Search results for: 'analysis perspectives on agentic ai'

This study examines the increasing use of artificial intelligence (AI) in quality management. The rapid advancement of AI has led to its use across sectors, particularly quality management, as is evident in the growth of predictive quality analytics and enterprise quality management systems (EQMS). With increasing competitive intensity, it has beco...

The AI landscape is undergoing rapid transformation, driven by disruptive technologies, evolving enterprise needs, and the imperative to innovate at scale. This study identifies the Top 10 Growth Opportunities in AI Technologies and Platforms that will shape global markets in 2025 and beyond. These opportunities reflect the convergence of strategic...

The human resources function faces significant disruption as artificial intelligence (AI) technologies mature. While the chief human resources officer organization has been adopting digital technologies to drive greater integration, automate processes, and improve workflows, Generative AI (GenAI) now offers the potential to create a transformative ...

This analysis examines the AI applications reshaping the automotive aftermarket ecosystem, exploring key adoption areas, regional dynamics, emerging use cases, and AI-driven investments driving change in operations and business models. Structured into six core segments, the report focuses on major value chain streams: Remote and On-Bay Diagnostics ...

Agentic AI now decides and acts inside live customer interactions, approving a refund, denying a claim, escalating a complaint, or promising a remedy the company must honor. The accountability and control architecture most enterprises rely on was built to review human work after the fact, and it cannot monitor a decision an AI agent makes in millis...

As enterprise adoption of AI enters a more demanding phase, the source of competitive advantage is shifting. In 2026, success is no longer determined by access to advanced models or the scale of experimentation, but by an organization’s ability to operationalize AI reliably within core workflows. This report examines how AI applications and use cas...

As generative AI becomes deeply embedded in enterprise operations, fueling copilots, assistants, and automated agents, organizations face a wave of new and dynamic security risks. Traditional cybersecurity architectures are ill-equipped to address the fluidity, autonomy, and unpredictability of GenAI systems. This report provides chief information ...

Frost & Sullivan s 2024 information technology decision-maker (ITDM) survey was conducted to gain insights on the following end-user organizations priorities: Top technical challenges for IT/telecom departments Top non-technical challenges for IT/telecom departments Current unified communications and collaboration (UCC) technology a...

Enterprise use of AI has moved past experimentation. Many organizations now run AI inside core workflows that affect customers, operations, and compliance. This shift has changed what enterprises expect from AI implementation and managed service providers. Success no longer hinges on launching a model or completing a deployment. It depends on wheth...