In an exclusive interview with The CanadianSME AI Business Review Magazine, Carlos Secada, Founder and CEO of Zagitas, shares how mid-market companies can move beyond experimental AI pilots and deploy automation that delivers measurable operational impact Drawing on his experience in enterprise transformation and operational strategy, Carlos explains how organizations can identify highvalue workflows, deploy AI agents as digital workers, and rapidly integrate automation into finance and supply chain operations.
Carlos Secada is the Founder & CEO of Zagitas, a Toronto based AI automation company helping mid market organizations operationalize Agentic AI across finance and supply chain workflows
He brings enterprise experience from Accenture and Citibank, where he worked on large scale transformation and operational leadership in complex environments.
Carlos holds an MBA from the University of North Carolina at Chapel Hill’s Kenan Flagler Business School and an Industrial Engineering background, bridging strategy and execution
At Zagitas, he works with leadership teams to identify high ROI automation opportunities and deploy AI agents that act as digital workers delivering measurable results quickly while maintaining strong governance and accountability
Many companies get stuck in AI pilots. What do you do differently to get Agentic AI into day to day operations?
Most companies struggle to move beyond pilots for three reasons: lack of internal AI talent, high upfront investment, and not knowing where to start At Zagitas, we address all three head on
We work closely with clients to identify, assess, and prioritize automation opportunities based on clear ROI not experimentation Our focus is on high impact, real operational workflows where AI agents can execute work, not just provide insights
Delivery is incremental and practical We operate in six week production iterations so value is realized quickly, not months later To reduce risk and friction, we offer a pay per use model that significantly lowers initial investment and avoids heavy CapEx commitments
Equally important, we work side by side with client teams throughout the process. This helps overcome knowledge gaps and the natural “fear of the unknown” that often slows adoption By ensuring the right pilot is chosen and successfully delivered AI becomes part of daily operations, not a stalled experiment
How do you choose which finance or supply chain processes are best suited for AI agents first?
We start by prioritizing business impact Each potential use case is evaluated based on costs reduced or avoided, risk or compliance improvements, and incremental business value or opportunity
From there, we analyze the operational reality: the systems involved, number of steps, time spent by staff, roles required, and overall technical complexity The goal is to identify workflows where AI agents can deliver the greatest impact with the least unnecessary complexity
This typically leads us to processes that are highly manual, repetitive, and span multiple systems common in finance and supply chain operations. By selecting use cases with strong ROI and manageable implementation risk, we ensure early success and build momentum for broader automation across the organization
Can you share a quick example where Agentic AI delivered clear ROI (time or cost saved) in weeks, not months?
A good example is a manufacturer of cleaning products that sells through large retail chains Their logistics operation was governed by strict SLAs, with significant penalties and fines tied to delivery errors and delays Those penalties were exceeding $2 million per year
We implemented an AI agent responsible for transportation order routing and shipment planning The agent automated order consolidation, routing decisions, and planning logic end to end a process that had previously required significant manual coordination
What safeguards do you put in place so AI agents stay accurate, compliant, and well governed over time?
Governance is built into our architecture from the start Every AI agent operates with role based access, least privilege permissions, and multi factor authentication All actions are logged, auditable, and fully traceable similar to financial transaction logs
Agents are designed to handle routine cases automatically while escalating exceptions or anomalies to humans This human in the loop model ensures accuracy and keeps people firmly in control
From a data standpoint, we enforce encrypted transmission, minimal data retention, and alignment with enterprise grade cloud security standards Built on Microsofts secure cloud stack, our solutions follow recognized security and compliance frameworks. Performance and rules are continuously monitored and refined to ensure agents remain aligned with operational and regulatory requirements over time
What first steps should a mid‑sized company take if they want to move from manual processes to AI‑driven automation this year?
The first step is clarity, not technology Companies need to understand where manual work is creating real operational friction today and what success looks like in measurable terms
To accelerate this, we often start with a one day automation discovery workshop. In a single session, we work with client teams to identify, assess, and prioritize AI automation opportunities based on ROI, feasibility, and risk This creates immediate alignment and a clear execution roadmap
From there, companies should start small but real selecting one end to end workflow that can move into production quickly and deliver results within weeks When AI agents are treated as digital workers with defined responsibilities and oversight, automation stops being experimental and becomes a durable competitive advantage
In Canada, AI has quietly shifted from testing to everyday work, and 2026 is proving pivotal for how this change affects leadership, skills, and employment According to surveys, 14% of Canadian organizations now use or intend to utilize generative AI; adoption is much greater among larger, more export-focused companies. At the individual level, generative AI is currently used by over half of Canadian workers, and daily usage is increasing However, instruments by themselves are not the whole story
HowAIIsChangingKnowledge Work,NotJustAutomation
According to data from Canadian company surveys, generative AI is being utilized more to transform knowledge work than to eliminate jobs The two main motivations for the 14% of companies that are already using or plan to use generative AI are to automate tasks without laying off workers (46%) and to speed up creative content (69%) Only 13% of respondents say they value generative AI primarily for worker replacement, indicating a preference for augmentation over replacement. According to early adopters, AI can be used to create marketing content, summarize papers, prepare emails and reports, assist with coding, and analyze large datasets, freeing professionals to focus on complex problem-solving, judgment, and client engagement
Leaders in Canada are increasingly framing AI as a skills shock rather than merely a fad in technology According to workforce studies, Canada's use of automation and artificial intelligence (AI) tools nearly doubled between 2021 and 2023 and has continued to grow, raising concerns about outdated capabilities.
Analyses of the job market also show a persistent need for leadership and teamwork, as well as a growing demand for digital confidence, data literacy, and sophisticated communication
A mismatch is revealed by HRfocused surveys: although 67.5% of upskilling initiatives now incorporate certain AI-related skills, just roughly 4% of job advertisements specifically mention AI, indicating that employers view AI understanding as a given rather than a formal necessity.
According to Canadian experts, jobs that combine technological literacy with human abilities such as persuasion, ethical judgment, and client problem-solving will become more valuable, while routine, process-heavy knowledge work such as basic reporting, standard copywriting, or simple analysis will be under the greatest pressure The message for leadership teams is clear: in an AI-intensive workplace, success will be determined by skill portfolios, not job titles
AI won't replace leadership, according to commentators on the Canadian workplace, but it will expose poor strategy and outdated operating models Instead of using AI to improve efficiency and redesign jobs, many firms still view it as an IT addon or a series of short pilots.
However, polls of Canadian CEOs and HR directors show that those who incorporate AI into their business strategy, talent strategy, and governance frameworks—rather than focusing solely on tool deployment— report the greatest performance gains
There is a limited window in 2026 for Canadian leaders to transition from reaction to intent Professionals outline a useful playbook First, rather than letting employees speculate, clearly explain how AI will be used to enhance people, enhance services, and address Canada's persistent productivity challenges. Second, make AI training available to everyone, not just technical teams According to polls, 83% of workers want their employers to provide AI training, and those who receive it are more likely to report improved performance
ImageCourtesy:Canva
According to a KPMG-linked study, more than half of workers already see performance gains from AI, including improved job quality (58%), enhanced efficiency (67%), and better access to information (61%), but they are also concerned about fairness, guardrails, and training.
