The difference is not the model. It is context: the ability for AI to understand how a business actually runs. 

Much of today鈥檚 AI discussion centers on agents, along with models and benchmarks. Which model performs best? Which system completes the most tasks? Which interface feels most natural? These factors matter, but they do not solve the central enterprise challenge.

Companies run workflows that cut across teams, policies, approvals, authorizations, and data. They plan, source, produce, hire, pay, and serve through systems that carry real business consequences. AI only creates durable value at scale when it operates inside this reality.

Models generate answers. An agent can complete a task. But running a business requires something more. It requires an understanding of how work gets done, who is authorized to act, which rules apply, and how decisions connect across functions. Without that context, AI simply can鈥檛 deliver on its promise.

That is one reason I believe AI raises the premium on software with deep business context. It allows companies to fundamentally reinvent how work gets done. When AI agents understand end鈥憈o鈥慹nd processes, they can operate across functions, execute workflows autonomously, and coordinate actions in real time. Instead of automating individual steps, AI can run processes end to end, freeing employees from repetitive coordination and enabling them to focus on higher鈥憊alue judgment, oversight, and strategy.

This is what we describe as the Autonomous Enterprise, a fundamental shift from systems of execution to systems that can reason, decide, and act. A vision where 麻豆原创 is poised to lead. 

For more than five decades, we have powered the core processes that run the world鈥檚 leading organizations. Our systems don鈥檛 just store data; they encode how businesses actually operate: their processes, rules, and decisions. Our ERP is the institutional memory and the brain of many companies across industries and around the globe. Our new 麻豆原创 Business AI Platform brings together enterprise data, processes, and governance into a unified context for AI.

Building on this foundation, Joule is the interaction layer that connects people with AI and redefines how they interact with software. Joule Assistants collaborate with users, while Joule Agents execute business workflows end to end. This is how intelligence becomes embedded directly into operations, not added on top. We call this the .

“Show me how my financial forecast for the year could change based on the latest pipeline and supply chain data.” On the surface, this looks like a simple prompt directed to a large language model.聽But disconnected from enterprise systems, the answer is聽mere聽speculation.

Grounded in the full context of the business,聽the system first identifies the correct business process from聽hundreds聽of聽mission鈥慶ritical processes and understands the specific configuration that governs how this process runs in your organization. It then selects exactly the right data from聽millions聽of聽data fields stored across the ERP landscape. Finally, every step is checked against identity, authorization, and access controls, ensuring the result is accurate, compliant, and trustworthy. This is how enterprises move beyond generic, probabilistic answers toward decisions they can rely on.

Reaching this state requires more than adding a chatbot or layering AI on top of existing systems. Many enterprises still operate with fragmented landscapes, data spread across systems, and processes shaped by years of incremental change. In this environment, AI cannot simply be “bolted on” or layered onto fragmented, outdated systems. It does not accelerate progress. It amplifies inefficiency and risk. Companies must rethink how their processes, data, and infrastructure work together and how humans and AI share responsibility. This is not only a technical shift. It is a change鈥憁anagement challenge. 

New technology only creates value when it is accompanied by real change. AI does not replace transformation. It raises the return on transformation done well. And it comes to life only when every element of the system鈥攖he agent, the process, and the human鈥攚orks together by design. People need to understand how to work with AI agents, and processes must be intentionally shaped to embed intelligence where decisions and execution happen.

This is why change management is foundational. It means reskilling employees, re鈥慹ngineering processes to connect them directly with data and AI, and modernizing the underlying landscape. 

That is why we are introducing new聽AI-led RISE with 麻豆原创 and 麻豆原创 GROW聽offerings聽and fundamentally resetting our services model: to help companies modernize, navigate change, and turn AI from potential into sustained business value at their own pace.聽

This marks the beginning of a new era of enterprise software:聽where intelligence is not separate from聽operations but embedded within them.聽The companies that lead will not be those with the most advanced models in isolation, but those that connect AI to the way their business actually runs鈥攚ith context, governance, and trust.聽

This is the dawn of the Autonomous Enterprise, and 麻豆原创 is uniquely positioned to help the world鈥檚 leading organizations realize its full potential. 

Christian Klein is CEO of 麻豆原创 SE.

Every week brings another announcement about smarter copilots, more capable agents, or new orchestration layers designed to automate work across the enterprise. The progress is undeniable. But much of the market is not optimizing for how businesses operate.

That distinction is more important than many realize. Because enterprises do not run on prompts. They run on execution.

