The Transformational Mindset in the AI Era
release time：2025-06-05
GAO FENG ADVISORYJune 2025
Ⅰ. The Rapid Transformation of AI Technology
We are entering a new AI era with unprecedented technological acceleration: AI has evolved from basic chatbots with simple language capabilities to reasoning engines solving complex problems, and is moving toward autonomous agents with self-planning and execution abilities. At the cutting edge, AI is expanding into scientific innovation and task coordination, paving the way for systems with collective intelligence and autonomous collaboration. From ChatGPT’s debut to the widespread use of reasoning models like DeepSeek, each model upgrade unlocks entirely new application scenarios.
This AI era differs fundamentally from the previous digital age in three core dimensions:
From data analytics to knowledge synthesis: Unlike the internet era’s reliance on big data algorithmic analysis and process automation, AI autonomously extracts knowledge and insights from massive datasets—intelligence derived from training on billions of users’ behavioral data, mirroring human collective behavior. It enables efficient capture of organizational tacit knowledge, supports strategic judgment under uncertainty, and realizes data-empowered intelligent decision-making.
Fewer architectural constraints, higher autonomous intelligence: Advanced large model algorithms exceed the boundaries of human explicit logical definition. Enterprises can leverage AI to autonomously process massive variables and adaptively seek optimal solutions, breaking through traditional application limitations and responding agilely to complex, unpredictable scenarios with end-to-end intelligence.
Ⅱ. Building Future Strategies and Competitive Advantages in the AI Era
AI is reshaping enterprise strategic direction with new paradigms, far surpassing the disruption of the digital/internet eras. Based on Gao Feng Advisory’s "The Third Way of Business Strategy" framework (enterprise boundaries = dynamic balance of opportunity and capability), AI has fundamentally transformed both dimensions, prompting a profound redefinition of enterprise boundaries.
On the Opportunity Side: From Efficiency Optimization to New Business Models
AI not only reduces operational and management costs but also spawns entirely new business models. The definition of "opportunity" has expanded beyond niche market penetration or new product launches to cross-industry, cross-ecosystem growth and industrial reconstruction. More critically, AI enables enterprises to redesign customer value propositions, explore new revenue streams, and reshape industry rules.
NEV industry example: "Automobile + AI" transcends autonomous driving optimization, creating AI-driven products across human-vehicle-home scenarios. Competitive logic has shifted from product-market fit to data-intelligence co-creation, and from linear expansion to nonlinear leaps.
Li Auto case: As a cross-sector NEV entrant, Li Auto leverages AI to expand from intelligent vehicles to humanoid robotics and other new verticals, repositioning itself from a carmaker to a comprehensive intelligent technology company and unlocking blue-ocean markets beyond traditional automotive boundaries.
On the Capability Side: From Individual Knowledge to Scalable System Capabilities
In the past, enterprise core capabilities often relied on the tacit knowledge of a few senior employees—knowledge that was hard to codify and transmit. AI has fundamentally transformed this by systematically extracting, structuring, and disseminating individual knowledge across the organization, enabling rapid capability replication and large-scale application.
Mengniu case: The dairy giant deploys AI agents across its entire value chain (production, supply chain, sales, R&D, management), with capabilities such as raw material quality forecasting, intelligent production scheduling, precision marketing, and AI-driven R&D recommendations. It has shifted from a product-centric to a consumer-operations-centric model, e.g., integrating AI and IoT to build a digital cooler network for real-time consumer engagement and continuous business model innovation.
AI enables enterprises to build on expert knowledge, achieve capability replication via data collection, knowledge modeling, and feedback refinement, and leverage AI’s scaling law (model performance improves with data volume, parameters, and computing power) for nonlinear functional growth. This creates a pattern of lower marginal costs and stronger marginal capabilities, helping enterprises overcome growth pain points like "headcount growth without proportional revenue".
