Reps lost time hunting across complex systems. Now, AI agents surface verified answers and automate notes, freeing humans to connect.
A global telecommunications provider supporting millions of customers and thousands of network engineers, where seconds of downtime can trigger massive service disruptions.
An imbalance between data velocity and human capacity left employees navigating layers of complex systems to do their jobs. Customer care...
“We chose Azure Cosmos DB because it is optimized for high-performance chat and agent apps, super-responsive user experiences, and scalability.”
Wireless, high-speed internet, and enterprise communication services provider.
Enterprise software, cloud infrastructure, and consumer electronics platform.
AT&T's Internal employee assistants is part of this use case:
Related implementations across industries and use cases
Opening enterprise data risked uncontrolled tool sprawl. Now, a tiered platform empowers staff to safely build their own AI tools.
Reps manually skimmed 12-page articles to resolve cases. Now, an AI assistant synthesizes live data to deliver answers in seconds.
Contextless forms and limited chatbots bottlenecked support. Now, AI agents resolve multilingual queries and auto-generate IT tickets.
Scattered systems delayed risk decisions. Now, an AI assistant grounded in live search lets experts instantly compare complex policies.
Routine, multilingual requests bottlenecked IT. An AI assistant in Teams now resolves staff queries instantly in six languages.
Pulling answers from a sprawling customer dataset once meant weeks of SQL; now anyone asks in plain language and explores it in seconds.
Call centers couldn't keep pace with demand peaks and rising costs. Now, agents handle 24/7 messaging, with AI summarizing and translating.
Basic troubleshooting consumed agents' time. Now, an AI fluent in local dialects independently resolves 73% of these cases.
Manual call evaluations delayed insights by weeks. Now, bots score calls at scale, empowering analysts to synthesize data in hours.
Reps lost time hunting across complex systems. Now, AI agents surface verified answers and automate notes, freeing humans to connect.
A global telecommunications provider supporting millions of customers and thousands of network engineers, where seconds of downtime can trigger massive service disruptions.
An imbalance between data velocity and human capacity left employees navigating layers of complex systems to do their jobs. Customer care...
“We chose Azure Cosmos DB because it is optimized for high-performance chat and agent apps, super-responsive user experiences, and scalability.”
Wireless, high-speed internet, and enterprise communication services provider.
Enterprise software, cloud infrastructure, and consumer electronics platform.
AT&T's Internal employee assistants is part of this use case:
Related implementations across industries and use cases
Opening enterprise data risked uncontrolled tool sprawl. Now, a tiered platform empowers staff to safely build their own AI tools.
Reps manually skimmed 12-page articles to resolve cases. Now, an AI assistant synthesizes live data to deliver answers in seconds.
Contextless forms and limited chatbots bottlenecked support. Now, AI agents resolve multilingual queries and auto-generate IT tickets.
Scattered systems delayed risk decisions. Now, an AI assistant grounded in live search lets experts instantly compare complex policies.
Routine, multilingual requests bottlenecked IT. An AI assistant in Teams now resolves staff queries instantly in six languages.
Pulling answers from a sprawling customer dataset once meant weeks of SQL; now anyone asks in plain language and explores it in seconds.
Call centers couldn't keep pace with demand peaks and rising costs. Now, agents handle 24/7 messaging, with AI summarizing and translating.
Basic troubleshooting consumed agents' time. Now, an AI fluent in local dialects independently resolves 73% of these cases.
Manual call evaluations delayed insights by weeks. Now, bots score calls at scale, empowering analysts to synthesize data in hours.