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Published: 2026-09-29 08:33:11 Views: 2278
Editor's Note
First-hand practical insights from Alibaba's latest AI-native organization, shared live at the Yunqi Conference, offering a reference for enterprises' AI organizational transformation. Key content: 1. The background of the AI-Native Organization Forum and industry development trends 2. The core current problem in enterprise AI transformation: emphasizing technology over organization 3. Alibaba's judgment on organizational AI-ification and analysis of key conditions for transformation
Yang Fangxian
Founder of 53AI / Tencent Cloud (TVP) Most Valuable Expert
"Code can get 10x faster, but the organization doesn't necessarily speed up in sync. Alibaba pushes the problem to managers, workflows, and organizational relationships."
On the afternoon of September 22, before the Mid-Autumn Festival, I attended the "AI Native Organization Forum" at the Yunqi Conference in Hangzhou, and it was packed. I had been following the topic of AI-native organizations long before the event, and this time I especially hoped to share the forum's content as completely as possible.
The scene of the "AI Native Organization Forum" at the 2026 Yunqi Conference
This is already the fourth consecutive year that Alibaba's Yunqi Conference has held an organization-themed forum.
At this time last year, "AI Native" was rarely mentioned, but this year it has become one of the hottest topics in the industry, and the forum made it its theme outright.
What's even more noteworthy is that, as Jiang Fang, Chief People Officer (CPO) of Alibaba Group, pointed out in her opening remarks, this year's forum was jointly hosted by the CPO and CIO lines for the first time. This matches a trend we've observed: some frontier companies have begun merging HR with information or intelligence centers. In the AI era, this may be the biggest difference in organizational evolution compared with the past.
"Business systems are being rewritten by AI, and organizations will inevitably be rewritten too. The people who manage business systems and the people who manage the organization are sitting together for the first time."
In the past, when companies talked about AI transformation, the subject was usually models, platforms, data, and tools. The AI Organization Forum asks another set of more critical questions: how will job boundaries change, how will decision-making and collaboration be rewritten, what should managers do, how should talent be selected and developed, and once digital employees enter the organization, who provides the Context and who is accountable for the results.
What is the code to the evolution of AI-native organizations? After the talks, no one on site gave a standard answer. The only certainty is that technology and tools are already running fast, but organizations haven't sped up in sync. Let's take a look at the core content of the forum:
AI has moved from answering questions to executing tasks and delivering results, and is beginning to generate real business value in end-to-end processes. Job boundaries, ways of collaborating, and relationships of authority and responsibility are loosening accordingly.
Jiang Fang relayed two observations from AI companies. The head of Claude Code believes that Anthropic's technical leadership fundamentally comes from leadership in organizational processes and culture; the founder of Genspark said that the output of 70-plus people can match the output of the 2,000-person team he once worked with at a big tech company.
"When model capabilities converge, the organizational gap is precisely the moat that others cannot copy-paste."
Jiang Fang, Chief People Officer of Alibaba Group, delivers the opening talk
Last year, the forum proposed four things for organizational evolution: rebooting cognition, rebuilding capabilities, restructuring collaboration, and reshaping culture. After a year of practice, Jiang Fang offered a clearer judgment: "Organizational AI-ification is a certain option." But the difficulty of transformation varies greatly across organizations.
She listed four relatively easy conditions:
The top leader deeply understands AI and can get hands-on; work is already highly digitized as data; the organization has fewer than 100 people; the core team has a high level of mutual trust. For large enterprises, these four points also serve as a reverse diagnostic checklist. Scale, legacy systems, offline information, and low trust all raise the cost of transformation.
The first three are easy to understand; the fourth, "high mutual trust," has rarely been placed separately into an AI transformation framework before. Models and tools can be bought, but Context inside an organization doesn't flow automatically. If important judgments remain for a long time on personal computers, in meeting rooms, and in the minds of a few people, agents can't get the full context, and employees can't share processes with each other either. The closer AI gets to core processes, the more trust resembles a piece of infrastructure.
