Why it’s so hard to bring enterprise AI/ML products to market and what you can do about it – Marketingwithanoy

In 2019 I led the sales force and growth strategy for a venture-backed AI company called atSpoke. The company, which Okta eventually acquired, used AI to improve traditional IT services management and internal business communications.

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 — more often than not, they’d become a customer. The problem was getting enough strong prospects to connect with the sales team.

The traditional SaaS demand generation playbook did not work. Buying ads and building communities focused on “AI” were both expensive and attracted enthusiasts who lacked purchasing power. Buying search terms for our specific value propositions — say, “auto-routing requests” — didn’t work because the concepts were new and no one was looking for those terms. Finally, terms like “workflows” and “ticketing,” becoming more prevalent, put us in direct competition with whales like ServiceNow and Zendesk.

In my role as part of the platform team at B Capital Group, where I advise as part of the platform team of companies in the growth phase, I see similar dynamics across almost all AI, ML and advanced predictive analytics companies I speak with. Generating healthy pipelines is the problem of this industry, but there is very little information on how to go about it.

Keep a link to categories known in early posts, even if the category isn’t the core of your value proposition or why people will end up signing a contract.

There are four key challenges that stand in the way of demand generation for AI and ML companies, and tactics to address those challenges. While there is no panacea, no secret AI buyers conference in Santa Barbara or ML enthusiast Reddit thread, these tips should help you structure your marketing approach.

Challenge 1: AI and ML categories to be defined

If you’re reading this, you probably know the story of Salesforce and “SaaS” as a category, but the genius needs to be repeated. When the company started in 1999, software as a service did not yet exist. In the beginning, no one thought, “I need to find a SaaS CRM solution.” The business press referred to the company as an “online software service” or a “web service.”

Salesforce’s early marketing focused on the problems of traditional sales software. The company memorably organized a “end of softwareprotested in 2000. (Salesforce still uses those posts.) CEO Marc Benioff also made it a point to repeat the term “software as a service” until it caught on. Sales team created the category they dominated.

AI and ML companies face similar dynamics. While terms like machine learning aren’t new, specific areas of solution like “decision intelligence” don’t fall into a clear category. Even grouping “AI/ML” companies together is tricky because there is so much crossover with business intelligence (BI), data, predictive analytics, and automation. Companies in even newer categories may refer to terms like continuous integration or container management.

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