Only leadership decisions, such as establishing precise rules for appropriate use, funding training for responsible AI use, and coordinating AI initiatives with specific goals like cycle-time reduction or service improvement, can manage that tension The competitive gap in 2026 will be more about who has the mindset and governance to deploy technologies at scale with confidence than about who has access to them
Third, incorporate AI into leadership and workforce development by revising leadership programs, promotion standards, and position profiles to reward the efficient use of AI and the human capabilities it cannot replace, such as sophisticated collaboration, ethical judgment, and change leadership
Lastly, measure what matters: monitor AIs impact on customer outcomes, employee satisfaction, and productivity not just usage metrics AI is projected to enhance good leadership and, in cases where it is present, expose inadequate strategy for organizations that take action on these fronts in 2026
Your role in staying informed is essential to our mission of building a strong community of AI-driven innovators. The CanadianSME AI Business Review Magazine is your go-to resource for insights, strategies, and updates shaping the future of artificial intelligence in business
Subscribe to our monthly editions at aibusinessreview.ca to stay up to date on the latest AI trends and developments in the Canadian business landscape Your engagement enables us to continue supporting and empowering the AI ecosystem
Disclaimer: This article is based on publicly available information and is intended solely for informational purposes The CanadianSME AI Business Review Magazine does not endorse or guarantee any products or services mentioned Readers are encouraged to conduct independent research and due diligence before making business decisions
As they prepare for electrification, data centers, and more severe weather, Canada's utilities and infrastructure operators are under tremendous pressure to maintain affordable and reliable power AI, robotics, and drones are "rapidly altering the Canadian electric utility business," according to Electricity Canada's technology trend studies, enhancing grid reliability, safety, and climate resilience While pursuing net-zero and controlling inflation, some provinces may need to significantly expand the grid and quadruple generation capacity to accommodate AI-related data centers and increased loads
In response, utilities are shifting from reactive solutions to proactive planning by leveraging AI for improved demand forecasting, predictive maintenance, and smarter grid operations By 2026, artificial intelligence (AI) will have evolved from a side project to a strategic tool for balancing innovation, energy transition objectives, and the financial strains that Canadian households and businesses face
To manage unpredictable generation and new demand types, Canadian utilities are shifting toward smart grids systems that combine sensors, automation, and artificial intelligence
AI-driven solutions are being used to assess outage patterns, optimize power-flow models, and combine data from field devices, smart meters, and Advanced Distribution Management Systems (ADMS), according to Electricity Canada's Technology Trends 2026 study As more rooftop solar, electric vehicles, and storage come online, AI algorithms are essential for forecasting load, identifying anomalies, and recommending switching actions that minimize losses and shorten outages
According to market research, Canada's AI for smart energy grid market, which includes demand response systems, fault detection, real-time load balancing, and grid management platforms, has already grown to over USD 1 billion and is predicted to continue growing rapidly through 2030 due to net-zero goals and the integration of renewable energy sources
To increase resilience against wildfires, storms, and heatwaves, Canadian utilities are integrating AI with storage, microgrids, and automation, as demonstrated at smart-grid conferences To preserve reliability and meet environmental goals, this smart-grid intelligence helps utilities reduce strain during peak hours, connect new loads more quickly, and maintain voltage within limits
PredictiveMaintenance
BuiltforReliability
Predictive maintenance, or the transition from planned or failure-driven repairs to data-driven anticipation, is one of AI's most obvious benefits for Canadian utilities. According to Electricity Canada's AI report, utilities are extending asset lifetimes, predicting equipment failures, and optimizing inspection routes by using sensor data, smart meter data, and historical asset records AI models evaluate temperature, load cycles, vibration, and environmental conditions to identify components most likely to fail, rather than inspecting lines, transformers, or substations on set schedules
AI in Canadian utilities is increasingly linked to discussions of affordability, climate, and efficiency A Concordia University remark describes AI as "Canada's green catalyst," noting that algorithms can improve electricity systems by forecasting demand and balancing loads in real time, reducing waste and enabling further integration of solar and wind power For instance, to better predict peaks and fluctuations in renewable output, Ontario's Independent Electricity System Operator (IESO) is using sophisticated forecasting models supplemented by AI analytics
At the same time, AI is creating new electricity demand through data centers that consume significant energy According to Osler's analysis, provinces such as British Columbia, Alberta, Ontario, and Quebec are being forced to reconsider grid-connection rules due to AI-driven demand, as single data center projects are consuming as much electricity as medium-sized cities. While managing limited grid capacity, new rules aim to prioritize projects that promote decarbonization and deliver economic benefits
Even though AI applications themselves consume significant electricity, they help utilities incorporate more clean energy, operate more efficiently, and justify expenditures In this sense, AI is both a problem and a solution
Concrete AI-grid trials that show the direction the industry is heading are already being funded by Canada Natural Resources Canada announced in December 2025 that Hydro Ottawa would receive a C$6 million investment to test AI-enhanced predictive analytics on the distribution grid Smart thermostats, EV chargers, and household batteries are examples of customer-owned assets that the project will use AI to estimate peak demand and convert into responsive resources that help balance supply and demand in near real time, reducing costs and enhancing reliability
In addition to power, Canadian water utilities are using AI to improve pumping, treatment, and storage, balancing cost-cutting and emissions-reduction goals in a changing climate In the future, successful utilities will combine AI investments with digital expertise, cybersecurity-by-design, and transparent communication with consumers and regulators about how AI supports affordability, climate goals, and dependability
Your role in staying informed is essential to our mission of building a strong community of AI-driven innovators. The CanadianSME AI Business Review Magazine is your go-to resource for insights, strategies, and updates shaping the future of artificial intelligence in business
Subscribe to our monthly editions at aibusinessreview.ca to stay up to date on the latest AI trends and developments in the Canadian business landscape Your engagement enables us to continue supporting and empowering the AI ecosystem
Disclaimer: This article is based on publicly available information and is intended solely for informational purposes. The CanadianSME AI Business Review Magazine does not endorse or guarantee any products or services mentioned. Readers are encouraged to conduct independent research and due diligence before making business decisions.
ImageCourtesy:Canva
Law25ConsentRulesfor AutomatedDecisions
When it comes to permission and automated decision-making involving residents' personal information, Québec's Law 25, which took full effect in September 2024, significantly raises the standard Regarding permission, Law 25 primarily prohibits pre-checked boxes, bundled consent, and consent hidden in lengthy terms and conditions Instead, it demands explicit, informed, opt-in consent before collecting, using, or revealing personal information. For every objective, including AI-based profiling or scoring, organizations must be able to demonstrate that consent was freely provided, clear, and specific
Law 25 goes beyond many regimes in its use of automated decision-making When a business utilizes personal data to make decisions solely through automated processing, it must:
When the decision is communicated, let the person know it was automated.
In Canada, AI-powered automated decision-making systems that underwrite loans, screen applicants, route claims, and target offers are no longer in the experimental stage However, if organizations cannot demonstrate legitimate authorization for the data that powers them or explain how decisions are made, these systems may easily violate privacy regulations While Ontario's new job-posting regulations require firms to reveal when AI is used in recruiting, Québec's Law 25 currently enforces some of the nation's strictest transparency and automateddecision rights.
Even before a specific AI law is in place, federal privacy regulators have signalled that AI is firmly within their enforcement jurisdiction by issuing rules for "responsible, trustworthy, and privacy-protective" generative AI In 2026, Canadian companies should transform these standards into a practical compliance checklist integrated into their business operations
Tell them about the usage of personal data as well as the primary justifications, variables, and constraints that influenced the choice. Give people the opportunity to correct inaccurate information, request a human review of the decision, and voice any concerns.
Many AI-driven workflows may fall under Law 25, which, in contrast to the GDPR, applies to any decision made exclusively by automated processing, regardless of whether it has formal "legal effects "
TransparentAIHiringinOntariofromDayOne
Although Ontario has not yet implemented comprehensive AI regulations, it has adopted transparency regulations that will affect hiring practices Employers with 25 or more workers are required under the Working for Workers Four Act, 2024 (Bill 190), to disclose explicitly in every publicly posted job advertisement and related application form whether AI is being used to screen, evaluate, or choose candidates as of January 1, 2026 AI is a broad term that encompasses chatbots, interview scoring systems, and resume screening algorithms
These AI-disclosure regulations coexist with new requirements on pay transparency (such as wage ranges) and bans on "Canadian experience" requirements in employment advertisements Standardized AI-disclosure wording should be incorporated into job-posting templates; applicant-tracking systems should be updated to indicate when AI is used; and recruiters should be trained to respond to candidate inquiries about how these tools affect decisions, according to employment law guidance Although Ontario's requirement is codified in employment standards legislation, it is expected to establish standards for hiring transparency throughout Canada
When combined, federal privacy guidelines, Ontario's hiring regulations, and Law 25 indicate a useful, crossCanada checklist for AI systems that make or assist with choices These can be translated by organizations into specific workflow steps:
Map data flows and automated choices. Determine which personal information AI or scoring algorithms use and where they make or significantly affect decisions (e g , hiring shortlists, credit approvals, and claim priorities)
Improve consent processes. Use specific opt-in consent in terminology distinct from generic terms for AIrelated purposes, such as profiling, behavioural scoring, or automated eligibility judgments, where Law 25 or comparable rules may apply.
Incorporate automatic decisionmaking alerts Include the following in any decision that might be deemed "exclusively automated" for citizens of Québec:
A Typical alert that the choice was made automatically
A route or link where people can ask for the data used and the primary causes
A straightforward method for requesting human evaluation
Standardize the language used in hiring to disclose the use of AI. Maintain a common, authorized phrase library and ensure that all public job advertisements and online application forms for Ontario positions specify when AI methods are used to screen, evaluate, or select candidates.
Explainability of documents. To enable staff to respond to inquiries and regulators to conduct audits, save internal "model cards" or summaries that list the data utilized, the model's functions, its main features, and its recognized limits.
EmbeddingComplianceThrough GovernanceandTraining
Organizations are expected by Canadian regulators to transition from check-box notices to systematic governance Before implementing AI that leverages personal data, especially for high-impact decisions, the Office of the Privacy Commissioner's generative AI rules advise conducting Privacy Impact Assessments (PIAs) and, when appropriate, Algorithmic Impact Assessments (AIAs) As systems change, these evaluations should be updated to reflect the types of data used, potential biases, privacy threats, and mitigation strategies.
Assigning explicit accountability (such as an AI or data governance committee), establishing guidelines for permissible AI applications, documenting decisions and exceptions, and educating frontline employees on how to communicate AI-assisted results to clients and applicants are all examples of practical governance It will be significantly simpler to comply with Law 25 rights requests and Ontario's transparency requirements in day-to-day operations through robust documentation and training
Your role in staying informed is essential to our mission of building a strong community of AI-driven innovators The CanadianSME AI Business Review Magazine is your go-to resource for insights, strategies, and updates shaping the future of artificial intelligence in business
Subscribe to our monthly editions at aibusinessreview.ca to stay up to date on the latest AI trends and developments in the Canadian business landscape. Your engagement enables us to continue supporting and empowering the AI ecosystem
Disclaimer: This article is based on publicly available information and is intended solely for informational purposes The CanadianSME AI Business Review Magazine does not endorse or guarantee any products or services mentioned Readers are encouraged to conduct independent research and due diligence before making business decisions
In an exclusive interview with AI Business Review Magazine
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When you start with a new client, how do you decide which AI use cases are worth pursuing and which to avoid?
