Hey there,

AI is shifting from scattered POCs and vague talks to stacks. Where Kore.ai, Dell, Microsoft, and bank factories bind data, models, and agents into one control layer.

Which platforms become core—and which pitches hit the cutting-room floor?

Kore.ai

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Kore.ai is an enterprise-grade agentic AI platform that lets you design, deploy, and scale specialized AI agents across business functions without starting from scratch each time.

Best for

• Enterprises deploying multiple AI use cases across departments needing consistent governance

• Companies stuck in "pilot purgatory" wanting production-ready agents that actually work at scale

How to use it

• Map your business functions where agents could add value (support, HR, sales ops)

• Use the low-code interface to configure agents with specific roles and system integrations

• Connect through its ecosystem to plug into tools and build more automated workflows

When not to use it
If you're only testing one or two AI prototypes, workflow automation is overkill. Start with simpler tools first.

Pro tip
Leverage the platform's unified control plane to manage agent behavior consistency across departments, this prevents the chaos of every team building fragmented, ungoverned agents that can't talk to each other.

🚀 Dell & Microsoft: Collapse Enterprise Stacks Into One AI Control Plane

Dell and Microsoft are rearchitecting enterprise IT by positioning AI as a complete operating model rather than scattered point solutions, integrating compute, storage, networking, and orchestration into one control plane that addresses the friction keeping companies stuck in perpetual experimentation instead of production deployment.

Read the full article here » 

Key Takeaways:

• 🔧 Unified Control Plane: Composable platforms merges the stacks into one interface managing analytics, models, and apps across cloud and on-prem.

• 🤖 Built for Agents: Knowledge graphs and protocols in the data layer let autonomous agents broaden intelligence without manual constraints.

• ☁️ Sovereign Private Cloud: Azure Local pairs public cloud capabilities with locked-down data residency for media, life-sciences, and EDA workloads.

• 📊 New Customer Segments: Neoclouds and GPU providers are pushing the market toward platforms delivering working results over isolated elements.

🏭 What's An AI Factory? Vendor Buildings, Server Racks, And Software Stacks

Infrastructure leaders seek vendor clarity before costly bets, forcing contract specificity on whether "AI factory" means data centers, server racks, or software stacks, turn vague remarks into traceable decisions that match power budgets, rack counts, and deployment needs.

Read the full article here » 

Key Takeaways:

• 🏢 Specialized Data Centers: Nvidia and Siemens define it as gigawatt facilities with liquid cooling, industrial controls, and reinforced concrete enhanced by digital twins for both power and placement.

• 🖥️ Preconfigured Server Racks: Lenovo and AWS frame it as managed stacks, one to hundreds of racks shipped fully assembled with networking, storage, and AI services ready to plug in on-site.

• 💾 Software Foundation Layer: MIT Sloan and Deloitte describe it as lifecycle stacks combining platforms, data repositories, and reusable algorithms that cut deployment time and liabilities.

• ⚠️ Clarify Before Buying: Pin down whether vendors mean literal buildings, refrigerator-sized racks, or set up software ecosystems. Misreading this distinction burns billions on the wrong bet.

💹 AI Bubble Leaks: Factories Lock In as the Stable Backbone for Real AI Returns

Bad vendor quarters, cheap Chinese models, and spend pullbacks pops the AI bubble, while BBVA, JPMorgan, and Intuit’s AI factories fuse platforms, methods, data, and algos so data scientists stop rebuilding the same base work.

Read the full article here » 

Key Takeaways:

• 📉 Bubble Deflation Ahead: Sky-high vals, burn rate, costly infra echo dot-com, gradual deflation aids long-term digestion, skips short froth.

• 🏭 AI Factories Emerge: BBVA, JPMorgan's 2019-20 tech-algorithm platforms now scale analytical, generative, agentic AI across sectors.

• ⚙️ Infrastructure Advantage: Factory-less teams redo tool picks, data maps, algos recent gradual AI costs, delays, pains every time.

• 🔄 Amara’s Law Applied: Short-term AI overhype, long-term underrate markets absorb tech as users chase energy-thrifty economic fixes.

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