AI in Canadian commerce: a practical guide for business leaders
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Summary: Artificial intelligence is already embedded in how Canadian businesses process payments, detect fraud and serve customers. This guide explains what AI is, where it delivers value in commerce today and what business leaders should consider as AI-powered systems become more autonomous. It is written for Canadian business owners and operators who want to understand AI practically, not theoretically.
AI is not a future concept for Canadian commerce. It is already part of how customers find products, how transactions are screened for fraud and how businesses manage day-to-day operations more efficiently.
Yet there is still understandable confusion about what AI actually is, what it is not and how it applies to running a business. This guide is intended to cut through the noise and explain where AI is creating real value today, where it is heading and what that means for Canadian businesses operating in a trusted, regulated commerce environment.
At its core, artificial intelligence refers to software that can recognize patterns, make predictions and take actions based on data, often faster and at a larger scale than people can on their own.
AI is not a single technology. It is an umbrella term that covers several approaches:
Many of these capabilities have quietly powered Canadian commerce for years. Fraud detection, transaction authorization, anomaly monitoring and network security all rely on AI and advanced analytics, even when they are not labelled that way.
For most businesses, the highest-impact use of AI today is not experimentation. It is optimization, making existing operations faster, safer and more efficient.
Across the commerce lifecycle, AI is helping Canadian businesses:
These applications work behind the scenes. They improve outcomes like higher approval rates, lower losses and faster service without changing how customers interact with a business. That distinction matters. The most successful AI deployments today are evolutionary, not disruptive.
At Moneris, these are not theoretical benefits. Machine learning and statistical models have been core to our fraud detection and transaction monitoring for years, flagging suspicious activity and screening for indicators of financial crime in real time. AI also plays a growing role in how we make merchant onboarding decisions and optimize authorization rates across the network. These are areas where speed and accuracy directly affect the businesses we serve, and where AI delivers measurable improvements without adding complexity for merchants.
Generative AI, the technology behind tools that can draft text, summarize documents or write code, has drawn significant attention over the past two years.
For businesses, its practical value right now centres on:
Generative AI is best understood as an assistant, not a replacement. It helps people work faster and more effectively, but it still requires human oversight, especially in regulated, high-trust industries like payments and financial services. As with any AI tool, businesses must consider data privacy, accuracy and governance before adopting generative AI.
Moneris has been putting all this into practice. On the customer-facing side of the business, we transformed Monique, our customer support chatbot, from a rules-based tool into a generative AI-powered assistant. Since relaunching in September 2025, Monique has supported more than 50,000 customer conversations in both English and French, achieving strong resolution rates (nearly double its pre-launch performance). It was our first customer-facing AI initiative and a practical example of how generative AI can improve service quality while freeing frontline teams for more complex customer inquiries.
Internally, we have rolled out Microsoft Copilot across the organization so every employee can use AI to support daily tasks, from summarizing documents to building personal AI agents. Our technology teams also use GitHub Copilot to accelerate software development, testing and documentation. These tools are governed by our enterprise security and AI governance standards, reinforcing the principle that generative AI works best when supported by clear guardrails.
AI is beginning to move from helping people make decisions to carrying out actions on their behalf. This is where the concept of agentic commerce comes in.
In simple terms, agentic commerce is what happens when AI-powered software agents can act on behalf of a business or a consumer, within defined rules, to complete tasks like placing orders, reconciling transactions or managing recurring purchases. Think of it as a new discovery and demand channel, where customers may start finding and buying from businesses through AI-powered platforms rather than traditional websites or apps.
This shift is still early. But it is gaining momentum. Industry research projects that agentic commerce transaction values could grow significantly over the next five years as infrastructure matures and consumer habits evolve. Major payment networks and technology companies are already building the systems to support it.
For Canadian businesses, this does not mean rushing into something new. It means being ready when customers begin buying in new ways. The businesses that will benefit most are those built on secure, reliable infrastructure that can adapt without locking them into a single platform or forcing them to rebuild their systems.
Moneris is already engaged for this shift. We are developing support for the Model Context Protocol (MCP), which will create a standardized interface for AI agents to interact with our commerce APIs while preserving existing security controls. We are also tracking and supporting emerging industry protocols, including Visa's Trusted Agent Protocol (TAP), Mastercard Agent Pay and Interac Konek. Our goal is straightforward: when AI agents begin initiating transactions on behalf of consumers, Canadian merchants using Moneris will be able to participate safely and securely without rebuilding their systems.
From a Canadian business perspective, agentic commerce raises important questions around authorization, accountability, reversibility and trust. These are areas that require thoughtful planning, not reactive adoption.
AI adoption is not the same everywhere. Canada's commerce environment is shaped by:
This means Canadian businesses need to approach AI with a focus on reliability, transparency and governance, not just speed. AI systems that influence commerce must be explainable, auditable and secure. They must support error handling and recovery. And they must comply with Canadian privacy and accountability obligations.
In practice, this places a premium on infrastructure and controls, not just experimentation.
Moneris has built its AI adoption around this principle. Our enterprise AI Policy, effective since November 2024, is grounded in six core principles: accountability, transparency, fairness, robustness, human oversight and risk-based compliance. We have established a Model Risk Officer role to oversee AI systems and integrated responsible AI practices into our broader enterprise risk management framework. Every AI model deployed at Moneris undergoes a risk assessment before and after deployment. As AI evolves faster than legislation in many areas, we believe governance cannot wait for regulation. It must be embedded in how a company operates from the start.
For business owners and operators, rather than focusing on how fast you can adopt AI, consider the following:
AI is not a single decision or a one-time investment. It is a set of capabilities that must align with your business goals, your customers' expectations and your risk tolerance. It can be overwhelming, so focus on things that will help your business.
At Moneris, that's all we think about when it comes to AI, “How can we improve to better help our customers?” While we have a number of initiatives underway to improve our business and customers’ experience, we have also enabled all employees with tools to create agents and improve processes. We see AI as a scale enabler and scale only works when the entire organization is equipped to use it.
AI in commerce will continue to evolve, from optimization to assistance to increasingly autonomous systems. Each step forward brings opportunity but also responsibility.
For Canadian commerce, success will not be defined by who adopts the most advanced AI first. It will be defined by who integrates AI in ways that preserve trust, deliver consistent experiences and hold up to regulatory and operational scrutiny.
In future discussions, we will explore how these principles apply to emerging models like agentic commerce and what they mean for customer experience and business growth in Canada.
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