Successful AI adoption starts with clarity Before pursuing any use case, we work with clients to define the problem and align on what success looks like based on their business values AI initiatives often come with uncertainty about realistic possibilities Our role is to bring clarity and define practical expectations
We typically begin with a structured strategy engagement Through interviews and workshops, we work with key stakeholders across the organization to understand day-to-day workflows and key pain points This creates alignment among those involved in shaping and using the solution, increasing the likelihood of successful adoption
Through these discussions, we assess data quality, system integration, process maturity, and overall readiness for AI adoption We consolidate those insights into a structured needs assessment, which forms the basis of a practical AI roadmap Potential use cases are then evaluated against clear criteria, including ROI, time to value, feasibility, and organizational readiness.
Based on that assessment, we typically sequence initiatives to deliver measurable results early In many cases, this begins with improving data quality, consolidation, and digitization, followed by workflow automation and process standardization Once that foundation is in place, we move toward more advanced AI initiatives identified during the strategy phase
Many firms struggle to move from AI prototype to stable deployment. What key steps help make that transition successful?
Successfully moving from AI prototype to stable deployment requires treating AI as an engineering discipline rather than a modelling exercise In our approach, deployment considerations shape the system from the beginning Before building anything, we analyze workflows, infrastructure, data pipelines, and operational constraints to ensure the solution is scalable, maintainable, and integrates effectively into existing operations
This discipline extends to how we build and package systems We don’t deliver isolated models or research prototypes We deliver production-ready AI applications packaged with their dependencies, ensuring they run consistently and reliably across environments - whether on-premise, in the cloud, or in hybrid setups
When AI is engineered as part of the operational ecosystem - not as a standalone model - deployment becomes sustainable by design
Lemay.ai works across sectors like fintech, defence, and public services. How do you adapt your AI approach for such different environments?
Lemay ai works primarily in regulated and mission-critical environments, so our foundation remains consistent across sectors: we build production-grade, audit-ready systems that integrate directly into operational and governance workflows
We follow recognized governance standards and bring that discipline into every engagement With several ISO/IEC 42001 Lead Auditors on the team, we structure our projects in line with ISO/IEC 22989 to ensure clear documentation, sound risk management, and proper oversight from start to finish
In defence and public sector projects, secure deployment, data sovereignty, and auditability are essential We design solutions that operate within controlled environments and meet Protected B safeguarding requirements Our team includes members with Reliability Status and Top Secret clearances, allowing us to work directly within government and regulated environments. Privacy is embedded from the beginning, with defined access controls, full traceability, and structured review gates involving the client’s IT lead
Our technical standards remain consistent What changes is how we structure governance, security, and deployment to fit each sector’s operational environment
ImageCourtesy:Canva
Knowledge transfer is part of your consulting model. How do you ensure clients can own and maintain AI solutions after you leave?
This is a concern for many of our clients, particularly organizations that are just beginning their AI journey and do not yet have an internal AI team to take ownership of the solution after deployment
We design and build solutions that are not only technically robust, but also operational, well-documented, and transparent Throughout the project, we actively involve key stakeholders in design reviews and technical discussions They are not simply observers of progress updates; they are contributors to the decision-making process This ensures that knowledge transfer happens progressively and organically, rather than being compressed into a final handover session
At the delivery stage, we hold structured working sessions to go through the architecture, pipelines, and maintenance processes in detail In some cases, we have also supported clients in hiring or onboarding the right technical profile to ensure continuity That is part of change management for us
After deployment, we provide structured first- and second-level support to address questions and ensure operational stability For organizations seeking ongoing optimization or continuous improvement, we remain engaged as a long-term partner
At the same time, internal concerns shouldn’t be ignored Employees may worry about disruption or job displacement, and that’s understandable When AI is treated as a way to support subject matter experts and strengthen workflows, it becomes easier to build something meaningful Without trust, even strong initiatives can fail
Ultimately, responsible adoption comes down to clarity, courage, and disciplined execution
What first steps should a Canadian SMB or public organization take in 2026 if they want to adopt AI responsibly and effectively?
For a Canadian SMB or public organization in 2026, the first step is to approach AI with both openness and discipline. There is a fine line between viewing AI as too risky to pursue and assuming it carries no real risk at all Neither extreme is productive Organizations can look at what others have achieved as a result of AI adoption Did processes become more efficient? Were decisions supported with better information? That perspective shifts the conversation from hesitation to informed evaluation
Responsible adoption also requires clear guardrails Data privacy, security, compliance obligations, and internal accountability must be addressed early That commitment to structured oversight is one reason we value frameworks such as ISO 42001, which formalize accountability around AI systems Clear roles, review mechanisms, and human involvement help ensure systems remain aligned with organizational standards and public expectations
The Artificial Intelligence and Data Act (AIDA), one of the first national AI laws in the world, was supposed to be passed by Canada in 2025 However, the bill quietly died in committee, leaving the nation without specific AI legislation at a crucial juncture AIDA, part of Bill C-27, was intended to regulate "high-impact" AI systems through risk management, transparency, and penalties However, the entire bill was removed from the Order Paper following months of stalled clause-by-clause examination and a change of government Canada continues to regulate AI through a combination of sectoral regulations, human rights, privacy, consumer protection, and public sector directives
Simultaneously, deepfakes, generative AI, and automated decision-making have evolved from theoretical threats into practical challenges for public services, jobs, and elections After 2025, a new strategy centred on a dedicated AI minister, a revised national plan, and more focused regulation became possible, driven by this regulatory vacuum and the political impetus to address it.
LessonsfromCanada’sAIDAMisstep
AIDA is a "regulatory experiment that failed," according to policy analysts, yet it continues to influence future developments The act was criticized for its ambiguous definition of "highimpact" AI, its lack of public consultation, and its excessive concentration of authority in the minister's hands, in the absence of an impartial regulator Industry stakeholders and civil society organizations contended that its development process was insufficiently transparent, its enforcement measures inadequate, and its scope unclear
The bill lost political momentum as parliamentary hearings dragged on into 2024; instead of reviving a contentious framework, the new administration allowed Bill C-27 to expire in committee after the 2025 election and cabinet turnover
However, many of AIDAs fundamental concepts riskbased classification, lifecycle governance, recordkeeping and documentation, and stronger sanctions for hazardous AI uses are generally regarded as essential and will probably reappear in a different form A new consensus is emerging from AIDA's failure: the next phase of Canadian AI regulation needs to be more specific, consultative, and supported by robust institutions and infrastructure
PuttinganAIMinisterattheCenterof Canada’sStrategy
Evan Solomon was named Canada's first Minister of Artificial Intelligence and Digital Innovation in May 2025, providing AI with a committed political advocate and focal point Solomon has characterized his regulatory stance in interviews as "light, tight, right" light enough to maintain Canada's appeal for investment and innovation, tight enough to manage actual risks, and right in the sense of being reasonable and grounded in evidence Leading a redesigned national AI strategy, managing the Sovereign AI Compute Strategy and associated infrastructure investments, and collaborating with colleagues responsible for privacy, competition, and sector regulation to harmonize AI regulations across government are the three main responsibilities of his mandate
Experts anticipate that Canada's post-AIDA regulatory agenda will focus more on tangible harms such as discrimination, deepfakes, and opaque automated decisions than on AI in general Following the 2025 federal election, when AIgenerated content sparked concerns about voter manipulation and institutional trust, deepfakes and synthetic media are a major worry Proposed policies include requiring platforms to authenticate and flag synthetic media, requiring the labelling or watermarking of AI-generated political content, and establishing guidelines for campaigns and advertisers on the use of generative AI
Another issue is bias and discrimination. Regulators are investigating how AI may exacerbate systemic bias in hiring, credit, insurance, and law enforcement, and how to mandate impact studies, testing, and documentation for high-risk systems The path is already defined by public-sector regulations: the federal Directive on Automated Decision-Making, which is being amended to incorporate generative AI, mandates impact studies, transparency, and human-rights analysis for government algorithmic systems Lastly, to enable people to understand when AI is used and to contest significant judgments, future law will likely formalize explainability and contestability rights for automated decisions, building on federal guidance and Québec's Law 25
WhyInfrastructureMatters asMuchasRegulation
Building capacity is just as important to Canada's post-AIDA path as creating regulations The Canadian Artificial Intelligence Safety Institute (CAISI) was established by the federal government to study, test, and assess cutting-edge AI systems and to provide guidance for future regulations.
