Search results for: 'chiplets ai'

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...

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 ...

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...

The Microelectronics Technology Opportunity Engine covers innovations pertaining to optoelectronics powering Hyperscale AI and Data Center Interconnects, Advanced Transceiver Chip For Increased Communication Efficiency, Low-Power System-on-Module (SoM) For Enhanced AI Applications, Next-generation Defect Review System with Faster Analysis of Nanosc...

Disruptive AI models are increasingly challenging traditional assumptions about “classic” cloud data centers. As training clusters scale up to thousands of accelerators and consume tens of megawatts of power, the conventional CPU-centric, air-cooled, monolithic server model is no longer sustainable. This research study explores the megatrend of AI ...

As artificial intelligence moves from experimentation into the operational core of the enterprise, the demands placed on implementation and managed service providers are changing fundamentally. Early AI success was defined by proof points: models deployed, pilots completed, productivity gains demonstrated. Today, success is defined by durability: t...

Incorporating AI into mobile operator business operations offers clear advantages—automation, customer-centric services, and operational efficiency. However, these benefits come with challenges, including high implementation costs and data security risks. Strong support from C-level executives and boards can help operators overcome these obstacles....

AI technologies and platforms are entering a new stage of maturity. Most organizations now have access to capable models and have run pilots across multiple functions. The real question is no longer whether AI works, but where it can create sustained growth and how it can be scaled without introducing cost volatility, operational risk, or governanc...