In an exclusive interview with AI Business Review Magazine, Liz Samson, Head of Industry Relations and Operations at Square Canada, shares how AI is moving beyond theory and becoming a practical tool for small business owners across the country At a time when entrepreneurs are overwhelmed with data but short on time, Liz Samson explains how Square AI is simplifying decision-making by turning real-time business data into clear, actionable insights.
InterviewBySKUddin
Liz Samson is Square Canada’s new Head of Industry Relations and Operations.
Liz is among the most senior women leaders at Square Canada, bringing with her a wealth of experience in tech and fintech, including at Wealthsimple and RBC
What is Square AI, how does it work and what does it offer small and medium-sized business leaders?
Small business owners operate a lot off instinct, relying on their accumulated experience, local knowledge, and natural intuition At the same time, their businesses generate a lot of data – data that can either support their gut feeling or shine a light on new insights We built Square AI, an integrated, conversational AI assistant, to help sellers easily make sense of their business data using natural language: they can easily identify trends, quickly analyze performance, and ask questions about their operations to gather actionable intelligence. We see Square AI as a tool that can supercharge our sellers’ business instincts
How does it work?
Our Square Dashboard has always been packed with useful data, but most business owners just don’t have the time to dig through reports and crunch numbers Square AI solves this Ask Square AI real business questions, like whether to launch a new drink – or how a new product is performing Square AI can answer the question and provide back-up data insights in seconds to help sellers move faster and make daily decisions with confidence
Square sellers who have tried Square AI so far tell us that it’s given them back hours each week It helps them ditch time-consuming spreadsheets, monitor data in real time to stay ahead of trends and focus on what matters most: running and growing your businesses.
What benefit does Square offer over other widely available AI tools?
The reality is that, to date, most AI development across the industry has been tailored to office workers, not small business owners We purposefully built Square AI for the needs of our sellers, with insights from real operators informing our development
One of the key benefits is that Square AI runs on a business’ realtime transaction data – so data insights are informed by their customers, their neighbourhood, seasonal trends and other inputs and trends that are unique to their business It even brings in web data so sellers can get guidance tailored to their community, the local weather and news and events in their neighbourhood And by integrating it directly into Square, sellers don’t have to worry about downloading data from the platform and uploading it into a thirdparty AI provider’s website – Square AI already has the information it needs It also means sellers' sensitive business data stays protected within Square's security infrastructure, rather than being shared with outside tools.
Is there anything special about Square AI for smaller businesses?
Every business, no matter the size, generates enormous amounts of data every day through payments and commerce For a long time, turning that data into real insight has been something reserved for larger organizations with analysts and operations teams Square AI levels the playing field by bringing powerful analytics into the flow of running a business of every size through a simple, conversational AI assistant. For our sellers, the accessibility to innovative AI is saving them time, helping them identify ways to drive down costs, and even unlocking new growth opportunities
What feedback have you had about Square AI so far in Canada?
It’s been really positive 10 DEAN, a Toronto multi-location café and bar was in our Square AI beta – and they love the fact that, instead of manually filtering and comparing data, they can ask a simple question and let Square AI build performance tables for them The manager says Square AI has changed how they review menu item performance and make decisions day-to-day
How ready do you think Canadian businesses are for AI?
Square recently surveyed 4,000 business owners across eight countries – and that showed that Canadian entrepreneurs are among the most pragmatic and openminded AI adopters worldwide In fact, 60% of Canadian entrepreneurs told us they are already using AI tools – and now, with Square AI, they can access a customized and builtin AI assistant, at no additional cost.
What else did the survey highlight about business adoption of AI?
Most Canadian entrepreneurs surveyed agreed AI has the potential to save time that can be reinvested into improving work-life balance (38%), developing new ideas to grow their businesses (37%), improving marketing (31%), and spending more time with customers (26%)
What other tools are new at Square?
