/PRNewswire/ –ZTE Corporation (0763.HK / 000063.SZ), a global leading provider of integrated information and communications technology solutions, announced that Cui Li, the company’s Chief Development Officer, was speaking at AI Innovation Asia 2025, organized by Economist Impact, the thought-leadership arm of The Economist Group.
ZTE CDO Cui Li speaks at Economist Impact’s AI Innovation Asia 2025
In the panel “How May AI Help You? Agentic AI and the Customer Experience,” Cui Li shared ZTE’s strategic vision for Agentic AI and illustrated how it is already reshaping the customer experience and operating models within the company. She also highlighted the role of AI in strengthening resilience, improving oversight and accountability and urged organizations to prepare now for the era of Agentic AI.
Question 1: Preparation – How is AI impacting the customer experience in your industry?
Agentic AI is redefining the user experience, not just in UI design, but from response to understanding and co-creation. Under our “AI for All” strategy, ZTE Agentic is integrating AI into four key domains: networking, computing, home and personal devices.
For example, we enable autonomous networks at level 4 and higher with three engines: Nebula Telecom Large Model, big data and digital twin. In practice, ZTE and China Mobile have jointly developed multi-agents that can detect network problems and enable self-healing, reducing troubleshooting time by 47%.
Question 2: Strategic Transformation – How will increasing digital autonomy through Agentic AI change the way organizations build resilience and adaptability?
In fact, we are now in a very uncertain era. Therefore, we must start by keeping the goal in mind, which is to always seek stability from uncertainty and build a strong moat like the snowball effect. We must also remain agile to detect changes and pivot on the fly so that we can evolve from a machine-like organization to an organic, adaptive organization.
And then we need to know how AI can really help us. Large AI models are already performing at or above PhD level. While agents go a step further by integrating memory and tools, acting as real applications of models. And Agentic AI can coordinate different agents to automate more complex, time-consuming tasks. Obviously this is an ideal status. The truth is that agents and Agentic AI are still in their very early stages and face many technical challenges. But given the hypergrowth of AI, I think the solutions will become a reality soon.
And furthermore, I believe that AI can only have an effect if we continue with it for a long time. Intelligence is built on digital and network foundations. Without digital transformation, a business won’t truly achieve intelligence, not to mention become more resilient or adaptable — it’s like trying to run before you can walk. Becoming intelligent also requires knowledge engineering, process restructuring and an AI focus, which is a marathon and not a sprint.
At ZTE, our digital journey started in 2016, then intelligent transformation started in 2022. Our experience shows: infrastructure first, but keep the balance between hardware and software; conduct systematic planning from the top down to ensure everyone is on the same page; make continuous investments, because big breakthroughs come from every small step; and finally, start with high-quality and concrete scenarios and then iterate quickly to address any uncertainties along the way.
Question 3: Oversight and accountability – With AI systems making more and more decisions autonomously, how can companies maintain overview, ensure accountability and maintain digital sovereignty?
In short, keeping people involved. Tasks such as design, assessment, decision-making and monitoring must still be carried out by people, who must remain ultimately responsible. Automation is only a means, not an end. What people should really worry about is not that they are being replaced, but that they are taking a step back or being absent from this process.
Every coin has two sides, and that also applies to these models. Its generalization, emerging capabilities, and constant evolution — yes, these are the hallmarks of a technology that can truly be a game-changer. But they are accompanied by hallucinations, black-box problems, and so on. Furthermore, humans have social intelligence and morality, something AI can never truly master. So essentially AI is built on statistical models — it lacks common sense in the real world, let alone the ability to make complex trade-offs like humans do.
More importantly, deploying AI in a company requires deep integration with know-how. We need to consider factors such as accuracy, security, compliance and division of responsibilities, and consider both workflows and KPIs. Based on ZTE’s practical experience, I would like to give some tips: First of all, companies should develop their own knowledge engineering projects and domain-specific large models – plus RAG and digital twins to be professional and reliable. Second, they need to identify the concrete problems that agents need to solve. A one-size-fits-all agent often fails to do anything right. Third, know when to use agents or workflows. Agents are better at handling complex tasks with variable execution paths, while workflows are more accurate and efficient in highly predictable scenarios. Finally, enable end-edge-cloud collaboration to ensure both cost-efficiency and security. Between all these important components, it is still people who drive AI in the right direction and create real value.
Question 4: Looking Ahead – How do you see Agentic AI evolving from task-based automation to integrated business partners and what immediate actions should organizations take to future-proof themselves for Agentic AI?
From a technical perspective, we can see agents or Agentic AI as proactive digital workers. In addition to simple or repetitive tasks, they can connect entire workflows, achieve cognitive automation, and even evolve themselves. Currently, agents work well in scenarios that are well structured, information-rich, fault-tolerant, and have clear feedback loops. But they often get stuck in the lab when real-world environments become more complex or risky. As I mentioned earlier, agents and Agentic AI are still in their infancy. In the next two years they will mainly focus on vertical industries. They will then be able to handle complex tasks with more autonomy, become more generalized and adaptive and able to learn and evolve. Agents are developing very quickly now. Gemini 3, launched last month, sets a new bar for AI models with SOTA reasoning, multimodal understanding and agentic capabilities.
I think embracing AI is the only path for organizations to take. Deploying AI is more than connecting to APIs, it is reimagining processes, structures and teams. Companies must first make medium- and long-term plans and be able to adapt to technological and market changes. Then assume business-specific, high-value scenarios and iterate quickly. This is how we can really master AI. It also changes our talent strategy. In the future, three types of talent will be most important: AI experts, who further develop this technology; AI power users, who drive innovation and improve efficiency; and people who go beyond AI, with high thinking skills and a healthy mindset. Finally, for AI to reach its full potential, companies must restructure and plan for a future of “human-AI symbiosis.”
AI Innovation Asia 2025 is a platform for high-level dialogue between business leaders, technology pioneers and policy makers. Through 15 in-depth theme sessions and insights from more than 40 industry experts, the platform focuses on the commercialization journeys of cutting-edge technologies, such as Generative AI and Agentic AI, to help companies turn technical insight into tangible growth and navigate sustainable digital transformation in the complex market landscape of Asia Pacific.
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