The recent webinar on “No Data, No AI: Bringing AI-Powered Procurement to Life” hosted by Stephany Lapierre, Founder & CEO of TealBook, and Patrick Marlow, Staff Engineer at Vertex AI, Google, was an insightful deep-dive into how generative AI (Gen AI) can optimize procurement processes. Here’s a recap of the key takeaways and highlights.
Top 5 Takeaways
1. Clean Up Your Data
Preparing and organizing data is a crucial first step in leveraging Gen AI applications effectively. Clean data ensures that AI models can function optimally, providing accurate and reliable insights. This process involves removing inaccuracies, duplicates, and ensuring consistency across datasets. By investing time in cleaning your data, you set a strong foundation for any AI-driven initiatives.
“The old adage of garbage in garbage out. It holds true here with generative AI.” − Patrick Marlow
2. Enrich Your Data
Enhancing your data through Gen AI technology can significantly improve the quality of the applications you develop. Enriched data is more valuable and can lead to more insightful analyses and predictions. This involves adding context, depth, and relevance to your data, making it more actionable and tailored for specific use cases. When your data is enriched, you’ll have updated NAICS, Tax IDs, diversity certifications, and other critical attributes.
“If you spend the time upfront enriching your data with generative AI or AI in mind, you’re going to get a lot more long term dividends on the quality of that application that you’re building.”− Patrick Marlow
3. Build Skilled Teams
Developing teams with strong skills in AI technologies is essential for achieving success with Gen AI. Investing in continuous education and training for your data scientists and software engineers ensures they are equipped with the latest tools and techniques. Skilled teams can more effectively build, manage, and optimize AI applications, leading to more innovative and successful outcomes.
4. Leverage Pre-Built Gen AI Models
Using existing Gen AI models can save significant time and resources. These models, built on vast datasets and refined through extensive experience, provide a robust starting point for AI initiatives. By sidestepping the need to build from scratch, organizations can focus on customizing these models to their specific needs, accelerating the development process and reducing costs.
5. Smart Small and Then Scale
Adopting a strategy of beginning with small projects allows teams to test, refine, and validate AI applications before scaling. This approach minimizes risks and provides valuable insights into potential challenges. Starting small means you can iterate quickly, learn from initial deployments, and build confidence and expertise, leading to more successful large-scale implementations.
“[Gen AI] may not have the perfect answer at first. You may have to train it to make it better, but over time it’s going to get smarter and better. And it’s going to allow you to focus on much more strategic initiatives and deliver value to the business significantly faster.”− Stephany Lapierre
By focusing on data readiness, enriching data with AI capabilities, and leveraging pre-built AI applications, procurement professionals can unlock new levels of efficiency and value.
If you’re ready to take your procurement strategy to the next level, explore how Gen AI can work for you and learn more about our Gen AI application, Aime.
