رؤى جارتنر: استكشاف مستقبل الذكاء الاصطناعي والتقنيات الناشئة

Gartner’s “Emerging Technology Watch” provides a concise overview of trending questions and expert answers concerning the future of AI and related technologies. This compilation, dated November 2025, tackles critical topics from the evolution of AI paradigms and the transformative potential of agentic AI to the disruptive influence of intelligent simulation and the ethical considerations surrounding emotion AI. This blog post summarizes the key insights presented, offering valuable perspectives for businesses looking to leverage these emerging technologies.

The Future of AI: ACI Over AGI and ASI

Gartner predicts that Artificial General Intelligence (AGI) is unlikely to be fully realized within the next decade, requiring substantial breakthroughs. They also highlight the potential risks associated with Artificial Superintelligence (ASI). Instead, Gartner proposes a third approach: Augmented Collective Intelligence (ACI).

• AGI and ASI Challenges: AGI aims to replicate human cognitive abilities, potentially replacing human roles. ASI, surpassing human capabilities, poses risks if misused or as a single point of failure.
• Augmented Collective Intelligence (ACI): ACI envisions a network of connected AI agents working alongside humans, combining AI styles with human intelligence to enhance human capabilities and improve quality of life.

Agentic AI: Aligning Needs with Capabilities

The Gartner Agentic Compass is designed to help organizations navigate the complexities of agentic AI solutions by aligning organizational needs with agentic capabilities.

• Framework: The Agentic Compass utilizes frameworks to assess both use-case needs and agentic capabilities.
• Insights: Based on analysis of 169 providers and 200 real-world deployments, these frameworks provide a grounded reference for defining requirements, constraints, and objectives, enabling better alignment with platforms, prebuilt agents, and governance tools.

Emotion AI: Enhancing Engagement and Mitigating Risks

Emotion AI, also known as affective computing, is identified as a crucial differentiator in computer vision and robotology.

• Customer Engagement: Emotion AI can enhance human-machine interactions by analyzing emotional states through voice and facial recognition, allowing businesses to tailor responses.
• Applications: Emotion AI finds applications in healthcare (diagnostics) and marketing (neuromarketing).
• Key Considerations:
  • Prioritize aggregated, context-based insights over individual emotion attribution to comply with regulations.
  • Utilize multimodal systems for better accuracy.
  • Establish clear metrics for precision, recall, fairness, and regulatory compliance.
  • Ensure transparency and prioritize ethical decision-making.
  • Safeguard user privacy using privacy-preserving machine learning techniques.

Intelligent Simulation: Transforming Decision-Making

Intelligent simulation integrates advanced simulation engines, digital twins, GenAI, synthetic data, and quantum computing to deliver unprecedented scale, efficiency, and accuracy in decision-making.

• Transformation: Intelligent simulation is moving from guidance to autonomously executing decisions based on simulated results.
• Essential Considerations:
  • Alignment with business objectives and operational goals.
  • Seamless integration with existing IT infrastructure (ERP, CRM).
  • Data accessibility and quality.
  • User experience and accessibility through low-code/no-code interfaces.
  • Cultural change management.
  • Demonstrating value and ROI.
  • Focus on predictive capabilities.
  • Continuous learning and adaptation.

AI and Robotics: Reshaping the Job Landscape

By 2030, Gartner predicts that 90% of humans will interact with smart robots daily, driven by advancements in intelligence, social interactions, and human augmentation.

• Key Trends:
  • Automation of routine tasks, freeing human workers for more complex activities.
  • Creation of new roles, such as AI engineers and ethicists.
  • Enhanced decision-making in areas like customer service.
  • Workforce augmentation through polyfunctional robots.
  • Continuous operation in hazardous environments.
  • Physical agency of AI in agent networks.
• Workforce Preparation: Reskilling, upskilling, and careful workforce transition management are critical.

Spatial Computing: Blending the Physical and Digital Worlds

Spatial computing enhances human perception and improves machines’ ability to understand and interact with the real world, with an expected market size of $1.7 trillion by 2033.

• Use Cases:
  • Embodied AI for orchestrated responses in environments like autonomous vehicles.
  • Agentic AI to connect diverse devices and content across public and private data sources.
  • Spatial web for just-in-time information and personalized experiences in various sectors.
• Challenges: Addressing data privacy, standardization, and integration into existing workflows is vital.

Reasoning Models: Driving AI Adoption

Reasoning models are evolving significantly, incorporating logical inference, complex problem-solving, and chain-of-thought processes.

• Evolution: Driven by advancements in reinforcement learning, these models mimic human-like thought patterns more effectively than traditional models.
• Investment: Gartner projects that by 2029, investment in reasoning models will underpin the success of over 70% of agentic AI applications, up from 0% in 2024.
• Impact: Domain-specific reasoning models will enable organizations to automate complex tasks, improve decision-making, and redefine workforce dynamics.

In conclusion, Gartner’s insights provide a valuable roadmap for understanding and navigating the complex landscape of emerging technologies, particularly AI. By considering the various approaches to AI development, the ethical implications of emotion AI, the transformative potential of intelligent simulation, and the evolving nature of work, businesses can make informed decisions about technology adoption and prepare for the future.

Source: Gartner