex and time consuming, he had the idea to set up an offline, server-based AI tool to help inmates with job applications as well as an AI agent to automate the complex tax paperwork for companies that offer jobs for inmates. But he and his organization didn’t have the skills or funds to create an AI prototype to realize his concept.

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A community of changemakers

This is when came in, a global support community for young social entrepreneurs and a long-standing partner of Â鶹ԭ´´. In 2025, this organization established a for social entrepreneurs and NGOs to experiment with AI capabilities and implement AI tools and features to fix one challenge common to all social enterprises: a lack of helping hands in combination with a large volume of small, sometimes repetitive tasks.

According to Matthias Scheffelmeier, co-founder of ChangemakerXchange, young changemakers are tackling the most pressing issues of our time but are often stretched and under-resourced. “We believe helping them mindfully and ethically adopt AI tools allows them to focus on their key expertise and therefore scale their impact in the world,� he said. “To address this, the ChangemakerXchange AI program provides customized support, in-person gatherings, and a public toolkit to help young entrepreneurs navigate AI.�

As the longest-standing corporate partner, Â鶹ԭ´´ has supported social enterprise ChangemakerXchange for more than eight years. Beyond just financial support from the company, Â鶹ԭ´´ employees joined local cohorts of social enterprises, shared knowledge on AI, and brainstormed how individual ideas could be brought to life.

ChangemakerXchange initiated the Possibilists, a global alliance for youth innovation. on the needs and challenges young change makers face with AI. The study showed that while 65% use AI almost daily, 70% lack knowledge on how to navigate AI tools proactively for their purpose.

From environment to politics

Entrepreneurs in the European cohort of the ChangemakerXchange AI program cover environmental, social, and political projects.

One of them, Romania-based social enterprise Station Europe, aims to make democracy accessible, especially for young people from rural areas. “We empower young people to engage in participatory democracy, embrace creative activism, identify and address disinformation campaigns, and design policy recommendations that reflect their communities’ needs,� said Alin Gramescu, president & co-founder of Station Europe. To support these goals, the organization launched a collaborative platform in 2024 called that allows young people to explore new formats of political participation, taking them right into the heart of the policymaking process. Participants in hands-on workshops learn how to start from an actual issue or need and create a policy recommendation—with AI clustering and processing workshop findings. This results in recommendations for government authorities based on the input of thousands of young people.

“AI will help connect policymakers and young people. Within one year, we condensed more than 1,400 papers from over 80 workshops,� Gramescu said. “Opening the platform to additional countries will exponentially increase the volume of data we will be dealing with.� When asked for the value ChangemakerXchange added for his organization, he said “I knew what I wanted to build to manage this content, but I needed the step-by-step technical guidance to make it happen.�

Education as foundation for progress

Education is often described as the cornerstone of progress, and for Alexia von Salomon, concept & learning designer at Education Innovation Lab, this belief is her daily motivation.

“For me, education is the foundation for social innovation,� von Salomon said. As a leader in educational transformation, she sees a lack of relevant future skills conveyed at schools in Germany and aims as high as transforming Germany’s education system.

Besides conducting workshops at schools, she creates learning experiences for teachers and pupils, like the learning platform “digital sparks for the future.� To scale reach, Education Innovation Lab focuses on self-guided learning platforms and train-the-trainer sessions for school teachers.

von Salomon uses AI to co-create and validate new concepts. “This helped me to be more creative and think outside the box,� she said. “Using AI for early testing how minors would interact with learning content and tools reduces the iterations we need before actually conducting tests in schools.�

She said that being part of the ChangemakerXchange program not only gave her the opportunity to get to know the right people in the tech industry, but to shift her perspective on AI and increase her use of AI tools. Her personal goal is to shape a future where learning is not just about knowledge, but about empowerment and transformation. “From my perspective, key skills for minors in a future influenced by AI will be creativity and critical thinking—to use the opportunity AI offers without suffering from the potential negative impacts,� she said.

Serving business and society

More than 1,500 social entrepreneurs in over 130 countries are part of ChangemakerXchange’s global community. “True innovation happens when changemakers challenge the status quo and create solutions that serve both business and society,� Scheffelmeier emphasized. For him, social enterprises are not just businesses, but catalysts for inclusive growth and sustainable impact. “By combining technology with the vision of social innovators, we can scale solutions that address global challenges and build a future where profit and purpose go hand in hand,� he said.

Rather than a one-size-fits-all approach, the solution was designed to empower small businesses and the lower midmarket with an ERP solution that can grow with them and meet their evolving needs as they grow and prosper.

Introducing Â鶹ԭ´´ Business One

Today, more than 83,000 customers and 1.2 million users across more than 170 countries rely on Â鶹ԭ´´ Business One, supported by a global network of 850 partners and over 500 industry and country-specific extensions.

“It’s designed to be easy to start with,â€� said Darius Heydarian, head of Partner Solution Enablement Â鶹ԭ´´ Business One. “Organizations can begin with just a single user and scale up as their needs grow, whether that means adding more people, locations, or subsidiaries.â€� The solution can adapt to how smaller organizations work, offering both quick setups for remote sites and coordinated rollouts across regions. It helps provide a modern, browser-based experience and can integrate smoothly with Â鶹ԭ´´â€™s analytic solutions and automation tools.

The role of partners

Â鶹ԭ´´ recently reaffirmed the strategic importance of Â鶹ԭ´´ Business One within the Â鶹ԭ´´ solution portfolio. Â鶹ԭ´´ Chief Partner Officer Karl Fahrbach emphasized Â鶹ԭ´´â€™s continued commitment to Â鶹ԭ´´ Business One, highlighting the crucial role of the partner ecosystem: “Â鶹ԭ´´ continues to invest in the future of Â鶹ԭ´´ Business One. Our partner ecosystem remains at the heart of this success—driving autonomy, resilience, growth, and winning new customers every day.â€�

Partners play a central role in the Â鶹ԭ´´ Business One ecosystem. Â鶹ԭ´´ works closely with a vast network of partners that are experts in implementing and supporting Â鶹ԭ´´ Business One. These partners have the expertise to tailor solutions to each customer’s specific requirements, whether it’s industry functionality, localization, or regulatory compliance.

“Partners also contribute to the extensibility of Â鶹ԭ´´ Business One, developing extensions and industry solutions that help customers address unique challenges,â€� Heydarian said. The collaboration between Â鶹ԭ´´ and its partners helps ensure that customers benefit from both Â鶹ԭ´´â€™s technology and the specialized knowledge of local experts.

Customers in scope

“Recently, one of our partners shared a customer example with me that perfectly reflects the nature of companies in the market segment we are targeting with Â鶹ԭ´´ Business One,â€� Heydarian said. “The business had three employees when they started using the solution. Over the years, they expanded and employ 250 people today—without outgrowing their software platform.â€�

A typical Â鶹ԭ´´ Business One customer is a small or midsize company looking for an affordable, flexible, and scalable ERP solution. These organizations often need to manage a range of business functions—from accounting and financials to purchasing, inventory, sales, customer relationships, and reporting—all in one place. Â鶹ԭ´´ Business One helps them gain control, streamline processes, and make strategic decisions based on real-time information.

The solution is customizable to meet evolving business needs and supports international expansion with 28 languages and 50 country-specific localizations. Local support is provided by over 850 partners, helping to ensure that customers can get help tailored to their specific requirements.

“We are proud of the fact that Â鶹ԭ´´ Business One is part of Â鶹ԭ´´â€™s solution portfolio,â€� Heydarian said. “Three thousand net-new customers choose Â鶹ԭ´´ Business One every year. On average, 10 customers select Â鶹ԭ´´ Business One every day.â€�

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An intelligent application includes three distinct layers: data products, domain content, and applications. When all these components come together, intelligent applications become a suite of adaptive, AI-powered applications that can learn from data, understand business context, and act on behalf of a user. They are built to complement Â鶹ԭ´´â€™s transactional applications and sit on top of Â鶹ԭ´´ Business Data Cloud.

But what makes these applications different from existing analytics solutions? “Traditional reporting and analytics is focused on producing insights and reviewing past performance,â€� said Brian Raver, vice president, Â鶹ԭ´´ Business Data Cloud Product Marketing. “With intelligent applications, we go a step further. We provide out-of-the-box data products, model them together for actionable insights, and close the loop by allowing the intelligent applications to continuously learn and adapt based on that data.â€�

Three layers of an intelligent application

Data products

These are ready-to-use, curated data sets that are delivered for analytics, AI, machine learning, and operational use cases and provide insights related to a specific business topic or challenge. “A data product consists of a refined data set, metadata describing the data set, and an access port or application programming interface (API), allowing to exchange data with third-party applications,� Raver explained. Metadata provides detailed descriptions of the data set, including business definitions and explanations of each field, ensuring that users understand the context and meaning of the data they are working with. Available data products are published in a catalog for developers and data professionals for easy discovery and direct access to various use cases from analytics to application development.