In addition to Square AI, Square just launched a suite of new tools for Canadian food and beverage businesses to streamline operations and improve their guest experience
Highlights include the improved, second generation of our popular Square Register; a new seat management feature that empowers servers to easily handle lastminute guest additions and seat changes; a new inventory management tool by MarketMan; and a house accounts feature that makes it easy for smaller businesses to offer the convenience of pay later options for trusted repeat customers
AI-DrivenCashflow CommandCentres
HelpingCFOsWithSteady
LiquidityintheMidMarket
By SK Uddin
CFOs in manufacturing, SaaS, and multilocation retail across Canada's mid-market are all concerned about profitable growth on paper but unpredictable cash flow Even for otherwise sound businesses, liquidity risk has increased due to rising interest rates, wage pressure, and slower-paying consumers The dichotomy is highlighted by a recent Canadian survey, which found that while the majority of small- and mid-sized businesses are optimistic about their long-term prospects, around two-thirds suffer ongoing cash-flow difficulties
Finance executives are being pushed by this tension to switch from spreadsheet-only cash planning to always-on, AI-enabled "cashflow command centers " These technologies generate rolling, scenario-based projections and earlywarning signals by pulling data from accounting, banking, payroll, and sales platforms The promise is simple: identify issues weeks in advance, negotiate with suppliers and consumers from a position of power, and dynamically adjust working capital and spending plans before a crunch turns into a catastrophe
InsideTheAiCashflowCommandCentre
The new "command center" approach is more of an integrated collection of predictive tools than a single product Real-time visibility is the first step for a typical mid-market Canadian company AIenabled solutions link bank feeds, credit card spend, accounts payable and receivable, and payroll to create a real-time view of cash situations and commitments To reduce manual reconciliations and data silos, tools marketed to Canadian SMBs increasingly combine bill pay, forecasting dashboards, smart corporate cards, and automated cost management into a single platform
To forecast cash over 13 weeks and beyond, AI models analyze historical inflows and outflows, automatically accounting for seasonality, client payment patterns, and recurring subscription or inventory cycles Pattern-recognition algorithms simulate "what if" situations, such as a 10% decline in sales or a significant client extending terms by 30 days, and flag delayed or anomalous transactions CFOs receive constantly updated forecasts in place of static monthly views, along with notifications when predicted balances exceed internal thresholds This makes cash management a dynamic, high-frequency discipline
AI forecasting is being used by mid-market manufacturers in Western Canada and along Ontario's industrial corridor to better align production, inventory, and receivables with demand To forecast when money will actually arrive, not just when invoices are sent, AI models consider sales orders, supplier lead times, and historical shipment data With that forward-looking perspective, CFOs can arrange staggered supplier payments, modify manufacturing cycles, and proactively use credit facilities only when models indicate a likely shortfall
When AI forecasting is combined with process modifications in purchasing and collections, advisors working with Canadian manufacturers claim increases in working-capital efficiency of up to 20% For instance, a factory experiencing a seasonal slowdown in Q4 can use AI-generated scenarios to decide whether to temporarily reduce raw material purchases or offer distributors early payment discounts.
Before making a commitment, finance can test both options using the "command center" interface to evaluate their impact on minimum cash covenants and borrowing costs These choices add up over time, lowering emergency borrowing and freeing up funds for R&D or capital expenditures
Instead of actual inventory, collections and churn are the largest levers for Canadian SaaS companies, particularly those situated in major cities like Toronto, Montreal, and Vancouver AI-powered solutions combine usage telemetry, subscription billing, and support ticket data to forecast which clients are most likely to downgrade, renew, or make late payments Weeks before renewal dates, mid-market CFOs evaluate these signals to adjust cash-flow projections and initiate focused contact with the customer success and collections teams
Seasonal fluctuations, such as back-to-school, Black Friday, and post-holiday lulls, are the lifeblood of Canadian retailers, from regional shops to e-commerce firms. Retail CFOs can create unified, rolling projections by combining point-of-sale data, e-commerce orders, marketing calendars, and supplier terms with the aid of AI-driven cash-flow tools They can understand how personnel plans, promotional efforts, and inventory purchases may affect cash positions several months in advance, once seasonality is fully modelled
Forecasting for slow seasons, creating buffers during peak periods, and using rolling forecasts to adjust as new data becomes available are increasingly key components of practical advice for Canadian firms AI improves this by continuously updating forecasts and recommending levers to smooth the curve, such as reducing variable costs, clearing slow-moving stock, or utilizing flexible finance options
A Canadian shop demonstrated how integrating automated invoicing and spending management with AI-enabled e-commerce solutions enhanced cash flow and doubled online sales Finance executives benefit from fewer liquidity "surprises" when demand declines and greater confidence to invest in growth areas despite turbulent macroeconomic conditions
There are trends among Canadian mid-market CFOs regarding which AI cash flow solutions benefit them most Instead of expecting technology to repair flawed fundamentals, they begin with data hygiene and process fundamentals, such as clean AR and AP data, disciplined invoicing, and clear ownership for predictions, and then overlay AI on top
Additionally, they consider their command center a cross-functional hub, incorporating sales, operations, and treasury into monthly scenario assessments rather than confining forecasts to finance alone Access to algorithms won't be the differentiator as AI becomes commonplace in Canadian finance stacks; rather, it will be how aggressively and wisely CFOs act upon the insights they produce
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
SecuringAIfor theQuantumEra
In an exclusive interview with AI Business Review Magazine, Jeremy Samuelson, Executive Vice President of AI and Innovation at IQT, breaks down the growing risks surrounding modern AI systems and what organizations must do next to stay ahead. This conversation explores how AI is shifting from a tool to a critical business asset, making it a prime target for extraction, exposure, and misuse.
