The competition in enterprise AI is ultimately not just a contest of model capabilities, but also a test of context quality, collaboration foundations, and organizational execution.
What the automotive industry chain has validated is a proposition applicable across industries: the key to enterprise AI competition lies not in the models themselves, but in the quality of context, the foundation of collaboration, and the organization’s practical capabilities.
Feishu AI Productivity Conference 2025 – Automotive Industry Special Session Held
On September 3, Feishu hosted the "AI Driving the Chain, Whole and Parts Renewed – Feishu AI Productivity Conference 2025 – Automotive Industry Special Session" in Shanghai. Nearly 300 executives, industry experts, and digital transformation leaders from OEMs, parts suppliers, and related sectors gathered to discuss R&D system transformation, supply chain collaboration, production and delivery, and enterprise AI implementation. Representatives from organizations including the China Society of Automotive Engineers, SAIC Motor, NIO, Baolong Technology, Dongfeng eπ, and Sunway Electronics attended, along with Qiu Wei, Chief Engineer of the Shanghai Commission of Economy and Informatization, Feishu CEO Xie Xin, and Feishu Commercial Vice President Zhang Di.
"AI Smart Manufacturing Takes Flight Together" Manufacturing Efficiency Acceleration Program Launched
At the event, Feishu responded to Shanghai's "AI + Manufacturing" strategy by launching the "AI Smart Manufacturing Takes Flight Together" initiative together with industry partners, officially unveiling the 2026 "AI Smart Manufacturing Takes Flight Together" Manufacturing Efficiency Acceleration Program. Starting in Shanghai, the program aims to accelerate the integration of AI capabilities, industry methodologies, and benchmark practices into real-world manufacturing scenarios.
From New Players to Full Industry Chain: Feishu Builds Collaboration Advantages in the Automotive Sector
As the automotive industry shifts from individual vehicle competition to organizational competition and expands from domestic to global markets, a company’s speed is no longer determined solely by a single product or technology, but also by whether the entire organization and supply chain can respond rapidly. An engineering change, material anomaly, or quality issue often spans multiple departments, systems, and enterprises—where the slowest link in the chain may delay overall vehicle development and delivery.
Zhang Di explained on site that since 2021, Feishu’s reach in the automotive sector has expanded from leading new-energy vehicle startups to large automotive groups, leading parts manufacturers, and multinational automakers. Among the top 10 Chinese listed automakers by market capitalization in 2025, nine have adopted Feishu at the group, research institute, new energy brand, or relevant business unit level. Over 70% of the top 30 new-energy vehicle companies use Feishu. Major OEMs such as SAIC and Seres, and supply chain companies including Sunny Optical, Horizon Robotics, Desay SV, Baolong Technology, Linglong Tire, Wanxiang Qianchao, and Sunwoda, all use Feishu. Feishu’s automotive clients also include multinationals such as Valeo, Bosch, and Toyota Research. Today, Feishu serves not only individual organizations but also connects OEMs and parts suppliers across the value chain.
Entering Core Processes, Accumulating Enterprise Context
More importantly, Feishu is now integrated into core processes such as R&D, production, quality, and supply chain. Without requiring companies to dismantle existing systems, Feishu leverages Feishu Projects, multidimensional tables, automation, the open platform, and related AI capabilities to transform scattered data into tasks, automatically feeding progress and outcomes back into the system. This allows process records, knowledge, and collaboration history to accumulate as enterprise context understandable by AI.
The automotive industry thus becomes a typical case for enterprise AI transformation: large-scale organizations, complex processes, long supply chains, and extremely high demands for safety, quality, and delivery. If AI can succeed within the automotive supply chain, it stands a strong chance of being replicated in other multi-party collaboration scenarios such as consumer electronics and smart manufacturing.
From R&D to Delivery: Enterprises Bring OEM-Parts Collaboration into Real Workflows
OEM-parts collaboration refers to deep, cross-boundary coordination between vehicle manufacturers and component suppliers across R&D, manufacturing, supply chain, and quality control. The challenge typically does not lie in internal process management, but in what happens when processes cross organizational boundaries—often reverting to meeting alignment, email attachments, and manual follow-ups. At this conference, NIO, Dongfeng eπ, Sunway Electronics, Baolong Technology, and SAIC Motor shared how AI and collaboration tools are being applied in real automotive workflows across material supply, new model development, order fulfillment, quality management, and R&D systems.
NIO introduced its IDM (Integrated Development Management) platform built on Feishu Projects, a collaborative industrial development management system for partners. The platform consolidates tasks, processes, deliverables, and risk information from hundreds of supply chain partners onto a unified development infrastructure. This enables project tasks to flow seamlessly across upstream and downstream enterprises, with progress, deliverables, and risk status continuously fed back into a single chain. It also creates continuous, traceable business context, laying the foundation for AI to assist in issue identification, risk alerts, and post-project reviews.