Domain content

Data products are then combined into Â鶹ԭ´´ Datasphere semantic models that power Â鶹ԭ´´ Analytics Cloud stories and planning models. These out-of-the-box, interactive dashboards enable customers to analyze performance, monitor key metrics, and plan for high-value business scenarios.

Applications

Pro-code applications transform the domain content and data products into recommended actions that are directly connected to Â鶹ԭ´´ transactional systems. Over time, they continuously learn and adapt based on business outcomes. The future vision is for these applications to go beyond recommendations and empower AI agents to act on their own. “Intelligent applications encapsulate end-to-end business processes, while AI agents act as users within these processes,â€� Raver explained. “AI agents can interact with intelligent applications to perform tasks typically done by human users.â€�

Benefits for customers

An important benefit for customers is that intelligent applications extend the value of existing investments in Â鶹ԭ´´ Business Suite. According to Raver, a key advantage lies in the intelligent applications’ deep connection with Â鶹ԭ´´’s transactional systems. “These applications provide out-of-the-box data assets that not only fuel intelligent applications but are also accessible for developers and data professionals to use in their own analysis,â€� Raver explained. This built-in access to curated, high-quality data allows customers to unlock insights and drive value faster, without requiring extensive data engineering resources.

Looking ahead, as AI capabilities continue to mature, these intelligent applications are expected to evolve from providing recommendations to taking actions autonomously. “In the future, these applications could act on their own, learning from user behavior to automate repetitive tasks, free up time for more strategic work, and reduce the need for manual intervention,� Raver said.

By combining trusted Â鶹ԭ´´ data, intelligent automation, and emerging AI agents, the intelligent applications are helping to close the loop between insight and action.

What sets Â鶹ԭ´´ apart

Unlike other vendors that need to reverse-engineer data, Â鶹ԭ´´ provides ready-to-use data products, helping to save customers significant time and effort. “This seamless integration and the ability to deliver immediate value set Â鶹ԭ´´ apart in the competitive data market,â€� Raver said.

In addition to the Â鶹ԭ´´-delivered data products, Â鶹ԭ´´ also enables bi-directional zero-copy sharing of semantically rich data products between Â鶹ԭ´´ Business Data Cloud and other partner data platforms, starting with the already available . This capability, called Â鶹ԭ´´ Business Data Cloud Connect, enables seamless access to data where it resides, helping to eliminate costly data preparation and loss of semantics and business context. As a next step, Â鶹ԭ´´ Business Data Cloud Connect for Google BigQuery is planned to be generally available in the first half of 2026. Additional partnerships with key players in the data platform market are expected to follow.

With the latest announcements, Â鶹ԭ´´ now offers three intelligent applications—Finance Intelligence, People Intelligence, and Cloud ERP Intelligence Private—and another three in restricted public preview—Spend Intelligence, Supply Chain Intelligence, and Revenue Intelligence. “Intelligent applications powered by curated data products will transform how businesses operate by making data-driven decisions faster and more efficiently. As the market continues to evolve, these apps will play a crucial role in helping companies stay ahead of the curve,â€� Raver said.

By using AI and advanced analytics, BRF managed to improve forecast accuracy, optimized inventory, and fine-tuned planning. Collaborative forecasting and real-time data alignment now increase operational efficiency, enabling BRF to manage its extensive operations in over 127 countries and adapt quickly to changes in these markets. 

Project outcomes that scale

With 44 production units, 103 distribution centers delivering more than 500,000 deliveries per month to more than 415,000 customers, representing 5 million tons of food produced per year, the scale of the achieved improvements is immense.

“BRF is transforming efficiency and productivity standards in the production chain, developing solutions that make processes more agile, simple, and efficient,â€� said Gilberto Andretta, executive technology manager at BRF. “We are one of the leading Â鶹ԭ´´ IBP organizations achieving daily demand planning and sensing, statistical modeling, forecast accuracy and embedded analytics, time-series analysis, and exception management, which is integral for our more than 400 planners.â€�

By using Â鶹ԭ´´ IBP, the process of accurately accessing and analyzing demand and forecast data is 33 percent faster.

Award-winning solution

For BRF, seeking innovative solutions is essential to advancing digital transformation and driving growth. The company considers the use of AI as fundamental to overcoming supply chain management challenges.

“The transformation of BRF’s supply chain through Â鶹ԭ´´ solutions has made it easier to integrate processes, predict future trends, and respond quickly to market needs,â€� said Miriam Hahn, customer success manager for Â鶹ԭ´´ Brazil, who is working with BRF on its supply chain journey. “This journey highlights the strong partnership between Â鶹ԭ´´ and BRF.â€�

Using Â鶹ԭ´´ Integrated Business Planning for Supply Chain, BRF can now analyze vast data sets, improve forecast accuracy, monitor order trends, and enable proactive decision-making.

Ensuring that forecasts and delivery deadlines are accurate requires detailed and flexible planning, enabling a quick response to changes, increasing operational efficiency and accuracy. Additionally, consolidating real-time information, aligning financial and operational planning, and improving interdepartmental collaboration are fundamental steps for BRF’s operation.

Use of artificial intelligence

BRF is a recognized early adopter of Â鶹ԭ´´ Business AI and Â鶹ԭ´´â€™s AI copilot Joule. By integrating AI-driven insights, the food giant can accurately forecast demand, optimize inventory levels, and ensure timely delivery of products. This advanced technology helps BRF anticipate market trends, adjust production schedules, and reduce waste — ultimately leading to improved efficiency and customer satisfaction.

Within Â鶹ԭ´´ IBP, BRF is utilizing demand planning and sensing, statistical modeling, forecast accuracy and embedded analytics, time-series analysis, and exception management. Examples where AI is improving the business include:

• AI algorithms combine traditional time-series forecasting, regression and predictive modeling, and machine learning to produce more precise daily forecasts.
• Used for master data analytics, AI intelligence improves data quality, enhances the planning processes, and supports better decision-making.

More to come

BRF is among the first customers to successfully incorporate Â鶹ԭ´´ IBP with Joule to leverage the Â鶹ԭ´´ Help Portal and a chatbot to assist BRF employees.

Currently, BRF is deploying a “brownfield” Â鶹ԭ´´ S/4HANA Cloud implementation upgrade of the existing Â鶹ԭ´´ ECC system based on its successful established Â鶹ԭ´´ environment. The company is in the process of incorporating Â鶹ԭ´´ S/4HANA Cloud features and capabilities, while retaining much of the existing infrastructure and core data services. In addition to Â鶹ԭ´´ S/4HANA and the cloud-based Â鶹ԭ´´ Ariba solution for procurement, BRF is also running Â鶹ԭ´´ SuccessFactors software for human capital management (HCM).

“BRF is an early adopter for many of Â鶹ԭ´´â€™s AI services and our strategic partner in the region,â€� said Caio Granzotto, account director for BRF at Â鶹ԭ´´ Brazil. “As a next step, they plan to use Â鶹ԭ´´ IBP’s AI assistant to improve supply chain processes and simplify the work of their planners. The company is also considering order-based planning using additional AI algorithms.â€�

Top image courtesy of BRF

Further, the AI catalog offers detailed business benefits and estimated business value for customers, as well as helpful resources like step-by-step product documentation and demos.

“Since its launch 10 months ago, the AI catalog has been visited heavily by customers and partners,� explained Holger Neuert, chief product manager for the AI catalog. “Users spend a significant amount of time browsing the documented AI use cases, and return to the AI catalog on a regular basis researching AI features, Joule Agents, and [related] assets.�

Showing customers the business value of AI

The AI catalog offers a great source of information as it combines solution details and potential business benefits.

Â鶹ԭ´´ Customer Success manager Nick Pearce uses the AI catalog to access important documentation, value information, and pricing — in combination with the AI estimator — for customers.

“By providing fast access to potential business value statements, such as 70 percent faster job application processing with AI applicant screening, I can have value-based discussions with my Â鶹ԭ´´ SuccessFactors customers about prioritizing where to start their AI journey,â€� Pearce shared.

