The Transformative Nature of Generative AI to Your Business

The Evolution of Generative AI

AI has evolved significantly since Alan Turing proposed the Turing Test in 1950, moving from machine learning and deep learning to today’s Generative AI. Defined as AI that generates new, original content (AIGC), it gained global attention with the 2022 launch of OpenAI’s ChatGPT—a chatbot capable of conversing on diverse topics and generating structured text, despite occasional inaccuracies. Beyond text, Generative AI efficiently produces content in code, image, audio, video and other formats.

Emerging Applications of Generative AI

1. Abundant and diverse datasets for AI algorithms to identify patterns and generate cost-effective output;
2. A focus on innovation, with Generative AI enabling exploration of unconventional ideas;
3. Emphasis on personalization, matching Generative AI’s ability to deliver tailored solutions;
4. Demand for efficiency and automation, driving the adoption of Generative AI to replace labor-intensive tasks.

Accelerating Transformation in E-commerce & Retail

Generative AI is reshaping e-commerce business models and digital transformation, enabling personalized consumer experiences and customized services at lower cost and higher efficiency:

• Content & recommendation: TikTok uses AI for cross-border live e-commerce and intelligent live content generation; Generative AI recommends products based on user behavior and creates realistic product images (e.g., Midjourney generates custom product images, reducing inventory costs and enabling on-demand purchases).
• Marketing: It powers personalized marketing campaigns and intelligent optimization of marketing materials.
• Supply chain & operations: Walmart applies Generative AI to automate supplier negotiations, cutting labor costs and improving communication efficiency; it also optimizes supply chain operations and enhances shopping experiences.

Pushing Creative Boundaries in Entertainment & Gaming

• Entertainment: Simplifies content creation and delivers personalized experiences (e.g., Spotify uses Generative AI to create personalized playlists; future applications will include generating music, videos, animations and stories).
• Gaming: Automates design/development tasks (e.g., generating 3D models, textures, characters and code) to cut production time and costs; creates dynamic, responsive in-game content (dialogues, quests, scenarios) to enhance gameplay by adapting to player behavior and preferences.

Personalized Learning in Education

• Generates customized learning pathways, adaptive assessments and targeted interventions (e.g., Carnegie Learning’s AI math tutoring system analyzes student performance to create personalized learning plans, enabling self-paced learning and improving outcomes).

• Recommends courses/resources for lifelong learning and upskilling based on individual progress and career goals.
• Streamlines administrative tasks, freeing educators to focus on teaching and student support.

Advancing Healthcare & Pharmaceuticals

• Clinical applications: Gathers patient information for pre-consultations and generates diagnostic reports (e.g., Good Doctor Online’s AI pre-consultation reduces average consultation time by 20%); analyzes medical imaging to aid diagnosis and predict disease risks; supports follow-up care and chronic disease management (medication reminders, adherence monitoring).
• Drug development: Addresses the high cost, low success rate and long cycle of traditional drug development by accelerating target identification, molecule synthesis and clinical trial design:
  • Discovers/optimizes drug candidates, studies drug combinations, predicts metabolism and optimizes dosages (e.g., Insilico Medicine’s Chemistry42 platform designs novel molecules and evaluates their efficacy, stability and synthesis difficulty).
  • Predicts protein structures and generates artificial proteins (e.g., XtalPi’s XuperNovo and BaiTuShengke’s De Novo design protein drugs for specific diseases) by learning sequence-structure mapping relationships.

Cross-Industry Implications

Generative AI’s Transformative Impact on Businesses

1. Customer Relationship Redefinition and Business Model Changes

• Understand and predict customer needs, personalize experiences and automate customer service;
• Rapidly respond to market trends by synthesizing multi-source information at super speed (no human analysis required);
• Reshape product/service stratification: it changes the cost structure of customization, creating new pricing models—human expertise-based customization commands higher premiums, while AI-driven customization does not inherently equate to premium products.

2. New Capabilities Needed

• Collaboration: Acquiring or partnering with Generative AI solution providers;
• Self-build: Establishing internal teams to develop custom AI capabilities aligned with unique needs and value propositions.

3. Organizational Transformation and Re-alignment

• Decentralization/flatness: More even distribution of tasks and decision-making power, creating a more democratized organization;
• Labor redivision: New roles for training/managing Generative AI models;
• Cross-departmental collaboration: Faster inter-departmental cooperation, fostering an agile, collaborative organizational culture.

4. Ecosystem and Value Chain Re-configuration

• Traditional model (consumer goods example): Brands design products, outsource production to manufacturers, and sell to customers via retailers;
• AI-driven model: New AIGC intermediaries enable customers to design product specifications; manufacturers process these direct customer orders (via AIGC tools), shifting value chain roles, goods/service flow and intermediary cost structures.

Early examples of this shift exist in interior design, medical diagnosis, 3D printing and creative stock platforms. Generative AI players are also entering traditional ecosystems.

Core Strategic Shifts

• Adopt an AI-first approach: Reimagine core business operations and position Generative AI as a leader in transformation to boost innovation and productivity;
• Accelerate strategy cycles: AIGC delivers faster, more comprehensive information, enabling iterative, rapid strategy formulation and implementation.