The Sovereign AI Compute Strategy and Budget 2025 allocate approximately C$926 million over five years to "large-scale sovereign public AI infrastructure," including compute in Canada that supports local innovation and safety assessment
The AI Strategy for the Federal Public Service 2025–2027 also sets procurement, training, and governance guidelines for the use of AI in government, thereby creating a real-world testbed for ethical AI practices that could eventually shape expectations in the private sector Together, these actions indicate that, rather than relying exclusively on a single omnibus statute, Canada's post-AIDA strategy will encompass institutional capacity, infrastructure, and regulatory adjustments
The main takeaway for companies is that, despite the absence of a unified AI law, AI oversight is becoming more stringent Businesses should expect increasingly stringent requirements for bias and explainability in high-impact uses (such as hiring and credit), specific regulations on political deepfakes, and increased scrutiny of automated decisions that affect rights or access to services Treating AIDA's key principles as "coming soon " adopting risk-based governance, categorizing AI systems by impact, conducting impact assessments for high-risk use cases, and documenting datasets, models, and decision pathways is the emerging best practice
Companies should also monitor upcoming results from the CAISI and AI Strategy Task Force, as these will likely influence sector-specific standards and guidelines Businesses that make early investments in testing, p protections will be upcoming AI
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The foundation of Canadas economy comprises small and medium-sized enterprises (SMEs), and by 2030, AI could boost their productivity by tens of billions of dollars. However, official data indicate that the use of AI remains in its infancy Although utilization has nearly quadrupled since 2024, according to Statistics Canada, just 12 2% of Canadian enterprises employed AI to create goods or deliver services in the preceding 12 months in the second quarter of 2025 Many small businesses remain on the sidelines, and adoption remains biased toward larger companies, knowledge-intensive industries, and exporters
However, according to confidential studies, a large number of SMEs are experimenting with AI technologies for marketing, automation, and customer service, even if they do not yet refer to them as "AI " In 2026, the story of Canadian SMEs and AI lies precisely between potential and current reality
WhatTheNumbersSay:CurrentUse AndPlannedAdoption
The most lucid national picture is offered by Statistics Canada's Canadian Survey on Business Conditions Businesses reported employing AI to create products or provide services in 12 2% of cases in 2025, up from 6.1% in 2024. Large and medium-sized businesses are much more likely to use AI than micro-enterprises, and SMEs in industries such as finance, professional services, and information and cultural industries are more likely to adopt it than those in local services, construction, or hospitality Only 14 5% of companies indicate they intend to use AI within the next 12 months, compared with approximately two-thirds who have no intention and nearly one-fifth who are undecided
Similar conclusions can be drawn from data particular to generative AI About 14% of Canadian organizations are early adopters of generative AI, either using it or planning to use it soon, while roughly 73% have not given generative AI any thought, according to Business Data Lab's "Prompting Productivity" research. While many smaller businesses report that they are still determining how AI fits into their operations, early adopters tend to be larger, more export-oriented, and more technologically advanced To put it another way, most SMEs are still cautious, but a small percentage are acting rapidly
Research from Canada and the G7 identifies several persistent obstacles for SMEs SMEs have greater adoption frictions than large enterprises, according to an OECD document written for Canada's 2025 G7 presidency These include limited financial resources, a lack of in-house digital skills, and uncertainty about returns, all of which hinder the proliferation of AI. According to Business Data Lab's research and Statistics Canada's study, a lack of knowledge about artificial intelligence, worries about cybersecurity and data privacy, and trouble hiring qualified employees are the main challenges faced by small firms
Despite these obstacles, an increasing proportion of Canadian SMEs are covertly incorporating AI into routine processes, frequently using low-code, easily accessible tools According to Statistics Canada, companies that use AI frequently use chatbots or virtual agents, text analytics for unstructured data, and software for customer segmentation and marketing automation According to Business Data Lab, early users of generative AI most frequently use it to automate tasks such as email drafting, document summarization, and visual creation (46%) and to speed up content production (69%), without reducing staff.
Concrete use cases are highlighted in SME-focused reports:
Chatbots for customer service and FAQ helpers on websites
Ad targeting and social media scheduling are automated.
Dynamic pricing and inventory forecasting in e-commerce and retail automated translation of documents and a simple examination of contracts
By reducing the burden of repetitive tasks, these solutions help SMEs overcome limited manpower and low margins without requiring significant upfront expenditures on proprietary models
Quick‐WinUseCasesForCanadianSMEs
Recent Canadian playbooks and government plans suggest a series of low-risk, quick-win use cases for SMEs unsure of where to begin Starting with frequent, manual, rule-based procedures where mistakes are not catastrophic such as automating intake forms, email triage, or simple bookkeeping tasks is recommended by the government SME AI Adoption Blueprint The Business Data Lab recommends concentrating on generative AI tools that assist in:
Product descriptions, blog drafts, and social media posts are examples of marketing content.
Sales support (follow-up emails, proposal drafting).
Meeting, policy, and report summaries are examples of internal knowledge management.
Other Canadian guidelines emphasize low-hanging fruit for customers: a well-configured chatbot or virtual assistant that can schedule appointments, answer routine questions, and refer complex issues to humans can improve service and save staff time without replacing staff. Off-the-shelf AI technologies for cash-flow prediction, inventory management, and demand forecasting can provide improved visibility with minimal integration effort for users with some experience Selecting small, quantifiable use cases that demonstrate value in weeks rather than years is crucial
ASimpleAdoptionPlaybookForSMEs
A clear roadmap for SMEs to move from interest to impact is provided by Canadian reports First, start with one or two specific issues rather than "AI" as a goal Some ideas include reducing the time spent on invoicing, expediting proposal development, or responding to client inquiries after hours Second, instead of constructing everything inhouse, use reliable, low-code technologies with transparent privacy terms and strong support Third, start small, monitor time saved and mistakes avoided, and only then increase utilization Fourth, invest in foundational training to help employees understand AI's promise and constraints, thereby preventing abuse and irrational expectations
Lastly, maintain a light yet authentic governance by establishing clear guidelines for data handling, acceptable uses, and human evaluation of critical choices By taking these actions in 2026, SMEs can transform AI from a trendy term into a useful tool for growth and resilience.
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Businesses in Canada are now under pressure to demonstrate tangible financial results from their research, having moved beyond the "AI curiosity" stage In the 12 months leading up to spring 2025, 12 2% of companies employed AI to create goods or provide services, up from just 6 1% the previous year, according to recent Statistics Canada statistics, indicating a sharp increase in actual installations However, this adoption is inconsistent While some industries are only getting started, others, like professional services, information and cultural industries, and finance, are expanding more quickly In 2026, "How do we turn pilots into profit?" will be the key question for leaders, rather than "Should we employ AI?"
WidespreadExperiments,LimitedMaturity
Two parallel realities emerge from the most recent Canadian Survey on Business Conditions On the one hand, more than 10% of companies now report using AI in production, up from 5% in 2024 With 35 6% of businesses utilizing AI, the information and cultural sectors top the pack Professional, scientific, and technical services rank second at 31 7%, followed by finance and insurance at 30 6% However, only 8 3% of Canadian businesses view AI as essential to their operations: 8 3% say investing in AI is "extremely important," 20 1% say it is "somewhat important," and more than 40% say it is "not relevant " This disparity underscores why ROI remains elusive Instead of integrating AI into their core processes, products, and decision-making, many businesses are still dabbling at the periphery.
Banking:FromChatbots ToRiskEngines
The transition from experiments to ROI is most evident in Canadian insurance and banking Approximately 30 6% of companies in this industry report using AI, placing them in the top tier nationally Early efforts frequently focused on front-office chatbots and virtual agents, which are now widely used: 40.8% of financial and insurance companies adopting AI use text analytics to mine client data and documents, while 35 0% rely on virtual agents or chatbots
The ROI narrative is changing AI is closely linked to income and risk reduction, as institutions use machine learning and data analytics to enhance credit scoring, detect fraud earlier, and customize product offers at scale To capture greater enterprisewide value, the next wave will likely focus on integrating these models with regulatory reporting and core banking systems
Although they are proceeding more carefully, Canadian retailers are gaining pace Even though only 16 0% of retail companies plan to use AI software in the coming year, that is more than twice as many as those who anticipated doing so in 2024 Text analytics and marketing automation are rapidly expanding among businesses that already use AI; in just one year, marketing automation adoption increased from 15 2% to 23 1%, underscoring retailers' emphasis on customer journeys and tailored advertising
Simultaneously, several shops are experimenting with virtual agents and recommendation algorithms to handle routine customer inquiries and free up staff for higher-value tasks It's notable that over 89 4% of AI-using businesses in the economy report no change in overall employment, indicating that AI in Canadian retail is still more about increasing productivity and reallocating activities than about eliminating jobs
WhatDifferentiatesROILeadersInCanada
Statistics Canada data suggest what sets AI leaders apart from laggards Businesses that use AI report that 40 1% have created new processes and 38 9% have trained current employees to use AI, underscoring the importance of process redesign and skill development alongside the technology itself Businesses that view AI as a strategic investment which is prevalent in the finance, professional services, and information and cultural sectors are also more likely to invest in cloud infrastructure, data management, and vendor relationships, and to believe AI is "extremely critical" to operations
Redesigning workflows around AI capabilities, selecting targeted use cases linked to cost or revenue outcomes, and investing in personnel to enable productive collaboration with these new technologies are the three steps that Canadian executives must take in 2026 to transition from experimentation to genuine ROI
UtilitiesandInfrastructureMake High-ImpactAIMoves
AI adoption is less obvious but strategically important for utilities and infrastructure players across electricity, transportation, and logistics Predictive maintenance, demand forecasting, and network optimization are becoming more popular, as evidenced by planned AI software implementations in manufacturing (16 2%) and transportation and warehousing (10 4%) Because these industries require substantial capital, even modest efficiency improvements can yield significant profits.
To better manage loads and predict equipment issues, many operators are adopting data analytics and machine learning models Canadian infrastructure is increasingly creating the digital backbone needed for larger AI initiatives in grid management, asset monitoring, and resilience, as more utilities purchase cloud services and specialized computing capacity, a trend already reported by about 25% of AI-using businesses
Your role in staying informed is essential to our mission of building a strong community of AI-driven innovators The CanadianSME AI Business Review Magazine is your go-to resource for insights, strategies, and updates shaping the future of artificial intelligence in business
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Disclaimer: This article is based on publicly available information and is intended solely for informational purposes The CanadianSME AI Business Review Magazine does not endorse or guarantee any products or services mentioned. Readers are encouraged to conduct independent research and due diligence before making business decisions.
