AI Acquisitions Accelerating Enterprise Use Cases and Adoption

Subscribe To Download This Insight

3Q 2017 | IN-4644

Artificial intelligence (AI) applications outside of the academic research lab are in a nascent stage. The first enterprise market adoption for AI-powered applications has been predictive data analytics using machine learning (ML) algorithms.

Registered users can unlock up to five pieces of premium content each month.

Log in or register to unlock this Insight.


Large Software Companies Eyeing AI-Related Startups to Accelerate Internal Programs


Artificial intelligence (AI) applications outside of the academic research lab are in a nascent stage. The first enterprise market adoption for AI-powered applications has been predictive data analytics using machine learning (ML) algorithms.

Several of the academic efforts have formed businesses and commercialized AI-based products and services to monetize their research. Larger companies, to accelerate their own AI program development, have increased investments to acquire AI talent and assets of these startups.

Thirteen Large Brands Invest US$4.2 Billion+ in AI-Related Startups


Many of the large suppliers of enterprise solutions are building out their AI capabilities through acquisition. Thirteen large brands are highlighted here for their purchases of ML specialists and conversational interface companies serving the B2B and enterprise market.

AI Type Acquired Acquired by Financials Date Notes
Machine Learning Amazon US$20 million Jan-17 Security services
Chatbots Amazon   Sep-16 Conversational commerce; Termed an "acqui-hire" for hire of Navid Hadzaad.
Deep Learning Orbeus Inc. Amazon   Apr-16 Image analysis startup
Machine Learning Emotient Apple   Jan-16 Facial expression analysis
Machine Learning Faceshift Apple US$18.2 million Nov-15 A motion capture platform that enables its users to design virtual avatars
Machine Learning Turi Apple US$200 million Aug-16 Turi is a Seattle-based company that has created a software platform for other companies to build apps that utilize ML and AI. Customers have used the platform to build recommendation engines and fraud detectors, as well as apps that predict customer churn, run sentiment analysis, and segment customers.
Deep Learning Perceptio Apple   Oct-15 Developing technology for smartphones that allows devices to independently identify images without relying on external data libraries
Machine Learning VocalIQ Apple   Oct-15 A platform for voice interfaces, making it easy for everybody to voice-enable their devices and apps.
Machine Learning RealFace Apple   Feb-17 Facial recognition technology for user authentication
  Raven Tech Baidu   Feb-17 Chinese version of Amazon Alexa
Virtual Assistant Facebook   Jan-15 Enables developers to add a voice interface to their device or app in a few minutes
Machine Learning DeepMind Google US$500 million Jan-14 Develops learning algorithms that use data or raw experience to better themselves
Machine Learning Moodstocks Google   Jul-16 Provides an API and ready-to-use cross-platform SDK for developers to integrate scanning
Machine Learning Nest Labs Google US$3.2 billion Jan-14 Nest Labs is a home automation company manufacturing sensor-driven, Wi-Fi-enabled, self-learning thermostats, and smoke detectors
Machine Learning Google   Sep-16 Conversational interface development
Machine Learning Hark Google DeepMind   Feb-16 Prioritizes who needs to do what, where and when across all aspects of hospital life
Machine Learning Iris Analytics IBM   Jan-16 Real-time fraud protection
Machine Learning The Weather Company IBM   Oct-15 Digital assets (not the broadcast assets)
Machine Learning AlchemyAPI IBM   Mar-15 Provides the natural language processing service via a SaaS API
Deep Learning Nervana Systems Intel US$350 million Aug-16 Silicon development to power a deep-learning cloud service
Machine Learning Itseez Intel   May-16 Itseez specializes in research, development and optimization of real world applications in computer vision, pattern recognition and machine learning.
Machine Learning Saffron Intel   Oct-15 Big Data analytics
Chatbots Wand Labs Microsoft   Jun-16 Develops a mobile application that allows users to share music, videos, and locations
Machine Learning SwiftKey Microsoft US$250 million Feb-16 Android and iOS keyboard software; typing prediction algorithm
Machine Learning Equivio Microsoft   Oct-14 Develops text analysis software for the legal market
Machine Learning Revolution Analytics Microsoft   Jan-15 Provides software and support for the open-source R statistics language users
Deep Learning Maluuba Microsoft   Jan-17 Toronto startup focused on using deep learning for natural language processing
Virtual Assistant Genee Microsoft   Aug-16 Automated meeting scheduling app
Machine Learning ColdLight Solutions PTC US$105 million May-15 Provides automated machine learning science and big data predictive analytics
Machine Learning MetaMind Salesforce US$32.8 million Apr-16 Automated image recognition
Machine Learning PredictionIO Salesforce   Feb-16 Open-source ML server
Virtual Assistant Viv Labs Samsung   Oct-16 Developed a conversational interface platform
Machine Learning Magic Pony Technology Twitter US$150 million Jun-16 Builds ML-based approaches for visual processing on web, desktop, and mobile
Machine Learning Whetlab Twitter   Jun-15 Develops AI-like technologies that make ML easier for companies to implement
Machine Learning Geometric Intelligence Uber   Dec-16 ML techniques that learn more efficiently from less data


Competition from Multiple Developer Platforms and Tool Fragmentation Could Be Achilles' Heel


Several of these acquisitions are related to ML—source code, algorithms, and proven models—that will enable the larger organizations to build tools and developer programs that attract both software developers and enterprise businesses to partner on new applications. In turn, virtual assistants and chatbots will be developed to streamline business operations within the enterprise.

Fragmentation among the various ML platforms remains a challenge for widespread adoption of enterprise applications. The initial platform brand selection for development tools will likely receive the bulk of future investments by the enterprise, as switching costs associated with moving between ML platforms can be quite high. The concept of “write once, deploy many” for cross-platform developer tools that exists today in the mobile devices developer ecosystem is still a few years off for ML developers.

For additional information on ABI Research market analysis of AI in the Enterprise, please refer to our recent publications covering machine learning applications and conversational interfaces (chatbots and virtual assistants).