Why it is so hard to market enterprise AI/ML products and what to do about it-TechCrunch

In 2019, I took the lead the sales and growth strategy team for an adventure -backed AI company called atSpoke. The company, which was eventually acquired by Okta, used AI to augment the traditional management of the company’s internal IT and communications services.

In the very early stages, our conversion rate was high. As long as our sales team can talk to a prospect – and that prospect will spend time on the product – they will more often than not become a customer. The problem is getting strong enough prospects to connect with the sales team.

The traditional SaaS playbook for demand generation does not work. Buying ads and building “AI” -focused communities are both expensive and attract enthusiasts who can’t afford to buy. Buying search terms for our specific value propositions – e.g., “automatic routing requests” – doesn’t work because the concepts are new and no one is looking for those terms. Finally, terms like “workflows” and “ticketing,” which are more common, have brought us into direct competition with whales like ServiceNow and Zendesk.

In my role advising on the growth phase of enterprise tech companies as part of the B Capital Group platform team, I notice similar dynamics across almost every AI, ML and advanced predictive analytics company I speak to. . Healthy pipeline generation is the bugbear of this industry, but very little is content on how to address it.

Keep a link to categories known in early messaging, even if the category is not the core of your value proposition or why people will sign a contract.

There are four main challenges that hinder the generation of demand 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 threads, these tips should help you adjust your marketing strategy.

Challenge 1: AI and ML categories are still being defined

If you’re reading this, you probably know the story of Salesforce and “SaaS” as a category, but the brilliance is repetitive. When the company started in 1999, software as a service did not exist. In the early days, no one thought, “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 has memorably performed “finishing software”Protest in 2000. (Salesforce still uses that messaging.) CEO Marc Benioff also said to repeat the term“ software as a service ”until it catches up. Salesforce created the category they dominate.

AI and ML companies are faced with a similar dynamics. Although terms like machine learning are not new, specific parts of the solution like “decision intelligence” are not in a clear category. In fact, even grouping “AI/ML” companies is difficult, as there is so much crossover with business intelligence (BI), data, predictive analytics and automation. Companies in even newer categories can map to terms such as seamless integration or container management.

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