Robust Exit Activity Across the Intelligent Software Market

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Robust Exit Activity Across the Intelligent Software Market

We believe that the commercial market for Intelligent Software—software applications and platforms that leverage Artificial Intelligence and Machine Learning—has reached an inflection point.  In this post we share some of the data points that demonstrate to us that real money is being made across this large and rapidly growing market.

For starters, we note that exit activity across AI and ML is growing rapidly and hasn’t shown signs of slowing amidst the economic downturn.  Venture Capital exit value nearly doubled in 2019 to $27 Billion across 179 deals, up from $14 Billion across 145 deals in 2018.


At Omega Venture Partners, we invest in AI- and ML-enabled Vertical Applications (e.g., Braze, Cylance – acquired by Blackberry) as well as Horizontal Platforms (e.g., Plaid – acquired by Visa, Datarobot).  Both these categories are demonstrating robust exit activity, including in the public markets, strengthening our conviction that this is an ideal time to be investing in the tsunami of value and wealth creation that lies ahead.

Large technology companies – including, for instance, Apple, Microsoft, Facebook, Google, Salesforce, SAP, Oracle, Cisco, Intel, Nvidia – have stated that AI & ML is a strategic priority for them.  These companies are acquiring startups up and down the tech stack, and indeed we expect that these technology giants will seek to take advantage of any economic downturn to compound their advantages.  Broadly, we note that exit activity in AI & ML has been growing quickly over the last several years.  All indications point to sustained demand from technology incumbents to sustain a robust M&A exit pipeline in the years to come.

Indeed, our friends at CB Insights have compiled good data showing that the large technology companies have been aggressively acquiring AI startups and also that the pace of acquisitions is increasing rapidly.



Notably, AI- & ML-powered Intelligent Software companies have been achieving outsized exits in the public markets.  Technological innovation in AI & ML over the past decade is starting to produce customer outcomes that can support the growth of very large businesses.  Most recently, Datadog, Crowdstrike, Livongo,, and Lemonade demonstrate the appeal of AI and ML technologies across Enterprise Functions (IT Operations), Digital Health, Information Security, Commerce, and Insurance respectively.  The results are driving high growth and attractive exit valuations.

Datadog (DDOG), an AI-enabled application monitoring company, is currently valued at $25 Billion.  Splunk (SPLK) scooped up SignalFx, an AI-powered infrastructure monitoring company, for a significant premium.  UI Path and Automation Anywhere are prime IPO candidates in the RPA (Robotic Process Automation) market as is Palantir in AI software.

Notable Recent VC Exits




Vertical Applications in Intelligent Software address specific problems within industries. These solutions typically differentiate based on the quality of the data they are able to access to train narrower models, which address use cases within large end-markets.  We focus on Vertical Applications across the following major buckets:

  • FinTech, Financial Services, & Insurance: A massive, data-rich market, this includes software that embeds AI &ML via automation of mid- and back-office functions, robo advisors, self-learning programs, lending analytics, fraud mitigation, payment optimization, predictive underwriting, and conversational assistants.
  • Digital Health: Includes technologies that leverage AI & ML to improve medicine and the provision of care. Product categories include AI-based drug discovery, clinical decision support, physician workflow automation, digitization of medical records, healthcare administration and personalized medicine.
  • Commerce: Includes the entire value chain from supply chain management and procurement automation to inventory and logistics, merchandising, pick/pack/ship, price optimization, assortment selection, personalized recommendations, and advanced analytics.
  • Enterprise Functions: Ranging from sales and marketing automation, customer service and contact center, IT operations, to recruiting, human capital management, productivity, learning and development. This category includes software that optimizes specific functions typically administered by IT departments, including both backend and frontend use cases, such as HR automation, information security automation, legal automation, and finance automation.
  • The Consumerized Enterprise: Includes technologies that use AI & ML to propel consumerized adoption (aka product-led growth) business models.  Some categories here include: AI in media & entertainment, AI & ML marketing tech, workflow and collaboration, knowledge management, and Ed Tech.


Horizontal platforms, we define as industry-agnostic solutions that empower users to build and deploy AI / ML models across diverse market segments and uses cases.  We typically segment horizontal platforms into three major buckets:

  • Enablers and Datasets: This includes differentiated datasets (e.g., Second Measure) as well as DevOps tools that facilitate the building and deployment of AI / ML models and algorithms.
  • Computer Vision & Natural Language platforms: processing visual information (“seeing”); processing and understanding written and spoken information (“speaking” and “listening”).
  • Process Automation: including robotic process automation (RPA), decision intelligence, operational automation such as IT ops automation, contract lifecycle automation, database optimization etc.