NearLink Alliance releases technical document on embodied AI connectivity with participation of over 30 authors

Despite the growing availability of artificial intelligence models, turning them into reliable commercial products remains a complex task, especially when enterprises face fragmented data infrastructure.

At the TechSparks 2026 event, Michael Cronin, Vice President of Sales for Asia Pacific, Japan and India at Couchbase, participated in a discussion with Shivani Mutanna, Senior Director of Strategic Content and Video at YourStory. They discussed the transition of AI from the experimentation stage to full production, management challenges, customer experience, and the increasing role of India in AI innovation.

For decades, large corporations have implemented various databases and highly specialized solutions to solve specific problems. While each solved an immediate issue, the result was often a fragmented data layer composed of multiple systems that interact poorly with each other.

Cronin considers this fragmentation a serious obstacle to AI adoption. He cites industry estimates suggesting that 70–80% of AI development projects never reach the production stage, and MIT research showed that 95% of such projects do not become profitable.

Couchbase's operational data platform is designed to address this problem by helping companies consolidate their data infrastructure. Instead of trying to fix everything at once, Cronin advised companies to start small, recommending that founders and enterprises struggling with data layer fragmentation first choose the most critical use cases.

He cited the example of UK retailer Tesco, which has a large Global Capability Center (GCC) in Bengaluru. Many of this retailer's systems negatively impacted the customer experience, especially during peak shopping periods like Black Friday and weekends. Tesco began consolidating its data layer, starting with one instance and one use case before expanding efforts across its entire technology stack. This consolidation allowed the retailer to process millions of transactions per second with less than a millisecond latency. In Cronin's view, this example demonstrates how companies can begin solving the fragmentation problem through one use case before scaling changes across their entire operation.

The pressure associated with developing AI products has prompted many enterprises to prioritize models and applications. However, pilot projects are often conducted on small datasets in controlled environments, which does not prepare organizations for real-world production demands. Cronin pointed to the growing scale of machine traffic. He stated: 'In eighteen months, machine traffic surpassed human traffic on the internet. That is the scale of AI. That is the scale of machine speed at which businesses must operate.'

When AI applications encounter real-world workloads, fragmented data systems can prevent them from functioning reliably at scale. The increasing use of AI agents exacerbates this issue. As agents become more autonomous and take on decision-making functions, enterprises must ensure they can access the necessary data within appropriate governance structures.

Fragmented systems can hinder communication and management, limiting agents' ability to act autonomously. Cronin also urged enterprises to reconsider their approach to allocating AI investment. While models evolve rapidly, corporate data remains a critical asset. He emphasized: 'Models can be rented. The best model today is yesterday's newspaper. The true asset for an enterprise is data.'

As enterprises began to realize that fragmented data was slowing down their AI initiatives, many responded by adding APIs and connectors to link existing systems. However, Cronin warned that this method could create additional complexity instead of solving the core problem. Every extra connection increases latency, risk, and creates another potential point of failure.

Instead, he recommended consolidating the data layer so that AI applications and agents could interact with it more directly, bringing data closer to the customers. According to Cronin, many enterprises, including large GCCs in India, are currently re-architecting to eliminate data fragmentation. Without these changes, further investments in AI development may lead to the same problems.

Data consolidation can also improve customer experience by helping systems handle growing demand, remain available, and respond quickly. As customers expect more from AI-based services, having data at the edge is becoming increasingly important.

Cronin noted that business cultures vary across the Asia Pacific region, but fragmented data infrastructure remains a common challenge for AI adoption. At the same time, India is emerging as a vital hub for AI innovation and decision-making. He noted that 80% of Couchbase's top 50 global customers have a significant presence in India, with much of their AI innovation and leadership based in the country. Couchbase is also expanding its presence in India, moving its Asia Pacific headquarters to Bengaluru. Among Couchbase's corporate clients in India are Air India, Airtel, BigBasket, and Mahindra Finance. In Cronin's opinion, India's role in global business extends beyond cost savings; innovation, leadership, and important business decisions are increasingly happening there. He mentioned Air India's next-generation entertainment platform, which is built on Couchbase. This platform aims to provide passengers with a personalized shopping and entertainment experience even without network connectivity on board the aircraft. For Cronin, this project illustrates how Indian enterprises are using technology to enhance customer experience in real-world operations.

Ahead of the APEC Economic Leaders' Meeting in Shenzhen on November 18-19, 2026, Zhou Chanzhu, President of the RoboCup Asia-Pacific Confederation (RCAP) and Vice President of the RoboCup Federation, was invited to share his vision. As one of the co-founders of the RoboCup Humanoid Football League, he draws upon years of competition-based research experience to analyze the opportunities, practical bottlenecks, and viable pathways for achieving comprehensive cross-border AI cooperation in the Asia-Pacific region, ahead of the upcoming RCAP tournament in Changsha.

As artificial intelligence (AI) transforms technology and industry globally, a key question is how economies can combine their technological capabilities and manufacturing advantages so that more people can participate in and benefit from these innovations. AI is moving beyond the digital world and penetrating the physical one. Humanoid robotics, as part of embodied AI, is advancing from laboratories and competitive arenas into factories and daily life.