A global manufacturer deciding how to reroute inventory during a supply chain disruption needs more than simply an answer. It must evaluate supplier alternatives, inventory availability, customer commitments, and financial tradeoffs simultaneously. A CFO forecasting liquidity exposure during market volatility needs context that a simple chatbot interaction can鈥檛 provide. These are interconnected operational decisions shaped by dependencies, preferences, approvals, financial consequences, and tradeoffs that ripple across the business in real time.

In countless conversations I鈥檝e had with executives over the past year, the discussion inevitably shifts from AI capability to operational reality. The models are improving quickly. The harder question is whether AI understands the business environments it is operating within.

Today, too much of the AI conversation still assumes that better models alone will produce better business outcomes. They will not. Enterprises are discovering that intelligence disconnected from operational context 鈥� the processes, the data, the rules and policies that govern and protect your organization 鈥� can generate activity without creating much progress. In some cases, it can create more fragmentation and risk.

A generated recommendation may sound convincing while missing critical dependencies elsewhere in the system. An AI agent may automate one workflow efficiently while disrupting planning assumptions in another. Enterprises do not suffer from a shortage of AI outputs. They suffer from a shortage of AI systems capable of understanding operational consequences.

That is the real challenge now emerging in enterprise AI and solving it requires something deeper than orchestration. It requires context.

For decades, enterprise software has quietly served as the operational backbone of the global economy. Finance systems, supply chains, procurement networks, workforce planning platforms, manufacturing operations, and customer fulfillment processes all run through interconnected systems that capture not just information, but the logic of how businesses function. They contain years of accumulated process knowledge and data, governance structures, authorizations, policies, and economic relationships that shape every decision a company makes. They are the institutional memory of the enterprise.

In the AI era, that business context becomes enormously valuable. Without it, AI鈥檚 outputs remain educated guesses rather than grounded judgments.

When AI is grounded directly inside operational processes, it can begin to reason across the full reality of the enterprise. That changes the role software plays inside organizations. Enterprise systems are beginning to participate directly in execution itself.

AI can identify risks earlier, coordinate responses across functions, recommend actions in real time, and automate routine execution within defined boundaries. Not as isolated agents operating independently, but as intelligence connected to the economic and operational fabric of the enterprise itself. 

Importantly, autonomy in enterprise does not mean removing humans from decision-making. It means reducing the friction, fragmentation, and administrative drag that prevents organizations from operating with speed and coherence at scale. 聽People still define priorities, make judgment calls, and hold accountability. But AI can help coordinate and execute the operational work surrounding those decisions.

Consider a supplier disruption affecting a critical manufacturing component. Most AI systems today can summarize the issue or predict likely delays based on learned patterns. But operationally grounded AI can move beyond insight into coordinated execution. It can identify affected production schedules, evaluate inventory positions globally, assess alternative sourcing options, estimate financial exposure, flag customer delivery risks, and recommend actions across procurement, logistics, finance, and customer operations simultaneously.

That is not simply workflow automation. It鈥檚 an entirely new way for humans and systems to interact.

This is also why I believe the AI era will increase the strategic importance of enterprise systems, not diminish it.

As AI moves closer to execution, the systems that matter most will be the ones capable of grounding intelligence in operational and transactional reality. The value shifts toward systems that understand permissions, policies, dependencies, processes, financial consequences, and organizational accountability at enterprise scale.

This shift also changes how leaders should think about transformation.

The first phase of enterprise AI adoption focused heavily on experimentation. Companies tested copilots, deployed pilots, and automated isolated tasks. Few delivered productivity gains and fewer fundamentally changed how organizations operate.

The companies that lead in the next phase will approach AI differently. They will connect intelligence directly to the operational systems where decisions carry real economic consequences. They will recognize that trustworthy AI depends not only on governance, but on context, data quality, process integrity, and transactional understanding.

Most importantly, they will understand that successful AI adoption in enterprises is not only a technical shift. It is a change management challenge. Real value comes to life only if AI agents, processes, and humans work in concert.

The future belongs to enterprises that strike this balance: humans defining priorities and holding accountability, while intelligent systems coordinate and execute with precision 鈥� enabling businesses to navigate an increasingly complex world with greater resilience, productivity, and intelligence.

Christian Klein is CEO of 麻豆原创 SE.

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As companies and governments face challenges around sovereignty, security and competitiveness, they need to embrace approaches that initially appear contradictory: investing boldly despite limited resources, sharing data while protecting it and competing while collaborating. These are not contradictions 鈥� this is the new operating model.

