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Retail technology and frontline workers: Delivering unforgettable customer experiences

IBM Big Data Hub

The retail industry employs millions of people, and next-generation retail employees will be significantly impacted by the rise of generative AI. With generative AI, retailers can fine-tune inventory and adapt store layouts in real-time, improving navigation, product visibility and stock management.”

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Retailers can tap into generative AI to enhance support for customers and employees

IBM Big Data Hub

As the retail industry witnesses a shift towards a more digital, on-demand consumer base, AI is becoming the secret weapon for retailers to better understand and cater to this evolving consumer behavior. Generative AI excels at handling diverse data sources such as emails, images, videos, audio files and social media content.

Retail 95
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IBM and TechD partner to securely share data and power insights with gen AI

IBM Big Data Hub

By enhancing user adoption and proficiency, clients can unlock the full potential of data while helping to ensure utmost privacy and security. You can tailor training programs to match the unique roles, responsibilities and proficiency levels of your team members.

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The most valuable AI use cases for business

IBM Big Data Hub

But right now, pure AI can be programmed for many tasks that require thought and intelligence , as long as that intelligence can be gathered digitally and used to train an AI system. Generative AI can produce high-quality text, images and other content based on the data used for training.

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Data is essential: Building an effective generative AI marketing strategy

IBM Big Data Hub

Generative AI needs the right data As with all AI implementations, generative AI requires attention to sourcing and maintaining the underlying data. The familiar IT adage, “garbage in, garbage out,” still applies; high-quality data is essential to yield a high-quality result.

Marketing 122
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Conversational AI use cases for enterprises

IBM Big Data Hub

DL models can improve over time through further training and exposure to more data. Predictive analytics integrates with NLP, ML and DL to enhance decision-making capabilities, extract insights, and use historical data to forecast future behavior, preferences and trends. Clean data is fundamental for training your AI.

Analytics 106