HubSpot adds AI CRM intelligence layer, marketing AI

HubSpot has pulled a Prince.

Just as the Minneapolis funk master changed his name to an unpronounceable symbol and was referred to as "the Artist formerly known as Prince" during the 1990s, HubSpot's popular user conference -- formerly known as "Inbound" for the last 15 years -- will henceforth be known as "Unbound."

The change, HubSpot said, reflects the evolution of marketing tech: What was once intensively focused on the sales funnel now permeates an organization and requires the interconnection of sales, service and operations with the help of AI.

Unbound's unveilings

The predicted 13,000 customers attending Unbound will see the latest major platform updates HubSpot has built this year around the idea of "growth context," or integrating sales, service and marketing with AI tools that analyze business needs across those teams.

The idea, said Adam Cuzzort, general manager and vice president of product at HubSpot, is that AI acts as a management layer -- and connective tissue -- bridging present technology and data gaps among the three CX tribes.

Fusing marketing data such as brand guidelines, ideal customer profiles and marketing language with sales information about past sales, buying cycles, and even unstructured data as deals develop over time can drive sales growth. At least that's how HubSpot sees the potential.

"You can think of [growth context] as sort of an insights layer," Cuzzort said. "It contains all the structured data that's been in CRM for many years. But it also includes new things that we're able to bring kind of modern AI techniques to bear -- all of that conversational data, and also temporal data."

Analyst Predrag Jakovljević of Technology Evaluation Centers said in an email that such contextual AI may help solve technical issues that plague many sales organizations today.

Messy CRM records that contain missing fields, duplicate contacts and stale pipeline staging frustrate everyday CRM users. Still, they are symptoms of a larger structural issue: information is disconnected across legacy systems and contrasting business metrics, and there is a lack of cross-departmental governance.

"That CRM context -- or what others might call a semantic and ontology graph -- is critical," Jakovljević said. "It gives you all the relationships and industry context -- e.g., customer, order, lead, employee, department, regulations, etc. -- for AI to infer stuff properly."