AI is now a budget line item in Canada, but many businesses are still in the middle of interminable trial projects that will never fully become business-critical Even with knowledge substantially higher, Statistics Canada reports that only 12 2% of Canadian enterprises had employed AI to create goods or deliver services in the 12 months before mid-2025 Just 14 5% of organizations plan to implement AI in the coming year, while two-thirds have no plans at all, according to survey data on planned usage, highlighting how many businesses remain hesitant or doubtful
Similar findings are found in polls conducted in the private sector. According to KPMG, 93% of Canadian business executives report that their companies currently use artificial intelligence (AI) in some capacity, but only 31% have fully integrated it into core operations, 32% are in partial adoption, and 20% are still in pilot mode The problem is obvious: while AI experimentation is booming in Canada, production-level implementation is trailing behind
WhatTheSurveysReveal AboutStalledAdoption
The figures depict a nation in the midst of implementation and intent AI use has more than quadrupled in a year, from 6 1% of enterprises in 2024 to 12 2% in 2025, according to the Canadian Survey on Business Conditions However, adoption remains concentrated in a small number of areas, including finance, professional services, and information and cultural services Looking ahead, only 14 5% of firms say they plan to implement AI in the next 12 months, while 66 7% report no plans and 18 9% are unsure
This is supported by KPMG's generative AI research: while nearly all CEOs report using AI tools to some extent, fewer than four out of ten say they have a clear plan to extract value, and 57% cite "understanding how to capture value" as a top difficulty Essentially, Canada's AI story is about a lack of reproducible, scalable pathways from proof of concept to production, not a lack of experimentation
WhatCanadianAI“Scale‐Ups” AreDoingDifferently
Evidence from surveys and case studies indicates a common trend among Canadian organizations that have progressed beyond pilots First, rather than focusing on nebulous innovation objectives, they clearly link AI projects to quantifiable business outcomes such as productivity gains, cycle-time reductions, or revenue growth According to Statistics Canada, 40 1% of AI-using companies have revamped their workflows, and 38 9% have trained staff to use AI, indicating an emphasis on people and processes Secondly, they treat AI as a change in operating model rather than an IT add-on
Third, they invest in data preparedness, improve data collection and management processes, and strategically engage consultants or suppliers to integrate AI into existing systems Lastly, rather than declaring a pilot "done" once the device functions independently, they continue to monitor results and refine models These actions set companies that scale AI apart from those that remain in the experimental stage
Surveys and early adopters are producing a useful blueprint for Canadian companies wishing to go from pilot to production The first step is to select specific, high-value use cases where benefits can be measured quickly, such as automating document processing in finance, enhancing demand forecasting in retail, or using AI to route customer care requests in telecom The second step is to build around workflows, not features: instead of implementing a model into an unaltered process, map the end-to-end process, pinpoint where AI adds value, and restructure roles, approvals, and metrics accordingly
As part of their AI journey, many successful businesses report changes in data management procedures and increased use of cloud services Step three is to invest in data quality and integration, making sure that the relevant data is available, labelled, and managed To deploy AI-enabled workflows, the fourth step is to develop internal capabilities by training frontline employees and data scientists The fifth and last step is to institutionalize measurement. To develop the business case for scaling, clearly specify KPIs, such as time saved, error reductions, or conversion uplift, and monitor them from pilot to rollout
Early examples of scaling in Canada can be seen in the financial and insurance, professional services, information, and cultural sectors Businesses in these sectors are more likely than average to say investing in AI is "extremely important" to their operations, and about one-third report using AI Typical use cases include machine learning models for risk assessment or personalization, virtual agents and chatbots for front-line support, and text analytics for document and customer data mining. Here, successful businesses typically begin with a few carefully selected workflows, including subscription churn prediction, claims triage, or loan processing, and then apply the same methodology to related operations
The federal government's new register of AI use in the public sector provides a clear picture of how pilots can develop over time, showing systems ranging from early research and proof-ofconcept initiatives to fully implemented tools for operations and service delivery Focus, governance, and repeated scaling rather than just one-time experiments are what ultimately drive AI into routine production in all of these situations
In the future, Canadian researchers predict that AI usage will continue to rise steadily, with more businesses preparing to deploy AI software and more pilots entering production According to reports, early generative AI adopters roughly 14% of Canadian companies are already experiencing increases in productivity and time savings, and they may continue to do so over the next three to six years as they compound learning The question for most people who are currently on the sidelines or locked in pilots is not whether AI will matter, but rather whether they can develop the skills necessary to grow it commercially and responsibly The speed at which Canadian businesses can go past the proof-ofconcept stage and integrate AI into routine tasks will determine how competitive the country is
Your role in staying informed is essential to our mission of building a strong community of AI-driven innovators. TheCanadianSMEAIBusinessReviewMagazine is your go-to resource for insights, strategies, and updates shaping the future of artificial intelligence in business
Subscribeto our monthly editions at aibusinessreview.ca to stay up to date on the latest AI trends and developments in the Canadian business landscape Your engagement enables us to continue supporting and empowering the AI ecosystem
Disclaimer: This article is based on publicly available information and is intended solely for informational purposes The CanadianSME AI Business Review Magazine does not endorse or guarantee any products or services mentioned Readers are encouraged to conduct independent research and due diligence before making business decisions
Although AI is already used daily by Canadian businesses and individuals, the technologies actually employed in the workplace often differ substantially from the hype.
According to surveys conducted by the Business Data Lab (BDL) of the Canadian Chamber of Commerce, 14% of Canadian companies now use or intend to utilize generative AI, with adoption rates significantly higher among larger, more technologically advanced companies
According to KPMG, 51% of Canadian employees currently use generative AI tools at work, more than twice as many as in 2023 Of those, 73% tap them regularly or occasionally
However, a comparatively limited set of writing, communication, marketing, and analytics tools is the focus of most of this activity Leaders who wish to go beyond pilots and into real-world, productivity-boosting deployments must have a thorough understanding of this "actual" AI stack, including what people are using, what value it produces, and where the skills shortages are
Commonplace generative AI tools for writing, summarizing, and communicating form the cornerstone of the Canadian AI stack According to BDL's "Prompting Productivity" survey, the two main motivations for the 14% of companies that are already employing or intend to use generative AI are to automate activities without laying off workers (46%) and to accelerate the production of creative content (69%) In practice, this includes AI-enabled note-taking or office-suite applications integrated into Google Workspace or Microsoft 365; ChatGPT and Claude for writing emails, reports, and code; and Otter ai and related apps for recording and summarizing meetings
AI is already integrated into well-known systems in marketing and sales, which constitute the next tier of the stack Tools like HubSpot, Mailchimp, Jasper, Salesforce Marketing Cloud, and Vancouver-based Hootsuite are recommended by Canadian SME guidelines as the best choices for AI-powered campaign planning, audience segmentation, subjectline and copy creation, and social media scheduling For instance, Jasper is marketed to small businesses in Canada as a quick and easy solution for developing SEO content, ad copy, and blog entries without hiring additional writers
According to BDL's statistics, early adopters of generative AI disproportionately use AI to expedite customer-facing communications and marketing content, rather than for back-office tasks Additionally, Canadian SMB sites recommend AI-enhanced technologies, such as Dialpad AI for call analytics and transcription, and Canadian-born platforms, such as Hootsuite, for performance prediction and AI-assisted post recommendations.
These social media and marketing technologies are essentially "AI on training wheels" for many businesses, as they integrate with existing processes and deliver quantifiable improvements in lead generation, engagement, and campaign turnaround times without requiring substantial technical expertise
OperationsandAnalytics
A less visible but equally important operational and analytics layer lies behind the front-office activity The use of Microsoft Azure AI and related cloud services for creating chatbots, document-processing pipelines, and custom models; UiPath and related RPA platforms for automating repetitive back-office tasks; and analytics tools such as Power BI and Tableau for AI-assisted dashboards and forecasting are prevalent in Canadian enterprise surveys and case studies.
Businesses that currently employ cloud, data warehousing, and modern analytics are far more likely to use generative AI, according to BDL's research, which demonstrates a strong correlation between AI adoption and overall digital maturity 61% of Canadian workers intentionally use AI at work, including built-in features in office software and analytics platforms, even if they don't necessarily refer to it as "AI," according to KPMG's broader AI trust survey
In industries such as finance, shipping, and professional services, where automating document flows, reconciliations, and reporting directly yields cost savings and fewer errors, RPA and AI-enhanced analytics are especially appealing The operational core of the Canadian AI stack comprises these tools
Headline data demonstrate how quickly generative AI is being adopted at the worker level 51% of Canadian workers now utilize generative AI tools at work (up from 22% in 2023), and 73% of those users interact with them regularly or many times per week, according to KPMG's 2025 generative-AI index Internal analyses indicate that many workers save approximately 1 to 5 hours per week on tasks such as drafting, summarizing, and research, and nearly eight out of ten report that these tools have increased their productivity
However, this use conceals a substantial lack of confidence and expertise According to KPMG, nearly half of workers fear losing their jobs if they are unable to keep up with AI developments, and 83% of workers want or need further training to use generative AI efficiently. This is supported by BDL's study, which shows that a lack of internal expertise, a lack of clarity regarding the real areas in which AI offers value, and privacy and security concerns are the main obstacles to wider deployment A two-speed environment results, with many colleagues remaining cautious or doubtful while eager early adopters experiment widely
WhatLeadersShouldDoWithThisStack
The actual AI stack ChatGPT-style tools, AI-infused marketing platforms, and analytics-rich cloud services offers Canadian executives instant opportunity, but only if they are supported by training and clear regulations Three priorities are suggested by experts To reduce risk and confusion, first establish written criteria for the appropriate use of generative AI, such as data-handling norms and human review for critical decisions Second, rather than focusing on general AI theory, invest in hands-on training on the products employees already use, such as marketing automation, CRM, and office suites
Third, link AI to specific business goals, such as reduced time, shorter sales cycles, or improved customer support, so that testing yields quantifiable performance improvements. The competitive edge in 2026 will come more from employing the current Canadian AI stack with assurance, focus, and discipline than from finding novel, exotic tools
Your role in staying informed is essential to our mission of building a strong community of AI-driven innovators. TheCanadianSMEAI BusinessReviewMagazine is your go-to resource for insights, strategies, and updates shaping the future of artificial intelligence in business
Subscribeto our monthly editions at aibusinessreview.ca to stay up to date on the latest AI trends and developments in the Canadian business landscape Your engagement enables us to continue supporting and empowering the AI ecosystem
Disclaimer: This article is based on publicly available information and is intended solely for informational purposes The CanadianSME AI Business Review Magazine does not endorse or guarantee any products or services mentioned Readers are encouraged to conduct independent research and due diligence before making business decisions.
