In a blog post, that’s tolerable. In a financial disclosure, a regulatory filing, or a supply chain commitment, it is simply unacceptable.

is statistical. Answers to enterprise level problems are a lot more deterministic. The distance between 90% and 100% accuracy is not incremental. In our world, it is existential.

In 2026, AI is no longer evaluated on novelty. It is evaluated on precision, governance, scalability, and business impact. As organizations move from pilots to scaled programs, five moments will define whether they capture lasting value or expose themselves to avoidable risk. I have seen these moments play out across every major market I oversee.

1. The governance moment: when agents become digital coworkers

The first moment arrives when stops being a tool and starts being an actor.

Agentic AI systems plan, reason, orchestrate with other agents, and execute workflows autonomously. They touch sensitive data and influence decisions at scale. If you are not already governing them as you govern your human workforce, you are exposing your organization to risk.

Agent sprawl will mirror the shadow IT crises of the past decade, but the stakes are categorically higher. Enterprises must establish agent lifecycle management, clear autonomy boundaries, policy enforcement, and continuous performance monitoring. Every board needs to answer three questions: Who is accountable when an agent makes the wrong call? How are decisions audited? When does the machine escalate to a human?

Geopolitical fragmentation compounds this urgency. Sovereign cloud, sovereign AI, and data localization are no longer theoretical concerns. They are regulatory realities in markets from New York to Frankfurt to Riyadh to Singapore. Governance in the age of AI is less about controlling risk at the edge and more about embedding deterministic control into probabilistic intelligence. That is a C-suite mandate, not an IT project.

2. The data foundation moment: when the last mile is the only mile that matters

The second moment is quieter, but it is where most enterprises will ultimately win or lose.

AI is only as reliable as the data and processes it operates on. Fragmented master data, siloed systems, and over-customized ERP landscapes introduce unpredictability at the worst possible moment: when AI provides a recommendation that affects your customers, your cash flow, or your compliance position.

Enterprise AI value will not come from generic large language models trained on internet-scale text. It will come from intelligence grounded in your enterprise data鈥攐rders, invoices, supply chain records, financial postings鈥攅mbedded directly in your processes. Relational foundation models optimized for structured business data will outperform generic LLMs in forecasting, anomaly detection, and operational optimization.

The question every board should be asking is not only “What AI can we add?”, but also, “Is our data estate ready, or are we layering probabilistic intelligence onto fragmented foundations?”

3. The employee interaction moment: when the interface disappears

The third moment happens in your employees’ daily workflows, and it will accelerate faster than most organizations expect.

In 2026, we are moving from static application interfaces to generative user interfaces. Instead of navigating between systems, employees express intent: “Prepare a briefing for my highest-revenue customer visit this week.” AI agents orchestrate the workflows, assemble the context, and surface recommended actions.

But adoption is not automatic, and trust is not given. Employees will embrace AI teammates only when they are confident that outputs respect governance boundaries, reflect real business rules, and deliver measurable gains. Role-specific AI personas tailored for the CFO, the CHRO, the head of supply chain, built on trusted data and embedded in familiar workflows, are what will close the adoption gap.

Organizations that invest in AI-native architecture will accelerate ROI. Those that bolt AI onto legacy interfaces will struggle with trust, usability, and scale. This is a design decision with strategic consequences.

4. The customer moment: when intelligence becomes a competitive moat

AI proves its enterprise value most visibly at the customer edge.

Trained on your own data, your own policies, and your own interaction history, customer-specific intelligence compounds in ways that competitors cannot easily replicate. This is especially powerful in exception-heavy environments: dispute resolution, claims handling, returns management, service routing. AI that can classify cases, surface relevant documentation, recommend policy-aligned resolutions, and learn continuously from outcomes transforms these high-cost, high-friction processes into sources of competitive differentiation.

In 2026, your customers will not reward novelty. They will reward reliability, relevance, and responsiveness. Organizations that use AI to absorb complexity, without losing control over outcomes, will build moats that generalist tools cannot breach.

5. The strategy moment: when you decide how far to go

The final moment is the one that falls squarely on leaders.