The New Logic of Competition: From Knowledge Advantage to Cognitive Advantage
AI drives knowledge democratization: competitors can quickly acquire proprietary expertise through AI tools, eroding traditional knowledge barriers and nullifying market advantages built on information asymmetry. In response, enterprise competition has shifted from possessing knowledge/technology to applying knowledge in unique contexts to generate insights and intelligence—cognitive advantage becomes the core of enduring competitiveness.
In the AI era, enterprise boundaries are no longer limited by existing assets/capabilities but by the integration of emerging market opportunities with creative and resource orchestration capabilities. Cultivating this new strategic mindset is essential for transformative growth.
Ⅲ. Why We Must Talk About the “AI Mindset” Now
Yahoo: Early to recognize algorithm/data-driven search but clung to human-curated directories, losing to Google’s PageRank algorithm.
Nokia: Held 40% of the global mobile phone market in 2007 but ignored smartphone-era platform ecosystem logic, focusing on vertical integration and technical specifications and being disrupted by agile competitors.
By contrast, successful enterprises adopt new cognitive frameworks:
Microsoft: Under Satya Nadella, shifted from "Windows-first" to "cloud-first and mobile-first", partnering early with OpenAI to lead AI innovation and achieve a "second growth curve".
Core Insight: Technological Disruption Redefines Resource Allocation Logic
Technological advancement not only reshapes products and services but also fundamentally alters how enterprises mobilize resources, scale operations, and drive growth:
Internet era: Expansion shifted from "customer growth synchronized with revenue" to "scale user base first, then monetize".
AI era: Dramatically fewer resource inputs are needed for the same revenue growth, requiring a fundamental rethink of traditional organizational structures, talent allocation, and decision-making processes.
Enterprises must cultivate an AI mindset—a sharp awareness of technological trajectories, and more importantly, a break from path dependency—to reinvigorate organizational vitality and strategic competitiveness, transitioning from passive followers to proactive leaders in the AI era.
Ⅳ. What is the “AI Mindset”?
The AI era brings fundamental changes to business logic: from the industrial age’s "operating assets" and the internet age’s "managing users" to "accumulating wisdom" and "creating insights". Enterprises’ core competency becomes leveraging AI to systematically extract explicit/implicit/counterexample knowledge and generate unique insights. Meanwhile, production relationships are transforming: organizations evolve from human-dominated structures to carbon-based wisdom (humans) + silicon-based intelligence (AI) composite entities, with more flexible, task-oriented processes and flatter/atomized structures, placing higher demands on talent’s critical thinking, innovation, and transformational leadership.
An AI mindset is a systemic new thinking paradigm that goes beyond understanding AI technologies/applications, requiring comprehensive restructuring of strategic positioning, organizational architecture, business processes, talent models, and corporate culture. It includes six core dimensions:
1. Business Logic: From Resource Management to Insight Creation
With AI’s widespread adoption, traditional knowledge barriers and experiential competency diminish due to knowledge democratization. Enterprises’ true competitive advantage lies in continuously generating knowledge and creating unique insights (distinguishing: data = raw materials, knowledge = structured cognition from data, insights = underlying patterns/principles). AI-driven knowledge/insight production enables value cycles and transformative growth.
AI breaks through traditional scale constraints: it improves decision-making efficiency, responsiveness, and low-cost replication, allowing enterprises to maintain high organizational efficiency even at large scales.
Insurance industry example: A leading insurer partnered with RollingAI to build an AI sales coaching system, extracting high-efficiency scripts from top sales dialogues. The system simulates customer interactions, provides real-time personalized improvement suggestions, reducing training time, improving conversion rates, and standardizing sales capabilities—redefining organizational boundaries through AI-driven capability deconstruction and reconstruction.
Key questions to assess AI-driven knowledge/insight capabilities:
Does it systematically capture valuable "counterexample cases" for AI training (beyond positive cases)?
Has it connected data, knowledge, AI, and business to build a sustainable closed-loop knowledge-learning/production mechanism?
Has it established human-AI collaboration to consistently create and apply insights for value generation?
