Machine Learning is Driving an Innovation Wave in SaaS Software

Navidar | August 22, 2017

SaaS software vendors are enhancing their solutions with machine learning (ML) algorithms. ML is the ability of a computer to either automate or recommend appropriate actions by applying probability to data with a feedback loop that enables learning. We view ML as a subset of artificial intelligence (AI), which represents a broad collection of tools that include/leverage ML, such as natural language processing (NLP), self-driving cars, and robotics as well as some that are tangent to ML, such as logical rule-based algorithms. Although there have been multiple “AI Winters” since the 1950s where hype cycles were followed by a dearth of funding, ML is now an enduring innovation driver, in our opinion, due in part to the increasing ubiquity of affordable cloud-based processing power, data storage—we expect the pace of breakthroughs to accelerate.

These innovations will make it easier for companies to benefit beyond what is available with traditional business intelligence (BI) solutions, like IBM/Cognos and Tableau, that simply describe what happened in the past. New ML solutions are amplifying intelligence while enabling consistent and better-informed reasoning, behaving as thought partners for business users. These endeavors will dramatically improve overall business operations, customer satisfaction, and workforces by eliminating automatable roles (e.g., telemarketer, dishwasher,
court clerk) and place more emphasis on social intelligence, creativity, perception, and manipulation¹. Enterprise SaaS providers (e.g.,, Workday, and ServiceNow) have launched first-generation ML functionality to enhance the value of existing applications while legacy software titans (such as Oracle, Microsoft, and SAP), that have been acquiring SaaS companies, are following suit.

While still in the early innings of ML enriched SaaS solutions, we expect the aforementioned companies to dominate horizontal markets (e.g., salesforce automation, customer support, marketing, and human resources) through both organic and M&A initiatives. As for vertical-specific solutions, we think that a new crop of vendors will embrace ML and achieve billion-dollar valuations. VCs are making big bets that these hypotheses will come to fruition as demonstrated by the $5B they poured into 550 startups using AI as a core part of their solution in 2016². Also, established vendors have been acquiring ML solutions, for example, alone has acquired BeyondCore, Metamind, PredictionIO, and RelateIQ.

As the preeminent technology investment bank in the middle corridor of the U.S., Navidar closely tracks trends like these. We are excited about ML because most enterprises have access to a plethora of data and by prioritizing ML initiatives, they will be able to better solve complex, data-rich problems. Companies that are quick to make the arduous business-process and data-management changes, in our opinion, will establish durable competitive advantages. In this article, we examine ML technology, implementation steps, leading vendors’ ML initiatives, and industries prime for disruption.

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