Until recently, predicting outcomes in sports was usually based on historical data compiled from player and team statistics. This approach, however, can’t guarantee outcomes; in fact, it often leads to dashed expectations and disappointing results.

Perhaps the most famous example of this predicament was the 2014 FIFA World Cup semi-final where Germany defeated Brazil 7-1. Traditional metrics favored Brazil, leaving the South American team stunned by their devastating loss and highlighting the inefficacy of existing statistical models in accurately representing team performance.

The Soccer Statistic You’ve Never Heard

Stefan Reinartz, a former midfield Bundesliga player and UEFA Champions League competitor, cited that example on a recent panel discussion hosted by Soccerex Miami. After experiencing his own “Aha!” moment, Reinartz became the founder of IMPECT, a German analytics company that revolutionized the role of statistics in the world of soccer.

“Common soccer statistics like ball possession, winning tackles, running distance, and so on actually have no correlation to the end result,” Reinartz said. “I was quite shocked when I realized they really can’t tell you anything about a player’s or a team’s performance.”

He came to that realization when he read about the Moneyball approach, which uses advanced statistical analysis to acquire undervalued players and thus assemble a competitive team despite a limited budget.

“Some sports such as baseball may be more conducive to such a data-driven strategy, while others like soccer are more complex, making it difficult to create the right KPIs,” he explained.

Soccer is a low-scoring game involving 22 players in continuous action, requiring complex strategies and a lot of hard work to get past opponents to score. Apart from the number of goals and passes, individual statistics are almost nonexistent, making it challenging to create universally reliable metrics.

Realizing that soccer statistics did not measure the true value of individual players changed his own assumptions. Together with another midfielder, Jens Hegeler, he founded IMPECT with the goal of developing soccer data that can be used to make valuable assessments of players and teams. They invented “packing” to quantify the value of a pass or dribble.

The basic statistics in soccer, such as goals and assists, primarily reflect strikers and attacking midfielders, the players who do most of the scoring and assisting. Midfielders, who do most of the running and passing to initiate offensive moves, are seldom recognized statistically. What they do best is move the ball past opposition players. That is what packing measures.

The methodology is simple: Players earn a point for any move — a cross, a dribble, a long pass — that causes the ball to move past opposition players. Receivers also get points.

Today, IMPECT is a thriving business collecting and selling packing data to help teams scout for players, analyze their own performance, and learn key facts about opponents.

What’s SAP Got to Do With It?

During the discussion, Achim Ittner, vice president of Sports at SAP, described the company’s journey from its roots in business software to developing specialized sports applications, emphasizing user-centric design and collaboration with renowned coaches and national teams.

“Like IMPECT, we’ve been focused on developing data-driven solutions and relevant tools for sports clubs using direct feedback and a design thinking approach,” Ittner said. He explained that SAP built a unified platform to address fragmented software ecosystems in sports. The platform integrates APIs and data sources to support processes like injury prevention, training recommendations, match preparation, and player well-being.

“Sports analytics embodies the blend of data science and athletic performance, providing a blueprint to guide decisions made by coaches and trainers,” he shared. “The significance of data in today’s sports ecosystem cannot be overstated. It’s necessary for everything from strategizing game plans to refining individual skills.”

SAP’s engagement with IMPECT is just one example of how the compay collaborates with a variety of teams and experts to further shape its sports technology. New technologies — like social media, mobile, and digital capabilities, as well as sensors in player uniforms and equipment — are disrupting the sports industry. SAP is committed to helping sports organizations take advantage of these new innovations to deepen fan engagement, improve team performance, grow revenue faster, and efficiently operate their business and venues.

Besides solutions for scouting and measuring team performance, SAP also has one for customer engagement and marketing. Afterall, the fan experience is just as important as the team’s performance. The SAP Sports One solution provides a 360-degree view of each customer and fan to better target individual preferences and purchasing behaviors by offering personalized experiences over online, mobile, and social channels. Teams can grow their fan base, manage their merchandise, and increase loyalty with personalized rewards.

