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Forbes: 3 Kinds of AI for Every AI Portfolio

According to an October 2023 Gartner survey, 73% of CIOs plan to increase AI investments in 2024. Yet, only 40% of businesses have an AI strategy, and just 38% believe their AI use differentiates them from competitors (Deloitte). AI strategies often falter between low-level developer tools and embedded platform features. To truly differentiate, businesses need a portfolio approach: Embedded AI boosts team efficiency but doesn’t integrate comprehensively with unique business strategies. Knowledge Assistants (RAG) enhance workforce efficiency by querying internal data but remain reactive. Streaming AI (STAG) adds proactivity, continuously querying dynamic data to unify and amplify AI impact across the organization. An AI portfolio allows for a balanced approach, integrating off-the-shelf solutions while fostering innovation. Companies that master this balance will outpace those sticking to a monolithic AI strategy. Read more from our CEO George Davis on Forbes.

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July 09, 2024

THE LATEST

Powering the Middle with AI

Powering the Middle with AI

Middle managers are crucial to organizational success but often spend a significant amount of time on administrative tasks, which limits their ability to focus on leadership and strategy. AI can alleviate these burdens by automating repetitive tasks and providing real-time insights, enabling managers to concentrate on higher-level responsibilities. Technologies like Frame AI’s STAG architecture go further by continuously analyzing data, proactively identifying risks, and offering performance feedback, which allows managers to take timely action. By empowering middle managers with advanced AI tools, organizations can enhance leadership, team performance, and overall operational success.

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What is Deep Personalization?

What is Deep Personalization?

Personalization has evolved from basic demographic targeting to more complex, individualized experiences, thanks to the rise of AI. Early personalization was limited by structured data, offering shallow insights into customer behavior. However, AI now enables businesses to tap into unstructured data, such as customer interactions and feedback, allowing for deeper, more context-driven personalization. With AI’s ability to understand customer intent and emotions in real time, brands can deliver highly relevant, proactive engagement that enhances customer experiences and builds long-term loyalty.

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Deep Dive: Automated QA

Deep Dive: Automated QA

AI-driven Automated Quality Assurance (AQA) is revolutionizing the traditional QA process in customer service by making evaluations more efficient and consistent. Traditionally, managers manually reviewed a small percentage of interactions based on rubrics measuring accuracy, empathy, and adherence to procedures, which was time-consuming and left room for inconsistencies. AQA automates much of this process by using AI to analyze entire conversations, pre-fill rubrics, and provide real-time insights into agent performance. This allows managers to focus on high-level feedback, improving scalability and ensuring more comprehensive and accurate evaluations across a larger number of customer interactions.

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