Over the past several decades, we have seen the standardization of customer relationship management software in the technology stack of every enterprise sales organization. These CRM systems provide a level of visibility and operational clarity into the productivity of sales organizations and have become the foundation of sales operations. However, the use of these tools is only as good as the data manually entered into the system by each sales professional—which, of course, then leads us to the running joke about the ongoing battle between sales management and sales reps to fill out the necessary information. It’s a constant battle that often results in reps entering the least amount of information necessary to simply avoid management scrutiny. Not only is the manually entered data subject to human error, but the burden on the sales rep for data maintenance is so high that many critical pieces of information never make it into the CRM. This problem is then multiplied across hundreds or thousands of individual sales reps responsible for mission-critical functions such as sales and company forecasting. It’s like building a house on a foundation riddled with holes and pockets of air.
This concept of manual data entry will, soon enough, be entirely forgotten in the enterprise. It is estimated that sales people spend between 10% and 20% of their time manually reporting their activity. It’s a comical cycle: do some (real) work; manually enter a report of what you did; do some more (real) work; manually enter another report of what you did… and repeat! It’s a complete waste of time, especially when the same underlying data already exists in the form of email, calendar, and phone records. Using machine learning and natural language processing to capture this data from existing sources can eliminate manual data entry by sales people, giving them back the time to do exactly what they do best: sell. The result is increased revenue and materially better forecasting that seems too good to be true.
Why is this so important? Beyond the direct productivity lost from spending up to an entire day each week on manual data entry, the impact of incomplete, inaccurate data can inflict even more damage throughout the enterprise. Without high data fidelity, the sales org can become a black box, and all of the enterprise workflows that live on top of data in the system of record suffer. How can sales management optimally coach reps without live visibility on what they are up to? How does marketing know what sales did with the latest batch of leads without continuity in the sales rep activity data? How can sales ops accurately forecast bookings without a granular snapshot of rep activity?
Enter People.ai, our new portfolio company that magically automates the curation of existing data to improve the effectiveness of enterprise go-to-market organizations. The company has world-class technology that allows organizations to seamlessly leverage email, calendar, and phone records to create an automated system of record. With tens of thousands of sales professionals on People.ai today, the product is deployed in large enterprise customers who’ve already seen dramatic improvements in sales force productivity and forecasting results.
Better yet, their vision goes far beyond sales and marketing alone. Each function in the enterprise has its own stream of existing data already produced by employees in their daily responsibilities. If properly captured, this data can inform processes and decisions that are mission-critical with direct commercial impact. This is the key insight motivating the People.ai vision as the underlying problems their technology solves appear in nearly every function across the enterprise.
In reflecting on the history of enterprise software, it’s clear that the last generation of SaaS winners started out as not much more than empty databases that employees would manually populate and at best benefit from light business logic encoded on top. As we believe the next generation of SaaS in the workplace will be won by applying ML to address all areas of knowledge work, People.ai is in the ideal position to become a foundational company in the future of enterprise software. Anyone who’s come across People.ai in the market is undoubtedly well aware of their rapid pace of execution, and from the moment you first meet their founder and CEO, Oleg Rogynskyy, you’ll know exactly where it comes from. In light of this, I’m thrilled to announce our Series B investment in People.ai and welcome Oleg and the entire team to the a16z portfolio.
Peter Levine
is an advisor at Andreessen Horowitz where he focuses on enterprise investing.
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