Delivering the Experience

“A great sports experience is about so much more than winning the game,” said Ittner. “It involves a mix of elements that includes players, spectators, and organizers.”

For the fans, food and beverage, merchandise, a good streaming experience, and clear communications on schedules and navigating the venue are critical. Having the right players for a winning team and providing them with the best care and training before and after the game is the job of team management. And finally, the venue must provide exceptional facilities, infrastructure, safety, and security.

SAP helps bring this all together, creating an intersection of sports, technology, and innovation.

Technological innovations such as artificial intelligence (AI) are reshaping entire industries and redefining the boundaries of what is possible. These advancements significantly boost productivity and help us reach new heights as societies.

According to the McKinsey Global Institute, AI could contribute between $17.1 and $25.6 trillion to the global economy annually, including $6.1 to $7.9 trillion from generative AI use cases alone.

Yet technology alone does not guarantee success in the Intelligent Age: People remain the true catalysts of innovation and growth. As businesses navigate rapid technological change, their ability to continually adapt, build resilience, and create sustainable growth depends on how well they interlock people, organizational development, and technological advancements. Together, this triangle forms a transformation engine that drives both individual and business growth, ensuring long-term success. It also helps organizations and individuals thrive and create a sustainable future.

Putting People and Skills First

Even in this high-tech era, people remain the driving force behind innovation and growth. In the Intelligent Age, skills are the new currency — encompassing the abilities, knowledge, and expertise that empower individuals to adapt and thrive in a rapidly changing world. Therefore, to bring out the best in their people, organizations must prioritize skills.

The skills-first approach is a paradigm shift that revolutionizes how organizations handle workforce planning, hiring, performance management, job architectures, learning and development, career pathing, as well as rewards — hyper-personalized and infused with AI. Putting skills at the center transforms people practices, products, and solutions across all stages of the employee life cycle.

In doing so, organizations create a people ecosystem centered on adaptability and growth. From skill-based job descriptions and skills assessments to prioritizing skills over experience and degrees when executing skill-based hiring, organizations can transform their people practices. This extends to skill-based learning and development, enabling cross-generational and regional development, internal mobility, recognition programmes, as well as career pathing and skill mapping from an organizational perspective.

This approach ensures that organizations attract and engage the right talent, provide individuals with personalized development opportunities aligned with organizational needs, and offer competitive rewards that recognize and incentivize skill growth and application.

A skills-led people ecosystem helps employees adapt quickly, almost in real time, to changing demands. Prioritizing skills allows organizations and employees to drive innovation and achieve sustained success. Ultimately, this approach will contribute to overall economic growth by ensuring a skilled workforce and sustaining high levels of employability.

Culture as the Organizational Foundation for Innovation and Resilience

Under the constant demand to transform, along with competition and geopolitical challenges, even the most resilient organizational cultures can be stretched. In the Intelligent Age, maintaining a strong culture requires continuous strategic attention and nurturing.

Businesses must instill a culture that encourages adaptability. An adaptive culture acts as an internal compass, guiding employees on how to work together, serve customers, and remain accountable for sustainable results. It guides decision-making through shared values and priorities, emphasizing a growth mindset for continuous learning and developing individual and organizational capability. This helps organizations adapt quickly to market changes, remain competitive, and foster innovation.

Leaders play a pivotal role here. By motivating teams, providing clarity and purpose, they create a cohesive workplace that empowers individuals and aligns their actions with the broader vision. The result is a unified effort where every team member contributes to shared goals, fuelling both performance and adaptability.

When culture is deeply rooted and intentionally nurtured, it aligns strategy execution with engagement from employees, partners, customers, and shareholders — strengthening commitment across all stakeholders and delivering consistent results.

People Technology Changing the Game

Technology helps organizations implement and amplify the impact of their people strategies. By leveraging the power of mega data and AI in the people sphere, companies foster data-driven and transparent decisions, a critical prerequisite for workforce transformation and future success.

Mega data and AI help organizations predict workforce trends, identify skill gaps, and improve talent insights and mobility, leading to more efficient team setups and equal opportunities to employees. This enables superior business outcomes based on a holistic and transparent view of employee capabilities and insights — available to all relevant decision-makers.

