Analyst Profile

Adrian Bowles

Vice President of Research and Lead Analyst
37 years of industry experience
Westport, CT

Adrian Bowles joined Aragon Research in 2017 as a Vice President and Lead Analyst. Adrian has worked at the intersection of advanced technology and business strategy as an academic, practitioner, consultant and industry analyst. His current research is focused on the business impact of modern AI/cognitive computing, advanced analytics, and the Internet of Things.

Adrian has practical AI experience ranging from co-developing a natural language simulation system published in the proceedings of Cybersoft 80, to leading a commercial design team on a DoD battle management expert system project, to developing an assessment tool used by the IBM Watson team for evaluating application ecosystem partner submissions. He co-authored Cognitive Computing and Big Data Analytics, published by John Wiley & Sons, 2015. Adrian's monthly SmartData webinar series with Dataversity has an audience of professionals in over 65 countries.

Previously, Adrian held executive positions at Ovum/Datamonitor, Giga Information Group, New Science Associates, and Yourdon, Inc. He also held academic appointments in computer science at Drexel University and SUNY-Binghamton, and adjunct faculty positions in the business schools at NYU and Boston College. He began his career with research and application development roles at IBM and GTE Laboratories.

Adrian earned his BA in Psychology and MS in Computer Science from SUNY-Binghamton, and his Ph.D. in Computer Science from Northwestern University.

Coverage Areas
Artificial Intelligence (AI)
Machine Learning
Deep Learning
Cognitive Computing


Adrian Bowles's Research Agenda

On-Demand & Upcoming Webinars

Modern AI and Cognitive Computing: Boundaries and Opportunities
May 5, 2017

Upcoming AI Research

  • The AI Strategic Report and Vendor Index
  • The Global Cloud AI Providers: How Amazon, Google, IBM, Microsoft and Salesforce will Compete
  • Separating Fact from Fiction: The Key Differences between Machine Learning, Deep Learning and Cognitive Computing