“Canadian utilities will not become smarter because they own artificial intelligence. They will become smarter when AI is connected to trusted field data, disciplined operations, and people who understand that reliability is still the first promise of public service.” – MJ Martin
Introduction
Artificial intelligence has been promoted as one of the great productivity tools of the modern workplace. It can draft documents, summarize meetings, analyze data, write formulas, generate images, support customer service, and accelerate research. On paper, it appears transformational. In practice, the results are more complicated. The real question is not whether AI is powerful. It clearly is. The better question is whether AI is actually making workers more productive in a measurable and meaningful way.
The Productivity Gap
Recent Canadian polling from the Angus Reid Institute suggests that the workplace productivity story is still uncertain. Among Canadian workers who use AI on the job, 49 percent said it has had no impact on their productivity. Only 38 percent said it has improved their productivity. That is a sobering result, because it shows that nearly half of actual users are not yet seeing the gains that technology vendors and business leaders often promise.
This does not mean AI is failing. It means many organizations are still in the early stage of experimentation. Workers may be using AI casually, inconsistently, or without proper training. A tool that is used poorly will not produce excellent results. AI can save hours for one employee while creating confusion for another. Productivity depends not only on the technology, but on the process around it.
Quality Matters Too
Productivity is not just about speed. A faster answer is not always a better answer. The Angus Reid Institute found that only 29 percent of AI-using workers said AI improved the quality or value of their work. Many reported no meaningful improvement. This matters because organizations should not confuse motion with progress. If AI helps produce more emails, more reports, or more analysis, but the quality is weak, the business has not truly advanced.
In professional environments, AI output still requires judgment. Someone must verify facts, refine tone, check assumptions, protect confidential information, and decide whether the final product is useful. AI can draft, but people must still think. The most productive workers are not those who simply accept AI output. They are the ones who know how to question it, edit it, and apply it to a real business problem.
The Policy Problem
Another concern is workplace readiness. The Angus Reid Institute reported that only 31 percent of employees who use AI said their employer has a formal AI policy. Another 30 percent said rules are still being developed, while 39 percent said their organization has no AI policy at all. That is a major operational gap.
Without policy, workers are left to make their own decisions about privacy, accuracy, copyright, customer data, and acceptable use. This creates risk and inconsistency. A company cannot expect enterprise-level productivity from a tool that is being adopted without enterprise-level governance.
AI in Canadian Utilities
Canadian utilities provide a practical example of where AI can become genuinely productive, but only when it is connected to operational data and field discipline. In electric, gas, and water utilities, AI can help detect abnormal consumption, identify leaks, forecast demand, prioritize maintenance, analyze outage patterns, and support customer service. However, AI does not replace accurate meters, reliable communications, good installation practices, or experienced operators. A utility cannot analyze what it cannot measure. The productivity gain comes when AI is layered on top of high-quality field data from AMI systems, SCADA platforms, pressure sensors, outage systems, and customer information systems. In that setting, AI becomes less of a novelty and more of an operational assistant. It can help utilities move from reactive work to predictive planning, but only if the data is clean, the governance is strong, and the final decisions remain accountable to qualified people.
A Balanced Future
The future of AI will likely be mixed. Most workers do not appear to see AI as entirely good or entirely bad. They expect benefits and drawbacks. That is probably the most realistic view. AI will eliminate some repetitive work, improve some workflows, and create new forms of efficiency. It will also introduce errors, overconfidence, dependency, and disruption.
Summary
AI is productive when it is applied to the right task, by trained people, inside a disciplined process. It is not automatically productive simply because it is new. The current evidence suggests that AI has enormous potential, but that potential has not yet been fully converted into workplace performance. The next phase will not be about whether companies have AI. It will be about whether they know how to use it well.
About the Author:
Michael Martin is the Vice President of Technology with Metercor Inc., a Smart Meter, IoT, and Smart City systems integrator based in Canada. He has more than 40 years of experience in systems design for applications that use broadband networks, optical fibre, wireless, and digital communications technologies. He is a business and technology consultant. He was a senior executive consultant for 15 years with IBM, where he worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX).
Martin served on the Board of Directors for TeraGo Inc (TGO: TSX) and on the Board of Directors for Avante Logixx Inc. (XX: TSX.V). He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology. He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now Ontario Tech University] and on the Board of Advisers of five different Colleges in Ontario – Centennial College, Humber College, George Brown College, Durham College, Ryerson Polytechnic University [now Toronto Metropolitan University]. For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section.
He holds three master’s degrees – in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and seven major certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has completed over 80 next generation MOOC (Massive Open Online Courses) [aka Micro Learning] continuous education programs in a wide variety of topics, including: Economics, Python Programming, Internet of Things, Cloud, Artificial Intelligence and Cognitive systems, Blockchain, Agile, Power BI, Big Data, Design Thinking, Security, Indigenous Canada awareness, and more.
Martin in a volunteer, a photographer, a learner, a technologist, a philosophizer, and a romantic optimist.