毕马威-正在改变企业的八大AI趋势（英文）-2019.9-16页_6mb

Altransforming the enterprise Summary

Core Content

The KPMG 2019 Enterprise AI Adoption Study highlights the rapid evolution of AI adoption in large enterprises. The research involved interviews with senior leaders from 30 Global 500 companies and secondary analysis of job postings and media coverage. These companies collectively employ about 6.2 million people and generate $3 trillion in revenue, making them a critical indicator of the direction of AI transformation in the corporate world.

Main Trends

The study identified eight key AI adoption trends that are shaping the future of enterprise AI:

1. Rapid shift from experimental to applied technology
   AI has moved from being a 'technology to watch' to a 'technology to deploy'. Enterprises are now focused on scaling and applying AI across the organization, not just experimenting.

2. Robotic Process Automation (RPA) adoption is growing

   • 26% of companies have deployed RPA at scale
   • 65% use RPA selectively and in silos
   • 83% expect to deploy RPA at scale in three years

3. AI and Machine Learning (ML) are being deployed more widely

   • 17% of companies use AI/ML at scale
   • 30% use AI/ML selectively
   • Half of the companies expect to use AI/ML at scale in three years

4. AI enables insight, augmentation, and full automation
   AI systems can recognize complex patterns, augment human expertise, and enable full automation of tasks that previously required human judgment when combined with bots.

5. Automation, AI, analytics, and low-code platforms are converging
   These technologies are being deployed together to create more effective and scalable solutions. Low-code platforms are particularly important for integrating and deploying AI in a flexible and user-friendly manner.

6. Enterprise demand for AI is growing

   • 188 out of 200 Global 500 companies are actively buying and deploying AI
   • These companies are posting 69% of all AI-related job postings
   • Companies with mature AI capabilities have an average of 375 full-time AI employees and spend $75 million annually on AI talent, expecting this to grow to 500–600 employees in three years

7. New organizational capabilities are critical
   AI success depends not only on technology but also on organizational capital, including human capital, values and norms, knowledge and expertise, and business processes. Many companies are investing in governance structures and creating Centers of Excellence (COEs) to support AI initiatives.

8. Rise of AI-as-a-Service (AlaaS)
   Enterprises are increasingly accessing AI through external services and platforms. Managed services, microservices, and bot stores are emerging as important components of the AlaaS market, but they do not replace the need for a comprehensive AI strategy.

Key Insights

• AI is a game-changer that can shift the competitive landscape and create new winners and losers.
• Governance and organizational capital are essential for successful AI deployment and scaling.
• Control frameworks are necessary to ensure trust, transparency, and ethical use of AI, especially as algorithms become more complex and opaque.
• AI-as-a-Service is becoming a viable option for enterprises, but it requires strong internal capabilities to be effectively leveraged.
• Productivity improvements are a key operational driver for AI and automation, with companies reporting an average of 15% improvement.
• AI is not just a technology but a strategic imperative that needs to be integrated into broader business strategies and innovation efforts.

Key Definitions

• Artificial Intelligence (AI): A broad set of technologies that can imitate intelligent human behavior.
• Enterprise AI: Advanced intelligent digital systems at scale and as-a-service, with strategic oversight.
• Robotic Process Automation (RPA): Technology that enables software bots to perform tasks that would typically be done by humans.
• Machine Learning (ML): A subset of AI that allows systems to learn and improve from experience and training.
• Low-code platforms: Tools that allow for the rapid development and deployment of enterprise applications with embedded business rules and automation capabilities.

Key Takeaways

• AI is rapidly evolving and is now ready to deliver real value.
• Investment in AI talent and infrastructure is increasing, with many companies expecting significant growth in the next three years.
• Governance and control are critical for AI success, ensuring trust, transparency, and ethical use.
• AI-as-a-Service is an emerging trend, but it requires strong internal capabilities to be effectively integrated.
• Organizational capital must be developed to support AI initiatives, including leadership, processes, and people.
• AI should be part of overall business strategy, not just a separate initiative.
• Combining AI with other technologies such as automation, analytics, and low-code platforms can create synergies and drive innovation.