英文_CMO_Council_AI生成技术的竞争优势之路_41页_13mb

Introduction and Executive Summary
Generative AI (GenAI) promises significant competitive advantages, with 79% of business leaders expecting it to deliver in the next 18 months. However, data readiness is critical; only 13% of leaders are confident in their organization's data-AI readiness, leading to a high failure rate for GenAI projects. The report underscores that GenAI's full potential can only be unlocked through AI-ready data, addressing issues like data quality, accuracy, security, and cost/ROI.

Key Findings and Challenges

• Data Silos and Quality Issues: Organizations face challenges with isolated data sources, limited data understanding, and poor governance, affecting GenAI reliability. For instance, poor data indexing leads to inconsistent GenAI outputs, and most enterprise data (70-90%) is unstructured, complicating preparation.
• Confidence and Success Rates: Only 40% of business leaders feel confident in their data-AI readiness, and 30% of GenAI projects are expected to fail due to inadequate data. Common problems include lack of data management practices, algorithmic bias, and security risks.
• Regional and Business Model Breakouts: North America leads in GenAI maturity, with APAC lagging notably. B2B companies show more advanced use cases in personalization, while B2C focuses on process automation. Company size correlates with structured data reliance, with larger entities having higher but narrower confidence.

Strategic Recommendations
To achieve GenAI competitive advantage, organizations should invest in modern technology stacks for data management, enhance internal AI and data literacy, prioritize high-value use cases, and implement robust data governance. Initiatives include data discovery, classification, and enrichment to ensure accuracy, reliability, and compliance. Cost/ROI considerations are evolving, requiring sustainable processes to avoid administrative overhead.

Expert Insights

• Emphasis on data trust: GenAI outputs are reliable only if based on high-quality, governed data, preventing hallucinations and compliance risks.
• Beyond productivity: AI's true value lies in innovation and efficiency gains, enabling new capabilities like video generation and personalized campaigns.
• Cultural and data shifts: Organizations need to foster human-AI collaboration, cultural adaptability, and active data strategies to harness GenAI effectively for transformation and competitive differentiation.

Conclusion
Data-AI readiness is essential for GenAI success, driving business value through improved processes, risk mitigation, and innovation. Companies failing to address data challenges risk missed opportunities, while those investing proactively can achieve sustainable competitive edges in an AI-driven landscape.