Technological advancements also allow unprecedented levels of personalization, making hyper-personalization a key focus in the people sphere. From targeted, skill-based learning programs to individualized career paths, AI-enabled tools can tailor experiences to each person’s needs, offering clear growth opportunities and driving engagement by making employees feel valued and supported in their development.

By using technologies like AI effectively and ethically, organizations will be more adaptive going forward — enabling quick pivots to meet external demands and build resilience.

By interlocking people, organizational development, and technological advancements, businesses create the foundation to continually innovate, adapt, build resilience, and drive lasting growth. This holistic approach helps organizations and individuals thrive in the future, ultimately contributing to a better tomorrow.

As we stand on the brink of the Intelligent Age, it is vital that businesses reimagine their strategies with people at the center. Together, we can create organizations that serve as engines of innovation and resilience — ready to lead us into a people-centered, growth-focused, and sustainable future.

Let us seize this moment to ensure that the Intelligent Age is defined not only by technological progress but also by how it uplifts humanity.

Gina Vargiu-Breuer is chief people officer and labor director, as well as a member of the Executive Board of SAP SE.

This piece originally appeared on the World Economic Forum website.

Artificial intelligence (AI) is accelerating at an astonishing pace, quickly moving from emerging technologies to impacting how businesses run. From building AI agents to interacting with technology in ways that feel more like a natural conversation, AI technologies are poised to transform how we work.

But what exactly lies ahead? We’d like to share five key themes for AI in 2025 that undoubtedly come with challenges for businesses but also the potential to redefine what’s possible. Ready to glimpse into next year and beyond? Let’s dive in.

1.  Agentic AI: Goodbye Agent Washing, Welcome Multi-Agent Systems

AI agents are currently in their infancy. While many software vendors are releasing and labeling the first “AI agents” based on simple conversational document search, advanced AI agents that will be able to plan, reason, use tools, collaborate with humans and other agents, and iteratively reflect on progress until they achieve their objective are on the horizon. The year 2025 will see them rapidly evolve and act more autonomously. More specifically, 2025 will see AI agents deployed more readily “under the hood,” driving complex agentic workflows.

Users will interact with a copilot for their tasks, which will deploy the request and coordinate among systems of multiple expert AI agents to complete more difficult tasks. Future AI agents, or multi-agent systems (MAS), can collaborate to understand the business user, have all the context, and structure the problem to subsequently interact with these domain-specific expert AI agents — each performing specific sub-tasks that together complete a much more complex task. In the future, users will not even need to trigger an action. Instead, AI agents will proactively respond to business events such as incoming customer inquiries, supply chain disruptions, or demand surges. They will automatically prepare a decision workflow as far as they can before pinging the human user for feedback.

If we look at a five-year horizon, AI agents will simplify significant portions of workflows, even aspects that have been resistant to automation, such as exceptions in customer service, long-tail administrative tasks, and specific programming activities like coding or debugging software. AI agents will be flexible and can plan, fail, and try something else or self-correct based on reasoning. AI agents will handle and complete routine, repetitive tasks end-to-end as effectively and often even more effectively than humans, leading to increased productivity and demonstrable cost savings. Agents will be more adaptable and robust than conventional robotic process automation (RPA) for longtail and highly extensive tasks. This means figuring out the best result out of many possible outcomes, which is almost impossible to hardcode in an RPA algorithm with classical automation methods.

Adopting AI in these domains will also shift workforce dynamics, with human roles evolving to focus on anticipating uncommon scenarios, coping with ambiguity, factoring in human behavior, making strategic decisions, and driving genuine innovation — complemented, not replaced, by AI capabilities. 

In short, AI will handle mundane, high-volume tasks while the value of human judgement, creativity, and quality outcomes will increase.

