NEW LAUNCHES

The latest features, products & partnerships in AI

• Vay’s Las Vegas car services will have human drivers… operating from miles away

• Easily embed RAG into your product with Ragie’s new ‘Connect’ platform

• Adobe’s new TransPixar system takes AI VFX to the next level

• Meta develops AI memory layer architecture to boost LLM accuracy and recall

• South Carolina is preparing to launch an AI Center of Excellence

IMPLEMENTATION

Announcements, strategies & case studies

• Small businesses using AI are 45% more likely to fill open positions

• Best practices for LLM-assisted software development

• Apple Intelligence expansion to TV and watch devices expected soon

• 41% of firms to plan workforce cuts by 2030 because of AI

• AI agents will drive the future of autonomous businesses

• AI ROI: 3 crucial questions CIOs must ask

• AI agents in 2025: What enterprise leaders need to know

AI INFRASTRUCTURE

Buildout, financing, policy, energy & hardware

• Louisiana approves $2.5B data center project with zoning update

• Big Tech group urges US government to halt AI chip export rule

• Microsoft promotes US-led AI development

• Digital Edge raises $1.6B for APAC data center growth

IN OTHER NEWS

Compelling stories beyond the usual categories

• Samsung announces date for Unpacked 2025 event — here’s what to expect

• AI drives rent prices higher, new study reveals

• OpenAI’s latest red teaming research offers essentials for security leaders in the AI age

• New research suggests consumers don’t want AI to seem human

• AI awareness is growing in Germany, but many workers have no interest in upskilling, study finds

• How to install an AI model on MacOS (and why you should)

What’s happening in AI right now

The race for enterprise AI agents

Nvidia introduced its Nemotron family of models, designed specifically to enhance AI agents' capabilities across language and visual processing. Meanwhile, Microsoft and Accenture released research showing how AI-powered autonomy will fundamentally reshape business operations, with over 75% of executives citing trust as crucial for realizing AI's full potential. These developments point to a larger trend: the enterprise adoption of AI agents is accelerating faster than many anticipated.

The new enterprise stack

The competition for enterprise AI is shaping up differently than previous tech platform shifts. Instead of a single dominant player emerging, we're seeing a layered ecosystem develop where companies like Nvidia are building specialized agent-focused models, enterprise software giants like SAP and ServiceNow are integrating these models into their existing platforms, and consulting firms like Accenture are developing frameworks for implementation and governance.

This layered approach suggests that success in enterprise AI won't come from controlling the entire stack, but rather from creating effective partnerships across layers. For the time being, this is essential, though it is also theorized that agents will ultimately reshape the dynamics of enterprise software entirely.

Beyond simple automation

A key distinction is emerging between traditional Robotic Process Automation (RPA) and agentic AI. While RPA excels at rule-based, repetitive tasks, AI agents offer autonomous decision-making and adaptability. The combination of these technologies creates a powerful new paradigm for workplace automation.

The World Economic Forum's recent survey reveals a nuanced approach to AI adoption: while 41% of firms expect staff reductions by 2030, a striking 77% plan to focus on retraining their existing workforce. This dual strategy suggests companies see AI agents as tools for augmentation and efficiency rather than wholesale automation for the time being.

The infrastructure challenge

Perhaps the most overlooked aspect of this transition is the massive infrastructure transformation required. Microsoft and Accenture's research highlights what they call the "Binary Big Bang" in enterprise technology capabilities. Organizations need robust digital cores and governance frameworks to effectively deploy AI agents.

This infrastructure challenge creates interesting dynamics. Established enterprises with legacy systems face significant hurdles, while younger companies might have an advantage in adopting agent-first architectures. Infrastructure providers could become unexpected winners in the transition.

Looking ahead

By 2025, an estimated 25% of enterprises will deploy AI agents, rising to 50% by 2027. The race is creating new priorities: companies must develop robust evaluation frameworks, optimize costs, and balance personalization with privacy concerns.

The enterprise AI landscape is evolving rapidly, but several key questions remain. Will specialized agent models (like Nvidia's Nemotron) outperform general-purpose LLMs in enterprise settings? How will organizations balance the need for standardization with the desire for customization? Can existing enterprise software providers successfully integrate AI agents, or will new players emerge?

One thing is clear: the race for enterprise AI agents isn't just about technology - it's about fundamentally rethinking how work gets done.

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