Dongfeng eπ shared its practice in material supply planning collaboration. Facing over 400 suppliers, Dongfeng eπ uses Feishu multidimensional tables, group robots, and automation to turn material shortage alerts into assigned tasks directed to responsible individuals. Suppliers provide online feedback on inventory availability, transportation status, and estimated arrival time, with responses consolidated into a single record. AI then analyzes response patterns, logistics data, and historical anomalies to identify root causes and drive review sessions. "Structured business data is where AI enters operations," said Dongfeng eπ.
Sunway Electronics demonstrated how to connect order data, workflows, and responsible personnel without replacing existing systems, focusing on short-delivery orders ("short交"). RPA automatically extracts data, Feishu multidimensional tables detect delivery exceptions and push tasks, automation handles overdue escalations, and relevant features of DOU+ Work generate weekly and monthly reports along with improvement tasks. According to internal documentation, the time required for short-delivery confirmation and approval was reduced by 19%, and customer complaints related to short deliveries dropped by 85%.
Baolong Technology Vice President Huang Junlin, speaking under the theme "People Stay Still, Data Moves," described the systematic upgrade of internal R&D collaboration at Baolong. Leveraging Feishu Projects, Baolong built a project management middleware platform connecting ALM, PLM, FMEA, and design tools, integrating APQP, engineering changes, review management, and risk control into a single system. Project schedules update automatically, engineering changes circulate online with full traceability, and past project risks are preserved as checklist items for new projects. According to Baolong’s internal assessment, this restructuring improved process standardization by 50% and enhanced information transparency and traceability by 30%.
Facing the profound shift in the automotive industry from single-technology competition to ecosystem-level competition, Wang Conghe, Dean of SAIC Motor’s Innovation Research and Development Institute, shared SAIC’s practices and insights on reshaping its R&D system in the AI era. He stated that vehicles are rapidly evolving from transportation tools into "super mobile intelligent spaces" and progressing toward embodied intelligence, necessitating a reconfiguration of R&D organizations, collaboration models, and industrial relationships. While AI enhancing individual efficiency is just the beginning, the future competitive edge lies in shifting R&D from "experience-driven by people" to "process-driven by systems" to achieve systemic efficiency gains.
Built on the Feishu collaboration platform, SAIC has advanced end-to-end R&D transformation in areas such as vehicle product changes, model project management, and OEM-parts collaboration, streamlining cooperation across organizations, resources, and the supply chain. For example, SAIC’s OEM-parts collaboration project with United Automotive Electronic Systems involves hundreds of engineers and 187 key process nodes, reducing planning and scheduling time from one to two hours down to just five minutes. "AI doesn’t lack computing power—it lacks context," said Wang Conghe. Only when real R&D processes are continuously documented can AI evolve from empowering individuals to empowering processes, organizations, and industrial ecosystems. Looking ahead, SAIC will collaborate with supply chain partners to build an open, cooperative, AI-native R&D system, forging long-term competitiveness in the intelligent vehicle era.
The practices of these companies point collectively to a fundamental shift: collaboration, once reliant on people repeatedly moving information and chasing updates, is now transitioning to process-driven task flows, automated data movement, and AI-powered early risk detection. The value of Feishu has thus evolved from internal organizational collaboration to enabling cross-enterprise collaboration between OEMs and parts suppliers on joint projects.
Feishu Holds the Context, DOU+ Work Turns Context into Productivity
At the event, Feishu CEO Xie Xin highlighted in his keynote that most tasks in enterprises do not occur in isolated tools, but span documents, messages, meetings, spreadsheets, workflows, and human collaboration. As Agents enter enterprises, work styles will shift from "humans using software" to "humans and Agents collaborating within the same workspace." Feishu provides the environment where work happens—along with knowledge, processes, and permissions—making the collaboration platform a critical infrastructure for Agents to understand context, invoke tools, and work alongside humans.
On August 25, ByteDance officially launched its new Agent product and brand, DOU+ Work. It can write documents, create spreadsheets, make PPTs, operate browsers and local computers, and be used directly within Feishu. This defines the relationship between Feishu and DOU+ Work: Feishu hosts messages, documents, meetings, calendars, tasks, and business processes, allowing knowledge, collaboration, and decision records to accumulate continuously within permission boundaries. DOU+ Work, upon authorization, leverages this context to invoke tools and complete tasks. They 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 actionable Agents, context cannot be fully transformed into productivity.
The newly launched Manufacturing Efficiency Acceleration Program will support Shanghai-based manufacturers through enterprise-grade AI capability trials, model invocation support, OEM-parts collaboration incentives, transformation assessments, scenario templates, benchmark visits, and policy coordination. Through mechanisms such as industry summits, benchmark site visits, AI Pioneer Competitions, enterprise diagnostics, and consultant-led coaching, the program extends a single launch into sustained implementation.
From a collaboration tool for new-energy automakers to a business platform used jointly by major OEMs, parts suppliers, and multinational corporations, Feishu’s role in the automotive industry is transforming. It not only helps individual organizations improve efficiency but also connects upstream and downstream players in the industrial chain, accumulating enterprise context that is understandable, actionable, and traceable by AI.
What the automotive industry chain is validating is a proposition applicable to many more industries: the competition in enterprise AI is ultimately not just about model capabilities, but about the quality of context, the foundation of collaboration, and the strength of organizational execution.
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