Core challenge: Unlocking AI value relies not on cutting-edge foundation models alone, but on enterprise data governance capabilities and knowledge production mechanisms—especially the management of failure case databases (which offer richer AI training insights than "correct answers"). Top AI adopters (e.g., pharmaceutical companies) establish institutional mechanisms to capture failure data (cross-departmental error sharing, model-feedback loops) and convert it into organizational intelligence. Only through robust data governance, knowledge accumulation, and business-aligned closed-loop training can enterprises evolve from "using AI" to AI-native (building unique intelligent advantages).
2. Collaboration Paradigm: From AI Tools to Silicon-Based Colleagues
AI collaboration is evolving from basic tool use to integration with silicon-based employees: AI agents are transforming from task-specific enablers to digital colleagues that manage multiple workflows and collaborate autonomously with other agents. This redefines collaboration models from "humans directing AI execution" to "humans-AI co-creating strategy", and ultimately to "AI-AI organizational collaboration with human high-level oversight" in a multi-tiered ecosystem.
In this model, AI agents are knowledge partners, decision-support assistants, and coordination orchestrators; the human-AI relationship shifts from unidirectional command to bidirectional knowledge supplementation and strategy co-creation. Humans focus on goal-setting, strategic guidance, and outcome evaluation, while AI handles task execution, knowledge integration, and data-driven scenario simulation—completing the full execution loop together.
Critical enterprise actions:
Develop AI agent training/management mechanisms: Frontline business experts become AI agents’ "trainers" and "product managers" (replacing IT/algorithm team-dominated development), teaching AI agents expert heuristics, judgment criteria, and adaptive strategies through demonstration, feedback, and contextual training (similar to human talent development).
No-code/low-code AI platforms: Mengniu, for example, built an AI assistant development platform, enabling business personnel to independently create, iterate, and manage dedicated AI agents—breaking down technology-business barriers and realizing distributed co-creation.
"Use it or lose it" management rules: Regularly assess AI agent performance, implement version iteration, design inter-agent collaboration protocols, and define clear human-AI role boundaries with supervisory/intervention mechanisms.
This paradigm shifts enterprise core competency to proprietary AI agent networks and management capabilities, transforming organizational design from position-based to dynamic task/agent-based, and managers’ roles to AI team leaders (orchestrating human-AI hybrid teams for business goals).
3. Organizational Processes: From Standardization to Unleashing Potential
Traditional enterprise management centers on process standardization to ensure baseline operational performance, but such processes only codify operational steps—not the full operational logic, leaving large amounts of tacit on-the-job knowledge uncaptured. AI extracts and formalizes this tacit knowledge, driving capability uplift and paradigm shifts in processes, structures, and production relationships.
As human-AI collaboration evolves, processes become dynamic and task-driven: AI agents reshape management architectures from the bottom up, orchestrating entire workflows and dynamically configuring processes to meet real-time task demands—ushering in highly adaptive, task-centered operating models.
Industry differences in transformation pace:
2C industries (FMCG): Short value chains, abundant customer touchpoints, and rapid feedback loops enable fast AI transformation in marketing, customer service, and content production.
B2B industries (pharmaceuticals/chemicals): Complex physical processes, specialized collaboration, and stringent regulatory compliance lead to cautious, measured process reconfiguration.
Organizational structure evolution: The traditional top-middle-frontline three-tier structure flattens; middle management roles are redefined. Knowledge-intensive teams may evolve into atomized models (cross-functional integration, e.g., marketing-sales convergence), with small groups of human experts leading AI agent networks. This even drives a shift in enterprise production relations toward Haier’s vision of "self-organizing micro-enterprise ecosystems"—enterprises as knowledge/capital platforms, and employees as empowered, capability-enhanced individuals—spurring innovation in performance management and incentive systems.
4. Talent Capabilities: From Skills Training to Cognitive Education
As AI masters foundational knowledge and task execution, human talent’s core value shifts to unleashing AI potential (cognitive approaches) and generating additional value from AI outputs (creativity).