2. Models: No Context, No Value

Large language models (LLMs) will continue to become a commodity for vanilla generative AI tasks, a trend that has already started. LLMs are drawing on an increasingly tapped pool of public data scraped from the internet. This will only worsen, and companies must learn to adapt their models to unique, content-rich data sources. Model improvements in the future won’t come from brute force and more data; they will come from better data quality, more context, and the refinement of underlying techniques. Companies must spend more time innovating to make better models through fine-tuning and model adaptation rather than just training larger and larger models. Neurosymbolic AI techniques, especially knowledge graph, will see a renaissance since they can provide both learning objectives for foundation models and context to significantly improve the performance of generative AI while reducing hallucinations.

We will also see a greater variety of foundation models that fulfill different purposes. Take, for example, physics-informed neural networks (PINNs), which generate outcomes based on predictions grounded in physical reality or robotics. PINNs are set to gain more importance in the job market because they will enable autonomous robots to navigate and execute tasks in the real world, from warehouses to manufacturing plants, or models trained on tabular, structured data, like SAP Foundation Model, and can handle tasks that LLMs cannot do well, like predictions of numeric values.

Models will increasingly become more multimodal, meaning an AI system can process information from various input types. AI applications will eventually evolve into “any-to-any” modality solutions capable of understanding, processing, and reasoning across text, voice, image, video, and sensor data within a single model. In addition, smaller and more specialized LLMs with scalable finetuning techniques and the ability to work on any device will become more common, a trend that may lead to hyper-personalized models for organizations or even individuals in the future.

Enterprises will shift toward strategies utilizing multiple foundation models (not to be confounded with multimodal capabilities in a single model, described above), leveraging a diverse set of AI models and techniques tailored to specific use cases. This is backed by the trend of fine-tuning small slices of models, which requires fewer resources and much less data, resulting in full model flexibility and enabling businesses to extract more value from their unique data and gain a competitive edge. Enterprise software vendors will offer or extend integrated AI model marketplaces and platforms that support seamless model deployment, management, and updating. Benchmarking and lowering model switching costs will help deploy the same use cases in heterogeneous environments. Context equals value. Knowledge graph technology has been around for 40 years and is now seeing a revival because it can overcome key LLM challenges, such as understanding complex formats, hierarchy, and relationships between business data. Knowledge graphs offer data meaning and explain the relationship between entities, significantly supercharging the abilities of LLMs. The next step in this journey will be large graph models, allowing further advancement in generative AI.

Implicit knowledge is power, and making knowledge explicit to others is a superpower.

3. Adoption: From Buzz to Business

While 2024 was all about introducing AI use cases and their value for organizations and individuals alike, 2025 will see the industry’s unprecedented adoption of AI specifically for businesses. More people will understand when and how to use AI, and the technology will mature to the point where it can deal with critical business issues such as managing multi-national complexities. Many companies will also gain practical experience working for the first time through issues like AI-specific legal and data privacy terms (compared to when companies started moving to the cloud 10 years ago), building the foundation for applying the technology to business processes.

From a technological perspective, while 2024 saw significant advancements in AI, 2025 will see companies focus on making these advancements more meaningful through seamless data integration, ultimately enhancing the accuracy and significance of AI-powered outcomes and boosting adoption. Lastly, in 2025, we might glimpse a shift in the software business model from building static software features and functions to an outcome-as-a-service model focused on achieving process objectives.

4. User Experience: AI Is Becoming the New UI

AI’s next frontier is seamlessly unifying people, data, and processes to amplify business outcomes. In 2025, we will see increased adoption of AI across the workforce as people discover the benefits of humans plus AI.

This means disrupting the classical user experience from system-led interactions to intent-based, people-led conversations with AI acting in the background. AI copilots will become the new UI for engaging with a system, making software more accessible and easier for people. AI won’t be limited to one app; it might even replace them one day. With AI, frontend, backend, browser, and apps are blurring. This is like giving your AI “arms, legs, and eyes.” While power users will still have singular, expert interfaces, most users will demand flexibility across multiple access patterns. At the same time, there will be a growing acceptance of longer inference times for high-quality answers to complex, previously unsolvable problems and actions in domains requiring deep analysis and research. Ultimately, users will recognize the trade-off between latency and complexity of tasks handled by AI.

