Trending Questions on AI and Emerging Technologies

By Daniel Sun

• Follow a three‑step approach. First, discover candidate agents and map their roles and potential business benefits. Then, evaluate fit against your business needs and context. Finally, implement governance, integration, security and change management to ensure value delivery of AI agents. 
• Prioritize agent candidates aligned to business value for digital channels. Map candidate agents to the specific business model component and expected value.
• Use a structured evaluation to filter and prioritize use cases. Evaluate task complexity, required oversight, environment volatility, data modality/volume, personalization needs, criticality of errors and human–AI collaboration mode to decide which agents to pilot and scale. 
• Establish clear management practices for AI agents before scaling. Manage agents across five critical areas: 
  • Governance and accountability
  • Integration and interoperability
  • Monitoring and compliance
  • Security
  • Business change 
• Implement life cycle processes and centralized orchestration. Define selection, design, test, deploy, update, efficacy auditing and decommissioning processes. Use centralized orchestration frameworks to manage integration, scale and agent interactions. 
• Measure success with clear, business‑linked success measures. Track seamless integration, adoption rates, increasing autonomous decision making (reduced human intervention), improved user experience/productivity, and demonstrable ROI with transparent cost‑benefit analysis. 
• Use telemetry, dashboards and real‑time alerts to monitor efficacy and costs. Implement telemetry and logging, dashboards and alerts to identify performance, usage, cost and failure modes so you can iterate quickly. 
• Pilot in controlled environments. Run controlled pilots, validate value and integration, then scale; maintain human‑in‑the‑loop safeguards for higher‑risk actions. 
• Plan for skills, roles and change management. Invest in employee reskilling and create safe experimentation environments so teams can learn to design, monitor and govern agents. 
• Apply responsible AI controls. Implement identity management for agents and secure agent‑to‑agent communication. Apply ethical, regulatory and audit controls as part of the governance model. 

By Marty Resnick

Biocomputing platforms leverage living biological materials, such as biochemically engineered neural networks, biological cells or organoids, either independently or integrated with traditional silicon-based technologies to perform computational tasks with high energy efficiency and adaptive learning. These platforms address the scalability and sustainability limits of silicon architectures, especially as AI workloads grow.

Organizations are exploring biocomputing platforms alongside neuromorphic, photonic and quantum resources, requiring unified software stacks and open standards. Commercial synthetic biological intelligence (SBI) systems and wetware-as-a-service (WaaS) are emerging for research and specialized workloads, with broader adoption anticipated. 

Engineered beings further bridge biological and digital systems, enhancing human-computer interaction and offering healthcare opportunities.

By Alizeh Khare

Generative AI (GenAI) combined with advanced computer vision (CV) is changing how organizations extract value from visual data. Amid market disruption, multimodal GenAI CV and vision language models (VLMs) are reshaping product categories, enabling new, defensible experiences and contributing to scaling and automation. Further, agentic orchestration is also enabling GenAI CV by coordinating perception, reasoning and action agents to adaptively interpret the scenes, generate insights and optimize decisions in real time. Although the data, compute and integration needs are significant and there are governance risks, generative AI-powered computer vision:

• Enables new product and revenue opportunities by turning previously passive image/video archives into actionable, searchable and monetizable assets (e.g., automated video transcription, multimodal visual search and VLM-enabled services) 

• Accelerates time to value for content and campaigns via GenAI-assisted image/video generation, enhancement and automated metadata/tagging — reducing production friction and enabling faster personalization at scale

• Improves real‑time customer experiences and retention by enabling on-device and edge inferencing for use cases such as augmented reality (AR)/virtual try‑ons, spatial/“phygital” experiences and visual question answering 

• Unlocks richer insights for strategy and targeting by correlating visual signals with other modalities (text, audio, event streams) through multimodal and world-model approaches that support generalized inference and prediction

• Reduces dependency on scarce labeled real-world datasets by using high-quality synthetic data and automated labeling to scale model training and expand coverage of edge cases

• Lowers latency and bandwidth costs by moving inference to edge devices (vision accelerators, small models) and hybrid edge-cloud architectures, enabling real‑time personalization and analytics in customer touchpoints

By David Sugden

The 2026 Gartner Emerging Technologies Adoption Radar highlights four themes shaping the future: 

• AI-powered enterprise transformation

• Security by design

• Efficiency first

• Foundational and frontier technologies. 

Expect rapid advances in generative and agentic AI, explainable AI, and domain-specific language models to drive business transformation and customer experience. At the same time, organizations are embedding cybersecurity by default, leveraging innovations like secure access service edge (SASE), zero trust and AI-native security controls. Efficiency gains will come from the convergence of AI, automation, process intelligence and digital twins, enabling self-managing systems and real-time decision making. Foundational investments in cloud modernization, digital sovereignty infrastructure and next-generation platforms will underpin scalable, future-ready operations.

To make the most of these technologies, prioritize tangible outcomes, such as productivity and customer experience, by integrating AI and automation into core processes while establishing centralized governance for security and compliance. Adopt a strategic, risk-aware approach: Start with technologies that align with your business goals and readiness and continuously assess new innovations for their relevance and impact. This helps balance innovation with risk, ensuring your technology roadmap delivers measurable value in a fast-evolving digital landscape.

