5 Selection Criteria Explained
The KHB AI Super Brain Head Plan employs a rigorous selection evaluation system, comprehensively judging from five dimensions: industry position, data assets, tech foundation, team capability, and collaboration willingness. Only enterprises meeting all five criteria can enter the formal cooperation negotiation phase. These standards are not set to create barriers, but to ensure every cooperation project has sufficient foundation for success.
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Industry Position Criteria
Top 3 in Niche Track · Revenue Scale · Market Share
Industry position is the primary threshold for joining the Head Plan. We only partner with true leading enterprises in each niche track, because industry leaders possess the deepest industry knowledge, the most industry data, and the strongest industry influence. Only such partners can support the positioning of "industry AI infrastructure."
Top 3 in Niche Track: Enterprises must rank in the top three in their niche track. The niche track here is not a broad industry classification, but a specifically defined vertical field. For example, in the medical aesthetics track, we distinguish between chain aesthetic institutions, aesthetic device manufacturers, and aesthetic consumable brands—selecting only the leading enterprise in each sub-track. Judgement is based on third-party industry reports, market research data, and public ranking information.
Revenue Scale: Enterprises must have a certain revenue scale, which is both a direct reflection of industry position and a basic guarantee for subsequent joint venture investment. Revenue thresholds vary by track, generally requiring annual revenue of over 500 million RMB, with appropriate flexibility for emerging tracks. Revenue scale represents commercial maturity and risk resistance capability, serving as an important guarantee for cooperation stability.
Market Share: The enterprise's market share in its core business area must reach a certain proportion, typically requiring 10% or more. High market share means the enterprise has numerous customer touchpoints and business scenario data—these data are the core fuel for training industry AI models. Meanwhile, high market share also means the enterprise has the ability to promote AI standards within the industry.
2
Data Assets Criteria
Data Volume · Data Quality · Data Compliance
Data is the core production material of the AI era and the most valuable asset of industrial AI infrastructure. Without sufficient high-quality data, even the most advanced algorithms cannot generate genuine industry value. Our evaluation of data assets covers three levels: data volume, data quality, and data compliance.
Data Volume: Enterprises need to have business data of sufficient scale, including customer data, transaction data, operational data, service data, and more. The larger the data volume, the better the AI model training effect, and the richer the business scenarios that can be covered. We evaluate data volume not just by record count, but more importantly by dimensional richness and time span. Generally, enterprises are required to have 3+ years of structured business data accumulation, with tens of millions of data records or more.
Data Quality: Data quality is more important than data volume. Low-quality data not only fails to help AI models learn, but can actually mislead them. Our data quality evaluation dimensions include: data completeness (field missing rate), data accuracy (error rate), data consistency (consistency of the same entity across different systems), data timeliness (update frequency), etc. High-quality data assets are an important indicator of enterprise digital maturity and a key prerequisite for AI project success.
Data Compliance: Data compliance is an insurmountable red line. Enterprise data assets must comply with the requirements of relevant laws and regulations such as the Data Security Law and Personal Information Protection Law. We evaluate the enterprise's data governance system, data security measures, user authorization mechanisms, etc. Only by ensuring data compliance can subsequent AI training and applications proceed safely and sustainably. For industries involving sensitive data, compliance requirements will be even stricter.
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Tech Foundation Criteria
Existing IT Systems · Digital Maturity · Tech Team
Industrial AI construction doesn't start from scratch—it's an upgrade and leap based on the enterprise's existing digital foundation. The enterprise's current IT systems, digital maturity, and technical team directly determine the speed and depth of AI implementation. We don't require enterprises to have cutting-edge AI technical capabilities, but we do require a solid digital foundation.
Existing IT Systems: Enterprises need relatively mature IT systems supporting business operations, such as ERP, CRM, OA, business management systems, etc. These systems are both the source of data and the carrier for AI capability implementation. The more complete the systems and the smoother the data flow, the lower the cost and the better the effect of AI integration. We evaluate the coverage, integration, and data connectivity of existing systems.