ShapesCanada’sDigitalFuture
For Canadian leaders, AI sovereignty is now a practical business necessity rather than just an abstract policy concept Businesses in the public sector, healthcare, energy, and finance are increasingly curious about who controls the infrastructure, where their data is stored, and which regulations apply to their AI systems Fundamentally, data and AI sovereignty means that, even when international cloud and AI providers are involved, information and vital digital infrastructure are managed under Canadian law and in accordance with Canadian principles
As Canada develops a national AI plan and enacts new laws such as the Artificial Intelligence and Data Act (AIDA), which aims to ensure that AI systems used in Canada are secure, non-discriminatory, and accountable, this becomes increasingly important For Canadian executives, sovereignty is now about long-term competitiveness, resilience, and trust rather than merely compliance
WhatAISovereigntyMeansIn TheCanadianContext
Location, control, and governance are the three pillars of Canadian AI sovereignty in practice Location refers to storing important workloads and sensitive data in data centers within Canadian territory, operated by organizations subject to Canadian law Control is making sure that Canadian organizations, not foreign governments or extraterritorial laws, have the last say over how data is accessed, processed, and utilized in AI systems From privacy regulations (such as PIPEDA and provincial regimes) to future AI-specific requirements under AIDA, including risk classification, algorithmic transparency, and audits for high-impact systems, governance encompasses the legal and policy frameworks governing AI
As experts have noted, because Canada cannot produce all cutting-edge chips and hardware, its concept of sovereignty largely depends on who controls the infrastructure and where data is stored, rather than on complete technological autarky
BuildingSovereignCloudand ComputeinCanada
Through cloud and compute policy, the federal government has started to establish AI sovereignty To effectively restrict high-security workloads to domestic or private hosting, federal agencies are required under updated Treasury Board guidelines to ensure that sensitive information (Protected B and above) is stored in Canada or in governmentcontrolled facilities By promoting new Canadian data centers and cloud capacity tailored to public-sector requirements, Canada's Sovereign Cloud Initiative seeks to reduce dependence on foreign-controlled infrastructure In addition, the Sovereign AI Compute Strategy is allocating C$2 billion over five years to provide Canadian businesses and researchers with access to cutting-edge AI computing, including financing for supercomputing equipment owned and located in Canada.
These actions are intended to maintain sensitive datasets under Canadian jurisdiction while providing Canadian entrepreneurs with consistent, local access to high-performance computing These activities signal to businesses that governments will increasingly favour vendors and architectures that comply with domestic data location and management regulations
HowSovereigntyShapesCloudChoices
The selection of cloud providers and architectures by Canadian enterprises is being directly impacted by AI sovereignty There is increasing pressure on public organizations and regulated industries to adopt "sovereign cloud" or "Canada-only" regions, where all processing and storage occur within national boundaries and are subject to Canadian regulatory scrutiny With a clear commitment to local data storage and regulatory compliance, including the Privacy Act and PIPEDA, providers like SAP can promote sovereign cloud options for Canada. Industry associations caution that excluding international providers could lead to overly stringent datalocalization regulations, causing market fragmentation and restricting access to best-in-class tools
AI sovereignty is perilously close to regulated industries To ensure that models used for credit scoring, fraud detection, or wealth management are explainable, auditable, and consistent with cross-border data limits, financial organizations must align their AI deployments with federal and provincial privacy regulations, as well as their upcoming AIDA duties Similar pressures apply to healthcare firms, which must be wary of sharing identifiable patient data with non-Canadian clouds or opaque third-party AI technologies, given provincial privacy regulations and health information rules Federal cloud and data residency regulations must be followed by public sector organizations, and they increasingly favour sovereign or Canadian-operated infrastructure for AI systems that affect citizens' rights and access to services
Another layer is added by Quebec's Law 25, which requires privacy impact assessments whenever personal data is processed, including by AI systems, and imposes stringent restrictions on cross-border transfers Sovereignty is essential for these industries to implement AI at scale
In addition to infrastructure and regulations, "made-inCanada" AI models are increasingly being discussed in terms of sovereignty According to Canadian specialists, locally developed models governed by domestic legislation and trained on Canadian data can more accurately reflect Canada's social context, linguistic diversity, and regulatory requirements Global foundation models should be combined with Canadianbuilt systems, domain-specific fine-tuning, and robust contractual protections over data usage and retention, rather than being excluded The need for suppliers that can provide Canadian data residency, open training procedures, and robust rights for those affected by automated decisions will increase as AIDA develops and provinces such as Ontario and Quebec impose more stringent privacy and AI accountability regulations
Your role in staying informed is essential to our mission of building a strong community of AI-driven innovators The CanadianSME AI Business Review Magazine is your go-to resource for insights, strategies, and updates shaping the future of artificial intelligence in business
Subscribe to our monthly editions at aibusinessreview.ca to stay up to date on the latest AI trends and developments in the Canadian business landscape Your engagement enables us to continue supporting and empowering the AI ecosystem
Disclaimer: This article is based on publicly available information and is intended solely for informational purposes The CanadianSME AI Business Review Magazine does not endorse or guarantee any products or services mentioned Readers are encouraged to conduct independent research and due diligence before making business decisions
forEverydayCanadian Businesses
In an exclusive interview with The CanadianSME AI Business Review Magazine, Sahan Rao, Founder of LeadAi Solutions, offers practical insights on harnessing the true power of AI in small and mediumsized businesses With over 15 years of experience in marketing and automation, Sahan cuts through the AI hype, focusing on the real-world, scalable solutions that businesses can adopt without the need for large tech teams or hefty budgets.
I'm Sahan Rao, Founder of LeadAi Solutions, an AI and marketing automation consultancy helping small and mid-sized businesses implement practical solutions that deliver real results
I've spent over 15 years in marketing spanning agencies, consulting, IT, banking, and SaaS and eight of those years were working with marketing automation before it became a trend That experience taught me what actually works versus what's just hype.
I do not treat AI as the default answer. Many teams need better workflows, clearer ownership, or to use the tools they already pay for more effectively. AI works best when it supports a solid process, not when it replaces one
SahanRao FounderofLeadAiSolutions
My background in accounting (Bachelor’s) and marketing (Masters) helps me focus on outcomes that matter Less manual effort Fewer errors More time for meaningful work
My work and insights have been featured in automation and technology publications
When you first walk into a small or mid-sized business, how do you identify the low-hanging automation opportunities where simple AI and workflow changes can deliver quick wins without needing a full-scale transformation?
I start with one question: "What's eating up your team's time that doesn't require human judgment?"
Usually, the answer comes fast It's the repetitive stuff like manually entering data between systems, chasing leads that go cold, answering the same customer questions over and over From there, I look at three things:
Volume How often does this task happen?
Daily tasks with small time savings add up fast.
Simplicity — Can we define clear rules for it? If yes, it's automatable
Existing tools — Most businesses use maybe 20% of what their current software can do Before adding anything new, I check what's already there
Based on these three pillars, I create an AI Assessment Report that categorizes opportunities into low, medium, and high complexity Low-complexity items are quick wins and easy to implement, fast to show value Medium and high-complexity opportunities require more time, deliberation, and deeper integration
This approach keeps things grounded You are not betting the farm on a massive AI overhaul You are starting with what makes sense now, then building from there as results come in
You describe yourself as an AI realist, not an AI evangelist. From your work with Canadian SMBs, where do you see the biggest gap between AI hype and what actually drives measurable results in marketing, sales, and operations?
The biggest gap is between expectations and readiness.
Most Canadian SMBs I work with have heard the hype that AI will transform everything, automate your whole business, replace half your team etc But when I walk in, the reality is different Their CRM is half-populated Leads are tracked in spreadsheets Sales follow-ups happen when someone remembers
The hype says "implement AI " The reality says "get your data and processes in order first " That's messy, but it's where results come from
Where AI actually drives measurable outcomes is in specific, process focused solutions Things like AI sales systems that ensure no lead slips through the cracks, automated follow-up sequences that trigger based on real behaviour, or chatbots that qualify prospects before they hit the sales team
Canadian businesses tend to be more cautious with new technology and honestly, that's an advantage here The companies seeing real ROI aren't just chasing trends, they are solving specific problems with targeted automation.