AI adoption is not a single journey. It requires leaders to orchestrate three layers in parallel:

• Embedded AI: Persona-driven productivity gains built into core applications for immediate returns
• Agentic AI: Multi-agent orchestration of complex, cross-system workflows
• Industry AI: Deeply specialized applications co-developed to address the highest-value challenges specific to your sector

The trap is false sequencing: focusing only on embedded AI leaves value on the table and jumping to deep industry transformation without governance and data maturity multiplies risk. The organizations that will lead are those that align ambition with readiness and invest in clean core architecture, modern data foundations, and cross-functional AI ownership, while moving decisively from pilots to programs.

The leadership test

In 2026, the winners will not be those with the most AI features. They will be those who treat AI as a core operating layer, governed like a workforce, grounded in trusted data, tailored to employees and customers, and calibrated to the realities of their industry.

The gap between 90% and 100% is precisely where enterprise value lives. It is also where leadership is tested. The decisions you make in the coming months will determine whether AI becomes your most powerful source of durable advantage or your most expensive lesson in misplaced confidence.

This is the moment to move with precision.

Manos Raptopoulos is global president of Customer Success Europe, APAC, Middle East & Africa, and a member of the Extended Board 51风流SE.

The problem is not the promise of AI. It鈥檚 the gap between intelligence in the system and connection in the moment. Customers are increasingly disengaging because intelligence is not being applied where it matters most.

Technology, particularly AI, has fundamentally changed what customers expect. They assume brands can recognize them across channels, understand context in real time, and anticipate their needs. When that doesn鈥檛 happen, the miss feels less like oversight and more like indifference. Timing is off. Service lacks continuity, and personalization stops at the surface, despite all the data behind it.

While many enterprises are trapped in siloed systems and disconnected data, consumer expectations are growing. Brands that don鈥檛 deliver the expected experiences are quickly abandoned.

In addition, global socioeconomic factors are increasing rapidly and unpredictably, challenging bottom lines and making customer loyalty more critical than ever鈥攁t a time when consumers are less loyal than ever.聽

When economies falter, companies usually take one of two approaches. Some hunker down, cut costs and staff, and hope to survive. Others zero-in on differentiators like to drive growth and boost profitability.

The importance of CX for key metrics like churn, retention, loyalty, new sales, and competitive differentiation is well-established, so not investing in customer experience could be considered akin to saying you are willing to let those mission-critical metrics falter.

The following 15 takeouts from SAP’s highlight some of the most common CX pitfalls and opportunities.

1. 82% of consumers say a brand has disappointed them

Modern customers do not go quietly into the bad experience night. A whopping 82% of consumers say a brand has disappointed them, even when the product itself meets their needs. The issue isn鈥檛 the product or service; it鈥檚 the experience of purchasing and post-purchase care.

This is the essence of the 鈥溾��: the distance between what customers expect in the moments that matter, and what brands are actually delivering.

2. 60% do not pay attention to brands anymore and 48% care more about experience

Consumer attention in a difficult economy has shifted from logos and taglines to experiences that feel useful, contextual, and personal. So, what鈥檚 a brand to do when 60% of consumers say they simply don鈥檛 pay attention to brands and 48% care more about the experience than the product?

This is where CX outcomes become clear: engagement is no longer about shouting louder; it鈥檚 about showing up better and building experiences powered by unified data and intelligent orchestration.

3. Left unread: only 16% of customers skim email headlines, while 29% read one or two sentences

Consumer behavior in the inbox shows just how fragile engagement is:

• Most consumers only read the subject line
• Others will read one to two sentences before deciding whether to delete or engage further

Combined with the fact that 58% of consumers think most marketing emails they receive aren鈥檛 relevant, brands are staring down a massive relevancy problem. Sending more emails into the engagement abyss doesn鈥檛 solve this problem, but gaining a holistic understanding of your customers as individuals does.

4. 37% do not think brands personalize to their needs

For well over a decade we鈥檝e been talking about the importance of personalization, but today 37% of consumers believe brands don鈥檛 personalize engagements to their needs. Surface-level personalization鈥攏ames in subject lines, basic segmentation鈥攊s no longer enough.

This aligns with our assessment that 79% of companies have low or moderate CEM scores, meaning teams can access portions of shared data and deliver basic personalization, but coordination across marketing, sales, service, commerce, and product teams remains limited. Experiences often feel disconnected, forcing brands to rely on short-term tactics rather than building deeper relationships.

Consumers expect real-time, behavior-driven personalization based on context, intent, and history, not just boiler-plate persona buckets. Customers can see and feel investments in personalization and it matters.