However, translating technological breakthroughs into practical applications and individual innovations into international industrial cooperation remains a complex challenge. Since its first competition in 1997, RoboCup has been developing AI and robotics research through international competitions for nearly three decades.

Humanoid football provides a useful window for such cooperation. During the game, fully autonomous robots must perceive their environment, control their movements, make decisions, and work together without remote control or human guidance. Five-on-five and eleven-on-eleven formats expand the scope from individual intelligence to collective intelligence and the coordination of multiple robots.

In August 2026, 24 international RoboCup teams from 16 countries and regions participated in the second World Humanoid Robotics Championship at the RCAP Beijing Masters tournament in Beijing. The teams used humanoid robot platforms provided by Chinese companies to test their AI algorithms and system integration capabilities within the common rules of the competition.

The significance of this event extended far beyond the matches themselves. Chinese companies provided robotic equipment and engineering support, while international research groups contributed AI algorithms, software, and system integration expertise. The training, testing, and competition helped both sides identify technical issues, improve hardware and software integration, and accelerate development.

RCAP is now extending this model from the competitive field to industrial applications. The RCAP Asia-Pacific Changsha Invitational 2026, scheduled for December 18-20 in Changsha, Hunan Province, will bring together teams from China, Portugal, Brazil, Thailand, Malaysia, and Singapore. The tournament will feature humanoid robots developed by a major Chinese heavy equipment manufacturer based in the host city, whose core areas include construction and agricultural machinery.

This company's expansion into humanoid robotics demonstrates how established manufacturers can leverage engineering expertise, production capacity, and real industrial conditions to advance embodied AI. Participants will use these robots for training, joint research, and international competitions while exploring potential applications in industrial manufacturing and domestic services. The event aims to connect China's robot platforms and application environments with the global RoboCup developer community and leading industrial enterprises.

By combining competitions, joint research, and real-world testing, the event seeks to create a clearer path from technical experiment to practical application. Industrial testing can also generate valuable operational data. Repeated trials can show how changes in lighting, object placement, or workflow affect robot performance, helping researchers identify weaknesses and refine their systems.

For these benefits to cross borders, partners require clear agreements on data access and reuse, backed by measures for security, privacy, and intellectual property protection. Such arrangements can help transform access to industrial environments into shared opportunities for research and innovation. China's extensive robotics manufacturing ecosystem, efficient supply chains, rapid engineering iteration, and diverse applications in smart manufacturing and services provide valuable opportunities for international researchers to develop and validate.

In turn, Chinese manufacturers can utilize diverse AI algorithms, software solutions, and technical feedback from global developers to improve their products and software ecosystems. This two-way exchange demonstrates how hardware, software, AI algorithms, and application scenarios can complement each other in global robotics value chains, creating more opportunities for collaborative innovation.

Despite rapid progress in AI and robotics, translating technology from the lab to everyday use remains difficult. Obstacles often lie not in the robot's intelligence itself, but in infrastructure, device interfaces, technical standards, and system compatibility.

Consider a simple task: how does a service robot call an elevator? Many robots can already navigate autonomously, recognize their surroundings, and avoid obstacles. Nevertheless, in some commercial buildings in Singapore and elsewhere, elevator control systems do not provide a user-friendly communication interface for third-party robots. The robot may be unable to call the elevator or select a floor digitally, and instead might require an additional mechanical arm to physically press buttons.

The contrast is striking: a robot capable of making complex autonomous decisions can be limited by something as basic as an elevator interface. Of course, elevators are safety-critical systems. Access to their control must comply with physical safety, cybersecurity, and building management requirements. Safe integration requires standardized interfaces, appropriate certification, and clear access rules.

Singapore's national standard for data exchange between robots, elevators, and automatic doors is a step in this direction, although upgrading existing buildings takes time. Similar problems arise in industrial automation. Robots from different manufacturers often use different communication protocols, software architectures, and control systems. An AI algorithm that works well on one platform may require significant software adaptation, parameter tuning, and testing before it can operate on another.

Differences in development tools, operating environments, data usage requirements, and intellectual property agreements can further complicate international collaborative research and the reuse of research findings. Technical compatibility is only part of the problem. A robot that performs well during testing may still require further evaluation or approval before deployment in another economy. Differences in certification requirements, procurement procedures, and market access can add time and cost to joint projects. Therefore, cooperation requires more than just compatible hardware and software; it also depends on transparent requirements and coordination among technology providers, regulators, and users.

These examples point to a broader lesson: technological sophistication does not automatically translate into practical applicability, let alone widespread benefits. A breakthrough in one component does not establish the maturity of the entire system, and success in the lab does not guarantee reliable operation in the real world. Therefore, wider adoption depends not only on stronger AI algorithms but also on interoperability, standardized interfaces, system security, and reliable performance in real operating conditions.