Core Talent Qualities in the AI Era
Systematic critical thinking: Essential for all employees to effectively input commands, review, feedback, and refine AI outputs—enabling human-AI co-learning and iteration. Enterprises become lifelong learning platforms, with cognitive training as an extension of formal education (e.g., Toyota embeds critical thinking in lean production; Globis offers tailored critical thinking courses for all employee levels).
Creative capacity: Beyond new idea generation, it includes complex knowledge recombination, novel solution design, and breaking conventional problem-solving boundaries—human cognitive patterns become the decisive factor.
Redefined domain expertise: Shifts from knowledge accumulation to two directions: (1) transforming expertise into AI-interpretable frameworks to maximize AI potential; (2) developing professional judgment to assess AI outputs’ value, limitations, and improvement areas. Future talent needs either interdisciplinary thinking to connect fields and guide AI’s high-value outputs, or deep domain insight to interpret AI results.
Irreplaceable soft skills: Interpersonal communication, emotional resonance, and empathy—critical for internal collaboration, customer relationship management, and organizational culture building in the AI era.
In summary, cultivating cognitive approaches is far more important than transmitting discrete technical skills in the AI era.
5. Organizational Culture: From Performance Orientation to Vision-Driven Purpose
Corporate values are the cornerstone of AI-era enterprises, guiding talent selection/development and leader communication. AI’s deep embedding redefines organizational culture’s value: as AI takes on information filtering, trend forecasting, and decision recommendations, humans are left with value judgments and strategic choices in complex, morally charged uncertain scenarios—where values play a decisive role.
AI is not inherently neutral/objective: its behavioral logic and evolution are shaped by training. Employees’ judgments, preferences, and biases during AI training/labeling/tuning form the enterprise’s technological culture; management’s values determine AI’s strategic deployment direction (who it serves, how it evolves)—defined by organizational mission and culture.
Core requirement: Building an AI-era leading enterprise relies on a highly transparent, widely shared value system—values are no longer a "soft" bonus but a "hard" foundation for safe, orderly, and responsible AI use. AI-native enterprises must be values-led to ensure AI applications align with corporate missions, balancing humanistic care with technological advancement.
Didi example: Shifted its algorithm from pure efficiency optimization to efficiency-safety balance—rooted in a fundamental redefinition of corporate values, significantly improving ride safety.
6. Leadership: From Command-and-Control to Thought Leadership
Leadership is irreplaceable in technological/organizational transformation; breakthrough-growth enterprises are led by leaders with strategic judgment, forward-looking vision, flexible leadership styles, and strong change-driving capabilities. The AI era redefines leadership in two core ways:
Flexible leadership styles: Enterprises still need decisive leaders (authoritative/transformational styles) to chart courses amid uncertainty; at the same time, knowledge-intensive, AI-empowered organizations require empowering/coaching styles to unleash human-AI collaboration potential—leaders must switch styles dynamically to manage diverse talent and decision-making ecosystems.
Redefined CEO/founder role: The traditional command-and-control model is obsolete—AI-empowered employees have access to real-time massive knowledge, eroding top-level information asymmetry and decision monopolies. Governance structures balance centralization and empowerment: leaders delegate partial decision rights to frontline teams to activate vitality, while holding fast to three anchors—strategic direction, value system, and core capability development. The CEO’s fundamental role becomes a thought leader: posing critical strategic questions, defining system architectures, nurturing organizational mindsets/culture, and safeguarding the enterprise’s foundational logic.
Summary: The AI mindset is not a fixed technology/knowledge set, but a comprehensive cognitive and systemic construction capability spanning strategy, organization, processes, talent, and culture, evolving with technology and business development. It is the fundamental driver for enterprises to navigate cycles and create sustainable value in the AI era.
