Artificial intelligence
fromInfoWorld
2 days agoBuilding enterprise voice AI agents: A UX approach
Voice AI in enterprises needs to prioritize human interaction over technical improvements for effective collaboration.
The new tracker features a simplified progress bar that shows just four stages of pizza creation. The new design was rolled out to all platforms, and there's also new Lock Screen widgets for iOS that bring the pizza chain's most famous tech feature to the Liquid Glass age.
The way Costco's automated pay stations work is that members stand in line and a Costco employee scans the person's membership card and all of the items in their cart. When the member reaches the self-serve payment kiosk, they scan their membership card and pay. The system eliminates the conveyor belt and any interaction with a cashier.
In 1952, Japanese technologist Masaru Ibuka learned that Western Electric was releasing its transistor patents to the public for $25,000, a significant investment for his struggling firm. This opportunity would allow access to essential patent portfolios and technical information, crucial for innovation in electronics.
The competitive landscape among AI apps in China is fierce. Companies have been dumping money into the market to try to win customers and show them how AI is useful in everyday life, in particular, for buying stuff.
These AI tools drive profitability because they reduce outages, save energy, and obviate the need for manual intervention said Chetan Sharma, CEO of Chetan Sharma Consulting, who contributed to the report. The focus on AI-native networks and autonomous operations has overtaken customer service optimization as the leading use case for investment, according to findings from the firm's fourth annual State of AI in Telecommunications survey. The report also showed that AI has become profitable for 90% of telco operators.
The fourth annual State of AI in telecommunications survey was carried out from September to November 2025, gathering responses from 1,038 respondents. It included a 60/40 split between management (including executives) and AI practitioners, including engineers, network operators, architects, cloud operators and IT. Respondents encompassed a range of industry segments, including internet service providers, independent software suppliers, network equipment providers, consulting services, operators and system integrators.
There were specialists monitoring dashboards, tuning AI behavior, debugging API failures, and iterating on knowledge workflows. One team member who had started their career handling customer questions over chat and email (resetting passwords, explaining features, troubleshooting one-off issues, and escalating bugs) was now writing Python scripts to automate routing. Another was building quality-scoring models for the company's AI agent. This seemed markedly different from the hyperbole I'd been hearing about customer support roles going away in large part due to AI.