Takeaways
- Organizations are adopting two distinct approaches to generative AI, first, as a broadly applicable tool for personal productivity and then as tailored solutions aimed at addressing specific business needs.
- Broadly applicable generative AI tools, like conversational systems, are versatile but come with challenges such as biases, lack of context, and the potential for producing inaccurate outputs, requiring critical evaluation by users.
- To manage generative AI risks, organizations should develop clear usage guidelines, invest in training, and standardize tool access with a select group of vendors.
- Tailored generative AI solutions focus on strategic business objectives, offering the potential for scale but also creating challenges such as the risk of shadow AI and uncertainty around vendor control and model performance.
- Organizations can maximize the value of generative AI solutions by establishing transparent innovation processes, creating clear development guidelines, and forming strategic vendor partnerships.
Summary
The research briefing by Nick van der Meulen and Barbara H. Wixom from the MIT Center for Information Systems Research explores how organizations are leveraging generative AI in two primary ways: broadly applicable tools for personal productivity and tailored solutions for specific stakeholder groups. Broadly applicable tools, such as conversational AI systems, are versatile and pose challenges like context lack, bias, and security risks. To mitigate these, organizations should develop clear usage guidelines, invest in training, and standardize vendor selection. Tailored generative AI solutions are driven by strategic business objectives and face challenges like 'shadow generative AI' and vendor control over foundation models. Organizations can overcome these by establishing transparent innovation processes, formulating development guidelines, and creating vendor partnership strategies.