5. 46% say customer service feels too impersonal, while 41% believe brands do not understand them as a person

Considering how much data brands collect, it鈥檚 striking that nearly half of consumers (46%) say customer service feels too impersonal.

Customers are asking a simple, and valid, question: 鈥淚f you have all this information about me, why isn鈥檛 my experience better?鈥� When data doesn鈥檛 translate into empathy and action, it starts to feel like surveillance, not service.

With 46% of consumers saying service isn鈥檛 personal, it should be no surprise that a nearly equal amount (41%) believe that brands don鈥檛 understand them as a person. However, 34% agree that AI can help brands better understand them and what matters most to them.

This presents brands with a real-time opportunity: use AI and data to close the perception gap. Instead of just predicting purchases, enterprises should also be anticipating customer needs and reducing friction.

6. 78% of brands say they deliver seamless cross-channel engagement, consumers disagree

Seventy-eight percent of brands say their engagement strategies offer seamless multichannel experiences with glowing outcomes like increased CLV, retention, and advocacy, but consumers are simultaneously reporting little emotional connection and frequent disappointment. In fact, 44% say that brand interactions feel less personal and more generic than ever before.

The takeaway: internal dashboards can create a if not tied directly to real customer sentiment and behavioral signals across channels.

7. 54% of enterprises cannot access and use real-time data, and 66% still rely on third-party data

Fifty-four percent of enterprises can鈥檛 access and use real-time data. On top of that, 60% suffer from 鈥渄ark data,鈥� which is information that鈥檚 collected but not used throughout the customer journey.

Without real-time, connected data, brands are mostly flying blind. AI, personalization, and omnichannel orchestration don鈥檛 fail because the ideas or execution are wrong; they fail because the foundations are.

Although privacy regulations and legislation are increasing while third-party cookies decline, a majority (66%) of enterprises are still heavily reliant on third-party data. Simultaneously, 55% say their data is too unstructured to use effectively.

The lethal combination of overreliance on external data plus underutilized internal data keeps brands from building strong, first-party relationships rooted in trust and value.

8. 78% of brands say AI is essential for customer retention in 2026

AI is everywhere, and 78% of brands view AI as critical to retaining customers in 2026. However, 66% report they can鈥檛 use AI to optimize campaign performance in practice, while many also note they can鈥檛 utilize real鈥憈ime AI optimization in day鈥憈o鈥慸ay campaigns.

A quick translation of the above stats: an AI strategy is crucial, but execution is lagging because of fragmented systems, poor data quality, and integration issues.

9. Only 30% share engagement data with a CX or CRM platform

Despite the collective agreement that a comprehensive customer profile is important, only 30% of brands share their customer engagement data within a CX or CRM platform. This means that most brands are attempting to deliver personalized experiences without having a unified engagement core.

If engagement data lives in campaign tools, service systems, commerce platforms, and ERP, but never gets connected via CX or CRM, customers will feel every fracture along their journey.

10. 30% of consumers have used AI agents that act on their behalf

AI is not just an enterprise capability; it鈥檚 also a customer behavior. Thirty percent of consumers say they鈥檝e used AI agents to make decisions and act on their behalf when buying from brands.

This is a game-changer when it comes to engagement. Brands are now engaging not only with humans, but also with AI buyers that ruthlessly and continuously optimize for relevance and value. If your systems can鈥檛 keep pace, AI will select your competitor whose systems are operationalized for success.

11. When it comes to customer engagement maturity, 79% of brands have yet to integrate data, systems, and teams across their business; only two in five decision-makers see their departments as actually coordinated

The Customer Engagement Maturity (CEM) scoring model assesses how well brands align people, processes, and technology to deliver cohesive, intelligent experiences. Looking at the 51风流Engagement Maturity Index:

• 16% of brands reside at low maturity
• 63% sit in the moderate middle
• 21% have high maturity

Despite year-over-year progress, most organizations are stuck in developing or evolving mode, able to execute campaigns but not orchestrate truly connected, enterprise-wide engagement. And leaders agree, with only two in five decision makers believing there is effective collaboration across departments.

12. Just 21% of brands are high-maturity, and they are gaining ground against their competition

High-maturity brands rise above the competition because they connect data and intelligence across marketing, service, sales, commerce, and operations. They use AI and automation to deliver personalized, omnichannel engagement in real-time, at scale.

