Generative AI and the Impact on Enterprise Software | Tinder on Customers
Apr 17, 2023
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Bonnie, an expert in generative AI, discusses the impacts and effectiveness of generative AI on software selection and implementation. She highlights the revolutionary nature of generative AI in organizing and synthesizing mass amounts of data. However, she also acknowledges its limitations, such as not being comprehensive in understanding customer needs and lacking directionality for creating roadmaps. The podcast emphasizes the importance of human involvement in successful projects and cautions against overestimating AI capabilities.
Generative AI can revolutionize the software evaluation process by summarizing high-level areas and comparing multiple vendors, reducing workload and accelerating decision-making.
Chat GPT can accelerate software implementation tasks by generating code snippets, assisting with data migration, and producing grammatically correct documentation, but human oversight and testing are still necessary for accuracy.
Deep dives
Benefits of Chat GPT in Software Evaluation
Chat GPT can assist in software evaluation by providing important considerations for selecting potential vendors. By inputting specific industry, company size, and needs, it can quickly summarize high-level areas and help compare multiple vendors, reducing the need for extensive meetings and product demonstrations. It can also aid in analyzing pricing models and future functionality product comparisons. With appropriate data feeding and vetting, Chat GPT has the potential to revolutionize the software evaluation process by minimizing workload and accelerating decision-making.
Chat GPT's Role in Software Implementation
Chat GPT can contribute to software implementation by assisting with integrations and data migration. It can generate code snippets and APIs, minimizing potential integration issues and providing a starting point for technical resources to test. Additionally, it can help automate the process of extracting and understanding data structures from source to target systems, facilitating data migration. Furthermore, Chat GPT can create grammatically correct and fast documentation, such as training materials and FAQs, which can be produced more efficiently than a project team could alone. While Chat GPT can accelerate these implementation tasks, human oversight and testing are necessary for accuracy.
Limitations of Chat GPT in Software Projects
Despite its benefits, Chat GPT has limitations in software projects. It relies on up-to-date and explicit data feeding for optimal performance, as its knowledge is based on information prior to August 2021. Additionally, Chat GPT cannot sell ideas or projects internally, understand the nuances of a specific business, or provide accurate timelines without extensive training and human oversight. While it excels at creating grammatically correct documentation, it still requires human review to ensure accuracy. Moreover, it cannot improve the quality of garbage data or assess the performance of team members. Ultimately, human involvement remains crucial in successful software projects.
Episode 28 | Generative AI and the Impact on Enterprise Software Purcashing, Use, Implementation
The Big Themes:
Extensive experimentation: Bonnie tested the impacts and effectiveness of generative AI around the area of software selection and implementation, prompting the tool to provide scenarios and advice around purchasing and implementing software, from the perspective of a customer.
Generative AI as "revolutionary": Generative AI, and tools such as ChatGPT, are revolutionary in the fact that it can organize and synthesize mass amounts of data in a relatively quick timeframe. "Some of the things that I think these LLMs really do significantly well take a lot of random information and net it out into simple bullet points."
The "not so good": Generative AI is not comprehensive in terms of its knowledge about customer needs, demands, buying habits, or issues. It is not directionally correct when asked to create a "roadmap" for purchasing and implementing software. Additionally, the data is trained from 2021 and prior.
The Big Quote: "AI is also not going to sell your idea internally, or your project internally, and get others on board. We know these projects are successful because of the people who run them, and the effort that gets the commitment of the organization behind it, and stakeholders behind it. AI and robots are not going to do that. So the successful projects can be aided certainly by AI, but it can't be run by them."
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