“The AI catalog and estimator on Â鶹ԭ´´ Discovery Center is a true game changer and the go-to resource for discovering Â鶹ԭ´´â€™s latest AI innovations across every industry and business function,â€� said Richard Grandpierre, head of Â鶹ԭ´´ Business AI Product Management. “This powerful platform highlights business value and delivers transformative results at scale.â€�

Dr. Michael Glueck, global head of Value Advisory at Â鶹ԭ´´, explained that the value estimates used in the AI catalog are based on Â鶹ԭ´´â€™s industry expertise and are backed by benchmarking data and third-party research as well as early customer feedback on each AI use case.

“By looking at benefits in terms of both efficiency, direct impact, and effectiveness, indirect and causal-related impact, we provide a holistic view of the value a specific use case within Â鶹ԭ´´ Business AI can deliver to our customers,â€� Glueck said.

Innovation from the Bottom-Up

It started with a hackathon for Â鶹ԭ´´ engineers with the goal to create a prototype combining the power of immersive technology and Â鶹ԭ´´ solutions. The Â鶹ԭ´´ Sports One team won with a prototype unlocking the potential of spatial computing in football match preparation.

is a comprehensive solution that helps sports organizations optimize performance on and off the field and is trusted by football clubs like TSG Hoffenheim and FC Bayern as well as the German Football Association.

Taking Â鶹ԭ´´ Sports One to the Third Dimension

Imagine being able to dive into a match, not just as a spectator, but through the eyes of a player on the field. The prototype Â鶹ԭ´´ Sports One Immersive allows exactly that.

Using Apple Vision Pro, analysts and players can replay and analyze match scenes in 3D from any player’s perspective. A user can switch between the perspectives of various players to better understand a specific situation during a match. This immersive experience enables a new kind of match analysis and can make it easier to identify situations where another behavior of a player would have potentially been better.

Through an app on Apple Vision Pro, this prototype aims to empower teams to optimize their performance—whether it’s preparing for the next big match or analyzing past performances. Timo Gross, head of Match Analysis for football club TSG Hoffenheim, believes there’s a huge potential in the simulation of game scenarios in immersive 3D. “This enhances player training and tactical understanding,â€� he said. “In fact, I believe that this has the potential to pave the way for a new approach in player development, especially in the youth space.â€�

Professional Feedback: TSG Hoffenheim Thinks Immersive

“TSG Hoffenheim is an innovative club, always looking for new technologies to improve match preparation and talent development,� Gross said. “Immersive match analysis could be a next big step.�

Using immersive technology, users can feel as if they are standing on the field. They can observe players, move around, and gain a comprehensive perspective of a game situation. “This immersive prototype shows a match analyst the exact perspective of a player in a specific moment,� Gross explained. “For the player, it makes the transition from viewing a theoretical situation on a tactic board to the real-life moment on the pitch much easier.�

For Gross, another powerful feature of the prototype is the 3D visualization of classical 2D tactics scenes. Using the Â鶹ԭ´´ Challenger Insights mobile app, coaches and players can sketch game situations on a tablet. These 2D sketches can be transferred into the 3D immersive view.

From Hackathon onto the Stage

Winning the Â鶹ԭ´´ hackathon on spatial experience was just the first step. On February 5, 2025, the prototype was presented at the SPOBIS Conference in Hamburg, Germany, Europe’s largest sports business event. In a live, on-stage presentation, Gross was joined by experts including Dr. Dietmar Blicker, deputy managing director at the Karlsruhe Institute of Technology, Sports and Sports Science; Michael Oenning, UEFA-Pro licensed coach; and Fadi Naoum, senior vice president Â鶹ԭ´´ Sports and Entertainment; to discuss the potential of immersive technologies in sports.

Oenning, who was head coach for German national division clubs Hamburger SV and 1. FC Nürnberg, is passionate about developing and promoting talents. “Training young people using technologies they encounter in their daily lives is something I would definitely use in my work as a coach,â€� he said. “Offering different perspectives of a game situation is an experience young players can benefit from tremendously.”

Blicker agreed that “3D experiences like these enhance the cognitive perception of a player.� Technology savvy by the nature of his role, he isn’t only involved in sports from an academic perspective, but also coaches the U23 team of the German second division team Karlsruher SC. “As a coach, I often sketch a tactical situation. But such a 2D drawing doesn’t easily translate to a real situation. The body height of a close opponent might cause a risk to an intended pass, or the distances on the field don’t match up in the end. Practicing situations in an immersive setting can help players to internalize certain behaviors,� he said.

Â鶹ԭ´´â€™s Naoum noted the importance of applying new technologies. “This prototype is a result of our continuous collaboration with partners like TSG Hoffenheim where we constantly test innovative technologies,â€� he said. “For Â鶹ԭ´´, sports serve as a front-runner to demonstrate new technologies that can potentially be applied in other industries as well.â€�

Hamburg has more bridges than any other city in Europe. For Germany’s second largest city it’s a complex task to maintain its 2,500 bridges and other structures like pedestrian tunnels, locks, flood barriers, and embankments.

The State Agency for Roads, Bridges and Waterways (LSBG) operates a large portion of the traffic infrastructure in Hamburg and is responsible for the entire lifespan of these public works.

“We’re an important player in flood protection and the prevention of water pollution as well as the maintenance of most roads and bridges in Hamburg,” Erdinc Karabugday, head of Planning and Controlling at LSBG, shares. “Together with our network, we build and protect everything that keeps Hamburg together.â€�

Â鶹ԭ´´ technology is one building block to perform this duty safely and efficiently.

Free Flow of Traffic as Top Priority  

Mobility plays a key role in the lives of people in Hamburg and other major cities. Bridges bear a particularly heavy load, because they are critical junctions in the city’s transportation network. Tracking the condition of bridges and planning for their maintenance needs and upgrades is vitally important to keep people moving in urban areas.

Karabugday describes the problem in a nutshell: “Our objectives are long term, so basing operational decisions solely on the current situation isn’t enough. We need comprehensive data for the strategic context – not only economic aspects, but social and ecological impacts as well. Our Â鶹ԭ´´ solution supports us with this.â€�

The manual entry of damage to structures like bridges used to be very expensive and time-consuming, a complex process made even more difficult by the fact that data entry and analysis are performed by both the agency’s own staff and external companies.

Digital Representation of Infrastructure

To reduce Hamburg’s renovation backlog and create a sustainable long-term solution, LSBG teamed with Â鶹ԭ´´ to develop a standardized data platform. One central aspect involves merging data from a variety of sources on , making it possible to blend data such as the location and condition of each structure. The results feed into a digital model of streets and bridges and can be used to guide decisions and generate precise forecasts for maintenance and budget planning. 

Ulrike Brecht, architect advisor in Customer Advisory Public Sector at Â鶹ԭ´´ Germany, explains: “The project was initially intended to handle bridge maintenance. Employees conduct the necessary maintenance work and inspections on site. They also document the condition of a structure, ensuring that necessary maintenance measures are carried out at the right time.â€�

All available data on the structures, including road and bridge data as well as geographic and monitoring data, is merged in Â鶹ԭ´´ HANA Cloud. When combined with aging models for the structures, this information can be used for forecasting when specific road segments will require resurfacing, for instance.

These data-driven forecasts put infrastructure budget planning on a new, firmer foundation and make it possible to define the priorities for object renovation to minimize the impact on traffic flows and the lives of urban residents.

Future Urban Platforms

Cities around the world face enormous challenges in the coming years when it comes to protecting and maintaining their residents’ quality of life. Dr. Philipp Schulz was a research assistant at the University of Heidelberg and wrote his doctoral thesis on digital urban development. Today, he advises cities and communities on topics like revitalizing inner cities, redesigning public spaces, and civic participation.

One trend Schulz has observed in the cities and towns he works with is the development of urban dashboards that show all the data collected in a city as important parameters such as air quality, weather forecasts, and movements of people.

For Schulz, developing a long-term digital strategy is one of the pillars of modern urban planning.

“From my perspective the term ‘smart city’ is outdated,� he says. “Today I call it ‘platform urbanism,’ an approach in which different players and their information are brought together on one platform.� Platform urbanism describes how apps and websites are changing the way people in cities live, work, and interact with one another. It involves the digitalization of individual areas, which are then brought together incrementally.

The city of Hamburg is on the right track. “Hamburg’s goal is to handle the full maintenance management of its infrastructure through a standardized data platform,� says Brecht.

The city is in the process of expanding the accomplishments of this project to other areas, such as the maintenance of public parks and municipal buildings.

Known for its extensive range of products, competitive pricing, and customer-centric approach, Hornbach has built a solid reputation over the years. Founded in Germany in 1877 as a small workshop, the retailer has grown into a major player in the European DIY market, employing 25,000 people. The company remains family operated, with a strong focus on customer satisfaction and long-term growth.