Interview By Varun K Sirohi
Jeremy Samuelson, Executive Vice President of AI and Innovation at IQT, is the Inventor of the AIQu™ platform and product VEIL™ By trade he is a data scientist and mathematician Samuelson is former Equifax and while there, he served as the Principal Data and AI Scientist for Digital Identity Engineering. He’s also held senior AI leadership roles at Mastercard and VICI Capital Partners, and led large-scale optimization at a Coca-Cola subsidiary. Currently he teaches graduate-level executive programs in AI and management at leading institutions, including John Hopkins and the University of Texas
JeremySamuelson
Recent “distillation” attacks like those disclosed by Anthropic show models can be probed and partially replicated. What did those incidents reveal about how vulnerable today’s AI systems really are?
The Anthropic distillation story made one thing very clear: many AI systems are still easy to study from the outside If a model can be queried at scale, it can often be mapped, imitated, and in some cases partially replicated That is not just an LLM issue It applies more broadly to AI systems that expose valuable behaviour through APIs and production workflows, including the classical ML and deep learning models already running fraud detection, risk scoring, forecasting, and other business processes today
For business leaders, the lesson is practical AI models are no longer just tools; they are operating assets that may embody proprietary know-how, sensitive data patterns, and competitive advantage. Yet most were built for accuracy and scale, not for hostile environments.
What these incidents revealed is that the industry has moved faster on adoption than on protection Perimeter security is not enough if the model itself can be probed, or if downstream workflows still expose raw inputs The next phase of enterprise AI has to be about both performance and resilience
It is also important to remember that the AI footprint in these sectors is much broader than GenAI Many of the systems in production today are classical ML and deep learning models used for credit, fraud, claims, triage, and forecasting Those systems are often connected to APIs, applications, and decision workflows, which makes them valuable and reachable targets As adoption expands, attackers are naturally following the value
Many companies rely on encryption, but VEIL uses anonymization via ICA instead. In simple terms, what’s the key difference for protecting data inside AI workflows?
Encryption remains essential for protecting data at rest and in transit The challenge is that, in a typical AI workflow, the data eventually has to be decrypted so the model can use it At that point, the raw information is back in the pipeline and potentially exposed.
VEIL approaches the problem differently With ICA, the raw input is transformed before the downstream model ever sees it The model works on a latent encoding that preserves the signal needed for machine learning while reducing direct exposure of the original sensitive data
Why are model-extraction and training-data exposure becoming such big targets for attackers, especially in regulated industries like finance and healthcare?
Model extraction and training-data exposure are attractive because they target the most valuable layer of the AI stack: the intelligence itself In finance and healthcare, models do not just automate tasks They encode risk logic, operational judgment, and patterns learned from highly sensitive data If an attacker can infer that behaviour, they may be able to copy years of tuning, expose confidential information, or identify where an organization is weakest
This matters especially in regulated sectors because the downside is not only technical It can become a privacy breach, a compliance issue, a reputational event, and a business continuity problem all at once
That difference matters operationally Encryption protects the package while it is moving or stored ICA changes what is inside the package before it enters the AI workflow. So instead of asking, “How do we protect raw data everywhere it travels?” the question becomes, “How do we avoid distributing raw data in the first place?”