Against this backdrop, it becomes clear that organizations must adopt a three-pillar approach to navigate this new normal of paradoxes.

First, they must ground themselves in flexible digital foundations; second, embed AI deeply and responsibly into their operations; and third, view collaboration as a strategic advantage rather than a compromise.

These principles form the backbone of a sustainable way of working in the Intelligent Age, where progress depends on navigating paradoxes with agility and shared purpose.

Laying the foundation for flexibility

Progress is moving at breakneck speed. Technologies that seemed futuristic yesterday are mainstream today and by tomorrow, they could even be obsolete. To keep pace, organizations need a foundation that is rigid but adaptable 鈥� a platform that can evolve as quickly as the world around it.

That foundation is the cloud. A cloud migration is more than an IT project: it is the digital foundation for a thorough modernization of the entire enterprise, for moving from 鈥済ood 鈥渢o 鈥済reat.鈥�

Modern cloud infrastructure enables data, applications and AI to interoperate seamlessly, creating an environment where innovation can flourish. It accelerates the deployment of software updates and new applications, reduces complexity and provides the scalability needed to respond to shifting demands.

True flexibility, however, goes beyond technology. Organizations must foster a mindset that embraces change, encourages experimentation and prioritizes resilience over perfection. This means empowering teams to adapt quickly, learn continuously and view change as an opportunity rather than a threat.

Drive AI innovation on your terms

As AI rapidly reshapes how we live, study and work, no organization can afford to ignore it, yet many still have questions about how to apply it.

In the business-to-business realm, AI cannot be treated as a standalone technology. To unleash its full potential, AI must be deeply embedded in business processes. This requires three pillars:

• Modern cloud software
• Advanced data management
• A consistent stack of AI technologies

Companies that move from legacy on-premises software to integrated cloud applications unlock AI鈥檚 ability to access, understand and facilitate transactions across the enterprise. This enables AI agents to function as digital coworkers, capable of executing complex workflows spanning the business.

The power AI offers is undeniable and in today鈥檚 volatile world, this often leads to questions around digital sovereignty. True digital sovereignty is about maintaining control over critical data and assets while leveraging the best technologies available in line with national interests.

Data protection and compliance are non-negotiable. Companies and governments must ensure that sensitive information remains under appropriate jurisdictional control.

Internationally aligned sovereignty standards 鈥� such as ISO (鈥嬧�婭nternational Organization for Standardization) and IEC (International Electrotechnical Commission) 鈥� would enable secure, compliant scaling across borders, unlocking the full potential of AI without compromising trust.

Not all data requires the same level of protection. Information essential to national security or public safety requires the highest levels of control. At the same time, less sensitive data can be managed in trusted cloud environments that comply with recognized cybersecurity standards.

This nuanced approach allows organizations to balance innovation with responsibility.

Compete with collaboration

The paradox of competition and collaboration is perhaps the most striking of all. In a hyperconnected world, no company or government can tackle today鈥檚 challenges alone. Cybersecurity threats, climate change and economic inequality are global issues that demand collective solutions.

The competitive advantage now lies in partnerships 鈥� across industries, sectors and borders. Public-private collaboration is essential to co-create AI use cases, build open ecosystems and invest in digital education. Such partnerships are strategic imperatives that strengthen our society and our economy for long-term growth.

Collaboration also extends to governance. Establishing shared frameworks for ethical AI, data privacy and sustainability will require dialogue among stakeholders with competing interests. Yet, this dialogue is the cornerstone of progress.

Dialogue: the operating principle

While the opportunities AI provides are immense, they are by no means guaranteed. The determining factor will be our ability to engage in meaningful dialogue 鈥� as companies and governments, technology experts and policy-makers, innovators and citizens.

In the Intelligent Age, the question is not whether we will face paradoxes, but how we face them. Dialogue must be our operating principle 鈥� the means through which we reconcile paradoxes, build trust and chart a course toward shared prosperity. The future will belong to those who embrace complexity, act with courage and collaborate across divides.

Christian Klein is CEO and member of the Executive Board of 麻豆原创 SE.

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From my work at a company that pioneers AI in business processes, I hold this hope too: AI has the potential to stimulate growth and make companies more productive. It can make economies more resilient by helping businesses navigate disruptions, whether through global trade crises, natural disasters, or regional conflicts.