You often emphasize that AI should support a solid process, not replace one. Can you share an example of how fixing workflows or ownership before adding AI led to better outcomes than jumping straight into “advanced” tools?
I worked with a professional services firm that wanted an AI chatbot to handle inbound inquiries
They were convinced it would solve their lead response problem, prospects were waiting days to hear back, and deals were going cold
But when I dug in, the issue wasn't speed of response It was what happened after Leads came in, sat in a shared inbox, and nobody owned them No clear assignment No follow-up sequence No accountability
Adding a chatbot would've just meant faster responses going into the same broken process.
So we fixed the workflow first We defined ownership like who gets which leads, based on what criteria We built a simple follow-up sequence with clear timelines We set up notifications so nothing sat longer than a few hours
Once that foundation was solid, we layered in automation: auto-routing leads, triggering followup reminders, and eventually a chatbot to qualify inquiries before they hit the team
The result was their lead-to-consultation rate improved significantly and not because of AI, but because the process finally worked AI just made it a little faster
This is what I see over and over: the "advanced" tool isn't the fix The boring work like ownership, process, accountability, thats where the real gains live.
Many Canadian SMBs feel they can’t compete with bigger brands’ budgets or tech stacks. How can practical AI workflows like lead qualification, pipeline nudges, and onboarding automation—help level the playing field without requiring a large in-house tech team?
This is actually where smaller businesses have an advantage, they can move faster
I've worked at both small and large organizations across my 15 years in marketing SMBs hands down have an edge They are agile with quick decision cycles Enterprise companies have layers of approvals, legacy systems, and a min of 18-month implementation timelines A Canadian SMB can identify a problem on Monday and have a solution running by Friday. The tools that used to require a tech team and six-figure budgets are now accessible to everyone
Lead qualification like an AI chatbot that asks the right questions and routes qualified prospects to your calendar Pipeline nudges can help automate reminders that trigger when a deal stalls Onboarding automation can be set up with a sequence that delivers the right information at the right time so the team isn't manually walking every client through the same steps
These really aren't complex systems They are just practical workflows that save hours every week and ensure nothing slips through
What smaller businesses need isn't a bigger tech stack. It's the right systems working together with a few smart automations connecting them. So there’s consistency in both sales and customer experience
For business owners who feel they’re falling behind on AI but don’t know where to start, what first three steps would you recommend they take in 2026 to adopt AI safely, affordably, and in a way that their teams will actually use?
First, audit before you act Map out where your team spends time on repetitive, low-judgment tasks Lead follow-ups, lead enrichment, data entry, appointment scheduling, answering the same questions You can't automate what you haven't identified This also reveals whether AI is even the right solution, sometimes it's better process or simply using your existing tools properly
Second is to start small and prove value fast Pick one workflow, not ten Automate one process, measure the result, and build confidence from there
Third is to get expert guidance on data and privacy This is especially critical for Canadian businesses You are dealing with PIPEDA, provincial regulations, and increasing customer expectations around how their information is handled Before plugging AI into anything customer-facing, understand what data you ' re using, where it's stored, and who has access. This is foundational.
With indigenous businesses now offering everything from chatbots and marketing tools to self-driving systems and quantum-ready software, Canada has unobtrusively emerged as one of the world's most dense AI ecosystems Three national AI institutes Vector in Toronto, Mila in Montreal, and Amii in Edmonton have been established by federal funding through the Pan-Canadian AI Strategy These institutes serve as both talent magnets and launchpads for commercial spinoffs At the same time, Toronto, Montreal, Vancouver, and Waterloo are now among the fastest-growing tech and AI hubs in North America, attracting both local innovators and international labs
This is more than simply a source of pride for Canadian companies Organizations now have local options that are attuned to Canadian markets, laws, and languages, thanks to an expanding stack of "made-in-Canada" AI solutions, ranging from support automation to traffic analytics and mining platforms These businesses are shaping how AI is used in everyday Canadian business operations as they compete more fiercely with multinational behemoths
SMEs can quickly integrate some of Canada's most well-known AI achievements into their marketing and customer experience stacks
Ada (Toronto): Ada Support Inc. is an AIpowered customer care provider that helps businesses automate voice and chat support. With a platform trained on over four billion customer conversations and integrating state-of-the-art generative AI and speech capabilities, Ada, which was founded in Toronto in 2016, already powers customer-service automation for over 300 businesses, including Meta, Verizon, and Shopify. It claims to automatically answer a significant portion of consumer questions across any language or channel, freeing human agents to handle more complex problems
Hootsuite (Vancouver): Although not a "AI company " by definition, Hootsuite is a Vancouverbased social media management platform that currently uses AI to analyze interactions, recommend posts, and optimize posting times. Hootsuite is frequently ranked among the best digital solutions for small businesses seeking AIassisted social media and brand management in Canadian SMB tool recommendations.
Both global platforms and local SaaS products, such as AI-enhanced marketing, scheduling, and CRM tools, are cited by Canadian small-business websites, demonstrating that Canadian-built applications are already part of regular SME stacks.
Often ranked among Canada's leading AI startups, Cohere (Toronto) develops enterprise-grade large language models and foundation models, emphasizing data protection, deployment flexibility, and business-friendly APIs. Cohere is positioned as Canada's response to US-based foundation-model providers, thanks to its models that enable chatbots, search, and knowledge-management systems for international clients
InsideCanada’sDeepTech andInfrastructureBuilders
Canadian businesses are constructing the cutting-edge technology and infrastructure that enable AI further down the stack
Waabi, a self-driving technology startup based in Toronto, is developing an "AI-first" strategy for autonomous trucks Waabi, which is listed in several rankings of Canadian AI businesses to watch, seeks to improve the safety and efficiency of freight by leveraging simulation-heavy training and applied research roots in Canada's AI ecosystem.
Xanadu (Toronto): Xanadu develops photonic quantum hardware and the PennyLane software architecture for quantum machine learning, fusing AI with quantum computing. Its study demonstrates how AI and quantum are converging in Canada's deep-tech ecosystem and how this convergence is closely linked to Canadian academic institutions.
Companies that operate at the infrastructure and tools layer rather than end-user apps, such as Tenstorrent (AI chips), BenchSci (AI for biomedical discoveries), and Apera AI (vision for industrial robots), are also among Canada's top AI companies Collectively, these deep-tech companies offer Canada a stake not only in applications but also in the underlying framework of the global AI economy
Beyond horizontal platforms, Canada hosts a growing crop of vertical AI startups targeting specific industries.
VRIFY (Vancouver):
VRIFY Technology Inc. is branded as an “AI assisted mineral discovery platform” that helps mining companies analyze geological data and communicate exploration results through interactive visualizations. Its tools support capital raising and investor relations by turning complex exploration data into digestible 3D presentations.
Miovision (Kitchener): Miovision develops AI powered traffic and smart city solutions that use computer vision and analytics to optimize signal timing, reduce congestion, and improve road safety As cities confront congestion and emissions targets, Miovision’s products help municipalities make data driven infrastructure decisions.
BenchSci and Apera AI: Apera AI provides "4D vision" for robotic automation in manufacturing, while BenchSci uses AI to accelerate biomedical research by helping scientists select the best tests and reagents
Top startup rankings and Canadian AI company lists highlight numerous more sector specific players from ShopVision (e commerce merchandising) to Optifab (manufacturing process optimization) and Stocky AI (food supply chain operations) that embed AI directly into vertical processes These regional companies provide solutions tailored to the operational, data, and regulatory requirements of Canadian mining, mobility, health, and manufacturing companies.
The strength of Canada's AI is not a coincidence With support from the national supply-chain AI supercluster SCALE AI and hubs at Vector (Toronto), Mila (Montreal), and Amii (Edmonton), the Pan-Canadian AI Strategy has invested over C$400 million in AI research and commercialization since 2017 These institutions build a pipeline from lab to market by developing talent, launching businesses, and collaborating with businesses on applied projects
This ecosystem offers three benefits to Canadian companies First, local alignment: Canadian privacy, language, and sector settings are deeply ingrained in AI firms that are built in Canada Secondly, dense regional clusters provide access to partners and skills Third, policy resonance, as future rules and Canada's national AI strategy progressively favour domestically grounded, transparent, and reliable options. Selecting Canadian-made AI technologies in 2026 can be a practical way to balance innovation, compliance, and local economic impact, while also being patriotic
Your role in staying informed is essential to our mission of building a strong community of AI-driven innovators The CanadianSME AI Business Review Magazine is your go-to resource for insights, strategies, and updates shaping the future of artificial intelligence in business
Subscribe to our monthly editions at aibusinessreviewca to stay up to date on the latest AI trends and developments in the Canadian business landscape. Your engagement enables us to continue supporting and empowering the AI ecosystem.
