Last Updated October 2025
In short, significantly. Gartner predicts that by 2030, 90% of humans will engage with smart robots on a daily basis, due to smart robot advancements in intelligence, social interactions and human augmentation capabilities, up from less than 10% today.
This transformation is characterized by several key trends:
Automation of routine tasks to allow human workers to focus on more complex and value-added activities.
Creation of new roles, like AI engineers, data scientists and AI ethicists. This indicates a transformation in job functions rather than outright job loss.
Enhanced decision-making, such as in customer service, for example, where AI can assist agents by providing real-time data and insights, thereby improving the quality of service.
Workforce augmentation, in part thanks to the rise of polyfunctional robots, which can perform multiple tasks and adapt to various environments. This shift not only transforms job roles but also enhances workplace safety and productivity.
Continuous operation through hot inspection and in-situ monitoring by smart robots, even in hazardous environments.
Physical agency of AI in agent networks, where robots act as the physical embodiment of AI agents — enabling digital intelligence to perceive, interact and act within the real world.
To prepare the workforce for new responsibilities, focus on reskilling, upskilling and carefully managing workforce transitions to mitigate fears and retain talent.
By Tuong Nguyen
Spatial computing boosts human perception and thinking and improves machines’ ability to understand, move through and interact with real-world locations and objects. By organizing and linking digital content to the real world, this technology unlocks new avenues for business and dramatically improves the effectiveness of interactions between people, systems and their environments.
The market for spatial computing is expected to reach $1.7 trillion by 2033. Potential use cases include the following.
Embodied AI/physical AI uses a shared content source describing the state of and relationships between things to orchestrate and respond. For example, autonomous vehicles respond to first responder vehicles to request green lights, reroute cars and alert pedestrians through connected devices.
Agentic AI is a unifying framework to connect diverse devices and content across public and private data sources using an appropriate graph. For example, enabling AI agents controlling thermostats and those controlling energy-storage battery systems to work together to maximize comfort while saving money and cutting emissions across a neighborhood.
Spatial web is a physical world wide web that delivers just-in-time information and hyperpersonalized experiences and services via an internet of spaces and places for a variety of use cases, including retail, advertising, industrial maintenance and prototyping as well as orchestration and collaboration between AI agents.
As organizations explore these use cases, they must also address challenges such as data privacy, standardization and the integration of spatial computing into existing workflows.
Reasoning models are expected to undergo significant evolution in logical inference, complex problem solving and multistep reasoning. These models will increasingly use chain-of-thought processes and self-reflection, allowing them to mimic human-like thought patterns more effectively than traditional models, which primarily rely on pattern recognition. This evolution is driven by advancements in reinforcement learning, where models are trained to validate their outputs in areas such as math, logic and coding.
Gartner projects that by 2029, investment in reasoning models will underpin the success of more than 70% of agentic AI applications, a significant increase from 0% in 2024.
The shift toward more sophisticated, domain-specific reasoning models will enable organizations to automate complex tasks, improve decision-making and redefine workforce dynamics, ultimately leading to a more integrated and efficient use of AI technologies in business operations. The anticipated growth in agentic AI applications underscores the importance of investing in these advanced reasoning capabilities to stay competitive in an increasingly AI-driven landscape.
September 2025
Claude Sonnet 4.5 signals Anthropic’s intention to shift toward domain specialization in an increasingly competitive generative AI (GenAI) model market landscape. Gartner recommends:
Agentic AI technologies are poised for significant transformation and growth, driven by advancements in AI capabilities, increasing demand for automation and the need for enhanced decision-making processes across various industries. As agentic AI matures, you can expect:
Increased agency and autonomy: This will enhance productivity and efficiency, allowing organizations to automate intricate workflows and processes that were previously labor-intensive.
Integration into business processes: By 2028, it is anticipated that 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. This will facilitate more sophisticated interactions between humans and AI systems, improving overall operational efficiency.
Focus on use cases with clear value: Gartner predicts that over 40% of agentic AI projects will be canceled by 2027 due to escalating costs or unclear business value. Prioritize agentic AI projects that demonstrate clear business value and ROI.
Challenges in adoption: These include complexity in implementation, the need for robust governance frameworks and ensuring data quality.
Collaboration and governance: Organizations will need to establish clear guidelines for the autonomy granted to AI agents that balance the benefits of automation with the need for oversight.
Market dynamics and competition: The market for agentic AI is expected to grow rapidly, with both startups and established companies investing heavily in this area. Yet, the market is currently rife with “agent washing” with many vendors branding as agentic, regardless of the underlying capabilities. True AI system autonomy — acting as reliable, communicative and collaborative multi-AI-agent systems — remains aspirational..
The business value of AI ethics is increasingly recognized among organizations leveraging AI. Each of its principles relate to business benefits:
Human-centric and socially beneficial: Implementing ethical AI practices helps build trust with customers, employees and investors. Organizations that prioritize ethical considerations in their AI initiatives are more likely to maintain a positive reputation, which can boost customer loyalty and strengthen the brand.
Fair: Fairness includes bias mitigation, which helps with regulatory compliance. Furthermore, when customers feel they are treated fairly, based on their specific circumstances, it can positively affect retention.
Explainable and transparent: When you understand what your AI is doing, you are more confident in bringing it to market. This also positively affects the organization’s risk metrics, such as reputation risk.
Secure and safe: The business value is in data security, privacy metrics and operational risk.
Accountability: Strong AI governance facilitates shorter time to response and lower cost of compliance.
Sustainability: Efficient use of resources and alignment with an organization’s ESG goals is cost effective.
Enhanced reasoning language models (RMs) are designed to tackle complex tasks that require logical inference, multi-step reasoning and structured outputs. The best applications for these models include:
Providing explainable and structured outputs: RMs provide visibility into AI’s decision process and generate clear explanations of complex analysis or decision logic.
Analyzing extensive and complicated information: These models can review and assess vast document sets, generate comprehensive summaries and facilitate complex code analysis, comprehension and refactoring.
Tackling complex technical and logical problems: RMs excel in scenarios that involve intricate decision-making processes, such as regulatory compliance checks, financial risk analysis and medical diagnostics.
Automating workflows and using tools: These models can coordinate the specialized skills of individual AI agents to collaboratively execute multistep processes, connect and interact with enterprise systems and integrate real-time external information retrieval.
While enhanced reasoning models offer significant advantages, not all generative AI use cases should be upgraded with them. Here are some considerations:
Complexity of use cases: For simple tasks that do not involve multiple steps and require elaborate outputs, traditional large language models (LLMs) may suffice.
Resource requirements: RMs typically consume a lot of computational resources and time due to their planning and validation processes. For applications where speed and efficiency are critical, traditional LLMs might be more appropriate.
Current limitations: While RMs show promise, they are still maturing. Their performance can vary, and they may not always provide the desired accuracy or reliability.
To see previously featured answers to client questions on emerging technologies, visit the archive.
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