“We have more than 170 stores operating in nine European countries, and we keep expanding our footprint across Europe,â€� Mueller says. “As Hornbach focuses on providing the best price to its customers, efficiency of processes – and especially an efficient software solution – is key for us.”

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Expanding the Business Model: From Stores to Reseller Platform

The DIY retailer focuses on four key pillars: a wide product range with more than 120,000 products in categories including building materials, tools, garden, and decorative supplies; large store formats often exceeding 10,000 square meters; a low-price policy with high-quality products; and well-trained staff providing support for DIY projects.

“Everything you need for your garden or home renovation, you can get at Hornbach,� Mueller explains. To offer customers even more products for their personal projects, the retailer decided to level up its online store and create a reseller platform where it will connect offerings from other retailers with its own customer base.

To build a system that would scale to accommodate the expected growth within the next 10 years, Hornbach implemented Â鶹ԭ´´ Order Management foundation as a stable foundation for its corporate strategy.

“Â鶹ԭ´´ Order Management foundation is a system that helps businesses process orders efficiently,â€� explains Pano Papadopoulos, business development manager for Â鶹ԭ´´ Retail.

“Â鶹ԭ´´ Order Management foundation uses an event-based architecture to rapidly channel any orders through various business systems, including ERP and fulfillment systems,â€� Papadopoulos adds. “This intelligent system not only processes orders swiftly but also harnesses order data for strategic analysis, enabling smarter business decisions for the future.â€�

Hornbach already realized the benefits of this solution, Mueller says: “Â鶹ԭ´´ Order Management foundation allows us to access new markets quite fast and connect all the systems we need. In the first month of its launch, we have processed more than 100,000 orders and we can respond to a customer’s order within milliseconds. Today we process up to 1,000 orders in under one minute. This will allow for the order volumes we expect in the future. We measure the success of the solution by using OpenTelemetry, an open source standard, and monitor the processing speed of incoming orders. This way we can tackle potential issues early.â€�

Besides dealing with large order volumes, Hornbach’s system landscape already proved its adaptability. Due to the flexibility of its modular system landscape, the retailer could integrate a recent acquisition into its operational system in a very short time.

Hybrid Architecture: Connecting to Cloud Services

Hornbach is running on a hybrid system architecture, following its Â鶹ԭ´´ S/4HANA transformation project step-by-step. “We chose a cloud-native architecture that can scale – for all system loads that we might not be aware of today,â€� Mueller says. Â鶹ԭ´´ Order Management foundation is a cloud-native solution connecting to an on-premise system backbone.

Hornbach wants to be able to add more channels fast and create a seamless customer experience across all its channels. “We aim for a composable enterprise architecture, and for that we use event-driven architectures,â€� Mueller says. “That’s where Â鶹ԭ´´ comes in, because we’re able to connect our enterprise event mesh to plug in business created through platforms of hyperscalers like Amazon or Azure.â€� By moving to an event-driven architecture, Hornbach will be able to leverage the power and innovations from hyperscalers as well as connect new systems with current on-premise systems.

In the future, Hornbach will keep transforming its system landscape. Â鶹ԭ´´ Order Management foundation will be rolled out to further countries and the scope of the solution will be extended: B2B orders as well as orders from external marketplaces will be routed through Â鶹ԭ´´ Order Management foundation. Additional solutions like Â鶹ԭ´´ Omnichannel Sales Transfer and Audit will be next on the retailer’s list.

“The combination of new technologies like event-driven architecture, AI, and cloud-native solutions will give Hornbach great power for growth in the next years,� Mueller says, smiling.

Key to BrandAlley’s success is its deep understanding of its members and the impressive fact that 70% of these customers keep coming back for more. Fifty-two percent of them buy instantly, purchasing an average of three items, or £140 of value.

Michelle Hurney, head of Marketing at BrandAlley, explains the company’s core principle: “We launch up to 40 campaigns every day that offer up to 75% off on some of the best brands such as Reiss, Dyson, Levi’s, and many more.� Campaigns are exclusively for members and interested customers have to sign up to browse the website and to shop. Registration is free.

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Focus on Brand Fans

“We have been rethinking the way we identify our best customers, moving away from the traditional view of the most valuable customers being those who spend the most,â€� Hurney says. For BrandAlley, the true value lies in its brand advocates who provide more than just what they’re spending. She refers to them as brand fans: “It’s about who they’re recommending to BrandAlley. When they tell their friends and family about our site, that’s where the real value is. Word-of-mouth marketing happens to be one of the most effective ways to acquire new customers.â€�

To segment its customers, BrandAlley doesn’t only rely on the lifetime value of a customer (total amount spent by one customer), but accrues the amount spent across a customer’s connections and network. “Through the Advocacy Intelligence platform, we’re tracking the true economic value, or ‘extended customer revenue’ of our VIPs, the people who are sharing their favorite brands,â€� Hurney adds. “This way, we can engage and reward them, and keep them doing what they’re doing.â€� According to her, other insights become visible as well, like the fact that customers overall are spending less these days while customers from referrals spend more on average and are also far more likely to recommend.

Integration of Mention Me Tactics with Â鶹ԭ´´ Emarsys Customer Engagement

To identify these advocates BrandAlley is working with Â鶹ԭ´´ partner Mention Me, the world’s first customer advocacy intelligence platform. The Mention Me platform uses advanced AI technology to track and provide detailed insights into customers’ advocacy behaviors, like recommending products or services to others. Additionally, the solution includes prebuilt Mention Me advocacy tactics that can allow companies to quickly build personalized campaigns through various channels designed to motivate fans to share and recommend more frequently. Prebuilt workflows make the integration of Mention Me’s unique advocacy data with Â鶹ԭ´´ Emarsys Customer Engagement seamless and easy.  

Kat Wray, director of Strategic Partnerships at Mention Me, explains the benefits: “Our integration feeds Mention Me’s first-party AI-powered advocacy data into Â鶹ԭ´´ Emarsys Customer Engagement in real time to identify BrandAlley’s current and future brand advocates and activate them to drive growth. The combination of advocacy know-how and AI-based insights with Â鶹ԭ´´ Emarsys Customer Engagement have led to a 25% increase of the repeat purchase rate at BrandAlley.â€�

The overall improvements are impressive: since the go-live of the solution in September, BrandAlley’s customer base has grown, with 12% more customers acquired organically through referrals and a £9.6 million increase in new customer revenue. Customers who are referred to the brand spend 63% more in their first six months, are two times more likely to purchase again, and are three times more likely to refer their friends.

Wray explains how advocacy insights impact customer engagements: “Traditionally one would offer a discount code to a customer who is about to defect, together with a message along the lines of ‘We’ve not seen you for a while, here’s a discount to come back.’ Based on advocacy data, we can now see that some of these customers might not be ready to spend but are more than happy to share and recommend the brand to friends instead – driving indirect revenue. They aren’t inactive customers but engaged advocates.â€�  

Machine Learning Advances Customer Loyalty

“At Mention Me, we use AI and machine learning to optimize referrals. We have our own AI engine with predictive capabilities that enable marketers to focus their activities on specific customer cohorts,â€� Wray says. She explains that it is key to understand that not all customers are ready to refer; some people just aren’t at that stage yet, but future advocacy behavior of specific individuals can be predicted. Machine learning also identifies a customer’s propensity to refer at a given moment. Customers with a high propensity to refer will be encouraged to do so. Once brand loyal, customers’ propensity to refer grows, and sometimes so does their propensity to purchase again.

Asked about BrandAlley’s next step regarding advocacy management, Hurney answers: “As we build on our strategic partnerships with Mention Me and Â鶹ԭ´´ Emarsys Customer Engagement, we’re excited to see how our shift of focus from VIPs to ‘very important advocates’ will play an even greater role in our marketing and growth strategy.â€�

Top photo courtesy of BrandAlley

Sights Set on the European Championship

“Â鶹ԭ´´ Sports One enables us to efficiently share tactical insights and key information about our next opponents with our players. And by accessing the software on a tablet, we can even give them the latest updates in the changing room shortly before kickoff or at half time,â€� says Benjamin Glück, assistant coach to the German national A-team.

“Â鶹ԭ´´â€™s sports management suite is very important to the national soccer team as a central platform for collecting and analyzing data and for presenting the insights gained from that data to our players,â€� explains Glück. “We use the video and visualization functions in Â鶹ԭ´´ Sports One to help us illustrate findings about our opponents and their key players. These features make conveying tactics and preparing for specific matches much easier and clearer.â€� The German team also uses analytical insights to prepare for penalty scenarios. In Â鶹ԭ´´ Penalty Insights, goalkeepers can study the behavior of potential penalty-takers, such as which area of the goal they usually aim for and even how these preferences change under pressure. Conversely, penalty-takers can gain insights into the tactics of their opponents’ goalkeepers.

