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Conversational AI use cases for enterprises

IBM Journey to AI blog

Beyond the simplistic chat bubble of conversational AI lies a complex blend of technologies, with natural language processing (NLP) taking center stage. This sophisticated foundation propels conversational AI from a futuristic concept to a practical solution. billion by 2030.

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Top 7 meeting intelligence platforms in 2025

AssemblyAI

acts as your virtual meeting assistant by joining calls across multiple platforms and turning conversations into searchable knowledge. The platform is great for how it structures meeting content—automatically categorizing discussions, flagging action items, and making sure nothing falls through the cracks.

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Evaluate conversational AI agents with Amazon Bedrock

AWS Machine Learning Blog

As conversational artificial intelligence (AI) agents gain traction across industries, providing reliability and consistency is crucial for delivering seamless and trustworthy user experiences. However, the dynamic and conversational nature of these interactions makes traditional testing and evaluation methods challenging.

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How AI Chatbots Mimic Human Behavior: Insights from Multi-Turn Evaluations of LLMs

Marktechpost

Researchers evaluated anthropomorphic behaviors in AI systems using a multi-turn framework in which a User LLM interacted with a Target LLM across eight scenarios in four domains: friendship, life coaching, career development, and general planning. The results identified relationship-building behaviors that evolved with dialogue.

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4 ways to empower small and medium businesses with generative AI

IBM Journey to AI blog

Watsonx.data allows enterprises to centrally gather, categorize and filter data from multiple sources. Conversational AI solutions can reduce call wait time by 30% and produce up to a 370% three-year ROI. Through workload optimization, watsonx.data can reduce the cost of an enterprise’s data warehouse by up to 50%.

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Sundial: A New Era for Time Series Foundation Models with Generative AI

Marktechpost

This tokenization scheme, used in frameworks such as TimesFM, Timer, and Moirai, embeds time series data into categorical token sequences, discarding fine-grained information, rigid representation learning, and potential quantization inconsistencies. Sundial combines several innovations in tokenization, architecture, and training methods.

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The Rise of AI Software Engineers: SWE-Agent, Devin AI and the Future of Coding

Unite.AI

The field of artificial intelligence (AI) continues to push the boundaries of what was once thought impossible. From self-driving cars to language models that can engage in human-like conversations, AI is rapidly transforming various industries, and software development is no exception.