Importantly, we will see organizations move beyond viewing AI as a collection of productivity tools and begin reimagining their workforce as a network of collaborative intelligence with AI agents and humans working to accelerate innovation within the enterprise. For example, combining human expertise in strategic thinking with AI’s strengths in large-scale analysis and pattern recognition will create new competitive advantages for companies that effectively orchestrate these hybrid intelligence networks to drive breakthrough discoveries and market opportunities. Next year will also mark the early stages of a significant shift in how humans and AI work together, with agents evolving into workflow partners, taking initial steps toward independently navigating software environments and automating routine tasks – from data analysis and report generation to schedule coordination and software testing. This will also start a longer journey toward transformed work processes and patterns, with forward-thinking organizations developing new roles, metrics, and training approaches for effective human-AI task collaboration.

5. Regulation: Innovate, Then Regulate

It’s fair to say that governments worldwide are struggling to keep pace with the rapid advancements in AI technology and to develop meaningful regulatory frameworks that set appropriate guardrails for AI without compromising innovation. The regulatory landscape will become even more fragmented, with the OECD AI Policy Observatory tracking hundreds of AI regulations under discussion worldwide. This requires evaluating model compliance with and technical interpretation of various regulatory frameworks.

In 2025, the discussion will shift from what we try to regulate from a technical standpoint to how we innovate and what we deem fundamentally human. This discussion will elevate the role of humans, contribute a much more positive perspective, and help shape a long-term vision for how we want humanity and AI to live and work together. 

In this environment, it will continue to be critical for companies developing and deploying AI technology to adhere to responsible principles around safety, security, and ethical use. This will also help set the stage for important precedents and compliance.

Executing on the Themes in 2025

Indeed, these are just a few of what we are sure will be many exciting advancements for AI in 2025. Overall, the biggest takeaway from the year ahead will be making existing breakthrough technology more meaningful. We will see AI much deeper and almost invisibly embedded in consumer and enterprise applications and witness more advancements in how vendors and organizations that use these applications embed their individual contexts and data into AI seamlessly.

Getting to the point of leveraging AI generally, however, will require businesses to take advantage of a modern cloud suite with unified data access and harmonized data models to overcome data silos and fully benefit from AI innovation that spans across the whole enterprise. This will drastically increase the accuracy and significance of AI-powered outcomes, ultimately boosting adoption, specifically in the enterprise space.

We can’t wait to see what the future holds.

Sean Kask is vice president and head of AI Strategy for SAP Business AI at SAP.
Walter Sun is senior vice president and global head of AI for SAP Business AI at SAP.
Jonathan von Rueden is head of AI Frontrunner Innovation for SAP Business AI at SAP.

Working together in a co-innovation project, SAP customer Etienne Lacroix Group, SAP partner STMS, and SAP Co-Innovation Lab created a prototype for a generative AI solution that produces shipping labels for dangerous goods.

As the world’s No.1 fireworks provider, French pyrotechnics company Etienne Lacroix ships fireworks globally and has supported events such as the Burj Khalifa Grand Opening Fireworks Show in Dubai and Bastille Day, France’s national holiday that is celebrated on July 14 each year.

Logistics play a major role for the global player in the pyrotechnics sector, as shipping hazardous materials such as fireworks requires special precautions, especially when crossing international borders. Labels typically include warning pictograms and complex regulatory information in various formats. Recipients such as customs officers must be able to tell at a glance what sort of goods are inside, what the required shipping conditions are, and who is authorized to open the freight.

“On top of that, customers have their own specific requirements,” Eric Marini, director of Information Systems at Etienne Lacroix explains. “Also, there are different color codes in place for hazmat, depending on the country of destination. Red may mean danger in European countries, but in China, for example, it stands for celebration. Green has a different meaning in the Middle East that it does in the U.S., and so on.”

With so many different regulations in place around the globe, labeling the shipments at the end of the production line is not only time-consuming, but also leaves a lot of potential for human error. Now, with support from SAP partner STMS and SAP, the prototype for a generative AI solution was introduced that has the potential to reduce human involvement to a minimum.