How can international cooperation reduce barriers to development and help more countries, companies, and researchers participate in AI innovation? Nearly three decades of RoboCup experience offer practical lessons. Even when teams from different countries work on the same task using identical equipment, they often choose very different technical approaches. Some specialize in motion control, others in computer vision and autonomous decision-making, and a third in software architecture or multi-robot coordination.

Common rules, shared test environments, and technical exchange allow teams to compare, verify, and improve these approaches. At international competitions, I have often seen teams fiercely competing on the field, and then, after the final whistle, exchanging AI algorithms, sharing experiences, and sometimes initiating long-term research partnerships. Common technical goals leave room for diverse development paths. This approach aligns with the principles of voluntary cooperation and APEC consensus. Economies should be able to exchange experiences and choose technologies suitable for their own needs, resources, and stages of development, rather than following a single prescribed model.

Joint research and talent exchange can support this choice by building capacity through sustained testing and adaptation. Three practical priorities stand out. First, creating open and compatible development platforms. Accessible software development kits, modeling tools, standardized interfaces, and technical documentation can reduce integration costs and encourage the reuse of algorithms and software modules. This allows developers to spend less time on routine integration work and more on core technical challenges.

Second, focusing joint R&D on real-world applications. Universities, research institutes, and companies from different countries can work on common platforms to develop and test solutions for smart manufacturing, logistics, public services, and home assistance. China's robotics supply chains and application scenarios can complement the AI algorithms and expert knowledge of developers worldwide, accelerating the transition from research to implementation.

Third, strengthening research partnerships and talent exchange in the Asia-Pacific region. Joint laboratories can bring together university teams with industrial partners to test robots on common tasks, while researcher exchange can provide practical training in deployment and maintenance. Participants can bring proven methods and engineering expertise back to their institutions and adapt them to local needs. International competitions can help initiate these relationships and support partners in working toward common technical goals.

Broader access to advanced products is as important as the ability to develop and adapt the underlying technology. Lowering barriers to innovation allows research groups in different countries to contribute according to their strengths and create long-term research and application opportunities. This capacity building is a gradual, long-term process, not a one-time transfer.

Many APEC economies face common challenges such as frequent extreme weather events, uneven capacity of public services, and growing energy demand driven by increased computational needs.

Experts in quantum and computing technologies, Professor Lin Jing from the University of Hong Kong and Professor Wen Yungang, Associate Vice President of Nanyang Technological University and member of the Singapore Academy of Engineering, discussed ways to utilize advanced technologies in the region.

AI-based early warning systems process real-time weather data to issue warnings for typhoons and floods, while also supporting local community preparedness for natural disasters. Furthermore, open AI tools are being shared across the region for public health, assisting local medical teams in providing routine care.

Embodied intelligence prototypes presented at humanoid robot competitions are viewed as promising means for elder care and on-site emergency rescue, thereby filling service gaps where labor shortages exist.

Professor Lin Jing, academic curator of the 'Future of Computing · Future Computational Power' forum at the World AI Conference 2026, noted that hybrid quantum and AI architectures offer unique research advantages for high-precision climate modeling and molecular-level drug discovery. However, this technology remains predominantly in the realm of laboratory research.

Lin emphasized that quantum computing is still in the fundamental research stage and is not ready for widespread adoption as standard digital infrastructure. Although it possesses theoretical advantages for modeling complex natural systems, it requires continuous funding from governments, universities, and research institutions to advance.

She proposed that Hong Kong could become a regional hub for quantum cooperation in the Asia-Pacific, connecting talent and capital from around the world. Partners could first jointly develop prototypes before determining suitable pathways for local implementation and broader regional scaling.

Lin pointed to a viable model of collaboration represented by open quantum computing cloud platforms and joint Asia-Pacific research laboratories. These initiatives focus not on one-off technology transfers, but on sharing computational resources, joint experiments, and cross-border academic exchange.

In her view, inclusive access to technology cannot be achieved overnight and requires voluntary long-term cooperation between economies at different stages of digital development.

Sustainable computing is another common regional concern. The increasing load on AI leads to higher energy consumption in data centers, placing a particular strain on economies with limited land resources in Southeast Asia.

Wen's research focuses on designing space-based data centers. He argues that computations located in space can leverage greater solar irradiation and natural radiative cooling in space, while orbital resources can help alleviate terrestrial land constraints.

Wen noted that many Southeast Asian economies have rapidly expanded their data center infrastructure. Some are studying Singapore's regulatory framework: aligning data center growth with renewable energy sources, implementing regular energy efficiency audits, and setting limitations to promote more ecological digital infrastructure.

He believes that innovators in the Asia-Pacific can forge unique technical paths for next-generation computing, similar to the diverging development trajectories observed in the global electric vehicle sector.

Both experts observed that the digital divide among APEC economies means that a uniform, top-down technology rollout does not suit regional conditions. For regional advanced technology cooperation for societal benefit, voluntary knowledge exchange, joint laboratory research, and capacity building are more practical approaches.

They stated that regional stakeholders aim to translate advancements in AI, quantum modeling, and sustainable computing into tangible outcomes: enhanced climate resilience, more accessible healthcare, and improved social services.