AI data enrichment has the potential to enhance CRM records, making them more actionable for RevOps teams. AI models like OpenAI can enrich CRM data by pulling in real-time insights such as 10-K reports, buying signals, or ICP attributes.
For instance, Salesforce accounts can be enriched with the latest news articles or blog posts that mention relevant keywords or pull information from an account’s website about open roles, security certifications, or technical document details. You can even bucket accounts into specific industries/sub-industries or bucket job titles into specific personas.
"AI enrichment can take many different forms. Tools like Clay excel at AI enrichment by pulling data directly from the web. This technology could take the form of AI-SDRs for prospecting efforts." - Mikhiel Tareen
Jump to clip for Mikhiel's definition of AI enrichment.
Historically, companies have relied on static databases that are bought or refreshed on an ongoing cadence, like Apollo or ZoomInfo. Unfortunately these give you little visibility into how accurate the data actually is and when it was last refreshed.
Today, we’re moving into more dynamic, real-time data acquisition methods. AI gives us the ability to digest large amounts of data without having to hire a large team to support it, allowing for more accurate, up-to-date information that can be tailored to specific needs and enhancing the overall quality and reliability of the data used in CRM systems.
Jump to clip for insight into how AI lets us be more scrappy about targeting the right accounts.
AI is being used to classify and make sense of unstructured data, a task that traditionally required large teams and significant manual effort. By customizing AI models to fit specific business needs, companies can automate the classification process, reducing the need for extensive human resources while improving data-driven decision-making. By understanding and reacting to data quickly, you can create a competitive advantage.
"Our ability to reclassify data according to our specific parameters has significantly enhanced the value of the data we process.” - Marshall Hamilton
Jump to clip to hear how AI can help organize data from sources with different structures and fromats.
Integrating internal data sources, such as Gong call transcripts and CRM records, with AI is another key trend discussed during the webinar. External databases don’t have the full picture because they’re missing 90% of the internal data that lives in Salesforce. This really limits their ability to be useful and proactive.
RevOps pros should be able to take every recorded call, every intent signal, and every email that’s been sent to and from a rep, and run it through a model to summarize the data then give it back in an output that’s accessible and useful to sales reps.
Jump to clip to learn about combining external and internal data for the biggest impact.
As AI continues to evolve, so do concerns around privacy and data security. The panelists touched on the importance of ethical data scraping practices and the potential for increased regulation in the near future. For RevOps professionals, staying ahead of these concerns is critical to maintaining customer trust and ensuring compliance with emerging legislation.
“When dealing with personal or signal-based data, it's crucial to strike the right balance. Some AI vendors excel at both, but you might need to live with some data quality issues." - Conrad Millen
Jump to clip to hear the panel discuss the future of data scraping.
Adopting AI isn't without its challenges. The discussion delves into the cultural hesitation that often accompanies new technologies, as well as the need for reliability in AI outputs. Some of these tools are in their earliest phases and many will mandate terms that say they can use the data you give them them to make their tools better.
It will also take a lot of work to make these tools consistent and reliable enough to deploy, especially in a customer-facing capacity. The results could be right 95% of the time, but that 5% is dangerous depending on how you use it.
Jump to clip to hear examples of how AI software isn’t quite ready for plug-and-play.
Finally, the panelists emphasized the importance of developing new skills to thrive in a future increasingly shaped by AI. Skills like prompt engineering, effective writing, and a deep understanding of systems design and data flows will be essential. These competencies will enable RevOps professionals to harness AI's full potential, driving more efficient and effective operations.
"I think prompt engineering is something we throw around, but really it's just effective writing. And it's something that I think everyone should work on in this context as we move toward a world where we interact with computers differently." - Marshall Hamilton
Jump to clip for guidance on up-skilling your AI competencies.
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