Generative AI, Demos and Data: Going from Cool to Useful (Series co-authors: Loren Sylvan & Dan Axman)
You’re tasked with delivering an exceptional software demo. Now what??
Background:
In May 2024, Loren Sylvan delivered a presentation entitled “Turbocharging your demo build with Generative AI” as part of the 赛仕软件 Tech Exchange program. The work was inspired by the need for a hospital admissions data set to build a SAS Viya demo with a short turnaround time, an all-too-common situation. It was one of countless times throughout his career where colleagues asked if he had a good dataset for an industry-specific demo. He reimagined this request in the context of Generative AI and LLMs. Several hundred colleagues attended this Tech Exchange, and the in-meeting and post-session engagement was fantastic.?
He spoke about his research and point-of-view in pushing the limits of LLMs (in this case, ChatGPT) as a trusted advisor to accelerate the creation of industry-specific synthetic data to help create world-class software demos. This iterative process led to many best practices and, most importantly, produced results far beyond what had been previously accomplished.?
In June 2024, Loren published a LinkedIn article entitled “Embracing Generative AI: Transforming Software Demos from Good to Exceptional,”? where he summarized these learnings on how GAI can be used to create relevant synthetic data and how SAS Viya can be used to validate the synthetic data quickly. He also reinforced the importance of ethical and transparent use of AI as part of the process.?
As the June 2024 article mentioned, "AI is not going to replace humans, but humans with AI are going to replace humans without AI." This insight, from Karim Lakhani's of the Harvard Business School, highlights the transformative potential of Generative AI (GAI) to enhance our professional and work lives.?
Foundation:
Creating a compelling software demo showing a software product's power and capabilities can be the difference between making a sale or losing an opportunity. Although all software products are unique, we found that the process of successful demo creation can be broken down into a fairly repeatable series of iterative steps. This is particularly critical when working with a new prospect in an industry that’s new to those in charge of creating the demo.???
From obsessive wanting to understand client and/or prospect needs to selecting the right capabilities to illustrate value, every step is crucial to delivering a demo that resonates and drives business value. And besides having a great teammate, having another trusted advisor to collaborate with helps ensure exceptional outcomes. In this series of articles, we’ll walk through the full demo creation process, breaking it down into manageable phases and showing how GAI is a trusted partner along the way
Narrative:
We have been given one week to create a compelling demo in which we have no knowledge of the prospect’s industry, no specific use cases to help guide our demo, and no relevant industry data to leverage in the demo. In this scenario, and throughout the process, three key roles will collaborate. One team member brings a strong business development and research focus, identifying the client’s needs and collecting data, while the second contributes a blend of business and technical expertise, ensuring that the demo’s capabilities are both relevant and insightful. And third, our GAI (ChatGPT 4o) brings a world-class LLM foundation model and inference engine.??
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Our prospect is a midsize company in the logistics space (3PL). The product we are tasked to demo is SAS Viya Enterprise, which provides industry-leading predictive analytics, reporting, model management, and decision management capabilities. Together, they set the stage for an industry-specific, data-driven story that addresses real-world business challenges.??
Over this series, we will touch on the following repeatable iterative steps and how ChatGPT evolved from cool to useful, assisting us in completing each step in an exceptional manner.?
The Series:
In our next article, we will focus on the Research phase to understand the client/prospect’s company and industry. This will help create the foundation for the demo and the structure of the narrative. We will also discuss how the collaboration began to take shape and how we each leveraged ChatGPT to improve and accelerate the research.??
Along this journey, we encourage you to provide feedback, share your best practices for using GenAI and LLMs, and share any success stories or challenges you may have had!?
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6 个月The focus on software demos and presentations as primary use cases for GenAI might overlook its potential in more complex data analysis tasks within the sports industry. For example, recent advancements in using AI for athlete performance prediction have shown promising results. Could GenAI be leveraged to create interactive simulations that help coaches make real-time tactical decisions during games?