埃森哲-人工智能报告：规模化（英文）-2020.1-22页_3mb

AI: BUILT TO SCALE

Core Content

This document outlines the journey of companies in scaling Artificial Intelligence (AI) across their organizations, highlighting the differences between those that experiment with AI and those that achieve industrialized AI growth. It presents insights from a global study involving 1,500 C-suite executives across 16 industries and 12 countries, revealing the key success factors and financial outcomes of successful AI scaling.

Main Points

• AI as a Strategic Imperative: 84% of C-suite executives believe AI is essential for growth, and 75% believe not scaling AI risks going out of business in five years.

• Three Stages of AI Scaling:

  • Proof of Concept Factory: Most companies are stuck in this stage, with low success rates and returns due to siloed efforts, lack of clear strategy, and unrealistic expectations.
  • Strategically Scaling: Only 15-20% of companies have progressed to this stage, achieving a 2x success rate and 3x return on AI investments. They have a clear AI strategy, multi-disciplinary teams, and strong governance.
  • Industrialized for Growth: A small number of companies have achieved a mature AI platform, enabling exponential returns and competitive differentiation.

• Strategic Scalers Outperform: Companies that are Strategically Scaling achieve significantly better financial results, including:

  • +35% in Enterprise Value/Revenue Ratio
  • +33% in Price/Earnings Ratio
  • +28% in Price/Sales Ratio
  • 70%+ success rate in AI initiatives
  • 70%+ return on AI investments

• Key Success Factors:

  • Drive "Intentional" AI: Align AI with strategic objectives and business outcomes.
  • Tune Out Data Noise: Focus on business-critical data, invest in data quality and governance.
  • Treat AI as a Team Sport: Embed multi-disciplinary teams across the organization and ensure leadership sponsorship.

• Challenges in Scaling AI:

  • Organizational Structure: Lack of a clear operating model and governance.
  • Data Foundation: Inability to manage and integrate large volumes of data.
  • Employee Adoption: Resistance to change and lack of understanding of AI's role in daily operations.

• Financial Impact of Scaling AI:

  • Strategic Scalers achieve 2x more accurate forecasting and 45% increase in operating income.
  • They also reduce costs and improve efficiency through better data management and AI integration.

Key Information

• AI Capabilities: AI includes machine learning, natural language processing, computational intelligence, and more.
• ROI Gap: The average ROI gap between Proof of Concept companies and Strategic Scalers is $110 million.
• Data Importance: 90% of data was created in the past 10 years, and Strategic Scalers are better at managing and leveraging it.
• Multi-Disciplinary Teams: 92% of Strategic Scalers use multi-disciplinary teams, which are essential for successful AI scaling.
• Responsible AI: Ensures ethical, transparent, and accountable use of AI, maintaining user trust and privacy.

The Great Divide

• Global AI Spend: $306 billion was spent on AI initiatives over the past three years.
• Company Size Not a Factor: No significant difference in scaling success rates or ROI between smaller and larger companies.
• Strategic Scalers’ Experience: They have an average of 114 AI initiatives, compared to 53 for Proof of Concept companies.

Real-World Examples

• A global brewer used AI to improve forecasting, segmentation, and sales incrementality, achieving a 4x return on investment in the first year.
• A convenience store chain implemented AI-driven pricing strategies and virtual agents, expected to deliver $300 million in annual gross profit uplift.

Conclusion

Scaling AI is not just about technology; it's about strategy, culture, and organizational alignment. Companies that move beyond isolated experiments to a holistic, responsible, and data-driven approach to AI are more likely to achieve competitive agility and significant financial returns. The document emphasizes the importance of investing in the right capabilities, mindset, and infrastructure to unlock AI's full potential.