AI can also play a critical role in our ability to achieve our sustainability goals, tackle climate change, and mitigate its impact. While many recognize AI鈥檚 positive potential, the growing fragmentation of our world greatly reduces our ability to take full advantage of the opportunities AI presents.

AI can only be as good as the data it can access and work with. The more restrictions, limitations, and political borders we impose on it, the less positive impact AI can have in our countries and companies. AI can鈥檛 thrive in a fragmented world. This is what I call the AI progress challenge.

Tackling the AI Progress Challenge, Step by Step

Finding solutions to something as vast and as complex as global fragmentation requires us to divide the problem into smaller, individual challenges that are easier to overcome. How can we make AI a stronger tool for progress? How can we navigate the AI progress challenge step by step?

1. Begin at company-level

It starts on our doorsteps — at the level of the individual company. Cloud migration is an integral first step. The cloud makes data accessible for AI, systematically and comprehensively, and cloud solutions can easily — even automatically — be kept up to date with the latest AI innovations. What is more, integrated cloud software offers a standardized environment for data and processes. It makes it easier for companies to keep their data structured, clean, and coherent across the company — and consequently easier for AI applications to use.

2. Create an industrial ecosystem

The second step toward maximizing the potential of AI is to bring individual companies together in an industrial ecosystem. To become more productive, resilient, and sustainable, companies need to exchange information with suppliers — sometimes thousands of them — as well as with their clients and innovation partners. In the age of business AI, the need to build intelligent business networks is more important than ever. The more broadly and widely data can be shared between companies, the better and more impactful AI outcomes can be. The whole ecosystem profits in terms of added competitiveness and innovation power.

3. Build international frameworks

The third step is to build international frameworks that allow AI innovation to thrive in a responsible way. Governments and regulators can reduce unnecessary barriers to the exchange of data within countries and between countries. In cooperation with industry, public actors can also work towards aligning and harmonizing technological regulations wherever possible, especially between the key economic powers of North America, China, and Europe.

At the same time, we must build trust. Lack of trust is perhaps the greatest driver of global fragmentation — and the biggest obstacle to collaboration. By joining forces, international companies can agree on common rules for data usage, data privacy, and responsible, ethical AI. Such “confederations of trust” encourage more frequent data sharing.

Putting the AI Puzzle Together

The cloud, business networks, and a more harmonized global environment based on trust; each of these steps reduces fragmentation and increases federation. And, each of these steps can help the organizations involved reap much greater benefits from AI.

It鈥檚 like putting together a 10,000-piece puzzle. It鈥檚 not easy to start with and it鈥檚 not something that can be accomplished in one go, but if we begin by putting a handful of pieces into place, the big picture gradually emerges. As the degree of federation increases, progress accelerates.

It鈥檚 on us to put the AI puzzle together and reduce fragmentation, one step at a time. This is how we maximize the potential of AI — not just for some, but for many.

Christian Klein is CEO of 麻豆原创 SE.

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Today, we鈥檙e at a revolutionary moment of similar proportions. Generative artificial intelligence (AI) has gone from niche technology to a topic of global discussion within little more than a year. It has happened at a critical moment, with the world facing multiple geopolitical, economic, and climate crises.

While these challenges urgently require our attention, global human, environmental, and financial resources are already stretched. Generative AI, however, offers hope that we can address all these competing priorities simultaneously. In other words, we鈥檒l be able to achieve more with less.

Accelerating Change

The enormous potential of generative AI is widely acknowledged, yet there is one key area where its impact for good has yet to be fully realized. If we apply generative AI to how we run business — as a tool to transform companies, supply chains, and entire industries — we鈥檒l accelerate the evolution of our world economy into one that is more sustainable, resilient, equitable, and prosperous.

Generative AI for business can, for example, help find better and faster solutions to the questions millions of organizations around the world face today. For example:

• What steps do I need to take to make my company carbon neutral?
• How can I improve the availability of critical supplies?
• What can I do to make my business more productive and competitive?
• How do we skill and reskill our workforce to meet the challenges of today and tomorrow?

Relying on the recommendations provided by generative AI for such critical matters, however, requires the underlying technology to be extremely trustworthy — much more so than in the consumer application space.

Building Trust in AI

Trustworthy means, first, that generative AI for business has to be relevant. AI can only be as good as the data it is trained on, and the generic data used for today鈥檚 most famous large language models (LLMs) will not help companies address their very granular problems. To provide context-specific proposals, relevant AI for business must train and work with real-life enterprise data.

