Shelf named a Cool Vendor™ by Gartner® in the Digital Workplace Applications report.
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Voice AI for Customer Service: Replacing IVR with Intelligent Agents

Customers have long been accustomed to contacting customer support whenever they encounter a problem. And of course, such customers aren’t impressed by IVR (Interactive Voice Response) menus. But you know, sometimes it’s easier to figure something out on your own than to navigate an IVR and follow...

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Contact Center Automation: 12 Workflows AI Agents Handle Today

The first contact centers appeared in the United States as early as the 1960s. Since then, they have come a long way, evolving from IVR menus to AI agents that will handle entire workflows by 2026. Contact center automation is no longer just about resolving issues. It is a vast, complex system...

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Multi-Agent AI: When Single Agents Aren’t Enough

The business world has changed dramatically with the rapid development of artificial intelligence. Tasks that used to take people hours to complete can now be handled by AI in just a few minutes. But even that has its limits, and in some situations, even a single agent is no longer enough....

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Agentic AI for Customer Experience: Beyond Chatbots to Autonomous CX

In 1946, Ford established the first “automation department” at one of its factories. Workers laughed and criticized the idea, saying, “A machine can’t replace a human when thinking is required.” But ten years later, the assembly line was doing what used to take a person an entire day. What does...

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Knowledge Management Is the Secret Weapon of Enterprise AI Agents

You probably think that artificial intelligence knows absolutely everything. No matter what question you ask it, it responds quickly – literally within 5-10 seconds. That’s why it’s being integrated into various businesses, and everything seems to be going smoothly. Until, at some point, a...

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Enterprise AI Platform: What to Look for in Your Agentic AI Stack

Enterprise AI Platform in 2026 is not just another AI tool. It is a unified, large-scale infrastructure environment in which AI agents can analyze data, make decisions, and interact with one another with virtually no human intervention. It is not an LLM API; it is something much bigger and more...

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Agentic Process Automation: Why RPA Is Dead and What Replaces It

Agentic process automation, or APA, is a necessary and entirely logical evolution of RPA. The key difference between APA and RPA is the ability of AI agents to perform their tasks autonomously. This means they make decisions and adapt within business processes WITHOUT relying on scripts....

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Three Days at NRF 2026: What 500+ Conversations Revealed About AI in Retail

We’re still buzzing from last week’s NRF event. Over three days, our team connected with nearly 500 retailers and industry leaders and the energy around what’s possible with AI was palpable. But beyond the excitement, what struck us most were the candid, unfiltered conversations...

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Unstructured Data Management: Why Traditional Data Management Tools Aren’t Equipped to Solve It

When it comes to managing data, unstructured data is the wild card. It’s messy, unpredictable, and doesn’t fit neatly into the boxes and grids we’ve relied on for years. Unlike structured data, which is easy to organize and analyze, unstructured data is chaotic. It’s not just that there’s more of...

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Unstructured Data vs. Structured Data And Why it Matters for GenAI

Data is classified into two main types: structured and unstructured. Structured data refers to organized information that follows a predefined format and resides in fixed fields within a record or file. Structured data is easily searchable, organized, and can be stored in databases. Unstructured...

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AI Projects Won’t Deliver Results Until You Fix Your Data

This post was created by Shelf with Insider Studios. We’ve all heard the explanations for why AI projects fail: the models aren’t advanced enough, they don’t remember past interactions, they hallucinate answers — the list goes on. However, those explanations overlook AI’s...

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Why 95% of Contact Center AI Projects Fail (And the Governance Blueprint That Saves Them)

Key Takeaways An MIT report reveals 95% of AI pilots fail. Contact centers are rushing AI deployments without the governance layer needed for success.  We are seeing poor data preparation and lack of feedback loops as the leading causes of AI project failure. Organizations that implement...

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