"Individual productivity gains don't necessarily bring a leap in organizational efficiency."
Engineers' coding efficiency can improve 10x, yet end-to-end development cycles haven't noticeably shortened. A collection of super individuals doesn't automatically become a super organization.
Last year, Jiang Linquan, Vice President and Chief Information Officer of Alibaba Cloud Intelligence Group, had already seen the same problem from the CIO's product and R&D team. After the team made heavy use of AI Coding tools, the AI-generated code rate kept rising, but the total time for real end-to-end (E2E) delivery of requirements did not shorten significantly. Developers actually spend only 20% of their total working time writing code, while the remaining 80% is still spent on collaboration around requirement communication, confirmation, testing, release, and operations. The more code AI generates, the more backbone employees get "DDoSed" by ever-larger volumes of verification work; moreover, mediocre code mass-produced by AI entering the system will very likely become a liability, and the system's burden grows along with it.
Jiang Linquan, Vice President and Chief Information Officer of Alibaba Cloud Intelligence Group, shares on site
Two people, from different positions, saw the same bottleneck. The CPO cares about managers, mutual trust, talent, and authority/responsibility; the CIO faces data, permissions, platforms, and processes. No matter how skilled individuals are with tools, if work still flows through old pipelines, organizational efficiency is hard to change.
Returning to how to do it, CPO Jiang Fang then grounded the answer in three things: leaders go into the water first, special zones pilot first, and workflows get restructured.
Jiang Fang told a story about a product leader inside Alibaba. Before going to Silicon Valley to learn, this manager felt the project was at a critical stage and he couldn't step away. After his mindset was opened up, upon returning to China he handed over the few things he had been reluctant to let go of to subordinates, picked seven or eight young people, rebuilt the business workflow from scratch, and immersed himself every day in how to help AI work better.
The change then spread to the team. People began proactively redoing their own workflows and began thinking about the business in terms of "delivering AI products."
The most critical action in this story is the manager's shift in self-awareness and thinking. Only when a manager first hands over the work he can least let go of does he have time to enter a new process that hasn't yet been proven. If you only ask employees to learn tools while you yourself still hold meetings, approve, and assign tasks the old way, the team will quickly return to its original track.
Jiang Fang herself is making similar attempts. Alibaba is advancing from individual AI efficiency gains to collaborative network efficiency gains, letting closely collaborating colleagues share interconnected Context through AI. The Qianwen Office team is exploring Context interconnection in the product, and she has started using the new version of Qianwen Office to accumulate work context and share it with her closest colleagues. She said this has just begun, and she hopes to come back next year and report concrete examples of overall efficiency gains.
Bao Chenxing, HR Vice President of Alibaba Cloud Intelligence Group: After 600-plus digital employees, Alibaba Cloud is still in the second stage
Bao Chenxing, HR Vice President of Alibaba Cloud Intelligence Group, shares on site
Bao Chenxing divides organizational evolution into three stages.
The first stage is AI tools entering individual work, where employees start using Copilots, workbenches, or AI Assistants, while the human organizational network remains unchanged.
The second stage is a hybrid work network of humans and Agents, where digital employees become formal nodes that can directly serve customers and employees.
Only the third stage is the AI organizational brain: interactions, memory, and Context between humans and Agents are deposited in a single central hub.
His judgment is restrained: Alibaba Cloud is still in the second stage, and the third stage remains a goal. Alibaba Cloud currently has roughly 600-plus digital employees. His business unit had about 2,500 people three years ago, originally run under a traditional functional structure, and was later reorganized into a three-tier network of business units. Now, digital employees and cross-team project Agents are also beginning to enter this network.
The manager's digital avatar is the most vivid case in this part.
The team distilled management experience through corpus distillation and harness engineering, and every day hundreds of employees proactively consult their digital twins. Bao Chenxing himself used Qianwen Office to prepare quarterly OKR feedback for more than 20 people: the AI read work materials and generated drafts, he first sent the content to employees for confirmation, then entered it into the system. Several colleagues told him, "This is the best feedback I've received since joining Alibaba," because it contained concrete work data instead of the one or two sentences of the past.