And the maturity gap is becoming a performance gap. As top performers turn real-time intelligence into growth, the cost of competing with them rises for everyone else.

13. Personalized means personal: 58% of consumers respond positively to localized content

Personalization is more than a word or industry term. It means actually understanding and empathizing with your customer, including their regional traditions and social norms.

When engagement is done right, consumers respond:

• 63% say their favorite brand delivers seamless, connected experiences across mobile, web, and in-store
• 58% value localized content and product recommendations
• 55% appreciate highly personalized content
• 50% believe their favorite brand uses data to make interactions better

Customers aren鈥檛 against data or AI at heart. However, they are opposed to wasted data collection and bad experiences. It鈥檚 the job of brands to provide a great CX. If that job isn鈥檛 taken seriously, you can bet that other brands are willing to roll up their sleeves to fill the gap.

14. 77% of businesses plan to invest in AI-powered engagement in 2026

When it comes to the future state, 77% of businesses plan to invest in AI-powered customer engagement in 2026, and 76% are investing in omnichannel engagement technologies. At the same time, 29% say their top priority is connecting customer and stakeholder data across marketing, sales, service, commerce, and ERP systems.

The signal is clear: investment alone won鈥檛 close the Engagement Divide. The winners will be the brands that invest in connection鈥攐f data, teams, and systems鈥攏ot just in tools.

15. 15% say seamless integration will be the biggest driver of success

Lastly, and possibly most importantly, 15% of businesses believe seamless integration of engagement systems will be the single biggest driver of success. While that may sound like a small number, it captures a critical strategic shift: engagement is no longer a marketing problem or a channel problem. It鈥檚 an enterprise discipline that depends on unified data, coordinated teams, and embedded AI.

Artificial intelligence provides an evolving service for businesses. Employing cloud-based systems that can store, analyze, and route data will be the differentiator for brands in the marketplace.

Loyalty is transactional, and driven by great CX and a connected enterprise

Digital engagement has raised the bar when it comes to customer expectations, with more demands and a plethora of competitive choices if a brand doesn鈥檛 deliver.

It鈥檚 not a big leap to state that better customer experiences increase customer loyalty, which in turn leads to more purchases, augmented product utilization, and increased brand affinity and sentiment. And let鈥檚 not forget that an enhanced CLV lowers customer acquisition costs.

After all, loyalty is transactional and forged by the experiences customers encounter. In my conversations with customers across the globe, it鈥檚 clear that only the brands with truly at the heart of their operations will retain and grow their customer bases in the enterprises of the future.

That ambition relies on a technology foundation that can consistently deliver those experiences at scale. For British-founded luxury fragrance brand Molton Brown, moving from legacy systems to 51风流Commerce Cloud provided a high鈥憄erformance platform built for peak鈥憇eason resilience and continuous innovation. The impact was immediate: 100% uptime during peak trading, even as volumes surged to one order every three seconds during major events.

This kind of reliability is increasingly critical as the moments that shape experience and loyalty expand beyond owned channels. As product discovery shifts to social platforms and AI鈥憄owered assistants, consistent content and availability help the brand remain visible and trusted wherever customers engage. SAP鈥檚 evolving agentic commerce innovations are designed for this reality, keeping products discoverable, credible, and actionable across both human and AI interactions.

Ultimately, technology and AI are not the goal鈥攖he experience is. The brands that succeed will be the ones that use AI to show up more human, not less, turning insight into relevance and automation into trust.

The future of CX is for companies that operationalize intelligence across the enterprise鈥攃onnecting data, systems, and teams so AI can orchestrate experiences, not just analyze them.

Manos Raptopoulos is global president of Customer Success Europe, APAC, Middle East & Africa, and a member of the Extended Board 51风流SE.

鈥�Beyond Tech 鈥� Expanding Perspectives鈥� is about their stories. With this series, I hope to provide a glimpse into the inspiring minds I encounter, capturing their ideas to spark insight and innovation.

For the , I had the privilege of speaking with Nisha Bakker, director of Partnerships at The Ocean Cleanup, an organization proving that, with the right vision, evidence, and engineering, we can solve global challenges at scale.

