A B2B company new to AI uses HubSpot Customer Agent to automate lead research, qualify prospects faster, and prep sales calls. The strategy starts with data enrichment to populate CRM fields, then layers in AI-powered workflows and a consistent brand voice across tools.
Our customer that we're featuring today, they're a mid to large B2B business.
They're very much established on HubSpot, been using HubSpot quite effectively around the marketing and sales area. They have a very active, growth-minded, and we love growth-minded clients, sales enablement and operations team, but they're very new to AI and AI adoption. They've got a strong focus on qualified SQLs and getting them, you know, really qualified, as well as the buyer intent, understanding the intent of each of those, and also finding ways to prep for the calls with their sales teams more efficiently.
A lot of the sales reps were manually researching companies before the calls.
So again, there's a lot of heavy lifting happening and they were updating the CRM properties inconsistently.
What we needed to really understand is where do we start?
How can we use multiple AI tools and how will they talk and fit together?
And also the big thing of how do I avoid wasted Breeze credits?
So we kind of started with a holistic approach. To an AI adoption strategy, aligning that to their business goals, some of which I've just mentioned.
So we started by mapping out a HubSpot AI capability statement in phases. We positioned Customer Agent off the bat because that kind of covers a lot of the things they need to do as a kind of a ground-level agent for the wider agent ecosystem. From that, we really started with some foundation and some quick wins. We wanted to create the brand voice.
So it was so important that all of these AI tools were speaking on brand so that it followed the process.
We also added in things like their ICP definition.
We did some content and some CRM personalization, and then we started to initially build out some AI-powered workflows and emails using the known customer data that was already in the system.
And then first and foremost, implemented data enrichment.
You know, data is king. All of this, these agents are going to be relying on the quality of the data. So we wanted to auto-populate CRM properties from unstructured data and start to clean up. And then pretty much from there, we started to deploy a whole range of agents. Including customer agent.
