Generative AI for business: Use cases, benefits, and adoption
Blog post from Cohere
Generative AI for business uses models that interpret natural-language instructions to create or transform text, code, images, audio, and structured outputs, supporting functions ranging from knowledge retrieval and content creation to customer assistance, software development, data analysis, and workflow automation. The technology can improve productivity, consistency, personalization, experimentation, and the ability to develop new products or services, particularly when combined with company data and existing systems. However, organizations face challenges in selecting commercially viable use cases, ensuring reliable outputs, protecting sensitive data, establishing governance, integrating technical infrastructure, and preparing employees for changed workflows and responsibilities. A practical adoption process involves defining business objectives and organizational readiness, choosing an approach that meets requirements for integration, security, scale, and cost, then testing, deploying, training users, and continuously monitoring performance, usage, costs, and business outcomes. As access to generative AI becomes more common, the article argues that competitive advantage will depend less on the technology itself than on how effectively businesses apply it to their distinctive data, expertise, operations, and services.
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