Plastic is one of humanity鈥檚 greatest inventions and one of its most persistent problems. Durable, cheap, and versatile, it has transformed food security, medicine, logistics, and manufacturing. But that same durability means most of the plastic ever produced still exists today. And much of it has ended up where it shouldn鈥檛: in our rivers, our oceans, our ecosystems鈥攅ven our bodies.

Today, the world produces . Production is still rising, projected to grow 66 percent by 2040, even as waste management systems are overwhelmed. Only nine percent of plastic is recycled globally. A third is mismanaged, left to leak into the environment through open dumping, unregulated landfills, and littering. As a result, have accumulated in rivers and lakes, far more than the 30 million tons in the oceans themselves.

Rivers are the main conveyor belt carrying waste to the sea. In 2020 alone, 1.4 million tons of plastic flowed from rivers into the ocean. Without intervention, this will more than double by 2060. Just a thousand rivers account for 80 percent of this flow, largely in rapidly developing economies where growth, urbanization, and weak waste systems collide.

This is where has focused its mission. The organization aims to rid the world鈥檚 oceans of plastic through a comprehensive strategy that includes removing legacy plastic accumulated in the ocean and along coastlines while also stopping new plastic pollution from entering the marine environment. Their ambition is bold and unambiguous: to put themselves out of business by 2040.

Data, vision the difference in cleanup efforts

During our conversation, Nisha explained how the work is driven not only by passion, but by evidence. 鈥淵ou could look at a river, see the problem, and start removing plastic immediately,鈥� she said. 鈥淏ut we first determine the best place to remove it, and then build the entire value chain around it鈥攊ncluding recycling, operators, permits, and long-term partners. Data is what sets us apart.鈥�

Behind every cleanup is an enormous amount of engineering and analysis. The Ocean Cleanup鈥檚 teams map political, economic, and social dynamics in each country with an affected river system. They deploy trackers to understand how fast plastic moves, where it gets stuck, and how seasonal changes from monsoons to dry months affect pollution flows. Cameras equipped with detection algorithms help quantify volumes and patterns. Modelling and simulations guide where to deploy Interceptor systems and how to scale them.

This foundation of data explains their success: more than 46 million kilograms of waste intercepted and removed from marine and freshwater environments, thanks to System 03, their towed ocean technology spanning over 2.2 kilometers, which can clean an area the size of a football field in five seconds; and over 20 Interceptor systems deployed across the world鈥檚 most polluted rivers. The organization recently unveiled plans to tackle up to a third of all plastic emissions from rivers through its 30 Cities Program, targeting urban centers with important waterways and major pollution problems.

But as Nisha stressed, cleanup is only one part of the solution. 鈥淲e鈥檙e buying time for systemic change,鈥� she told me. 鈥淯ltimately you need governments, producers, recyclers, and communities working together.鈥�

There are signs of progress: more than 90 countries now have plastic bag bans; extended producer responsibility regulations are expanding; and negotiations toward a global plastics treaty have brought unprecedented international attention to the issue despite agreement remaining elusive.

The importance of systems

What struck me most in our discussion was the philosophy that drives The Ocean Cleanup. With employees from 40 nationalities, they are building bridges across sectors, disciplines, and geographies. They are proving what is possible when a global movement is anchored in evidence-based design and relentless experimentation.

At SAP, we recognize this mindset. Helping the world run better and improving people鈥檚 lives requires more than intention; it requires agile systems capable of putting insights at the fingertips of business. That鈥檚 why The Ocean Cleanup relies on 51风流to deliver on its mission. Every hour they spend building business systems is an hour not spent developing ocean systems, river systems, or new engineering solutions. Our role is to provide a stable, integrated digital foundation so they can focus on innovation, not administration. Technology should accelerate impact and enable scale, not get in the way of it.

The same is true for every organization. Whether fighting pollution, reimagining supply chains, or transforming business models, the biggest breakthroughs happen when you combine purpose with technology that can support it. Clean, connected data, intelligent processes, and applications that automate what can be automated so people can focus on what matters most: This is why 51风流is more relevant than ever.

The Ocean Cleanup shows what is possible when bold ideas meet the right technology and the right partnerships. This is exactly the type of conversation I look forward to bringing you through Beyond Tech 鈥� Expanding Perspectives, stories of inspiring minds that demonstrate that the future is not something we predict, but something we build together.