Disclaimer: This article is based on publicly available information and is intended solely for informational purposes The CanadianSME AI Business Review Magazine does not endorse or guarantee any products or services mentioned. Readers are encouraged to conduct independent research and due diligence before making business decisions
Deepfakes and synthetic media are on the rise in Canada, which is altering perceptions of internet safety, particularly for kids and teenagers Significant flaws in current criminal and online platform regulations have been revealed by weaponized false photos on platforms such as X, sexualized deepfakes of women and girls, and AI-generated child sexual abuse material (CSAM) Nearly 4,000 sexually explicit deepfake photos and videos of children and teens were reviewed by the Canadian Center for Child Protection's Cybertip ca tipline in a single year, many of which were connected to sextortion cases and educational settings
There is currently pressure on the federal and provincial governments to update criminal laws, enact new internet safety regulations, and establish guidelines for AI technologies that can quickly generate damaging yet realistic content This is not a far-off policy debate for platforms, brands, and schools because deepfakes are already affecting Canadian classrooms, feeds, and reputations
ChildrenAtRiskfromDeepfake SextortionandCSAM
AI-generated sexual imagery is already changing the environment of internet exploitation, according to child protection organizations The Canadian Centre for Child Protection (C3P) and Cybertip ca have issued a warning about "incidents involving sexually explicit AI-generated photographs and films " that are "increasing in schools across Canada," including phony student nude photos that have been made and shared by classmates.
Police and C3P stress that even if the photographs are artificial, it is still illegal to create, distribute, or possess AI-generated CSAM under Canada's child-exploitation statutes In response, they are publishing revised "Parenting in the Online World" tools and new lesson plans for grades 9–12 (as well as updated materials for grades 3–8) that specifically address sexually explicit deepfakes within the Kids in the Know program The message is very clear: parents and educators are now the first line of defence against a type of abuse that can occur even in the absence of a camera
CriminalLawGapsonDeepfakesand OnlineHarms
Even though CSAM is obviously prohibited, legal experts point out that non-consensual deepfakes involving adults continue to be a problem for Canadian law Advocates point out that while federal laws punish images of actual or imagined child abuse, they do not yet fully cover intimate photos of adults that have been digitally manipulated, leaving victims to rely on a patchwork of civil remedies, harassment, and voyeurism. In late 2025, federal MPs suggested modifications to the Criminal Code that would specifically criminalize non-consensual sexual deepfakes involving adults, in reaction to highprofile incidents employing technologies like Grok
AI chatbots have become a distinct but connected online safety issue as kids engage with more and more systems that can produce sexual content, risky advice, or deceptive answers In a widely reported Canadian case, a Tesla-integrated chatbot (Grok) requested nude images in response to a 12-year-old's soccer inquiry, sparking public indignation and calls for stricter regulations In response to mounting concerns about mental health risks, self-harm instructions, and sexualized interactions, Canada's AI Minister, Evan Solomon, has stated that an impending federal privacy bill may include age limitations on access to AI chatbots to protect kids
Additionally, Solomon is consulting on whether " age assurance " technology should be required for large language model chatbots and has proposed a legal "right to delete deepfakes," which might be included in future online safety or privacy legislation Advocates for policy contend that in addition to content filters, childfacing AI devices will require built-in crisis intervention processes, safeguards against sexual content, and explicit accountability for harm Even before a comprehensive AI regulation is passed, Canadian regulators are closely monitoring international litigation related to chatbot-related deaths and have indicated they are ready to act in "particular critical situations "
Organizations can proactively plan for deepfake and chatbot dangers even as laws change. The implementation of more aggressive detection, authentication, and reporting methods for synthetic media, as well as prompt collaboration with law enforcement and child-protection agencies in the event of CSAM or sexual deepfakes, is being recommended for platforms and major companies operating in Canada Experts in brand safety advise establishing explicit guidelines that forbid non-consensual synthetic images, educating moderation staff on new deepfake trends, and releasing easy-to-use reporting tools and takedown procedures
Incorporating C3P's revised deepfake lessons into the curriculum, initiating clear conversations about synthetic images starting in grade seven, and establishing non-punitive reporting routes for children who are targeted or witness abuse are all options available to educators and schools
It is advised that parents handle deepfakes the same way they would other types of online danger, having candid conversations with their children about sextortion, phony nudity, and what to do if they are singled out or under duress to submit pictures Organizations that start early on education, detection, and support will be better positioned to safeguard children and uphold trust as Canada creates new regulations on deepfakes, online dangers, and chatbot age limits.
Your role in staying informed is essential to our mission of building a strong community of AI-driven innovators. The CanadianSME AI Business Review Magazine is your go-to resource for insights, strategies, and updates shaping the future of artificial intelligence in business
Subscribe to our monthly editions at aibusinessreviewca to stay up to date on the latest AI trends and developments in the Canadian business landscape. Your engagement enables us to continue supporting and empowering the AI ecosystem
Disclaimer: This article is based on publicly available information and is intended solely for informational purposes The CanadianSME AI Business Review Magazine does not endorse or guarantee any products or services mentioned Readers are encouraged to conduct independent research and due diligence before making business decisions
WhatBusinessesNeedToKnow
WhileNavigatingCanada’s FragmentedAIRules
In 2026, Canadian companies will face a complex patchwork of regulations that will affect how they develop, implement, and manage AI systems Since the original Artificial Intelligence and Data Act (AIDA) expired on the order paper in early 2025, there is still no single, comprehensive federal AI law in effect Instead, companies must navigate conflicting privacy laws, industryspecific regulations, and new provincial laws that directly affect AI, such as Ontario's Law 25 and new transparency requirements for AI use in recruiting
AI expectations will be included in updated privacy and consumer protection frameworks under federal privacy reform, including Bill C-27 and associated measures, with potentially severe penalties for non-compliance The practical challenge for companies operating across multiple provinces is coordinating compliance: establishing AI governance and documentation that simultaneously meet the strictest provincial standards and federal requirements
For the time being, Canada is shifting away from a stand-alone AI statute and toward sectoral and privacy regulations for AI governance at the federal level To control "high-impact" AI systems, the first AIDA was developed under Bill C-27 It imposed duties on risk management, transparency, and record-keeping and was supported by fines of up to C$25 million or 5% of global turnover These particular proposals were shelved when Bill C-27 passed away in January 2025, but their ideas remained Since then, Ottawa has indicated that many of the concepts of AIDA, including explainability for automated decision-making, bias reduction, and governance standards, will instead be implemented through investment programs, policy guidelines, and revised privacy law (the Consumer Privacy Protection Act).
Strong enforcement authorities and substantial fines support the law, which applies to any private sector entity managing personal information in Québec, regardless of its headquarters This means that companies using AI will have to either redesign for "human-in-the-loop" or create strong explainability and appeal procedures because fully automated hiring screeners, creditscoring engines, or customer-scoring tools that function without significant human involvement will trigger disclosure and review rights
With the complete implementation of Law 25 in September 2024, Quebec currently has some of the most stringent AI-related regulations in Canada Law 25 updates Québec's private-sector privacy law and establishes clear requirements for choices made "exclusively by automated processing," such as AIpowered choices in customer service, credit, and employment According to section 12 1, companies are required to notify people when a decision is made entirely through automated processing and, upon request, disclose information about the personal data that was used, the primary elements that influenced the decision, and the person ' s right to have the decision reviewed by a human.
RulesInHiring
Ontario is adopting a different, more focused strategy by prioritizing automated hiring and transparency in job advertisements
Employers in Ontario will have to state in job advertisements whether they use artificial intelligence (AI) to screen, evaluate, or choose applicants under rules that are anticipated to go into effect on January 1, 2026 (via the Working for Workers Four Act and related legislation)
This requirement is anticipated to cover enterprises over a specific size threshold (e g , 25+ employees) and will apply generally to tools like chatbots used to evaluate applications, interview scoring systems, and resume screening algorithms In order to further promote equitable and responsible hiring practices, employers are now required to provide wage ranges and other transparency information.
HowQuébec’sLaw25RaisesthePrivacyBar
To prevent both compliance and reputational issues, legal commentators advise firms to update job-posting formats, provide clear descriptions of AI technologies used in recruitment, and guarantee meaningful human review of AIassisted judgments
Ontario's disclosure rule essentially establishes a baseline norm for national employers that is expected to affect practices across the country
OtherProvincesAndSectoralPressures
In addition to Ontario and Québec, several governments are reviewing data-protection and privacy regulations in ways that will affect the use of AI.
It is anticipated that Alberta's ongoing review of its Personal Information Protection Act (PIPA) may result in revisions that will impact AI usage involving personal information These amendments will include enhanced protections for children's privacy, stronger enforcement, and clarifying regulations on deidentified data
Regulators across the country are closely examining AI-driven practices such as targeted advertising, dynamic pricing, and automated credit or insurance decisions, and British Columbia is also considering revisions to its private-sector privacy law
Simultaneously, industry regulators, including financial services supervisors and critical infrastructure agencies, are releasing guidelines that specifically include AI systems in relation to algorithmic risk management, cybersecurity, and operational resilience
The end effect is a multi-layered ecosystem in which sector-specific standards, provincial privacy regimes, and federal privacy law can all be applied to AI systems at the same time.
PracticalCompliance PlaybookFor2026
By creating AI governance to satisfy the "highest common denominator" across Canadian regimes, firms can lower risk in this fragmented environment Experts in law and policy suggest a number of specific actions
First, map your AI systems: note which jurisdictions and authorities are involved, as well as where AI (including generative tools) affects automated decisions, personal data, or essential services
Second, make explainability and human-inthe-loop a default feature so that systems can comply with Law 25's disclosure and review obligations as well as new standards for accountability and justice
Third, revise internal policies, job ads, and privacy warnings to provide clear explanations of AI use while adhering to Ontario's disclosure regulations and expected federal transparency standards
Fourth, even before such rules become legislation, create a risk-based AI framework that is in line with AIDA-style ideas, such as risk assessments, monitoring, escalation, and classification of high-impact uses.
Businesses who take this proactive stance in 2026 will be better equipped to adjust as national and local AI regulations change
