Subscribe AI & Machine Learning

Our Artificial Intelligence & Machine Learning coverage assesses and maps the value proposition offered by these technology implementations. Our research assesses the various AI and ML business models including the platform as a service, technology as a service, software licensing models, and edge device applications. We aim to provide technology implementers with insight into how these technologies are shaping new applications and business models.

Featured Research

Hot Tech Innovators: AI and ML

The AI Hot Technology Innovators looks at 14 different AI technology companies, delivering a mixture of solutions to end users and developers. The report gives an insight into the disruptive potential of each company's technology and business model, at the cutting edge of the AI start-up area.

ABI Research has been impressed by a number of companies coming to the fore with what it would describe as non-traditional AI techniques outside of established machine learning – companies highlighted here using automate reasoning, and unique neural language processing are some of the most exciting challengers in the area of AI.


Reports & Data


Executive Foresights

Why Quantum?

4Q 2018

If the twentieth century was all about the mechanization of physical work, the twenty-first century is all about the mechanization of mental work. We can think of “mental work” as the process of thinking that unfolds when making difficult decisions. These decisions are ultimately what establish a state of understanding or belief. In computing terms, this state of understanding or belief is manifested in cached memory. The challenge of working with cached memory in the world of IoT is heterogeneity—of endpoints, applications, data, and suppliers—and the resulting need to shift from physical-time to logical-time order computing, which prioritizes the relative order in which data is generated, transmitted, and/or processed (think blockchain).


IBC 2017 Preview – Artificial Intelligence (AI) and Machine Learning (ML) in Media

3Q 2017

The International Broadcaster Conference runs from September 15th to 19th. One of the most significant new trends promoted at this conference will be related to the implementation of artificial intelligence (AI) and machine learning (ML) in video services. Some solutions targeted as AI or Machine Learning simply migrate from editor- or developer-coded optimization methods to neural-network trained solutions, a host of new solutions leverage video analytics to generate metadata.

Analyst Support

Every client is assigned a key member of our research team, based on their organization’s needs and goals. And, an unlimited number of Analyst Inquiry calls are available to answer your specific questions.

Rian Whitton

Research Analyst

Malik Saadi

Vice President, Strategic Technologies

Lian Jye Su

Principal Analyst

Jack Vernon

Industry Analyst

Dimitris Mavrakis

Research Director

Michael Inouye

Principal Analyst

Pierce Owen

Principal Analyst

Stephanie Tomsett

Research Analyst

Nick Finill

Senior Analyst


Market Forces Driving Edge AI: Enabling Technologies, Value Proposition and Key Use Cases

AI is going to see a dramatic growth in adoption in many verticals. However the currently popular model of having AI inference and training take place in the cloud is simply not appropriate for many use cases, creating a sizable opportunity for edge AI hardware to flourish. In this webinar Malik Saadi, Vice President of Strategic Technologies and Jack Vernon, Industry Analyst, will cover the main drivers for shifting AI to the edge, how the technology stack for AI is changing to reflect the shift, and the market opportunity in edge AI.

This webinar will address the following questions:

  • How is AI currently being implemented?
  • What is the case for shifting AI processing to the edge?
  • What are the use cases that will drive edge AI?
  • What are the hardware options for implementing AI at the edge?
  • How big is the market opportunity in edge AI implementation?