Manos Raptopoulos is global president of Customer Success, Europe, APAC, Middle East & Africa, and a member of the Extended Board at SAP.

Across the world, 95 percent of companies say growth is a priority for the year ahead. Their top focus areas鈥攊ncluding expanding market presence, broadening distribution through partners, and scaling operations鈥攕peak to leaders鈥� determination to create value in a climate of uncertainty and change.

In my engagements with customers, I see this reality every day. Companies everywhere want to grow, but they want to grow with confidence. They are looking for partners who understand their unique challenges, who support them with their long-term ambitions, and who can help them keep pace with rapid change.

Technology is central to this ambition. Nearly all respondents in the study rank simplifying work and improving processes alongside growth. Here, artificial intelligence stands out. Nine in 10 organizations have already made generative or agent-based AI a priority, and more than 70 percent have some form of AI in use. While concerns about data quality and talent remain, the message is clear: AI has moved beyond experimentation into the mainstream of how companies operate and unleash value.

From Frankfurt to Dubai to Singapore: How regional differences shape opportunities and risks

Regional differences tell a powerful story. In Europe, AI adoption comes with caution. Large enterprises put compliance, privacy, and transparency first, while many mid-market firms are still piloting solutions. In Asia-Pacific, the pace is different. Mid-market companies there already report strong AI use above global averages, and growth expectations run high. For them, AI is a way to seize advantage quickly in a fast-moving market.

These contrasts show why cultural intelligence matters so much for global leaders. Whether in Frankfurt, Singapore, or Dubai, I see how local realities, regulations, and expectations shape both risks and opportunities. In Europe, energy costs and geopolitical uncertainty drive supply chain strategies. In Asia-Pacific, digital adoption and market dynamism set a different pace.

Sustainability is another area where nuance matters. European companies place it near the top of their priorities, tracking or slightly exceeding global benchmarks. Asia-Pacific firms value sustainability but often rank it lower than growth and speed to market. Each is weighing trade-offs in its own context, creating exciting opportunities for 51风流to bring the most relevant technology, data, and practices to each region to help organizations achieve both economic and environmental goals.

The through-line in all of this is agility. Supply chain fragility, geopolitical conflict, inflation, and regulation continue to test even the best-run organizations. Technology can enable agility, but only if leaders embrace change themselves, rethinking processes, investing in skills, and building cultures of continuous learning and exploration. Security and ethical standards must also be the cornerstones of every AI conversation.

Turning AI potential into outcomes by centering value creation and integration

I believe this is a time for grounded optimism. The appetite for growth is real and the technology to achieve it is more advanced than ever. Innovation is accelerating at an extraordinary pace, with daily breakthroughs showcasing the expanding potential of AI.

There is a recent example that demonstrates AI’s ability to process multi-step tasks for over 30 hours. This achievement highlights not only the rapid evolution of AI, but also how increasingly accessible and capable these technologies are becoming.

However, as AI systems grow more autonomous and context-aware, organizations must recognize that true value doesn鈥檛 come from raw capability alone. To harness AI effectively, especially in enterprise environments, a consistent semantic layer is essential. It ensures alignment among data, tasks, and outcomes, enabling AI to reason reliably across systems and scale impact without losing coherence.

Companies must also move beyond simply adopting AI to actively testing and refining applications to gain a significant advantage. Equally important is a deliberate approach to managing the human element of a transformation, rooted in structured and human-centric change management.

Realizing AI鈥檚 true promise requires a fundamental shift in how people, applications, and data connect. Success relies on deeply connecting every part of an organization鈥檚 business, delivering end-to-end transformational value. A seamless, integrated suite provides insight and agility, whether responding to a problem or ensuring readiness when opportunity knocks.

This is where 51风流Business Suite is a game changer, integrating applications, data, and AI in a virtuous cycle that delivers tangible business outcomes. At our inaugural 51风流Connect event earlier in October, we showcased new applications, strategic data partnerships with Google Cloud and Databricks, and a new network of role-based AI assistants in Joule across every line of business.

Altogether, our marks the beginning of a new era powered by self-reinforcing AI, data, and applications. By keeping customer needs and value realization at the center and leading with innovation, businesses can not only navigate uncertainty, but build a more resilient, intelligent, and sustainable future.

Manos Raptopoulos is chief revenue officer of APAC, EMEA, and MEE, and a member of the Extended Board of 51风流SE.