Frost & Sullivan was invited to attend the 2026 China Glasses Industry Innovation and Development Conference to share insights on building brand moats in the AI era

In his speech, Wang Peng systematically outlined the evolution of brand competition logic, proposing the establishment of a real and traceable AI-friendly knowledge system to guide the industry from unregulated growth to a new stage of long-term compliance. He highlighted the key insights from Frost & Sullivan’s latest “2026 China AI Brand Asset Development White Paper” and reviewed the brand competition logic during the “search era” over the past two decades. Users first search for information, view links, then click, compare, filter, and make decisions. The core aspects of brand competition are visibility, traffic, click-through rate, and conversion rate, with the ultimate goal being “to be seen”. In the AI era, AI recombines information, and users receive preliminary answers before clicking. Thus, the focus shifts to system recognition, understanding, and proper citation of key issues, aiming to “be recognized, understood, and expressed properly by the system”.

Wang Peng emphasized that the biggest challenge for brands in the AI era is not the traditional issue of “invisibility”, butcognitive dilutionand implicit exclusion, where brands gradually disappear or are misrepresented in AI-generated answers. To address this, Frost & Sullivan introduced the new concept of “AI Brand Asset (AIBE)”, which refers to the overall value of a brand being accurately recognized, naturally recalled, consistently expressed, and credibly cited in mainstream AI large models and application scenarios. The white paper further constructs a three-level cognitive framework for AI Brand Assets. The first level is “being discovered”, where the system can accurately identify the brand’s identity and category; the second level is “being understood”, where the system truly grasps the brand’s boundaries, applicable scenarios, product features, and service characteristics; the third level is “being cited”, where in high-value, high-decision-making, and high-risk scenarios, the system has sufficient credible evidence to present and explain the brand.

Regarding specific implementation paths, Wang Peng pointed out that the industry urgently needs to move from conceptual hype to practical construction methods, helping brands establish three key infrastructures. First,Trusted Knowledge Network, which is a structured knowledge system that is real, verifiable, traceable, and easy for AI to understand; second, authoritative high-quality corpora, which are high-quality knowledge supply systems based on product boundaries and compliance information; third, accurate asset systems, which are information assets that are recognized, analyzed, cited, and reused by the system. Wang Peng stressed that in the AI era, brands must not only manage consumer mindsets but also pay attention to their information status in AI scenarios. They should shift from traditional brand asset models focused on creating communication materials, one-way advertising copy, and emotional imagery to building brand fact libraries, answer assets, citation assets, and trusted knowledge networks.

Wang Peng also introduced the AI Brand Asset Performance Index System (AIBV). This system uses a “dual-engine” evaluation framework that serves both brand growth and brand governance. The left engine “brand growth” measures the breadth of recognition and recall, focusing on market presence and awareness, helping companies understand “how much exists”; the right engine “brand governance” measures the accuracy of expression and citation, focusing on risk prevention and data compliance, helping companies determine “whether it appears accurately, consistently, and credibly”. The core mission of AIBV is to help companies accurately diagnose four major governance issues. First, misinterpretation risk prevention: whether the brand is misunderstood by AI models, misattributed, or overstated; second, incorrect citation risk screening: whether wrong parameters or false brand cases are used as decision-making bases in AI-generated conclusions; third, expression consistency verification: whether there are serious conflicts and inconsistencies in brand statements across different mainstream large models and application scenarios; fourth, credibility authority measurement: whether authoritative sources and third-party verifications supporting brand statements are missing in AI scenarios.

Finally, Wang Peng noted that the significance of the white paper lies not only in discussing phenomena but also in establishing long-term development guidelines, guiding the industry from the unregulated growth phase of “simple conceptual hype and blind competition with system algorithms” to the principle phase of “healthy language systems, rigorous evaluation frameworks, and realistic construction directions”. AsAI glassesand other smart wearable devices are included in national subsidy policies, and industry standards are officially released, the optical industry is accelerating from “single-product breakthroughs” to “ecosystem construction”. In this process, brands must not only manage consumer mindsets but also pay attention to their information status in AI scenarios. The complete system proposed by Frost & Sullivan, including KNIT as the foundation, GEO as the implementation tool and operational mechanism, AIBV as the evaluation tool and framework, and AIBE (AI Brand Asset) as the top-level goal, will help optical enterprises build strong brand moats in the AI era.