Digital Maturity: The enterprise's core business processes need a high digitalization rate, with key business links already online and data-driven. Enterprises with high digital maturity have employees who are more receptive to new technologies, encounter less resistance to organizational change, and implement AI applications faster. We evaluate digital maturity through indicators such as digital investment ratio, online business proportion, and the degree of data-driven decision-making.
Tech Team: Enterprises need their own IT or technical team that can effectively interface and collaborate with KHB's technical team. The technical team doesn't need to be large in scale, but needs core personnel who understand both business and systems. After the joint venture is established, the enterprise side needs to send technical backbone personnel to participate in the co-building, deeply integrating with KHB's AI team to jointly complete the construction and operation of industry AI infrastructure.
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Team Capability Criteria
Executive Cognition · Execution Team · Change Capability
All cooperation is ultimately cooperation between people. Even the grandest strategic vision requires an excellent team to execute on the ground. Our evaluation of team capability unfolds from three levels: executive cognition, execution team, and change capability, because industrial AI co-building is a profound organizational transformation requiring full recognition and promotion from top to bottom.
Executive Cognition: The enterprise's core decision-makers (Chairman/CEO) need deep cognition of AI's industrial value, truly understanding the strategic significance of the Head Plan, rather than treating it as an ordinary technology project or marketing gimmick. The cognitive level of executives determines the ceiling of cooperation. What we need are entrepreneurs who take industrial AI as the core future strategy of the enterprise, not followers chasing trends. Through in-depth exchanges with executives, we can judge their cognitive level and strategic determination.
Execution Team: Enterprises need a strong execution team that can translate strategic decisions into concrete actions. The execution team needs to include core roles such as business负责人, technical负责人, and operations负责人. This team needs both industry experience and an open learning mindset, able to collaborate efficiently with KHB's team. The capability of the execution team directly determines the speed and quality of project progress.
Change Capability: Industrial AI construction is not about patching up existing business—it's a profound business transformation and organizational change. Enterprises need the determination and ability to drive organizational change, able to break down internal departmental walls, adjust利益 patterns, and establish organizational mechanisms adapted to the AI era. Change capability includes organizational flexibility, resource allocation ability, cultural openness, etc. Enterprises that have successfully driven major transformations in history will receive bonus points in this criterion.
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Collaboration Willingness Criteria
Strategic Alignment · Resource Investment · Long-Term Commitment
The Head Plan is not a one-off transaction, but a deep binding lasting years or even decades. The strength of collaboration willingness directly determines whether the partnership can survive cycles and withstand tests. What we seek are strategic partners who truly identify with the shared vision, are willing to go all-in, and commit to long-term investment.
Strategic Alignment: Enterprises need to highly identify with the vision and philosophy of the KHB AI Super Brain Head Plan,认同 the positioning of "co-building industrial AI infrastructure," and认同 the values of long-termism. Strategic alignment is not just verbal表态, but reflected in understanding of the cooperation model, attitude toward benefit distribution, views on short-term gains and losses, etc. Only partners with highly aligned strategies can pull together in difficulties rather than blaming each other.
Resource Investment: Enterprises need both the willingness and ability to invest necessary resources, including data resources, human resources, channel resources, financial resources, etc. Co-building is not KHB's one-sided affair—it requires both parties to invest real resources. We don't require the more investment the better, but we require investment matching the enterprise's strength and matching the cooperation goals. The determination to invest resources is the most direct manifestation of cooperation sincerity.
Long-Term Commitment: The construction of industrial AI infrastructure is not an overnight matter—it requires 3-5 years or even longer of continuous investment and iteration. Enterprises need willingness for long-term commitment, not pursuing short-term monetization, not wavering because of temporary difficulties. We have designed corresponding equity lock-in periods and incentive mechanisms to ensure long-term binding of both parties' interests. Only enterprises willing to be friends with time can truly enjoy the long-term dividends of industrial AI.