mike tong has over a decade of experience leading GTM strategy and operations for tech and data companies as part of McKinsey TMT, AtSpoke, Splunk and the VC firm B Capital.
In 2019, I led the sales team and growth strategy for a venture-backed AI company called atSpoke. The company, which Okta ultimately acquired, used AI to augment traditional IT services management and internal company communication.
At a very early stage, our conversion rate was high. As long as our sales team could talk to a prospect — and that prospect spent time with the product — they would more often than not become a customer. The problem was getting enough strong prospects to connect with the sales team.
The traditional SaaS playbook for demand generation didn’t work. Buying ads and building communities focused on “AI” were both expensive and drew in enthusiasts who lacked buying power. Buying search terms for our specific value propositions — eg, “auto-routing requests” — didn’t work because the concepts were new and no one was searching for those terms. Finally, terms like “workflows” and “ticketing,” which were more common, brought us into direct competition with whales like ServiceNow and Zendesk.
In my role advising growth-stage enterprise tech companies as part of B Capital Group’s platform team, I observe similar dynamics across nearly every AI, ML and advanced predictive analytics companies I speak with. Healthy pipeline generation is the bugbear of this industry, yet there is very little content on how to address it.
Maintain a link to categories that are well known in early messaging, even if the category is not the core of your value proposition or why people will eventually sign a contract.
There are four key challenges that stand in the way of demand generation for AI and ML companies and tactics for addressing those challenges. While there is no silver bullet, no secret AI buyer conference in Santa Barbara or ML enthusiast Reddit thread, these tips should help you structure your approach to marketing.
Challenge 1: AI and ML categories are still being defined
If you’re reading this, you likely know the story of Salesforce and “SaaS” as a category, but the brilliance bears repeating. When the company started in 1999, software as a service didn’t exist. In the early days, no one was thinking, “I need to find a SaaS CRM solution.” The business press called the company an “online software service” or a “web service.”
Salesforce’s early marketing focused on the problems of traditional sales software. The company memorably staged an “end of software” protest in 2000. (Salesforce still uses that messaging.) CEO Marc Benioff also made a point of repeating the term “software as a service” until it caught on. Salesforce created the category they dominated.
AI and ML companies face a similar dynamic. While terms like machine learning are not new, specific solutions areas like “decision intelligence” don’t fall within a clear category. In fact, even grouping “AI/ML” companies is awkward, as there is so much crossover with business intelligence (BI), data, predictive analytics and automation. Companies in even newer categories can map to terms like continuous integration or container management.