Ⅴ. How Business Leaders Can Build AI-Ready Enterprises
We stand at an AI era inflection point: the technological transformation tipping point has arrived, even as fully fledged AI-native enterprises (founded with AI as the core driver of strategy, organizational design, and product-service reinvention—unlike traditional enterprises that use AI as a functional tool) are not yet dominant. Visionary leaders (e.g., Li Auto) are already leading AI-era transformation: Li Auto embeds AI across product development/user experience (cockpit, voice interaction, ADAS) and expands to new product landscapes, builds cross-functional intelligent teams, and constructs a user data + AI capability operating mechanism—its AI application penetrates underlying capability systems and organizational structures, embodying the next-generation AI-driven enterprise.
1. The Future Profile of AI-Driven Enterprises
Enterprises with a mature AI mindset exhibit core characteristics:
Flatter, more collaborative organizational structures;
Managerial mindset shifted from control to orchestration;
Human-AI hybrid teams as core production units;
Data and knowledge as primary production assets;
Evolution into self-learning, self-evolving intelligent systems—driven by tasks, intelligent feedback, and AI agents to form a continuously optimized operational loop (no longer reliant solely on processes and hierarchies).
AI is a structural variable determining enterprise survival: history shows that hesitant enterprises (Kodak, Nokia) are quickly overtaken, while transformation pioneers (Microsoft, Amazon) achieve exponential valuation and strategic influence growth—this dynamic intensifies in the AI era.
2. Five Critical Tasks for Corporate AI Transformation
CEOs and top leadership teams must complete five core tasks to transition to AI-driven enterprises, based on the RollingAI AI Maturity Model (four phases: AI Aware → AI Active → AI-Driven → AI-Native; most enterprises are at L1, few at L2/L3, none yet at L4):
a) Define Strategic Direction and Focus on “Must-Win Battles”
Enterprises must clearly identify AI deployment priorities (e.g., back-office functions with mature data/measurable ROI as a test ground, or core business process embedding to reshape products/services) and allocate resources accordingly. The core challenge is not introducing foundation models, but building the ability to continuously convert data into knowledge—requiring a robust technical architecture and solid data foundation. AI transformation demands a holistic approach across strategy, operations, talent, organization, culture, and technology (e.g., Mengniu builds AI agent platforms and trains business teams to advance to AI-Driven).
b) Reshape Organizational Architecture and Operating Models
AI-native enterprises rely on task-driven, intelligent-feedback, self-optimizing systems (highly agile/adaptive) rather than fixed processes and rigid hierarchies—AI becomes an integral part of operations with sensing, analysis, and execution capabilities. Enterprises must:
Tailor AI integration to their business context/maturity;
Embed AI agents into key workflow points to drive process redesign and organizational restructuring;
Deeply integrate AI across sensing, analysis, prediction, and execution layers;
Break down data-model-decision-execution barriers to build an efficient, autonomous data-model-decision-execution closed loop (foundation for future intelligent operations).
c) Redefine the Talent Competency Model
Valuable AI-era talent is no longer defined by technical expertise alone, but by AI co-creation ability and insight creation capability (generating business insights in complex scenarios by questioning, guiding, and refining AI outputs). Enterprises must:
Revamp talent models from "role-matching" to "capability-matching", emphasizing value alignment with corporate mission, critical thinking, interdisciplinary literacy, data/market sensitivity, and AI co-creation ability;
Evolve organizational structures to human-AI hybrid collaborative teams (AI as a creativity/execution force multiplier seamlessly integrated into human workflows).
d) Build Data and Technology Infrastructure
AI transformation requires comprehensive data asset governance: integrate structured/unstructured data, break down data silos, and incorporate overlooked resources (manual records, unstructured documents, edge-process data) to systematize "scattered knowledge"—enabling AI to decode enterprise behavioral patterns and drive targeted knowledge generation/business optimization.
