Go-To-Market Transformation with Generative AI |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-7 |
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| Year of Publication : 2024 | ||
| Authors : Hrishikesh Joshi | ||
| DOI : 10.14445/22312803/IJCTT-V72I7P103 | ||
How to Cite?
Hrishikesh Joshi, "Go-To-Market Transformation with Generative AI," International Journal of Computer Trends and Technology, vol. 72, no. 7, pp. 17-25, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I7P103
Abstract
Generative AI is transforming GTM (Go-To-Market) operations, just as it is revolutionizing countless other sectors across the business world. This study presents the profound impact of Generative AI on GTM strategy, highlighting its potential to optimize decision-making, enhance productivity and efficiency, uplevel customer experience, and streamline the entire product lifecycle. Furthermore, it outlines the implications of generative AI in accelerating go-to-market timelines, enabling businesses to adapt swiftly to dynamic market conditions and gain a competitive edge. This study continues to discuss the integration of Generative AI into the current day-to-day processes of various business sub-functions under the GTM umbrella. It elaborates on various use cases pertaining to these sub-functions to provide insight into the application of Generative AI technology through the enterprise architecture of pre-integrated assistants, custom frameworks, data integrity or a combination of all of the above. Along with business growth and strategy, it is essential to factor in the security considerations and legal challenges associated with the adoption of Generative AI. This article also emphasizes the need for responsible, secure and private implementation with transparent communication highlighting potential risks and limitations of Generative AI, urging businesses to approach its integration with caution and foresight. In a nutshell, the intention of this study is for readers to understand the true potential of Generative AI in the GTM operations space, reshaping traditional business models and empowering organizations to drive innovation, agility, and sustainable growth in an increasingly complex and dynamic market landscape.
Keywords
Enterprise architecture, Large language models, Retrieval augmented generation, Co-pilots, Assistants.
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