By Frank Buytendijk and Pieter den Hamer

Gartner predicts that AGI (artificial general intelligence) will not materialize for at least a decade, likely requiring several breakthroughs beyond scaling current technologies. By 2035, there will be progress toward AGI, but it will not be widely recognized as achieved.

AGI will only be achieved if AI technology matches the skills of all people on all cognitive tasks. Some technology providers are already openly discussing artificial superintelligence (ASI), AI that far surpasses human capability on all cognitive tasks. Because AGI and ASI require different approaches, they are almost parallel development paths.

Gartner believes both AGI and ASI have significant challenges and should be avoided. AGI performance is directed compared with people, whose ultimate goal it is to replace. Superintelligence, far surpassing human capability, can become problematic when in the wrong hands or be a single point of failure.

As such, we propose a third approach to general intelligence: augmented collective intelligence (ACI). Picture a swarm of connected and dedicated agents, combining different styles of AI, working side by side with people who contribute human intelligence. General intelligence emerges from this network. ACI best matches the goal of technology: to enhance human capability and improve quality of life.

By Danielle Casey

The Gartner Agentic Compass aligns organizational needs with agentic capabilities to help organizations navigate the increasingly complex landscape of agentic AI solutions. Its main objective is to enable enterprises to align their needs with appropriate AI capabilities, ultimately driving better business outcomes.

The Agentic Compass is composed of two frameworks — one for assessing use-case needs and the other for evaluating distinct agentic capabilities. Based on analysis of 169 providers and 200 real-world agent deployments, these frameworks offer a grounded reference for defining requirements, constraints and objectives. This enables better alignment with platforms, prebuilt agents, governance tools and optimal technology combinations across multiple vendors.

By Alizeh Khare

Emotion AI is a critical differentiator in the next wave of deployment of computer vision and robotology. To effectively leverage emotion AI to drive innovation while mitigating risks associated with its implementation, CIOs evaluating the technology (also known as affective computing) must consider the following:

• Enhancement of customer engagement. Emotion AI can significantly improve human-machine interactions by analyzing emotional states (e.g., through voice and facial recognition) and enabling businesses to tailor responses that better resonate with customers. 

• Applications in other sectors. Emotion AI is used in healthcare for diagnostics and in marketing for neuromarketing strategies that gauge product reactions. Its ability to analyze emotional signals across multiple modalities allows businesses to refine their approaches and improve customer retention and satisfaction.

• Privacy concerns. Current usage scenarios indicate a need to prioritize aggregated, context-based insights (e.g., “25% of customers showed frustration”) over individual emotion attribution to support regulatory compliance and build trust.

• Bias and accuracy. Relying solely on expression detection is insufficient for understanding intent; multimodal systems that analyze multiple cues perform better. Clear metrics — such as precision, recall, fairness, false alarms, latency, user satisfaction and regulatory compliance — are essential, especially in areas with strict privacy laws.

• Ethical oversight and transparency. Ensure that systems are transparent about how emotional data is used and prioritize ethical decision-making processes that avoid applications related to mass monitoring or surveillance.

• Regulatory compliance. Safeguarding user privacy by using privacy-preserving machine learning (ML) techniques such as federated learning, on-device processing, confidential computing, explicit opt-in consent and explainable AI will continue to be a top priority.

By Tsuneo Fujiwara

Intelligent simulation is rapidly emerging as a transformative technology that integrates advanced simulation engines, digital twins, GenAI, synthetic data and quantum computing to deliver unprecedented scale, efficiency and accuracy in decision making. It is moving from a tool that offers guidance by driving innovation through generating, analyzing, and designing at scale, to one that can autonomously execute decisions based on simulated results. Its use cases are vast.

The following considerations, particularly in the context of digital marketing and broader business operations, are essential in implementing intelligent simulation solutions:

• Alignment with business objectives. Ensure that intelligent simulation technologies address relevant challenges and enhance decision-making capabilities while aligning with marketing and operational goals.

• Technology integration. Effective implementation relies on seamless integration with existing IT infrastructure, including enterprise resource planning (ERP) systems and customer relationship management (CRM) tools. Prioritize platforms that can easily interface with current business solutions to maximize operational efficiency.

• Data accessibility and quality. The success of intelligent simulation is heavily dependent on access to quality data. Focus on strategies that ensure data integration from various sources to facilitate accurate modeling and scenario analysis. Address potential data silos and ensure that data management practices support high-quality inputs for simulations to optimize their accuracy.

• User experience and accessibility. Support the development of low-code or no-code interfaces that allow users to leverage intelligent simulation tools without needing extensive technical expertise. This will help democratize access to insights generated from simulations across the organization.

• Cultural change management. Implement a change management strategy that engages stakeholders at all levels to ensure they understand the benefits of intelligent simulations and how these tools can enhance day-to-day operations.

• Demonstrating value and ROI. Emphasize tangible business value outcomes such as improved efficiency, better decision making and enhanced customer insights. 

• A focus on predictive capabilities. Facilitate the development of models that not only provide real-time insights but also help anticipate future operational needs and market trends.

• Continuous learning and adaptation. Intelligent simulation applications should be designed to learn from their execution context, allowing them to adapt over time. Ensure processes are in place for ongoing evaluation and refinement of simulation capabilities to keep pace with changing business environments and user needs

To see previously featured answers to client questions on emerging technologies, visit the archive.