A new division of labor thus emerged in managers' work. AI expands information coverage, while managers calibrate judgment, confirm boundaries, and own the outcomes. The span of management can expand, but evaluation cannot be handed over to a black box.
The approach large organizations know best is to do top-level design first, then decompose it layer by layer and roll it out comprehensively.
Jiang Fang believes this path cannot be directly applied to AI transformation. Large enterprises have diverse business forms, and much of their work has not yet been fully digitized, so neither people nor AI have enough information to make a single global design.
She stated the second lesson clearly: "Let special zones try first—don't expect everyone to march in lockstep." She used "renovating an old city district" to describe large-organization transformation: rather than waiting for all conditions to be ready, it is better to give pioneering teams an independent space and let the bold run first.
The Qoder team is the example she cited. Five people developed QoderWork in 7 days—plans were discussed, decided, and revised the same day, with no cumbersome approvals and no need to pull in collaboration from outside the team. Ideas that typically take months to validate in the industry took them a week to run through one cycle, and they immediately went back to practice to make revisions.
The Qoder case directly illustrates the relationship between decision chains and experimentation speed. Problems, plans, decisions, and revisions all stayed within the same small team, so work didn't have to pass back and forth through departmental interfaces. The four conditions Jiang Fang previously listed—the top leader personally getting involved, highly datafied work, a team under 100 people, and mutual trust within the core team—are also more likely to coexist in such a unit.
There is also a broader experiment inside Alibaba Cloud. Bao Chenxing introduced that starting in February 2026, the team held an AI Maker Competition lasting 100 days, with all staff participating and everyone submitting a project. Projects could be work-related or not, letting employees start from interest and get hands-on. This also continues the principle he mentioned earlier: not turning token consumption into a mandatory metric for employees. The finals featured both a family companion robot dog and an AI video platform built by a Gen-Z management trainee.
Beyond the competition, new capabilities began entering two real cases with real customers. A short-drama platform had once hired an outsourcing team of 5 people for two months, and the delivered results were still unsatisfactory. A TAM technical service engineer discovered the problem, rebuilt the production platform in two weeks, re-orchestrated the workflow of models and video tools, and wrote a large number of Service modules.
Another salesperson was a liberal arts graduate with no coding experience. On a Friday, the customer's CTO asked to see a training platform proposal by the following Monday; two days later, what he delivered was already a runnable demo: hosts completed practice sessions in a simulated livestream room, with the AI evaluating, giving feedback, and scoring on the spot. The customer then wanted to continue building a product around this demo.
Kunlun Digital offered another approach for large enterprises at the roundtable forum. Facing the new FDE business, they built a relatively independent "1+N+X" system: "1" is the Industrial ModelScope empowerment platform, "N" is each business unit building professional capabilities such as agents, MCP services, and Skills, and "X" plans to establish joint innovation centers for about ten oil and petrochemical domains. This framework is still under construction, currently piloting in Lanzhou, with plans for full replication once it proves out.
The "special zone" here is not simply about having fewer people. Small teams have relatively complete goals, information, and decision-making authority, so work doesn't have to be shuttled back and forth between departmental interfaces. Qoder-style small teams first compress experimentation and decision-making into a short chain to validate direction as quickly as possible; at the diffusion stage, the proven methods must then be consolidated into shared platforms, professional capabilities, and business-facing scenario nodes. Without that second layer, small teams' experience is hard to reuse by other businesses.
The "special zone" also has boundaries. It cannot remain a showcase forever, and even less can it become an excuse to bypass safety and accountability. What a small team proves out must ultimately be integrated into real business, legacy systems, and formal structures of authority and responsibility.
At this point, the question moves into the third thing Jiang Fang discussed: restructuring workflows.