For organizations trying to scale AI responsibly, that is a meaningful shift
How can organizations start preparing their AI infrastructure now for emerging post-quantum threats, without having to rebuild all their existing pipelines from scratch?
The biggest mistake organizations can make is treating post-quantum preparation as a future project that starts later They do not need to rebuild their AI stack now, but they do need to reduce unnecessary exposure now
A practical first step is to map where raw data is exposed across training and inference workflows: where it is decrypted, copied, cached, logged, or sent to models and APIs If you reduce those exposure points today, you improve security immediately, and you also reduce what could be vulnerable in a harvest-now, decrypt-later scenario tomorrow
From there, the smartest path is layered modernization Keep the pipelines that work Add stronger access controls, monitoring, and abuse detection Introduce architectures that let downstream models operate on protected representations rather than raw sensitive inputs wherever possible Then plan the cryptographic migration separately, based on asset sensitivity and business priority
That is a much more realistic path for Canadian enterprises
What practical first steps would you recommend to Canadian enterprises that want to use AI more aggressively this year, but are worried about privacy, compliance, and future quantum risks?
My advice is to start with discipline, not fear. Canadian enterprises should absolutely move faster on AI this year, but they should do it with clear rules around data, ownership, and acceptable use
First, prioritize use cases where the business value is measurable and the workflow is well understood In many organizations, the most important AI systems in operation today are not chatbots They are classical ML and deep learning models used for fraud detection, risk assessment, forecasting, quality control, and operations
Second, map where sensitive data enters those workflows and minimize how often raw data is exposed downstream The fewer places raw data travels, the easier privacy, compliance, and security become to manage
Third, put basic governance in place early: approved tools, vendor review, human accountability for high-impact outcomes, and clear escalation paths when something looks wrong
Quantum risk should be part of that roadmap, but it should not be a reason to delay adoption.
Artificial intelligence is now essential to the management of credit, market, and fraud risks for Canadian banks, insurers, and credit unions, but it also poses a risk in and of itself AI can increase prudential, operational, and consumer protection risks if it is not adequately managed, according to OSFI and the Financial Consumer Agency of Canada (FCAC) This has led to a greater regulatory focus on model risk and data use At the same time, top Canadian banks are seeing quantifiable improvements in credit-risk categorization, suspicious-activity monitoring, and fraud detection speed and accuracy thanks to AI and machine learning models.
ImageCourtesy:Canva
Boards and CROs face a strategic dilemma: either advance AI and risk regulatory, privacy, and reputational blow-ups, or retreat and risk falling behind In Canada, the solution is becoming evident To scale AI while meeting the increasing demands of regulators, consumers, and supervisors, institutions are re-platforming their AI risk models within robust model risk management (MRM) and privacy frameworks
Machine-learning algorithms are being used by Canadian lenders to supplement traditional scorecards on the front lines of credit risk, especially in retail and SME portfolios Compared to traditional methods, these models can process significantly more behavioural and transactional data, thereby enhancing risk-based pricing, early warning signals, and portfolio segmentation AI algorithms help market-risk teams in capital markets and treasury by generating scenarios more quickly, identifying anomalies in trade patterns, and developing more dynamic hedging strategies across rates, foreign exchange, and commodities
AI has advanced the fastest in the areas of fraud and financial crime One large Canadian bank is transitioning from overnight batch review to near-real-time analysis by using CGI-layered machine-learning models on top of a vintage rules engine This modification decreased false positives that irritate clients while increasing detection accuracy and inquiry speed
Additionally, Canadian banks are testing GenAI to help investigators by creating case narratives, summarizing massive document sets, and cross-referencing trends with historical typologies As AI grows more complex over time, regulators seek to ensure that organizations understand how their systems operate, are continuously monitored, and communicate accurate results to supervisors and customers
The foundation of Canada's new AI risk framework for federally regulated financial institutions (FRFIs) is OSFI's updated Guideline E-23 on Model Risk Management, which takes effect on May 1, 2027 The guideline establishes expectations for an enterprise-wide MRM framework that is technologyagnostic yet AI-aware, and it expressly recognizes the increasing use of AI and machine learning models