The coaches and analysts of the German Football Association (Deutscher Fußball-Bund, or DFB) work with data created within Â鶹ԭ´´ Sports One as well as data and analyses from third-party sources. The Â鶹ԭ´´ solution serves as the central platform for consolidating and analyzing all the data.

An Â鶹ԭ´´ App for All Players

Germany’s national players stay in close contact with their coaches and analysts through the Â鶹ԭ´´ Sports One messaging app – Â鶹ԭ´´ Team One – which they have on their phones. Among other things, the mobile app is used to pass on match preparation information, such as opponent analyses, annotated clips, and videos.

The players also use the mobile app to share information about themselves. For example, they complete questionnaires about how intense they perceived the last training session or match, how well they slept, and how they are feeling overall. Their responses are used to generate an RPE (rating of perceived exertion) value. By gauging each player’s general health and well-being in this way, coaches can manage intensity levels according to each individual’s situation. Both the DFB and the player’s home club monitor this RPE value to guard against overtraining and injury. Â鶹ԭ´´ Analytics Cloud can draw on data from Â鶹ԭ´´ Sports One to create player-specific reports that are forwarded to the player’s home club after courses, tournaments, and matches the German team takes part in.

DFB and Â鶹ԭ´´: An Established Partnership

The DFB is a familiar name around the globe. With almost 7 million members spread across more than 25,000 clubs, it is the world’s largest national sports federation. At the World Cup in Brazil in 2014, which Germany won, the country’s national team was already using Â鶹ԭ´´ Match Insights, a prototype of the solution that is now part of Â鶹ԭ´´ Sports One. This prototype helped the DFB evaluate large volumes of data for match analysis for the first time.

Today, more than 1,000 DFB users – players and the “teams behind the teamsâ€� – work with Â鶹ԭ´´ Sports One. “Our successful collaboration with the German national team was a key factor in Â鶹ԭ´´â€™s decision to turn the prototype we developed for the DFB into a standard product. Now, clubs and associations in 19 countries are using Â鶹ԭ´´ Sports One,â€� says Fadi Naoum, senior vice president and head of Development, Â鶹ԭ´´ Sports One. More joint projects followed, including the development of Â鶹ԭ´´ Penalty Insights and Â鶹ԭ´´ Challenger Insights, a mobile app that helps teams prepare for upcoming matches. On the horizon of this co-innovation partnership are plans to leverage the power of AI to make the functions in Â鶹ԭ´´ Sports One even more efficient.

Shaping the Future

In addition to Germany’s national men’s and women’s soccer A-teams, the DFB manages 12 junior and U21 teams, too. “The challenge for us is to keep a close eye on the pool of talented young players we have and to help them reach their full potential,â€� says Christofer Clemens, the DFB’s head of Scouting, Match Analysis, and Diagnosis. “Our coaching teams watch an average of 1,500 games every season and identify a large number of gifted talents in the process. To qualify these players, the coaching teams compile a huge volume of scouting reports, which we manage in Â鶹ԭ´´ Sports One. These qualitative insights are vital for us. However, at the moment, the task of judging the development of a player or comparing individual players based on that information is time-consuming,â€� says Clemens. “We’re hoping that AI will make the complex task of analyzing these vast amounts of data much simpler for us.â€� Alongside scouting reports, the DFB plans to use publicly available data sources and match statistics to complete player profiles.

“Another area where the DFB sees enormous potential for AI is in analyzing opponents before international matches,â€� says Martin Vogelbein, a member of the DFB’s Scouting, Match Analysis, and Diagnosis team and project lead in managing the Â鶹ԭ´´ Sports One solution. Â鶹ԭ´´ Sports One is already helping the DFB prepare for big games, he explains, but the plan is to go a step further and use AI to automatically analyze insights from opponents’ past matches.

“We are hoping that Â鶹ԭ´´â€™s AI copilot, , will bring enormous benefits in terms of consolidating match reports. That would minimize the manual tasks our match analysts need to complete and reduce the time it takes to get answers to a range of key questions we have when preparing for games,â€� says Vogelbein. Typical questions are: What patterns are visible in the opposing team in different phases of the match? What does this tell us about the team’s strengths, weaknesses, and about the options we have? “We already have a huge amount of information – particularly qualitative data – available to us before games. Â鶹ԭ´´ Challenger Insights, a component of Â鶹ԭ´´ Sports One, allows us to visualize insights from that information and discuss them with the team. But, looking ahead, AI-driven analyses could help us prepare key background information for our players even more easily,â€� says Vogelbein.

Clemens and Vogelbein are very pleased with the results so far. “We see enormous potential in our work with Â鶹ԭ´´ on the AI-driven evaluation of information that is relevant for scouting and match analysis. The prototypes Â鶹ԭ´´ has presented to the DFB look very promising, and we are looking forward to using them live soon.â€�

Top image courtesy of Thomas Böcker

Companies that prioritize sustainability in their operations, such as using recycled materials, reducing carbon emissions, or supporting social causes, are gaining popularity among consumers. But those consumers demand proof of these values and product promises at the point of sale.

“The rise of sustainability isn’t just a movement; it is a shift in consumer consciousness,â€� said Dania Fayyaz, Â鶹ԭ´´ North America sustainability program manager. “Today’s consumers are redefining their purchasing patterns, opting for products and brands that align with their values, and driving a demand for eco-conscious practices and ethical sourcing.â€� In her conversations with retailers that visited Â鶹ԭ´´â€™s booth at NRF 2024, the world’s largest retail fair, she saw a high interest in sustainability solutions.

Consumers – and Markets – Demand Visibility

When the EU Deforestation Regulation (EUDR) comes into effect for large enterprises later this year all products sold in the EU containing palm oil, cocoa, soy, rubber, cattle/leather, coffee, wood, and their derivatives will need proof that their harvest didn’t contribute to deforestation. Non-compliance will result in potential fines of up to 4% of companies’ EU annual turnover. To help businesses simplify this complex task, the Â鶹ԭ´´ Green Token solution can track the geolocation of where raw materials are harvested and automate the creation of due diligence statements (DDS). The solution can also help improve visibility for consumers on where the raw material for their clothing was sourced and offer information on potential reuse and recycling.

Take the example of a cotton shirt made from 50% certified recycled materials. Cotton can be sourced from multiple farms or recycling services and taken to a cotton mill where it’s spun into thread and later fabric. It becomes impossible to distinguish the different cotton elements – conventional versus recycled cotton – once they are co-mingled and the manufacturer loses insight into the sustainability attributes of the raw materials. That’s why it’s important to have a sophisticated tracking solution in place. “Â鶹ԭ´´ Green Token can enhance transparency for bulk-traded, co-mingled raw materials by digitally storing sustainability attributes on tokens that traverse the supply chain,” said Fayyaz.

In addition to making conscious decisions on raw materials and products, consumers can also consider circular economy models. “I’m really intrigued by the idea of revitalizing clothing items,â€� said Fayyaz. “Imagine being able to exchange a well-loved jacket you’ve had for years and receive credit in return. And the jacket might find a new owner who will genuinely appreciate it.â€� The Â鶹ԭ´´ Recommerce solution enables that scenario, helping brands and retailers take back, manage, and resell secondhand inventory to help accelerate the shift from linear to circular business models. On an even larger scale, Â鶹ԭ´´ Green Ledger is Â鶹ԭ´´â€™s vision to make transactional carbon accounting a reality. It can enable organizations to track carbon like they do cash, with every financial transaction having a corresponding carbon entry. “This is incredibly desirable because we will see taxes for carbon imposed in the future, and decision-makers will want insights into which processes are utilizing the most carbon,â€� Fayyaz said.

A Glance into the Crystal Ball

At the same time, low-price online platforms continue to attract consumers with their affordable and wide range of products. They offer competitive prices on trendy clothing and accessories using economies of scale and direct-to-consumer models. Despite concerns about their environmental and ethical practices, these platforms remain popular among price-conscious consumers. However, there is growing awareness of the environmental and social impacts of fast fashion and low-price models. As a result, consumers are becoming increasingly discerning in their purchasing decisions, prioritizing quality, longevity, and sustainability over cheap prices and fast fashion trends.