Second, generative AI for business has to be reliable. The stakes in business can be very high: single decisions can affect thousands of customers, colleagues, and the company鈥檚 long-term future. That鈥檚 why business AI outputs must be provided with the greatest accuracy and quality. And, while AI 鈥渉allucinations鈥� may be entertaining in the consumer world, they鈥檙e a no-go in business.

Third, generative AI for business has to be responsible. There is an ongoing discussion about how AI models trained on and working with public Internet data may infringe on privacy and copyright regulations. In the business world, this kind of 鈥済ray zone鈥� mode of operation is unthinkable.

For businesses to trust generative AI, they need to be sure their data is handled safely and confidentially. They need to be sure that generative AI tools respect and observe data privacy, data ownership, and data access restrictions by their very design, and that they operate only in areas where explicit consent has been given.

These three 鈥淩鈥漵 — relevance, reliability, responsibility — are the cornerstones of trustworthy AI for the business world. They are also key to building trust in technology as a tool to tackle the biggest challenges of our time.

A Once-in-a-Generation Opportunity

As a global software company, 麻豆原创 has made relevant, reliable, and responsible AI a top strategic priority, training and working with real-life business data based on the explicit consent of thousands of customers. By design, it follows the access and privacy settings already built into 麻豆原创 databases and software.

The 聽is ensured by clear guiding principles, internal governance structures, and an advisory panel of external experts. Most importantly, 麻豆原创 is pushing the quality of generative AI results to be not just 鈥済ood enough,鈥� but to have the integrity and quality customers expect when they make consequential business decisions.

I believe there is also a once-in-a-generation opportunity on a larger scale: nations and regions that pioneer trustworthy AI for business will see a much faster and broader adoption of generative AI across companies and industries. They will reap the benefits of greater competitiveness, resilience, and sustainability. And they will contribute immensely to a better running world — much like the World Wide Web did three decades ago.

Christian Klein is CEO and a member of the Executive Board of 麻豆原创 SE.

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What we consistently hear is that a growing company needs the ability to scale without increasing costs and complexity. The last few years have also clearly shown how crucial agility is to any company鈥檚 success. In other words: growth cannot come at the expense of agility, and vice versa.

Whether a company has simply reached a point where its current technology or systems aren鈥檛 keeping up, or it has made a fundamental business model change 鈥� for example by shifting from selling products to selling a subscription service 鈥� at some stage, companies find they need to pivot to a more scalable and more capable solution. That is when they turn to cloud ERP.

麻豆原创 understands what it takes to help customers realize value from adopting cloud ERP. Based on our highly successful RISE with 麻豆原创 offering, 麻豆原创 is now launching a new offering for midsize customers to harness the proven benefits of cloud ERP: GROW with 麻豆原创.

GROW with 麻豆原创: Adopt cloud ERP with speed, predictability, and continuous innovation

Designed specifically to help midsize companies take full advantage of cloud ERP and all the benefits that cloud solutions offer, GROW with 麻豆原创 gives customers the confidence that they will be up and running quickly with technology that allows them to keep growing effectively and efficiently.

For more than 50 years, 麻豆原创 has worked hand in hand with customers across every industry to benchmark and define best-in-class industry-specific processes. With GROW with 麻豆原创, companies can immediately adopt these pre-configured, industry best practices, while embedded AI and automation capabilities enable customers to see rapid results in time and cost efficiency, not just effectiveness.

Every company has unique differentiators that cloud ERP can support. With GROW with 麻豆原创, we provide a business technology platform where customers can define their own apps and processes in a cloud-native way. Together with our partners, we have built thousands of unique processes for customers that work with 麻豆原创鈥檚 cloud ERP. And because we recognize that business experts know their business process needs best, we also enable them to create unique processes and create the solutions they need without having to write code.

Businesses require the agility to add new products, services, and customers 鈥� and they need a technology solution that can keep pace with their development. As a company that already supports the world鈥檚 largest enterprises, at 麻豆原创, we know that our cloud ERP can scale to support even the most extensive product lines, complex service offerings, and ambitious sustainability goals.

The GROW with 麻豆原创 offering also features services and tools to streamline delivery at a fixed rate that delivers a technical go-live in as little as four to six weeks and gives customers the assurance of the rapid time to value they need. Midsize customers adopting GROW with 麻豆原创 also have access to a global community of experts and free learning resources designed to ensure they see meaningful business results.