Formal risk evaluations of each model's materiality and weaknesses, a comprehensive inventory of models throughout the organization (including AI and vendorsupplied models), and governance commensurate with the model's risk are among the main objectives Clear roles and responsibilities, independent model validation, and continuous monitoring of performance drift, bias, and unintended consequences are all requirements for FRFIs. Given the complexity of AI risk, OSFI further highlights the necessity of interdisciplinary oversight that combines knowledge of risk, technology, law, compliance, and ethics
Canadian organizations using AI for risk must manage changing privacy and AI-specific duties in addition to prudential regulations Stronger consent, accountability, and algorithmic transparency standards will be introduced under the proposed Consumer Privacy Protection Act (CPPA), which is intended to replace PIPEDA These requirements would include duties to explain automated choices that have a substantial impact on individuals. Concurrently, the now-defunct Artificial Intelligence and Data Act (AIDA) established principles that continue to shape regulatory thinking, including "high-impact" AI systems, risk assessments, and mitigation strategies
In practical terms, this implies that whether AI is used for credit approval, fraud screening, or transaction monitoring, Canadian institutions need to be ready to explain choices in simple terms, outline the key variables involved, and address client questions Legal experts anticipate that future privacy changes will incorporate AI governance into more comprehensive data-protection requirements, with potentially harsh fines for non-compliance up to C$25 million or 5% of global turnover under the draft CPPA
A few design concepts are being adopted by Canadian organizations, leading the way in AIdriven risk management To make credit, fraud, and market-risk choices understandable to risk committees, internal audit, and, when applicable, consumers, they first invest in explainability through methods such as model simplification, surrogate models, and post hoc explainers Second, they are limiting the use of sensitive data and bolstering controls over third-party data sources by incorporating privacy-by-design and data-minimization principles into model development
Third, they regard AI risk models as live assets, constantly checking them for bias, performance drift, and adversarial risks such as deepfake-facilitated fraud A movement in Canadian banking culture from " move fast and innovate" to "innovate safely and demonstrably under control" is being accelerated by OSFI's E23 and changing privacy regulations Institutions that can strike that balance will be best positioned to harness AI for superior risk management while maintaining trust with regulators and the public
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
AITransforming CashFlowin SupplyChains
By Varun K Sirohi
For Canadian exporters, logistics operators, and manufacturers, paper profits are meaningless if cash is locked in warehouses, containers, and customer invoices. Working capital has become a vital metric due to rising interest rates, supply chain disruptions, and tighter bank credit According to industry forecasts for Canada's supply chain sector, AI and automation will be critical to the next wave of resilience and efficiency, helping businesses forecast demand, shorten cycle times, and free up capital tied up in operations
At the same time, government policy reinforces the trend Budget 2025 invests billions in trade corridors, logistics infrastructure, and AIenabled productivity. However, it also emphasizes the need for enterprises to increase their capital efficiency to remain competitive AIdriven working capital optimization transforms the way Canadian organizations manage inventory, payables, and receivables across global supply networks
Cana chain comm realrecei Cana acco provi with and a artific recon trans that custo Beyo addr learn vario and s selec while costimpo opera automation and improved data flows, to decrease the "drag" on working capital AI serves as the driving force behind working capital, rather than a mere reporting tool
SmarterInventoryStrongerCashFlowwithAI
Inventory is a significant drain on working capital for Canadian manufacturers and exporters AI-powered inventory management solutions accurately estimate demand by analyzing historical sales, seasonality, supply chain delays, and external factors such as macroeconomic trends and sector-specific indicators In manufacturing, AI-based multi-echelon inventory optimization considers the entire network, including suppliers, central warehouses, regional depots, and plants, to estimate optimal stock levels at each node, rather than focusing on a single location
According to Canadian manufacturing trend studies, AI demand-sensing and dynamic reorder-point adjustment reduce stockouts and excess stock, resulting in cheaper capital investments in raw materials and finished items Real-time dashboards enable planners to monitor inventories across multiple sites and model how changes in production or supplier disruptions affect working capital and service levels These tools for exporters take into account shipment lead times, port congestion, and customs fluctuation, all important factors in Canada's tradedependent economy AI-assisted inventory optimization, along with improved S&OP processes, can automate weeks of laborious spreadsheet analysis, freeing up cash and ensuring on-time delivery