“Overall, the consumer landscape is evolving, with a growing emphasis on sustainability, conscious consumption, and ethical practices,â€� said Sven Denecken, chief marketing and solutions officer for Â鶹ԭ´´ Industries & CX. “Companies that can adapt to these changing preferences and demonstrate a commitment to environmental and social responsibility are likely to thrive in the long term.â€�

Most have been in this situation before: one of the providers or services we use is a victim of a data breach and we want to determine if our personal user data has been impacted. This is where fully homomorphic encryption (FHE) comes into play. With FHE, the encrypted, personal password is compared against the data set of stolen user data and potential matches are identified without ever revealing the user’s password.

Use cases for this type of privacy-enhancing technology (PET) are numerous. They range from applications in medicine, where third-party service providers can analyze health data without compromising a patient’s privacy, to performing machine learning and AI algorithms on encrypted data, allowing organizations to derive insights from sensitive data sets without exposing the data to potential breaches or privacy violations.

How It Works

Fully homomorphic encryption allows calculations to be performed on encrypted data without having to decrypt it first. Confidentiality is maintained, as even the results are encrypted and can be viewed only with the appropriate decryption key. Further techniques for processing encrypted data are multi-party computation (MPC) and trusted execution environments (TEE).

Mathias Kohler, research manager at Â鶹ԭ´´ Security Research, outlines the differences: “While FHE is the most known of the encryption technologies, MPC is the ideal candidate if working with several parties exchanging encrypted data across company borders. And it can be substantially faster than FHE.â€� While both are software-based technologies, TEE is hardware-based, which makes it the fastest choice. The downside: TEEs, unlike MPC and FHE, require decrypting the data for processing. While decryption happens in a trusted hardware environment isolated from the operating system, it can allow data leakage via side-channel attacks. Notably, PETs do not need to be considered in isolation and can augment each other. For example, MPC can encrypt and distribute an FHE decryption key, protecting the FHE key and ensuring no single party can decrypt everything.

Why It’s Relevant

There is a demand for this kind of technology. By 2025, 60% of large organizations will use at least one privacy-enhancing computation technique in analytics, business intelligence, or cloud computing, according to .

Fully homomorphic encryption has numerous applications, especially in scenarios where privacy and security are paramount, such as secure computation in the cloud, privacy-preserving data analysis, and secure outsourcing of computations. As long as one party is performing the data processing centrally, FHE is the encryption method of choice. FHE enables organizations to share encrypted data with partners or third parties for analysis or monetization purposes while maintaining data confidentiality. This is particularly relevant in industries such as advertising and market research.

Interesting use case scenarios from Â鶹ԭ´´â€™s perspective could be secure benchmarking and predictive maintenance.

Secure Benchmarking

Companies often assess their competitiveness relative to industry peers and compare business-relevant KPIs, such as automation rate or return rates, with peers and even competitors. With fully homomorphic encryption, all participating parties can share encrypted KPIs without revealing individual data. As a result, they learn about relevant statistics, such as averages or medians, to assess their relative competitiveness and decide where to improve and invest.

Predictive Maintenance

Predictive maintenance is a machine learning technique to forecast demand for maintenance or spare parts based on historical data. “In certain industries, required data, such as usage patterns and failures, is considered sensitive and is not easily shared with data scientists or maintenance operators,â€� says Anselme Tueno, senior researcher at Â鶹ԭ´´ Security Research. By computing on encrypted data, however, no sensitive information is revealed while still allowing for the required insights to be gathered for prediction tasks.

Carbon Footprint Calculation with Multi-Party Computation

While it is early days from a product availability perspective, Â鶹ԭ´´ is working on potential use cases with customers and partners. One key example is calculating carbon footprints of products.

Prime examples for complex collaborations are today’s supply chains, intricate networks that encompass various levels of suppliers, manufacturers, and processed goods. Unfortunately, there is often a lack of comprehensive visibility across the entire process – either for technical reasons or because businesses are often reluctant to share sensitive data across supply chains that often include direct competitors.

However, to accurately assess and disclose a product’s carbon footprint, sensitive production details and associated carbon costs for production-relevant parts and materials are required. Here, MPC can reveal only the required carbon footprint without disclosing associated, proprietary manufacturing details with other supply chain participants.

Currently, Â鶹ԭ´´ is working with Bosch on cloud-native software for secure multi-party computation called .

“Â鶹ԭ´´ participates in this open-source project and supports the development of Carbyne Stack’s storage and processing services and the deployment of Carbyne Stack on Amazon Web Services (AWS),â€� Kohler explains. “For Bosch, Carbyne Stack is a type of cloud-native operating system for MPC workloads that manages resources to run as efficiently as possible in multi-cloud deployments.â€� This effort can help Â鶹ԭ´´ in the long run to integrate MPC as technology into Â鶹ԭ´´ solutions and services while running in a cloud-native environment.

What’s Next?

Despite all the benefits around processing data, encryption introduces significant computational overhead due to the complexity of performing operations on encrypted data. Slow processing speeds, especially for complex operations and large data sets, makes fully homomorphic encryption impractical for real-time applications or large-scale data processing. Although the performance of FHE has greatly improved in recent years, its practical adoption is still limited due to the processing overhead and performance considerations. Ongoing research is focused on the design of FHE-specific hardware accelerators.

“PETs for computing on encrypted data have the power to amplify data-driven business collaborations and reshape the future of cloud computing,â€� explains Jonas Böhler, senior researcher at Â鶹ԭ´´ Security Research. By safeguarding data, they enable access to previously untapped information while minimizing privacy risks and thwarting data breaches. The future of computing is encrypted.

Someone who seized this opportunity is Jonny Rowland. Having spent a big part of his childhood in Uganda, he saw the opportunity in the growing thirst for sustainable, high-quality coffee – and an opportunity to ensure that coffee farmers would receive their fair share in this business. Together with his sister Beth, he founded , a profit-for-purpose business that works directly with local farmers to achieve higher yields and the high quality coffee that is sought after on the global market.

With the support of local experts working for Agri Evolve, farmers have improved their productivity and increased income for themselves, their families, and their communities. The main idea of this social enterprise is to use digital technology to improve established supply chains for coffee cherries in the Rwenzori Mountains, one of Uganda’s key habitats for Arabica coffee plants, and share the latest agricultural practices with local farmers.

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A Long-Lasting Tradition: Coffee in Uganda

Uganda is one of the few countries in the world where coffee plants are native. The processed beans are an integral part of Uganda’s export economy, making the country one of the 10 largest coffee producers in the world. In recent years, Uganda has made a name for its specialty Arabica, which thrives in the local climate with humid days and cool nights. But Arabica coffee, a climate sensitive plant, is beginning to struggle as global warming shortens the cooler phases it needs to thrive. This requires adjusting the traditional ways of cultivating the cherries.

Over 1.8 million households in Uganda grow coffee, and coffee contributes nearly a third of the country’s export earnings, paying for critical infrastructure like roads, hospitals, and schools. While many families grow coffee, it was hard to make a living out of it in the past and motivation to produce high quality was low as prices on global markets wouldn’t justify the effort.

Joyce Birungi is one of the coffee farmers living high in the rainforest of the Rwenzori Mountains who registered with Agri Evolve as a supplier. Like most farmers in this remote area, she took care of the plants based on traditions passed down over generations. The cherries were often picked too early and only ripened while drying in the sun for longer periods, which caused a loss in quality and therefore lower prices. Climate changes, like increases in floods, droughts, and heat waves, put additional pressure on the traditional ways of farming.

“In the past, I used to pick coffee cherries of poor quality. So, I lost a lot of money,â€� Birungi said, explaining her challenges as a small local producer before working with Agri Evolve. “Transportation was a big challenge. I had no way of selling the coffee and I didn’t know about best practices in farming and agribusiness.”

Collins Kifula is a field coordinator at Agri Evolve and an expert for sustainability and quality. He works closely with the farmers, registering each farmer using a mobile app based on the Â鶹ԭ´´ Rural Sourcing Management solution and collecting data like the size of the farm and the state of the coffee trees. Using the mobile app saves time – time he can use to talk to the farmers and solve issues they might be facing or suggest improvements to their current setup. One important focus of his work is education on soil protection. With floods happening more frequently these days, farmers benefit from terraces and planting grass patches to avoid the fertile soil being washed away by rain.

As Agri Evolve is specialized in high-quality, sustainable coffee, certification is required. “We need to hold up to a certain level of quality and we do that by getting, for example, the . To receive this, we need to raise data and have the transparency. Here, the mobile app within Â鶹ԭ´´ Rural Sourcing Management is of huge help,â€� Kifula explained.

“Technology Is Changing Things�

The biggest challenges the farmers faced before starting to work with Agri Evolve was the quality of the cherries and the lack of price transparency. Sometimes there was even fraud along the process of collecting the cherries. “Technology is changing things,� Kifula said.

