埃森哲：100年前是电机 今天是AI 企业价值正在重新排序

This article presents Accenture's latest research on the development of the global AI industry and enterprise AI transformation. Key takeaways are as follows:

1. Currently, most corporate AI deployments improve employee efficiency but fail to drive top-line business growth. The problem does not lie with AI technology itself, but with enterprises' continued reliance on outdated production organization and management models — a parallel to the early days of electrification 100 years ago. Only 10% of enterprises today successfully convert AI capabilities into sustainable performance advantages.

2. AI transformation does not require a full one-time organizational and operational overhaul. A more feasible approach is to start with targeted use cases: first boost efficiency through automation, then redirect freed resources into new innovation cycles, and advance transformation incrementally.

3. In China, 63% of enterprises have already provided AI assistants to their employees, but only 18% have completed systemic process restructuring. Adjustments to human-AI collaboration models will become the primary change in the workplace, and individual workers should prepare to adapt to this shift in advance.

This article outlines new consumer trends and competitive directions for brands in the AI era. Key insights are as follows:

1. Consumer trends: Accenture forecasts that 71% of consumers will use generative AI to inform their purchasing decisions in the future, and 37% of existing brand followers are willing to switch brands based on AI agent recommendations. The new competitive battleground for brands is optimizing products and brand identities to be easily understood, verified and recommended by AI agents.

2. Cross-border brand growth: AI enables brands to gain deep insights into the authentic needs of local consumers in different regions, and connects global marketing, supply chain and other resources through AI agents to enable efficient cross-regional collaboration, addressing the core pain point of localization for brands going global.

3. Brand value growth: AI allows brands to transform their revenue models, shifting from simply selling products to delivering full-lifecycle outcomes. Brands can continue engaging users with AI after a product is sold, generate sustained higher value, and increase their own value density.

This article analyzes new opportunities, risks and transformation paths for sellers in the AI era. Key takeaways are as follows:

1. Growth opportunities: Generative AI has become a new entry point for consumer decision-making. Sellers can capitalize on this trend by optimizing product information and brand presentation to align with AI agent recommendation rules, unlocking entirely new traffic增量. Cross-border sellers can use AI to gain local consumer insights, coordinate global resources, quickly improve localization operations, and open up new growth opportunities overseas.

2. Risk warnings: As AI scales, "token tax" costs from token consumption will rise rapidly. Sellers should proactively evaluate AI ROI across different use cases to avoid blind overinvestment. Isolated point AI deployments will see growing incremental costs and rarely hit ROI targets, so sellers should avoid only deploying scattered, disconnected AI tools.

3. Transformation paths: AI transformation does not require a full one-time overhaul. Sellers can start with targeted use cases to deliver immediate efficiency gains, then gradually upgrade processes and organizational models to reduce trial-and-error costs during transformation.

This article charts a clear direction and identifies new business opportunities for factories advancing AI-driven digital transformation. Key insights are as follows:

1. Production and design: AI greatly enhances factories' ability to handle complex scenarios. Dynamic optimization that was unachievable under traditional models can now be delivered with AI, allowing factories to turn previously avoided complexity into a competitive advantage. For example, Xingxing Charging uses AI to match real-time charging demand, electricity prices and station resources to upgrade customer experience.

2. AI transformation lessons: Most factories currently only use AI for isolated efficiency improvements. To deliver real value growth, factories need to restructure production processes and human-AI collaboration models, and resolve lingering issues from past digital transformations including disconnected data, technical debt and organizational debt. Scattered point AI deployments lead to rising incremental costs and rarely hit ROI targets.

3. New business opportunities: AI creates new opportunities for factories expanding overseas. Factories can use AI to gain deep insights into demand across different overseas markets, optimize product design, and coordinate global supply chains through AI to improve cross-border competitiveness and unlock new growth.

This article maps development trends for the AI services industry, summarizes core pain points of enterprise clients and outlines priority strategic directions. Key takeaways are as follows:

1. Industry trends: The scaled adoption of AI among Chinese enterprises has accelerated notably: the share of enterprises using advanced AI applications has risen from 46% last year to 88% this year. Enterprise demand has shifted from pilot testing to scaled deployment, driving rapid expansion of the AI service market.

2. Core client pain points: Most enterprises struggle with technical debt, data silos and insufficient organizational capacity left over from traditional digital transformation. Scaled AI deployment has also added three new pain points: excessive token tax costs, uncertainty in AI-generated outputs, and the need for traceable and trustworthy AI decision-making. Most enterprises lack the capacity to build foundational AI capabilities independently, leading to strong demand for third-party AI services.

3. Priority solution areas: Service providers can build offerings around five core needs of enterprises undergoing AI transformation: helping enterprises frame AI as a core strategic priority, building new technology infrastructure to support AI operations, developing an AI cognitive layer, building organizational capabilities for AI, and establishing a trusted, scalable AI operation system.

This article analyzes shifting enterprise demand for platforms in the AI era and outlines new development directions for platforms. Key insights are as follows:

1. Shifting platform demand: Enterprises' scaled AI transformation requires support from new technology infrastructure and trusted AI operation systems. Traditional digital platforms can no longer meet these new demands, so platforms must upgrade their capabilities as soon as possible to align with the new requirements of enterprise AI transformation.

2. New platform growth opportunities: Cross-organizational AI agent networks will be the future industry direction. Platforms can extend AI agent capabilities across upstream and downstream industrial chains, connecting customers, suppliers and partners, and improve overall ecosystem efficiency through inter-agent collaboration. This model, already partially implemented by platforms like Alibaba International, has enormous room for expansion.

3. Operations and risk mitigation: Platforms need to help onboarded enterprises resolve AI-related cost, trust and compliance issues, launch AI transformation solutions tailored for enterprises at different development stages, and guide enterprises to start with targeted use cases and upgrade incrementally to avoid transformation failure from blind overinvestment. Platforms can also build AI-powered global collaboration tools for cross-border enterprises, capitalize on soaring outbound expansion demand to attract new merchants, and scale platform size.

This article discloses Accenture's latest research data, identifies new industrial trends, unaddressed problems and emerging business model directions in the AI era. Key takeaways are as follows:

1. New industrial trends: AI is accelerating value migration across global enterprises. Over the past decade, total global enterprise value migration has reached $27 trillion, and the generative AI boom has significantly speed up this process. Future enterprise competition will center on three core capabilities: autonomous intelligence, value density and multi-polar operation. Turnover in the leading enterprise ranks will accelerate, and the gap between leading and lagging firms will widen further.

2. Unresolved industrial problems: Most current AI deployments improve efficiency but fail to boost business performance, rooted in enterprises' continued use of outdated production organization and management models. Scaled AI deployment also faces three new major challenges: rising token tax costs, uncertainty in AI outputs, and the requirement for traceable, trustworthy AI decision-making. To date, only 10% of enterprises can successfully convert AI capabilities into performance advantages.

3. New business model directions: Leading enterprises will adopt new business models in the future, with value creation shifting toward four directions: outcome-based revenue, harnessing complexity, trusted decision-making, and AI agent ecosystem networks. The combination of autonomous intelligence and multi-polar operation will become the core business model for leading enterprises.