In technology architecture, build forward-looking, flexible, and scalable infrastructures to balance stability and adaptability to rapid model evolution and diverse technology pathways—addressing AI-era challenges of technology cost and complexity management. For enterprises still in digital transformation, adopt a dual-track strategy: strengthen digital infrastructure/data capabilities on one track, and gradually introduce AI tools/enterprise-specific models into business scenarios on the other (foundation + business progression).
e) Cultivate an AI Transformation Culture and Leadership
After completing strategic, organizational, talent, and technical restructuring, the ultimate challenge is activating an AI transformation culture and leadership system (leaping from "partial AI application" to "enterprise-global intelligence"). This requires:
A forward-thinking, change-driving leadership team that defines AI-era strategic direction;
Cultivating a culture of data trust, co-creation, and experimental willingness (cultural foundation for full AI business integration).
Under this paradigm, enterprises evolve into self-learning/self-adaptive intelligent systems; leaders become human-AI collaboration architects, AI capability enablers, and cultural transformation stewards. Building human-AI centered decision-making mechanisms enables continuous business insight generation, process optimization, and compound value growth in dynamic environments—this is the core of AI-era leadership: guiding the co-evolution of organizations and AI.
Ⅵ. Conclusion: The Transformative Mission of Leadership
In the AI era, business leaders are no longer just strategic formulators, but core drivers of organizational transformation. They must integrate technological insights with forward-looking vision, mobilize organizations amid uncertainty, make decisive decisions, and continuously evolve—today’s strategic decisions shape the enterprise’s next-decade development trajectory and competitive position. In an era of rapid technology/business model evolution, only enterprises that embrace change first and proactively reconstruct themselves will secure leadership in the new commercial civilization.
AI transformation requires a balance of strategic foresight and practical execution: leaders must grasp AI’s long-term profound changes, and focus on phased, measurable value-creating scenarios to build organizational confidence. Most importantly, successful AI transformation goes beyond technical application—it involves the fundamental reconstruction of organizational mindset, value-creation logic, and corporate culture. Only enterprises that fully embrace the AI mindset, break through established boundaries, and redefine development models will gain competitive advantages in this technological revolution and create sustainable new growth patterns.
Gao Feng Advisory’s AI Transformation Support
Gao Feng Digital: A dedicated arm enabling AI-era enterprise transformation, collaborating with ecosystem partners (RollingAI, Lark, DingTalk, Laiye, Globis) to deliver end-to-end solutions (AI strategy, readiness assessment, scenario identification, POC design, operations improvement, data/AI solutions, organizational/talent integration). It helps enterprises build AI-native mindsets, core competencies, and high-impact application scenarios, accelerating AI from conceptual adoption to deep integration.
Gao Feng Academy: Drives organizational strategic alignment through thought leadership, capability-building sessions, and co-creation workshops—helping enterprises define AI-era strategies, identify high-impact initiatives, and establish shared transformation mechanisms.
About the Authors
Edward Tse: Founder & CEO of Gao Feng Advisory Company, a pioneer in China’s management consulting industry. He built and operated the Greater China operations of BCG and Booz for 20 years, consulting hundreds of Chinese/foreign companies, investors, start-ups, public-sector organizations, Chinese government agencies, the World Bank, and the Asian Development Bank. Author of hundreds of articles and six books (e.g., The China Strategy (2010), China’s Disruptors (2015), Mindset for Mega Changes 2 (2025)).
Yifan Jie: Partner & Managing Director at Gao Feng Advisory Company, with 12+ years of high-quality consulting experience in strategy, operations, and digital transformation. He serves domestic/international clients in industrial manufacturing, automotive, energy, healthcare, life sciences, and 2C industries, and has extensive China/global project experience in strategic breakthroughs and transformational upgrades. Previously an Associate Partner at McKinsey & Company and a manager at leading domestic enterprises.
Derrick Wang: Senior Consultant at Gao Feng Advisory Company (Shanghai), with cross-industry experience in healthcare, finance, automotive, new energy, and ESG. He supports clients across all development stages in strategic planning, multi-dimensional market analysis, policy/regulatory analysis, and market entry/positioning strategies.
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