Business AI Co-Innovation Project with SAP partner STMS

In early 2024, Sébastien Faure, general manager at STMS SOLUTIONS, a longtime partner of both SAP and Etienne Lacroix, participated in a Hack2Build event where SAP partners leveraged SAP technology to address pain points of their customers. The idea for a generative AI use case came up, and STMS approached its customer Etienne Lacroix.

At that time, IT and business experts at Etienne Lacroix were very interested in how generative AI could assist them in making their processes easier. 

“But I have to admit, I was also a little skeptical,” Marini says. “Everybody is currently talking about AI, about how to introduce it to the industry and the huge benefits we will reap from leveraging it. But suggestions are scarce when it comes to implementing it in a way that guarantees the company will benefit from it.”

“Together, we looked at the pain points of the company and discussed possible use cases with SAP Co-Innovation Lab,” Faure says. “We received the requirement from the business that AI should help avoid human tasks that are error-prone and don’t actually add value.”

Supply Chain Use Case with Generative AI

The team quickly identified that the administrative process of creating labels for shipping was a task currently performed by human employees that required a huge amount of time and focus. The huge potential for the usage of generative AI was evident. 

“With guidance from experts from the SAP partner organization, we were able to build a specific SAP app on SAP Business Technology Platform (SAP BTP) and SAP S/4HANA to organize and use AI,” Faure says. “We then collected feedback from the business experts at Etienne Lacroix and, in the next step, brought in the generative aspect.”

To meet the regulations from the customer, the developers from STMS aggregated all the necessary data and created a model on SAP HANA Cloud vector engine.

Thanks to SAP HANA Cloud vector engine, which was newly introduced in 2024, SAP Business AI can allow customers and partners to leverage large language models (LLMs) in a much more accurate way on multi-model database SAP HANA Cloud. In case of the generative AI use case for Etienne Lacroix, the LLM was ChatGPT 3.5 Turbo.

“Technically, this prototype leverages everything SAP has to offer in terms of AI right now,” Faure says. “The app uses SAP’s generative AI hub and SAP HANA Cloud vector engine to draw on all the necessary information and generate the label.”

The remaining human task is to validate whether everything on the label is correct. “That was the most important requirement,” Marini says. “Human intervention must be guaranteed, as with all AI use cases.”

“It’s the generative aspect that makes all the difference,” Miliau Pape from SAP Co-Innovation Lab says. The LLM suggests what should go on the specific label—such as warning pictograms—based on legal regulations, historical customer requirements, cultural standards, and so on. The Retrieval-Augmented Generation (RAG) provides the LLM with context and makes the outcome relevant and reliable. “Simply put, when the AI is trying to be as creative as possible, the RAG provides guardrails, so it doesn’t potentially go wild and come up with nonsense,” Pape says.

For each shipment, a prompt draws on destination, shipping route, specific customer data, such as the storage and language this customer required the last time, or the colors or signs used to indicate that the shipment contains hazardous materials and can only be opened by experts with a certain certification.

“My first thought when I saw this, was—simply put—wow,” Marini says. “Wow, they actually did it. Wow, this will make our lives easier. This generative AI use case, at this point, may still be only a prototype, but it’s an applicable idea, an actual use case for an actual pain point in our company.”

“To show the world of possible, push the boundaries of the technology—that is the purpose of the exercise,” Pape says.

Etienne Lacroix Group is in the process of migrating to SAP S/4HANA. With the components of the app ready on SAP Business Technology Platform, go-live is planned for 2025.

“Our ERP was aging and no longer very popular with business users,” Marini says. “Migration to SAP S/4HANA gives us access to the newest technologies such as generative AI. This motivates users to take ownership of their IT solutions again and brings our business and IT together. All in all, it allows our company to fully exploit the potential of our SAP system.”

He adds: “The generative AI solution for labeling shipments is a tangible proof of the power of AI, a real use case, and we have it in our company. It is a very decisive first step in our AI journey.”