The competition in enterprise AI is ultimately not just a contest of model capabilities, but rather a race in context quality, collaboration infrastructure, and organizational execution.
The automotive industry chain is validating a proposition applicable to many other industries: the competition in enterprise AI ultimately hinges not only on model capability, but more critically on context quality, collaboration foundation, and organizational action capacity.
From New-Energy Automakers to the Entire Industry Chain
Feishu Builds Collaborative Advantages in the Automotive Industry
As the automotive industry shifts from single-vehicle competition to organizational competition and expands from the Chinese market to global markets, what determines an enterprise's pace of development is no longer just a product or technology, but also whether the entire organization and supply chain can respond quickly. An engineering change, material anomaly, or quality issue often spans multiple departments, systems, and companies—wherever the slowest link exists along the chain, it could delay overall vehicle development and delivery.
Zhang Di, Vice President of Commercialization at Feishu, introduced that since 2021, Feishu’s service scope has expanded from leading new-energy automakers to large automotive groups, leading component manufacturers, and multinational automakers. Among the top 10 publicly listed automakers in China by market capitalization in 2025, nine have adopted Feishu; among the top 30 new-energy vehicle makers, over 70% have chosen Feishu. Entire vehicle manufacturers such as SAIC Motor and Seres, alongside supply chain enterprises including Sunny Optical, Horizon Robotics, Desay SV, Baolong Technologies, Linglong Tire, Wanxiang Qianchao, and Sunwoda, all use Feishu. Clients also include multinational corporations such as Valeo, Bosch, and Toyota R&D. Today, Feishu serves not just individual organizations, but possesses the collaborative foundation to connect vehicle OEMs with component suppliers across the industry chain.
More importantly, Feishu is now deeply embedded into core processes such as R&D, production, quality control, and supply chain management. Without requiring enterprises to dismantle existing systems, Feishu leverages features like Feishu Projects, multi-dimensional tables, automation, open platform, and AI capabilities to transform fragmented data into actionable tasks, automatically feeding back progress and outcomes, thus solidifying process flows, knowledge, and collaboration records into enterprise context that AI can understand.
The automotive industry has become a typical case for enterprise AI transformation: large-scale organizations, complex processes, long industry chains, and extremely high demands for safety, quality, and delivery. If AI can succeed within the automotive industry chain, its experience can potentially be replicated across consumer electronics, smart manufacturing, and other multi-party collaboration scenarios.
From R&D to Delivery
Enterprises Advance OEM-Supplier Collaboration into Real Processes
OEM-supplier collaboration refers to deep coordination between vehicle manufacturers and component partners across organizational boundaries in areas such as R&D, manufacturing, supply chain, and quality control. The challenge lies in how cross-enterprise workflows often degrade into meeting alignment, email-based file transfers, and manual follow-ups. At this conference, NIO, Dongfeng Yipai, Shengyi Electronics, Baolong Technologies, and SAIC Group demonstrated how AI and collaboration tools are being integrated into real business operations.
NIO built a Partner Industrial Development Management (IDM) platform based on Feishu Projects, centralizing task management, workflows, deliverables, and risk information from hundreds of supply chain partners. Project tasks flow seamlessly between upstream and downstream enterprises, with continuous synchronization of progress, deliverables, and risk status, forming a continuous and traceable business context. This lays the foundation for AI to participate in issue identification, risk alerts, and project reviews.
Dongfeng Yipai shared its practice in collaborative material supply planning. Facing over 400 suppliers, they used Feishu multi-dimensional tables, group bots, and automation to convert material shortage alerts into targeted tasks. Suppliers provide online feedback on inventory readiness, vehicle dispatch, and estimated arrival time, with all responses consolidated into a single record. AI then performs root cause analysis based on response data, transportation logs, and historical anomalies, driving review processes. Dongfeng Yipai stated: "Structured business data is where AI enters operations."
Shengyi Electronics tackled the issue of short delivery cycles, connecting order data, workflows, and responsible personnel without replacing existing systems. RPA automatically extracts data, Feishu multi-dimensional tables identify delivery exceptions and push tasks, automation handles overdue escalations, and Doujia Work generates weekly reports, monthly summaries, and improvement tasks. Relevant data shows that the time spent on confirming and approving short deliveries decreased by 19%, and customer complaints related to short deliveries dropped by 85%.
