Intelligent App Enablers are the most clear and well-known group of companies. Several like Databricks, DataRobot and Fivetran have emerged over the last 5+ years as market leaders, and many of the earlier stage companies like OctoMl and dbt are already gaining significant market traction.
Machine to Human interactions are starting to become more ubiquitous - as the categories of sales (Gong), Marketing (Amperity), Customer Success (Cresta, Moveworks) grow.
Machine to Machine interactions continue to rise in adoption as cybersecurity platforms (Abnormal), physical security platforms (Anduril), and horizontal enterprise process automation (Celonis) reach the growth stage.
Cloud Providers: While the whole journey of intelligent applications (including model design and training) is hybrid and multi-cloud, most intelligent apps today leverage and operate on one or more of the major cloud platforms (AWS, Azure and Google Clouds)
Investors: The investor group that has backed these Intelligent Apps largely represents some of the most venerable VC brands and current trends (for example, Tiger Global backed the most companies voted on to the list with A16Z and Sequoia tied for the second most companies)
Expectations for 2022
Several IA Enablers will go public as these companies see broader adoption in the market.
Building on the Q3 record of greater than $25 billion in disclosed value, there will be increased M&A and consolidation in the intelligent apps space - as both traditional software companies buy Intelligent Application companies and consolidation amongst emerging market leaders in key sub-sectors.
Assuming macro-economic conditions hold, 2022 will set another record year in VC funding for intelligent applications across all stages of private financings - we are still in the early innings!
Data rights and regulations will increasingly impact strategies for IA companies - which can be both headwinds and tailwinds for individual companies as they attempt to responsibly access, use and leverage the fuel that powers intelligent applications.
Flywheels will start to emerge in various sub-sectors of intelligent applications. The flywheel of leveraging diverse and robust data to create contextually relevant machine/deep learning models that are then deployed to help solve real-world problems and then the learnings from those inferences (which is more data) are incorporated in to further improving the intelligent applications will be a source of sustainable competitive advantage for successful companies. And, it will encourage usage and engagement go-to-market and pricing models that facilitate customer adoption.