Generative AI Risk Management and Ethical Concerns

Core Operational Risks

1. Job displacement: AI automation may eliminate human jobs in some industries;
2. Data privacy: Large-scale training data may include sensitive personal/business/government information—poor security leads to privacy breaches, identity theft and other harms;
3. Disinformation: Inaccurate training data generates misleading content; bad actors may exploit it to spread deliberate disinformation.

Ethical Risks in Data Usage

1. Biased content: Biased training data leads to prejudiced AI output and lacks transparency in data collection/use;
2. Harmful information filtering: AI may fail to identify harmful online content, damaging the model and end users;
3. Cultural/religious insensitivity: AI cannot recognize users’ cultural, national or religious backgrounds, risking offensive content (especially on controversial topics);
4. Unpredictable consequences: Generic/inaccurate output may cause real harm (e.g., incorrect medication dosages, traffic law violations); AI models may evolve beyond design parameters (e.g., Google’s Bard learned unprogrammed languages to respond to users).

Understanding China’s Generative AI Regulations

Key Regulatory Requirements

1. Content moderation: Generate content that reflects socialist core values; prohibit subversive, separatist, terrorist, violent, pornographic, false or economy/social order-disrupting content; improve data legitimacy checks and model filtering systems to ensure content accuracy;
2. Data privacy: Desensitize/anonymize personal information, delete data periodically and optimize user complaint mechanisms;
3. IP protection: Ensure the legality of pre-training/optimization training data sources, avoiding infringing content;
4. Anti-discrimination/ethics: Halt services for unethical behavior (malware programming, biased comments, discrimination based on race, culture, religion, nationality, etc.); prevent discrimination in algorithm design, data selection, model optimization and service provision.

Regulatory Orientation

Geopolitical Considerations in Generative AI

1. Regulatory divergence: Different countries adopt distinct AI regulatory approaches, requiring MNCs to adapt AI adoption to local compliance requirements;
2. Cross-border data privacy/security: Cross-border data transfers raise privacy/security concerns, especially as AI models interact with massive data—MNCs need robust risk management mechanisms;
3. Cultural/ethical differences: Ethical and value definitions vary by country; unintentional offenses can severely damage corporate reputations—regional AI model training is needed to avoid sensitive topics and respect local users;
4. System fragmentation: MNCs may need to deploy region-specific Generative AI systems to align with local regulations and cultural norms.

Implications for Businesses

Strategic Recommendations for Generative AI Transformation

1. Partner with AI experts: Gain AI application knowledge, skills and experience to manage risks/ethics and stay updated on regulations;
2. Cultivate responsible AI use: Be transparent about AI applications; educate employees and customers on AI’s benefits, risks and proper use;
3. Foster a learning/collaborative culture: Encourage cross-departmental idea sharing and collaboration to maximize AI’s value.

Balancing Innovation and Risk

• Assess potential risks;
• Balance the costs and benefits of AI investments (avoid overinvesting in business model upgrades);
• Mitigate ethical conflicts and regulatory compliance issues.

Acknowledgment

About the Authors

1. Dr. Edward Tse: Founder & CEO of Gao Feng Advisory, a pioneer in China’s management consulting industry. He led 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), Strategic Thinking in the Era of Mega Changes (2022)).
2. Rachel Hu: Associate at Gao Feng Advisory (Shanghai), with experience in consulting and private equity across China, Australia and Latin America. Focuses on helping MNCs expand in China via growth strategy, market entry and business model design, serving automotive, semiconductor, healthcare, industrials and technology clients.
3. Jocelyn Yu: Consultant at Gao Feng Advisory (Shanghai), with cross-industry experience (automotive, cosmetics, healthcare/pharmaceuticals, semiconductor, IT services). Specializes in market research, policy analysis, data analysis and model assessment.

1. Gloria Li: Consultant at Gao Feng Advisory (Shanghai), with cross-industry experience (AI, energy, public sector, finance, healthcare). Conducts strategic analysis and market research for foreign and local enterprises.
2. Caroline Deng: Consultant at Gao Feng Advisory (Shanghai), with extensive experience in oil services, chip manufacturing, IT, power and medical technology. Specializes in policy analysis, data analysis, model assessment and market research.

About GAO FENG ADVISORY COMPANY

• Prioritize client interests, view engagements as long-term relationships (not one-off projects);
• Provide end-to-end support: formulate solutions and assist with hands-on implementation;
• Foster cross-level teamwork to create value and solve client problems.

Operating Model

1. Corporate Finance: M&A advisory (candidate identification, due diligence, valuation, deal advisory, integration planning);
2. Training Consulting Academy: Management/strategy training for medium-sized, fast-growing Chinese enterprises; senior executive coaching;
3. Consulting: Premium strategy/management consulting for core challenges in strategy, organization and operations;
4. Operation/Implementation: Hands-on implementation support for client strategies/organizations/operations; interim management by senior professionals;
5. Digital: Build digital capabilities; co-create disruptive digital business models (ideation to scale-up); develop corporate entrepreneurship models.

Gao Feng Advisory 

Gao Feng Advisory Company  is a professional strategy and management consulting as well as investment advisory firm with roots in China coupled with global vision, capabilities, and a broad resources network