Jiang Fang compared "grafting AI tools onto old processes" to fitting a steam engine onto an old waterwheel: the power source changed, but the transmission mechanism remained the old one. She relayed OpenAI's research on site: frontier enterprises in the top 10% of AI usage consume 8.3 times more tokens per capita than average enterprises. OpenAI just released a set of data: leading enterprises opened up an 8.3x token gap in six months, with legal applications growing fastest at 108x—the gap comes not just from the number of calls. Frontier enterprises hand complete context, tools, and rules to agents, letting AI enter the workflow itself.
Jiang Linquan, Vice President and CIO of Alibaba Cloud Intelligence Group: product and R&D roles compressed to 2 types using Half-stack, and roles in the CIO team also changed
At the workflow level, Jiang Linquan dissected the end-to-end product and R&D process in even finer detail.
As mentioned earlier, a developer's time actually spent on code is only 20%. Refining further, core and complex code accounts for only 20% of that, while the other 80%—routine code—takes up no more than about 35% of coding time. By this calculation, without changing other collaboration stages in product and R&D, the portion where AI can improve efficiency only affects 7% of E2E time—extremely limited.
In early 2025, Jiang Linquan had already seen the limits of this kind of productivity improvement in product and R&D organizations. He returned to the essence of software engineering and rethought organizational problems in the AI era.
The classic software engineering work The Mythical Man-Month discussed a problem: adding more people to a delayed software project often doesn't make it faster. Newcomers need to understand context, veterans have to re-split tasks, explain, and mentor, and the larger the team, the faster communication links multiply.
Jiang Linquan feels AI has begun to change two important conditions: first, adding several agents to an engineer doesn't simultaneously add several interpersonal collaboration relationships—while single-node efficiency improves, communication nodes in the organization also decrease. Second, moving from one role to another specialty usually requires long training, but AI can reduce learning costs to as low as 1% of the original, which also creates conditions for reducing communication nodes.
These directly changed the original division of labor in product and R&D organizations. So the Alibaba Cloud CIO's product and R&D team took the lead last year in completing role reshaping: using the Half-stack concept, multiple roles were consolidated into two—one customer-facing, one engineering-facing.
Customer-facing: product managers, designers, and frontend were merged into the PDFE role, responsible from user intent all the way to user interface architecture—pushing requirements confirmation to the extreme "left." Engineering-facing: backend and architecture were also merged into the ABE role, responsible from data structures all the way to system stability—pushing quality responsibility to the extreme "left." The two sides connect through API contracts, which greatly improved the overall interaction chain.
Scaling up from product and R&D organizations to the entire company's business systems is an organizational problem of much larger scale.
Since this year's lobster craze began, general-purpose agents have been continuously exploding. Jiang Linquan found the same problem: general-purpose agents have indeed improved individual office productivity, but the organization's overall E2E has not significantly improved.
His CIO team began in February this year to fully prepare the foundation for opening business systems to agents, but opening systems brings another layer of pressure: general-purpose agents open browsers on their own, retry at high frequency, consume tokens at high speed, and can "flood" the company's business systems. The underlying permissions and security issues are crucial. Jiang Linquan's principle for opening up is: when agents act on behalf of employees, they must use the employee's original permissions.
In four months, data from the business systems he is responsible for showed: general-purpose agent call volume grew 1,000-fold, quickly surpassing GUI interactions and the original first-party agents.
Here he gave an example of product commercialization: configuration that used to take two months is now shortened to half a day using skills. He particularly emphasized that merely opening up MCP is far from enough—you must also reverse the process, return to the real business, re-sort workflows, unify business concepts, and sort out platform permissions, investing enormous effort in these invisible "dirty, tedious jobs." You must also help many previously "offline" business activities catch up by going "online."
Only when these things are done can business systems truly open up to agents, and long-pent-up business demands can be fully unleashed.
With that, the CIO's role also changes: business demands, which used to be centrally routed to the CIO team to wait for scheduling, now see business teams closing the loop themselves on simple, vertical demands, while the CIO continues to support complex, cross-domain special projects; meanwhile, a new "developer operations" role has been added — Jiang Linquan treats business teams as new developers, and his team assigns a developer operations person to each business team to help them solve development challenges.
