Gartner-2020年十大战略技术趋势（英文）-2019.10.21-52页_392kb

2020 Top 10 Strategic Technology Trends Summary

Core Content

Gartner's 2020 Top 10 Strategic Technology Trends provide a roadmap for enterprises to navigate the evolving technological landscape. These trends are not only shaping the future of business but also influencing how people interact with digital environments. The focus is on people-centric smart spaces and the integration of technologies that enable continuous innovation and operations. The report highlights the importance of combinatorial innovation, where multiple trends interact to create new opportunities and drive transformation.

Main Trends and Key Points

1. Hyperautomation

• Definition: The combination of various automation technologies, including RPA, iBPMS, and AI, to automate business processes.
• Key Components: Robotic Process Automation (RPA) and Intelligent Business Process Management Suites (iBPMS) are central.
• Impact: Enables automation of knowledge work, shifts from rigid rule-based automation to dynamic, adaptive systems.
• Benefits: Improves business outcomes, increases agility, and allows for the creation of digital twins of organizations.
• Challenges: Requires a holistic view across functional and process silos, and the ability to manage complex models.

2. Multiexperience

• Definition: Enhancing the way people interact with the digital world across multiple touchpoints and devices.
• Focus: Conversational, immersive, and ambient experiences.
• Tools: Multiexperience development platforms that support various interfaces (mobile, web, AR, etc.).
• Importance: Supports the development of new digital business moments and customer engagement.

3. Democratization

• Definition: Simplifying the use of technology for non-technical users.
• Components: Low-code and no-code development, citizen developers, and citizen data scientists.
• Impact: Enables more people to contribute to digital transformation, reduces dependency on specialized skills.
• Challenges: Managing "shadow AI" and ensuring ethical use of data and AI.

4. Human Augmentation

• Definition: Enhancing both physical and cognitive capabilities through technology.
• Aspects: Cognitive augmentation via AI and machine learning, and physical augmentation through wearable tech and IoT.
• Ethical Considerations: Organizations must address cultural and ethical implications of augmenting human capabilities.

5. Transparency and Traceability

• Focus: Ensuring trust in AI and data systems through explainable AI, ethical algorithms, and data privacy.
• Need: Clear governance, policies, and best practices to manage AI-related security and ethical risks.
• Outcomes: Increases transparency, reduces legal and reputational risks, and supports better decision-making.

6. Empowered Edge

• Definition: Leveraging edge computing to process data closer to the source.
• Enablers: 5G networks, IoT, and real-time analytics.
• Impact: Enables faster, more efficient data processing and reduces latency.
• Use Cases: Smart buildings, cities, and industrial environments benefit from edge computing.

7. Distributed Cloud

• Definition: A shift from centralized data centers to distributed cloud services.
• Benefits: Offers flexibility, scalability, and improved performance by delivering services at the point of need.
• Challenges: Requires new infrastructure and management approaches to support distributed cloud environments.

8. Autonomous Things

• Definition: Physical objects enhanced with AI and automation to perform tasks with minimal human intervention.
• Capabilities: Perception, interaction, movement, and manipulation.
• Integration: Works in conjunction with AI, IoT, and edge computing to create autonomous systems.
• Outcomes: Supports smart spaces and improves operational efficiency.

9. Practical Blockchain

• Definition: Blockchain applied to real-world enterprise use cases.
• Focus: Scalability, security, and trust in transactions.
• Use Cases: Includes supply chain, identity management, and contract execution.
• Expectations: By 2023, blockchain will be technically scalable and support private, secure transactions.

10. AI Security

• Definition: Securing AI-powered systems and anticipating malicious uses of AI.
• Challenges: Cybersecurity threats that exploit AI systems (e.g., training-data poisoning, model theft).
• Solutions: Use AI to enhance security defenses and establish robust governance frameworks.

Key Recommendations

• Center Innovation on People: Use personas and journey maps to understand user needs and evaluate technology impact.
• Build an Overarching View: Integrate tools like RPA, iBPMS, and AI across silos to enable efficient and adaptive systems.
• Embrace Multiexperience: Develop platforms that support diverse user interactions and experiences.
• Establish Governance: Develop policies and best practices to ensure transparency, traceability, and ethical use of AI and data.
• Prepare for Disruption: Anticipate how trends will shape the future of business and take proactive steps to adapt and innovate.

Strategic Planning Assumptions

• By 2022, 70% of enterprises will experiment with immersive technologies, and 25% will deploy them to production.
• By 2021, at least one-third of enterprises will use a multiexperience development platform.
• By 2024, most cloud platforms will offer services at the edge.
• By 2023, blockchain will be technically scalable and support secure transactions.
• By 2022, over 75% of data governance initiatives will overlook AI security risks, leading to financial losses.

Conclusion

The 2020 Top 10 Strategic Technology Trends emphasize the convergence of AI, automation, and digital transformation. Organizations must adopt a people-centric and smart spaces approach to leverage these trends effectively. The key is to understand the human context, embrace combinatorial innovation, and establish robust governance to manage the ethical, legal, and security challenges. As these technologies mature, they will enable more dynamic, efficient, and trustworthy digital ecosystems.