When it comes to , it鈥檚 just as important to consider where you want your business to be as where it is today. And while GROW with 麻豆原创 is tailored to the immediate needs of midmarket companies, it also provides the agility and innovation midsize companies need for success in years to come.

Today鈥檚 cloud ERP is designed for the growth companies, and we are excited to watch a new set of customers GROW with 麻豆原创.

Christian Klein is CEO and a member of the Executive Board of 麻豆原创 SE.

Today鈥檚 world is characterized by economic and non-economic disruptions that are deeply intertwined: volatile markets, inflation, geopolitical tensions and war, the energy crisis, and climate change. No one business, government, or society can tackle challenges on this scale alone.

To reunite our fragmented world, we must change — both within our own four walls and beyond — and technology plays a key role in this.

Business Models: From Analogue Companies to Intelligent Enterprises

Faced with strong fluctuations in supply and demand, dynamic purchasing behavior and growing pressure to innovate, companies recognize the need to become more agile and resilient. But for many, fragmented process landscapes prevent them from reacting quickly to change. Data is often stored in silos and so is not equally available to all decision-makers.

Digitalization and the automation of core processes end-to-end is not only a competitive advantage, it is critical to an organization鈥檚 survival. This is not about replacing people with tech. It’s about giving people back the freedom to do what they do best: be creative. With reliable data and the help of artificial intelligence (AI), companies are better able to keep track of what is happening in their business and why. This not only makes them more efficient, but also more flexible and faster, especially in times of crisis.

However, it is no longer enough to be resilient as an individual company. This is just the first step toward a new way of doing business.

Supply Chains: From Linear Connections to Transparent Business Networks

Globalization has made our supply chains more complex and, as a result, also more vulnerable. At the height of the COVID-19 pandemic, about . Climate change, the pandemic, the war in Ukraine and geopolitical tensions worldwide have shown the limits of our current economic models, with the impact of this hitting the agricultural, energy, and hi-tech industries particularly hard.

Resilient supply chains have, therefore, become a priority, and technology the enabler — where linear one-to-one connections are prone to disruptions, networks of many-to-many connections allow companies to collaborate with partners along their value chain and exchange data in real-time. The 360-degree transparency across the entire value chain provides businesses with the flexibility and resilience to navigate even in the most dynamic environments. They can anticipate risks and manage sourcing, trading, and distribution all the way to the consumer. They can optimize inventories, match supply and demand, and identify bottlenecks before they even occur. In case of supply chain disruptions, companies can quickly select alternative or more sustainable suppliers.

The future belongs to companies that understand how to operate profitably, resiliently, and sustainably together with their ecosystem. And this mindset, the understanding of the power of ecosystems, is one of the most important prerequisites for solving global challenges.

Sustainability: From Image Driver to Social and Economic Imperative

The recent聽 (WMO) shows that the past eight years have been the warmest on record. The sea level rise rate has doubled since 1993, with the increase over the past two-and-a-half years accounting for 10% of the total increase over the past 30 years. In addition, with growing sociopolitical pressure and increasing social inequality, the importance of sustainability is changing.

Business leaders feel the urgency from all sides. Investor awareness around global challenges such as climate change, pollution, and inequality have increased, as has customer demand by a factor of seven from 2021 to 2022. Employees are making career choices based on their employer鈥檚 sustainability commitments and track record while governments are introducing new regulations. Sustainability, therefore, needs to become the North Star of every company, an integral part of the corporate strategy.

There is no business without sustainable business, and when it comes to the planet, the connection between digital and climate is fundamental to solve human problems. Promoting digital solutions for energy efficiency, scope 3 transparency, circularity, and carbon data sharing — in collaborative networks led by industry leaders and climate coalitions — will become a powerful blueprint for future sustainable business strategy, particularly in high-emission sectors like energy, materials, and mobility.

Ultimately, collaboration and networks are at the heart of the solutions to our global challenges. In a business network, companies can not only measure environmental, social, and corporate governance (ESG) in their own company, but across their whole value chains. They record verified data based on actuals, not averages. They can report against a quickly evolving set of ESG standards and, most importantly, they can act beyond ambitious targets by embedding sustainability across all of their business processes and value chains. This enables companies to create fair and safe working environments, reduce waste, and decarbonize the entire value chain (scope 1-3) — providing the basis of the circular economy. At the end of the day, companies are only as sustainable and resilient as their ecosystems.

In an increasingly fragmented world where global challenges threaten to divide us, technology plays a vital role in bringing us together.

Christian Klein is CEO of 麻豆原创 SE.
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