Today, farmers still deliver their cherries to so-called middlemen who check the quality, but they now document the weight using the Â鶹ԭ´´ app. The farmers instantly receive an SMS message showing the delivered quantity and its value. “This price transparency is a big motivation for the farmers and creates trust,â€� he said. Further, Â鶹ԭ´´ Rural Sourcing Management enables the calculation of the accrued delivery quantities and therefore the yield a farm is producing, which are used as the assessment basis to provide loans to the farmers.

Keeping Track of Data

“Our mission is to do sustainable farming for future generations. As a social enterprise, Agri Evolve works with a growing number of farmers, which can quickly add complexity to our supply chain. Today, we deal with around 22,000 smallholders. We collect a lot of data that needs to be analyzed,â€� Roset Biira, field supervisor at Agri Evolve, said. “We needed a system that collects and analyzes our data in an efficient way – to be able to be transparent to all our farmers and even our clients.â€� Using Â鶹ԭ´´ Analytics Cloud, Biira showed how Agri Evolve can identify if coffee trees are getting old or are weakened by pests early on. Based on these predictions, they consult with the farmers on planting new trees or suggest trainings on pest control.

Realizing Change

Agri Evolve is building a network of trust with the farmers and middlemen, making sure to give the best price possible to the farmers so that the local societies participate in the higher prices sustainable coffee can generate on the global markets.

“I’m now able to increase my coffee production and send my kids to school, and I hope that they will graduate from university and have better lives. We used to live in semi-permanent houses, but now we are moving to permanent housing,� Birungi said when asked what impact this new way of collaboration has had on her and her family.

Talking about the future of the growing profit-for-purpose business and its network of local coffee producers, Beth Rowland, co-founder of Agri Evolve, said: “We will continue to push the boundaries of supply chain transparency through digitalization to allow us to provide the best service to smallholder farmers in the Rwenzoris.� For the family business, it is important to ensure connected farmers have access to a fair and transparent market for their coffee cherries.

While sharing sustainable practices to counteract the impact of environmental changes is a requirement for the production of certified sustainable coffee, it will also make sure that the tradition of coffee production will live on in the Rwenzori mountains of Uganda.

It is the nature of artificial intelligence not to give any further importance to the emotional side of this explanation in the first place. Yet it’s precisely this emotional aspect that makes a discussion on the opportunities and risks of the metaverse or virtual realities so complex. Opinions here can vary from extreme enthusiasm to outright rejection. Some people are avoiding the topic altogether because they don’t know much about it or simply aren’t interested; others see massive potential and opportunity.

Â鶹ԭ´´â€™s latest art exhibition – The Metaverse: Dreamland or Dystopia? – shines a spotlight on these differing standpoints. Thirteen artists each present their take on how reality and virtuality – and art and technology – are converging. Working in a variety of media, they playfully explore space and time, traversing the real, the augmented, and the virtual. You can choose to visit the exhibition in the real world at the Â鶹ԭ´´ International Training Center in Walldorf or you can experience it in a . The exhibition runs until September 1, 2023.

Alexandra Cozgarea, curator of the exhibition, takes a close look at the impact of virtual worlds and artificial intelligence on art. “Artists have always used tools to create their work, be it hammer and chisel, paint and brush, or, in the age of technology, graphic design software, algorithms, and artificial intelligence. Today’s rapid advances in technology create tools that enhance our physical and mental abilities. The possibilities for the future are endless,â€� said Cozgarea during her speech at the opening of the exhibition, stressing how important it is for Â鶹ԭ´´ to use innovative technologies responsibly.

But for Cozgarea and the artists featured in the exhibition, the deeper question is whether digital and physical art can coexist. Is art on Instagram still art or must art only exist in the real world? But then isn’t Instagram also real? “How we perceive the metaverse is not only a technical question, but one about our vision of a desirable future and our ability to shape it ourselves,� added Cozgarea.

Her intent was to create a hybrid exhibition, grounded with one foot in the real world and the other in the virtual world – two places with blurring borders. The works impart their thoughts on how we as a society want to define ourselves as humans and how machines fit in.

During the opening of the exhibition, Jörg Kraus, who was instrumental in putting together the exhibition and is himself one of the artists featured, gave a speech about the metaverse, in which he connected current artistic viewpoints to all the ambivalent questions about the relationship between man and machine.

In his view, whether the metaverse is a dreamland or a dystopia comes down to our beliefs and our mindset. “Digitalization has already changed our lives and it will continue to do so at high speed,� he began. “Are you ready to contemplate the impact this might have on you?�

“As artists, we wonder whether the abundance of images that our society is already exposed to will continue to grow and level everything out. Will it really matter to anyone whether a work of art was created by a person or by a machine? Will we still have a notion of beauty and be critical of things – or will that all fade into the background?�

When asked where he stands on the metaverse, Kraus replied, “A key question for me is: where do I direct my attention when multiple elements are competing for it? We can focus on only one thing at a time.�

The Metaverse: Dreamland or Dystopia? is about art and therefore mainly about the artists’ perceptions and the aesthetics with which they express their thoughts. Images, sculptures, installations, videos, and a take us on a journey through the real and the digital, the two dimensional and the three dimensional.

When speaking to the artists about their work, it became clear that digital art, media art, or online art are challenging the notions of authorship and possession that underpinned the art market in the past. Virtual worlds not only offer artists new tools and opportunities, they also force them to face the ongoing discussion on how we as a society will use and consume creative content in the future. Has our society reached the point where the debate is no longer just about the pros and cons of technological progress? The exhibit suggests that we as a society should ask about the “deals� between man and machine, between man and economy.

So, is the metaverse a dreamland or dystopia? Even among the showcased artists, there is no single answer to this question.

A few of the exhibiting artists share their thoughts:

Michaela Schrabeck uses AI to create her work.

“For me, digital technology is a contemporary expression of human creativity. I’m drawn to machine learning and the fanciful artworks that artificial intelligence dreams up. Do the pieces I taught the machine to create pass as art and will they be accepted by my audience? Or must they be translated into traditional art forms?�

Paul Hirsch creates his sculptures twice: out of wood and in a virtual space.

“Didn’t virtuality and reality converge a long time ago? We already had powerful technical tools. Have we just added more – or do they represent a shift into a new and different world? Wrong questions – and much too passive. I think it depends on what we make of it. The space between the virtual and the real – and where the two overlap – that’s what I find so exciting.�

Christiane Rath builds “human nests� out of branches and leaves and invites the observer to make themselves comfortable inside of them. She reflects on what we might lose in the virtual world: the haptic experiences and smells that etch themselves into our memories.

“The virtual world is exciting and thrilling. It opens up a wealth of possibilities for new fantasy worlds and sensory adventures. Yet whenever I catch myself becoming too enthusiastic about the latest innovations, I think about what we might lose along the way.�

Jörg Kraus works with location data from Google Maps.

“The entire world is being rasterized and converted into virtually reproducible data. I’m relieved whenever I discover a gap – that even the “machine� misses something. That gives it a human-like quality. I’m worried, though, that the gaps are closing and that those fleeting escapes that we can still enjoy today will disappear.�

Image copyright: Klaus Kirchner
Disclaimer: This article is not about technological details, nor is it an assessment of artificial intelligence, virtual realities, or the metaverse. Instead, it contains different responses to the questions that using these technologies has raised – with a special focus on how they affect artists.

Even as far back as the 1950s, computer scientists were already experimenting with this concept of data modeling. In the 1980s, knowledge graphs were a much-discussed topic in the context of expert systems, and in the 2000s they once again came to the attention of the scientific community, as they took on a fundamental role in Semantic Web. Finally, in 2012, it was a search engine that gave knowledge graphs their big appearance among a wider audience: Google announced that its users would be able to search for “things not strings.� Rather than just trying to match keywords from a query, the search engine would also place them in the right context – all with the aim of delivering better, more intelligent results.

“Put very simply, knowledge graphs are a technology that seeks to turn data into machine-interpretable knowledge,� says Michael Burwig, innovation engineer at Potsdam. Their key focus is to model the relationships between objects, which are shown as a network of interconnected points.

“Knowledge graphs allow us to map how we humans understand the world – how we go through life, accumulate knowledge, and how we contextualize that information in our minds,� Burwig explains. This ability allows humans to draw conclusions that produce fascinating “aha� moments. Ultimately, this is also the goal of business software: to connect knowledge and find solutions, ideally in an automated way. There are many potential use cases of knowledge graphs, such as knowledge and data management and chatbots. Detecting insurance fraud is one example, but, in short, they can be used in any scenario in which patterns are examined to identify exceptions and anomalies.