In other words, the CIO line must both maintain data consistency, permissions, and the open platform, and help these "broadly defined developers" turn their ideas into processes that run smoothly.
On the much-discussed topic of AI native, Jiang Linquan's understanding is that an AI Native organization is more like a "state": as AI capabilities keep evolving, teams must constantly revolutionize themselves — inwardly breaking old mental seals and pushing past the organization's limits on efficiency and value realization, and outwardly exporting battle-tested products to help more enterprises transform with AI — the Rui series is exactly such a set of products, distilled from the Alibaba Cloud CIO team's internal practice and deliverable externally, covering "Rui Zhaobao" for recruiting, "Rui Yibao" for translation, and more.
Yuan Yimin, VP of HR at Alibaba Cloud Intelligence Group: Writing the context of the sales front line into organizational weights
Yuan Yimin, VP of HR at Alibaba Cloud Intelligence Group, sharing on site
Yuan Yimin turned the lens to Alibaba Cloud's international business. Her organization covers both product/R&D and sales, and the difficulty of AI-ifying these two types of work is very different: code can naturally be read by machines, but sales' key judgments are scattered across visit sites, a customer's moment of hesitation, and employees' experience.
She broke the AI organization into six interconnected elements: architecture, information, decision-making, learning, people, and supporting environment. In her words, "it's not six puzzle pieces, it's a chain."
At the architecture layer, she proposed first determining a single objective function. In the past, online advertising, offline events, and market intelligence each carried their own KPIs — local metrics were all met, but it was hard to say how many business results the leads actually brought. Alibaba Cloud's international business began making these stages jointly point to lead conversion ROI, then adjusting task routing based on real contribution. Division of labor is no longer only written into job descriptions; it also changes with outcome data.
On information, Yuan Yimin used a very distinctive phrase: "From Facts to Contexts, written into weights." CRM, BI reports, and approval forms mostly store structured conclusions, while the raw field context that decisions depend on has long never entered the system. Now, agents can cheaply complete listening, transcription, and summarization, with humans judging which information matters. Only then can an enterprise's unique unstructured context have the chance to become its post-training data.
Experience in employees' heads and on their local computers remains privately held weights. Yuan Yimin said: "When the person leaves, the weights are zeroed out." Only by continuously writing field perception into organizational systems will an enterprise form its own long-term memory. This also connects to the Context interconnection Jiang Fang mentioned.
Yuan Yimin also used a set of numbers to explain decision cost: 5 people in a review produce 10 communication links, while 15 people increase that to 105. The most expensive hidden cost in an organization is attention. She advocates layered decision-making by scenario, compressing the management view, and drilling down into the full Context only when necessary.
The most important thing in organizational learning is finding stages that come with "built-in referees," which also relies on verifiable outcomes and controlled experiments; otherwise, retrospectives easily write correlation up as causation.
On talent, she offered a sharp judgment: "Post-training is activation, not expansion." Products, tools, and processes can be taught, but judgment, reasoning density, and learning speed are hard to make up through onboarding. HR needs to identify what kind of "pre-training environment" a candidate has been through and what environment rewarded them; for the most frontier roles, one must also look at a person's self-learning and transfer speed.
The final roundtable was moderated by An Xiaopeng, VP of the Public Affairs line at Alibaba Cloud Intelligence Group. Li Xianqi, Party Committee Member and Deputy General Manager of CNPC Kunlun Digital, Ruan Zhongyou, CIO of Yihai Kerry Arawana, and Sun Dengke, Co-founder and CFO of Galbot, discussed AI Native organizations from three vantage points: industrial agents, large-enterprise IT, and embodied intelligence.
Roundtable discussion at the AI Native organization forum
The three guests offered very different ways of measuring. Li Xianqi looks at cost reduction, efficiency gains, output, and yield; Ruan Zhongyou looks at how much AI-generated code reaches production and how many marketing proposals are adopted by customers and continue on to sampling and transactions; Sun Dengke's north star metric is more distant — he hopes that within five years, "one person, in one month, can build a commercially viable robot." Token consumption can be an observation item, but cannot directly serve as the report card of organizational transformation.
