Editors note: The following special feature is brought to you courtesy Industrial Alliance Securities and analyst Blair Abernethy. It will run in multiple parts over the next two weeks in Cantech Letter. For Part Two click here. For Part Three click here.
In various forms, Artificial Intelligence has been around for decades, but as a sub-sector of the broader technology space AI seems poised to reshape nearly every corner of of our lives, from medicine to law, to the auto industry and beyond.
Of course, what concerns most readers of this site is how AI will impact Canadian technology, and this report dives into how it is being employed by names that will be more than familiar to our readers, including names in the broader Cantech universe such as Shopify, Kinaxis, and OpenText.
We thank Industrial Alliance for this comprehensive and report and encourage our readers to examine the disclosures that are provided at the end of this first section.
Given the widely reported advances in Artificial Intelligence (“AI”) applications over the past year, we thought that it would be timely to review the enterprise software companies under our coverage in order to gain a better understanding of their current positioning with respect to AI technologies and the potential opportunities and challenges that AI might present for them.
In this report, we provide a brief overview of the current AI technology landscape and discuss ways in which AI could impact, both positively and negatively, the enterprise software industry, including both on-premise and SaaS vendors. We then summarize a review we undertook of the Canadian companies we have under coverage to assess the potential for incorporating AI technologies into their enterprise software product lines.
We believe that several of the companies we cover, including Shopify, Kinaxis, Symbility and ProntoForms, could significantly benefit over the next few years as they incorporate a variety of AI technologies into their respective product stacks. In the long run, we expect AI technologies to become very broadly embedded into enterprise software applications and software, much as business intelligence (“BI”), reporting and analytics features have increasingly been directly incorporated into enterprise applications in the last decade.
What is AI?
Artificial Intelligence is a broadly applied term that is not easily defined. In 1955, early AI researcher John McCarthy described it as “that of making a machine behave in ways that would be called intelligent if a human were so behaving.” A long-time leading AI textbook by Stuart Russell and Peter Norvig titled “Artificial Intelligence – A Modern Approach” describes AI machines along four dimensions: thinking humanly, acting humanly, thinking rationally, and acting rationally. A recent report on Machine Learning by The Royal Society defined artificial intelligence as “an umbrella term for the science of making machines smart.”
In our opinion, AI should not be looked upon as a distinct technology but rather as a collection of software technologies, techniques, and statistical methodologies that can be applied independently and in combinations to create more “intelligent” software solutions. We view all software as essentially process automation, with AI technology being the next stage in software evolution. In the past, all software was programmed manually. Today, AI approaches, such as Machine Learning, are automating part of the programming task as well. Software with embedded AI technology can be deployed in countless applications for monitoring and/or mining large data sets, predicting outcomes, interacting with and augmenting the work of humans, and directly interacting with other machines (such as computers, sensors, and internet-of-things (“IoT”) with little or no requirement for human interaction.
At its core, a typical AI solution has an algorithm, which is a set of rules that a computer program follows in order to solve a problem, make a recommendation, or decide upon a course of action. AI algorithms are not necessarily “fixed” in nature, but typically evolve and improve as the system gains more experience with the data, or the input data itself (such as customer behaviour and buying patterns) evolves over time.
There are a variety of sub-disciplines in AI technology research, all of which are continuing to advance and rapidly evolve. Some examples of AI technology subfields include, but are not limited to, the following
•Deep learning (using neural networks with hidden layers);
•Machine learning (supervised, reinforcement, and unsupervised approaches);
•Machine vision (and image processing);
•Expert systems (knowledge-based systems);
•Natural language generation;
•Natural language processing;
•Predictive analytics and prescriptive analytics;
•Autonomous software agents (aka “bots”);
•Autonomous vehicles; and
Stay Tuned for Part Two in this series “Why is AI (Finally) Happening Now?”
Company related disclosures:
Issuer Company Ticker Applicable Disclosures
Constellation Software Inc. CSU-T 7, 9
Shopify Inc. SHOP-N 7, 9
OpenText Inc. OTEX-Q 7, 9
Kinaxis Inc. KXS-T 7, 9
Descartes Systems Group Inc. DSGX-Q 7, 9
Absolute Software Inc. ABT-T 7, 9
BSM Technologies Inc. GPS-T 7, 9
Symbility Solutions Inc. SY-V 7, 9
ProntoForms Corp. PFM-V 1, 3, 7, 9
Redline Communications Inc. RDL-T 7, 9
See legend of Disclosures on next page.
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Blair Abernethy, CFA August 17, 2017 Page | 27
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