The Potential of Hybrid AI

Burwig believes, however, that automated insights like these have not yet become part of everyday practice. “Hybrid AI is a major step in this direction. It combines semantic technology, in other words knowledge graphs, and static machine learning, which is now being used for a host of scenarios of this kind,� he says.

Dr. Jan Portisch, lead architect at Value Accelerator Delivery, describes the difference between classical machine learning concepts and hybrid AI machine learning as follows: “Classical machine learning methods use extracts of existing databases. These methods draw on such data sets, which are effectively snapshots without any context, to create machine learning content. With knowledge graphs, on the other hand, new data can be added at any time so that they keep on ‘learning’ and, unlike static methods, stay current.�

Even though knowledge graphs are not new, it took the massive increase in computing power that we have seen since the 2000s to unleash their full potential, Portisch explains. And since this technology is not yet widely taught at universities, few developers know much about it. “Another difficulty is that designing graphs is highly complex, with developers having to think beyond their own application. The modeling behind the graphs has to be complete and semantically accurate. Nonetheless, their potential is huge,� he says.

As an architect, Portisch is involved in creating a central graph for process knowledge at Â鶹ԭ´´. The Â鶹ԭ´´ Signavio Value Accelerator Delivery team collates and models the process knowledge Â鶹ԭ´´ has built up over the past 50 years to give customers an integrated view. In the future, the accelerator will offer a semantic search where users will be able to formulate a business problem they’d like to solve and the system will display a list of processes and data that are impacted by this problem, Portisch explains. Further, this capability could be a helpful transition tool in migration projects or could provide presales teams with the visibility they need when tailoring solutions to customers’ specific situations.

Felix Sasaki, expert in Knowledge Graphs & Semantic Technologies in the Â鶹ԭ´´ AI unit, explains additional benefits: “Standards-based knowledge graph technologies facilitate the modeling of business scenarios. So-called constraints complement the existing logic-based modeling. Since constraints can easily capture the knowledge of business experts, modeling becomes easier. In addition, knowledge graph-based vocabularies like schema.org have found widespread adoption and thus help to find a more ubiquitous language.â€�

Decoupling Business Expertise and Application

For Â鶹ԭ´´, the power of this technology lies in how graphs can be combined in different ways and, therefore, in how data models – and ultimately the applications themselves – can be integrated and composed.

Portisch explains how it’s important to recognize that RDF – Resource Description Framework, a syntax used to model metadata for Internet resources – is a publicly acknowledged standard and that knowledge graphs are already widely used outside the corporate world. One of the best-known knowledge graphs is ; another example of a large-scale graph is . “These graphs are public resources that can also be used commercially,� Portisch says.

That creates interesting use cases. For example, private corporate data could be combined with public data. A supplier system running on Â鶹ԭ´´ software could automatically import metadata from a graph about companies it interacts with, such as the type of business, company logo, and who their managing directors are.

“Semantic Glue� – Integration by Design

Burwig sees another advantage of these graphs in their “semantic glue.� Because of their flexible structure that can be enhanced in real time, individual graphs can be “glued� together to link up data silos. Unlike graphs, the table structure of a relational database has to be defined at the beginning – and changing that structure later requires a lot of work. But graphs can store and link metadata in a semantic data layer that is separate from the data stored in an application’s tables. This makes it possible to consolidate data across different products and data silos.

Graphs offer huge advantages over relational databases in certain scenarios, such as calculating the shortest route or associating a specific material with a customer material. “If such a scenario is based on a relational database, the query would have to touch applications from across the entire Â鶹ԭ´´ world. But with a graph, it would be a simple problem to solve,â€� Portisch says. While relational databases usually offer greater performance within their applications, graphs always have an advantage when it comes to creating bridges, known as joins, between data structures and recognizing the correct context.

Because it is possible to extend knowledge graphs, they represent a perfect data model for businesses to start small with and then, as an iterative process, to roll out to more locations and build on. For example, a company could begin with just one use case or department and then gradually roll the model out to the rest of the organization. Any additional data models are “glued� to the graph as new nodes and edges.

The Situation Knowledge Graph: One of the First Knowledge Graphs at Â鶹ԭ´´

One of the most advanced knowledge graphs to date at Â鶹ԭ´´ serves as the basis for the Explore Related Situations app, which is part of the Intelligent Situation Automation service on for situation handling in Â鶹ԭ´´ S/4HANA.

The business background: around 4% of the automated business processes within a business require manual intervention because of an unforeseen event, such as a late payment, a delayed delivery, or a transport issue.

The situation knowledge graph links these exceptional events to their business entities, enabling the user to better understand the situation in a business context and therefore helping solve the problem and optimize business processes. Further analyses of these situations often reveal relations of issues to certain materials or partners. “Today, this kind of knowledge is often hard-coded,â€� says Dr. Torsten Leidig, an architect in Â鶹ԭ´´â€™s Situation Handling team. The knowledge graph that underpins the Intelligent Situation Automation service enables a business expert or a key user to model processes and understand them within a comprehensive business context. A problem that has been detected can be resolved automatically based on simple rules and without additional programming.

Dr. Knut Manske, engineering lead for Situation Handling and Responsibility Management at Â鶹ԭ´´, describes the graph as a layer that stretches across various Â鶹ԭ´´ applications. To summarize the capabilities of situation handling, he explains: “The knowledge that is embedded in Â鶹ԭ´´ applications is extracted and made usable for algorithms. Situation handling runs alongside the application, analyzing data and reacting to information about data changes or events. The aim is to show solutions that span various applications or business areas.â€� Customers and partners can define situations themselves without ever touching the application. A selected group of customers is currently validating the solution.

Looking Ahead

Currently just a prototype, the Â鶹ԭ´´ “Business Decision Simulatorâ€� innovation project simulates the effects of internal and external factors on a company and presents the user with potential future scenarios and recommended responses. “One possible question could be: what does a bushfire in Australia mean for my global pharmaceutical company’s value chain and thus the achievement of our targets in Europe?â€� Burwig explains. “On the one hand, knowledge graph technology can help express the complex relationships between real-world events and business-related processes. Additionally, an extensive knowledge base makes it easier for the user to choose the best possible response to opportunities and risks.â€�

When asked about the possibilities of graph technology, Burwig says: “The potential of knowledge graphs – at least that’s the dream – is that someday there will be extensive graphs modeled on deep knowledge and experience that can be accessed by programs. This means that with every change in technology, we’d only have to recode the application and not the knowledge stored inside it.�

While the decoupling of data from applications and the accompanying scalability of solutions and programs are important aspects for an IT company, Burwig also sees a vision that goes beyond the Â鶹ԭ´´ context: symbolic AI, artificial intelligence that is linked to a large number of interdisciplinary knowledge networks, could have the potential to generate new knowledge that has not been explicitly modeled. Linking scientific content could have the power to accelerate the discovery of new drugs or forge innovations between different science disciplines that don’t interact on a large scale today.

In the history of humanity, things have often been – and still are today – “invented� multiple times, at least in part from lack of knowledge about other relevant findings. Connecting global research projects within one given discipline has increased sharply within the past years, thanks to the availability of extensive digital content. Uniting scientific knowledge across the individual disciplines – for example, mathematics, biology, medicine, and chemistry – could uncover new insights and lead to exciting interdisciplinary inventions, Burwig concludes.

The Virtuous Circle Between Knowledge Graphs, Machine Learning, and Natural Language Processing

Academic communities of symbolic AI and machine learning in the past had few touchpoints in terms of research methods and researchers. This is changing – among others leading to so-called hybrid AI. This results in various virtuous cycles in which knowledge graphs, machine learning, and natural language processing (NLP) cross-pollinate each other. Johannes Hoffart, head of the Chief Technology Office in the AI Unit at Â鶹ԭ´´, explains the relation between machine learning and knowledge graphs: “Knowledge graphs enable data scientists to work with complex and heterogeneous data sources. Their flexible schema can be easily extended and contains powerful data validation capabilities. At the same time, knowledge graphs facilitate access to and exploration of data, as they represent data and its schema such that it’s easier for humans, and also large language models, to understand.â€�

Christian Lieske from Â鶹ԭ´´â€™s Language Experience Lab adds about the relation to natural language processing: “Knowledge graphs can feed NLP and can be fed by NLP. Take the detection of new business entities as an example: a knowledge graph can inform NLP about known entities and NLP can add additional entities to a knowledge graph.â€�

To get further insight around knowledge graph technology, read this study co-authored by Portisch: .