Baolong Technologies’ Vice President Huang Junlin, under the theme “People stay still, data moves,” presented a systematic upgrade of internal R&D collaboration. Leveraging Feishu Projects, Baolong established a project management middleware platform integrating ALM, PLM, FMEA, and design tools, bringing APQP, engineering changes, review management, and risk control into one unified system. Schedule changes trigger automatic updates, engineering changes flow online with full traceability, and risks from past projects become checklist items for new ones. According to evaluation, process standardization improved by 50%, and information transparency and traceability increased by 30%.
Wang Conghe, Dean of SAIC Group’s Innovation Research and Development Institute, pointed out that the automotive industry is shifting from single-technology competition to ecosystem-level competition. Vehicles are rapidly evolving into "super mobile intelligent spaces" and progressing toward embodied intelligence, necessitating a complete restructure of R&D organizations, collaboration models, and industrial relationships. While AI enhancing individual efficiency is just the beginning, the future competitive edge lies in transforming R&D from being "driven by human experience" to being "process-driven," thereby achieving systemic efficiency gains.
Built upon Feishu’s collaborative foundation, SAIC has advanced end-to-end R&D transformation in areas such as whole-vehicle product changes, model project management, and OEM-supplier collaboration, establishing seamless coordination across organizations, resources, and the supply chain. For example, SAIC’s joint project with United Electronic Technologies involves hundreds of engineers and covers 187 key process nodes, reducing schedule synchronization time from one to two hours down to just five minutes. Wang Conghe stated: "AI does not lack computing power—it lacks context." Only through the continuous accumulation of authentic R&D processes can AI evolve from empowering individuals to empowering workflows, organizations, and entire industrial ecosystems. In the future, SAIC will collaborate with supply chain partners to build an open, collaborative, AI-native R&D system, forging long-term competitiveness in the era of intelligent vehicles.
The practices of these companies collectively reveal a trend: the traditional way of collaboration—relying on manual information transfer and repeated status chasing—is giving way to a new model driven by automated workflows, self-flowing data, and AI-powered early risk warnings. The value of Feishu is likewise expanding—from internal enterprise collaboration to enabling industry-wide collaboration between OEMs and component suppliers to jointly execute projects.
Feishu Hosts Context
Doujia Work Transforms Context into Productivity
Xie Xin, CEO of Feishu, noted in his keynote speech that most tasks within enterprises do not occur in isolation, but span documents, messages, meetings, spreadsheets, workflows, and interpersonal collaboration. As Agents enter enterprises, work styles will shift from "humans using software" to "humans and Agents completing tasks together within the same environment." Feishu provides the setting—knowledge, processes, and permissions—where work happens, becoming critical infrastructure that enables Agents to understand context, invoke tools, and collaborate with people.
On August 25, ByteDance officially launched its new Agent product and brand, "Doujia Work." It can write documents, create spreadsheets and presentations, operate browsers and local computers, and be used directly within Feishu. Feishu hosts messages, files, meetings, calendars, tasks, and business processes, ensuring that knowledge, collaboration, and decisions continuously accumulate within defined permission boundaries. Doujia Work, after authorization, leverages this contextual data to invoke tools and execute tasks. The two are not substitutes, but two sides of the same coin in enterprise operations and AI transformation: without continuously accumulated context, Agents cannot understand business; without action-capable Agents, context cannot be fully transformed into productivity.
The newly announced 2026 "AI Smart Manufacturing, Together We Fly" manufacturing efficiency acceleration program will support Shanghai-based manufacturers through enterprise-grade AI experience, model invocation support, OEM-supplier collaboration incentives, transformation assessments, scenario templates, benchmark visits, and policy alignment. Through mechanisms such as industry summits, benchmark site visits, AI Pioneer Competitions, enterprise diagnostics, and consultant-led coaching, the program aims to drive sustained implementation of AI capabilities.
From being a collaboration tool serving new-energy automakers to becoming a widely adopted business platform among vehicle groups, component enterprises, and multinational automakers, Feishu’s role in the automotive industry continues to deepen. It not only helps individual organizations improve efficiency but also begins to connect upstream and downstream players across the supply chain, accumulating enterprise context that is understandable, callable, and traceable for AI.
The automotive industry chain is once again validating a proposition relevant to many industries: the competition in enterprise AI is ultimately not just about model capabilities, but more fundamentally about context quality, collaboration foundation, and organizational action capacity.