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Creating Intentional Customer Experiences With Generative AI

Dec 09, 2024 - forbes.com
The article discusses the strategic adoption of generative artificial intelligence (GenAI) by software companies to enhance product delivery, create new revenue streams, and reduce technical debt. It emphasizes the importance of having a well-defined use case before integrating GenAI into products, as this technology can significantly improve operational processes and drive innovation. Companies like Iron Mountain and Canva are already leveraging GenAI to differentiate their offerings and streamline user experiences. However, the article cautions that simply embedding GenAI features does not automatically create value; customer reactions and potential price increases must be carefully considered.

Furthermore, the article highlights the need for new technical skills and capabilities to successfully implement GenAI. Data literacy and a strong data foundation are crucial, as is understanding responsible AI ethics and governance. Companies must balance accuracy, performance, and cost when evaluating large language models. By focusing on intentional design and continual testing, software companies can create disruptive products that delight customers, rather than merely following technology trends.

Key takeaways:

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  • Generative AI offers significant potential for creating disruptive customer experiences and driving growth in software products, but requires a strategic approach to maximize returns.
  • Companies should define clear use cases for generative AI, focusing on either operational improvements or embedding AI features into products, while managing associated risks.
  • Customer-centric design is crucial when integrating generative AI, as customer reactions and potential price increases can vary; companies should prioritize customer needs and feedback.
  • Developing technical skills and a strong data foundation is essential for leveraging generative AI, alongside considerations for responsible AI ethics, governance, and model evaluation.
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