Ruan Zhongyou made a wry remark: "Colleagues who truly understand AI are now canceling weekly meetings and weekly reports, while colleagues who don't understand AI are busy holding meetings about AI transformation." This points to leaders' way of working. Managers must know how to use AI themselves, and must think clearly about which part of the process it enters and what business problem it solves; when a project starts, an Evaluation must be set up first. He also introduced a recruiting experiment: have candidates use AI on the spot to accomplish something they had never done before, to observe their learning and transfer ability.
Galbot's approach picks up the "special zone" Jiang Fang mentioned. Small teams and young people can freely explore in different directions; once a direction shows value, the company quickly invests people, compute, and data. An Xiaopeng summarized this mechanism as "emergence, selection, replication": first let new ideas surface, then judge their commercialization prospects, and finally scale up what works. When to switch between distributed exploration and concentrated resources remains a judgment the organization must keep making.
Li Xianqi's case pushed AI into an industrial site with very high safety requirements. On the very day the 5.5-million-ton atmospheric and vacuum distillation unit at Lanzhou Petrochemical was connected to a diagnostic agent for anomalies, a real alert occurred. On-site experts judged that its root-cause analysis and handling recommendations were close to those of an engineer with more than five years of experience.
To bring the agent into this process, the team built tens of thousands of entity nodes and more than twenty thousand ontological relationships, and configured tools and permissions, verifiable loops, execution boundaries, human final decision-making, and full-process observability. The agent took 45 days to complete; under the old information-system development approach, it would have taken at least half a year, even seven or eight months. The cycle shortened, but safety standards did not loosen. What AI can see, how far it can execute, and when it must hand back to humans must all be written into the system in advance.
The roundtable's consensus was consistent: measure AI by real business value, have managers use it personally, redesign core processes, and let large enterprises have independent teams run first. The difficulties are equally clear: how high-risk industries handle model uncertainty, who is responsible for agent permissions and auditing, and whether FDEs are ultimately priced by project, by Token, or by business outcomes — all are still being tested. An Xiaopeng concluded that the most basic unit of the organization is expanding from person-to-person to person-and-digital-employee. The technology has already entered the workplace; the organizational mechanisms are still catching up.
At the end of her speech, Jiang Fang brought the topic back to talent. She relayed two important observations that Alibaba colleagues brought back after a recent study trip to Silicon Valley: NVIDIA still rigorously assesses values — the $5-trillion NVIDIA and its 5 values | worth a look for every company and every person — with business output and values each accounting for 50% of the total performance score; Anthropic conducts multiple rounds of training for its internal interviewers, including how to judge whether a candidate's values align with the company's mission.
From this she proposed "Talented but small ego." The AI era requires judgment and practical ability, and also people willing to pursue collective success and collaborate back-to-back with their teams. In the coming year, human colleagues will work alongside digital colleagues. Digital colleagues have no Ego; humans must learn to share Context, cede some control, and help digital colleagues do better.
Thus the AI Native organization becomes a problem for both the CPO and the CIO. The CIO must make systems, data, and permissions agent-friendly; the CPO and HR must adjust mechanisms for managers, talent, collaboration, and evaluation; business leaders provide real scenarios and are accountable for results. If any one of the three is missing, AI can only stay at local efficiency gains.
The signal from this forum is more concrete: AI is moving from employees' toolkits into managers' daily work, and is beginning to touch enterprises' processes, systems, talent, and accountability. The CPO and CIO co-hosting a forum for the first time is only a beginning. The gap going forward will come down to who can make people, agents, data, permissions, and business goals run within the same working system.
Technology keeps advancing, and organizations continue to evolve. Let us close with a quote from CPO Jiang Fang's speech, as encouragement to all:
"No one has the answers; the only way is to throw ourselves fully into this exploration. We will eventually find a path that lets AI truly penetrate the fabric of the organization and everyone's work."
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