ImageCourtesy:Canva
Working capital in Canadian supply chains is also highly influenced by payment behaviour how quickly companies pay suppliers and receive payment from customers AI-enabled cash flow and AR technologies segment consumers by payment habits, risk, and profitability to offer tailored terms, credit limits, and collection techniques Predictive algorithms can identify clients who are likely to pay late or default, leading to earlier engagement or stricter terms Conversely, low-risk consumers may benefit from more flexible terms to encourage growth without endangering liquidity
AI-powered procurement and payment solutions enable CFOs to model the cash effect of various payment techniques and early-payment discounts for Canadian and international suppliers. Canadian organizations are employing dynamic discounting and supply chain finance programs, underpinned by AI-based risk assessments, to selectively prolong DPO (days payable outstanding) and provide suppliers with cheaper financing When combined with automated invoice matching and approval workflows, these technologies reduce manual errors and shorten internal cycle times, enabling businesses to manage cash more effectively AI-enhanced AR/AP coupled with rolling cash-flow predictions can help Canadian SMBs avoid costly lastminute borrowing and negotiate from a position of strength, according to industry guides
WhatSmartCFOsAreDoingAboutCashFlow
Canadian businesses that benefit the most from AI in working capital exhibit some common characteristics They begin by cleaning and standardizing data from ERPs, WMS, TMS, and financial systems, then create simple dashboards before adding more powerful AI models. Working capital is considered a cross-functional KPI shared by finance, supply chain, sales, and procurement, rather than a finance-only metric assessed quarterly
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.
AIvs Traditional Forecasting inCanadian Finance
By SK Uddin
Canadian CFOs and FP&A executives are more concerned with whether their projections are genuinely closer to reality than with "fancy AI" in a world of higher rates and increased volatility Global research on AI-enabled forecasting demonstrates that conventional methods, such as manual spreadsheets, straightforward trend lines, and static assumptions, are unable to keep up with complex demand drivers and rapidly evolving circumstances In contrast, AI and machine-learning models can continuously absorb new data, learn from error patterns, and update forecasts much more frequently, all of which typically result in higher success rates
Depending on data quality, process discipline, and use case, quantitative benchmarks from vendors and research syntheses indicate that AI forecasting can increase accuracy by 15–50% when compared to traditional techniques That uplift is more than simply a technological improvement for Canadian financial teams managing supply-chain interruptions, FX fluctuations, and interest-rate instability. It might mean the difference between managing liquidity proactively and rushing to secure financing at the last minute, or between meeting and missing guidance
HowAccurateIsTraditionalForecastingToday
Simple statistical models, such as linear regression and moving averages, are typically combined with management judgment in traditional financial forecasting, often using spreadsheets This method is clear and well known, and it can perform well in comparatively stable contexts It frequently achieves 60–70% directional accuracy, but when markets or behaviours shift, it has large error bands These models are slow to incorporate new information, such as abrupt demand shocks, supply disruptions, or changes in the channel mix, because they rely on a small set of internal variables and infrequent updates
Another limitation is human bias When forecasts are debated line by line in budget discussions, they are particularly vulnerable to political influence, optimism, and sandbagging According to research syntheses cited in academic and industry assessments, businesses that rely solely on conventional techniques achieve baseline accuracy, which can increase by about 15–30% when AIdriven predictive analytics are added According to Canadian financial leaders surveyed internationally, it can take days or weeks to update estimates across many scenarios using traditional approaches, which limits how often they can adjust plans or respond to new signals
AI-driven forecasting employs machinelearning algorithms to identify intricate, non-linear relationships among a wide range of variables that are beyond human capability or straightforward regression methods According to vendor benchmarks and pooled studies, AI may improve forecast accuracy to the 80–95% range for many use cases, reducing forecast errors by 20–50% compared to traditional methods By combining historical, real-time, and external demand variables, an AI-based demand forecasting system helped a major retailer reduce inventory carrying costs by over $100 million and increase prediction accuracy to 87–94%
ImageCourtesy:Canva
AI systems are also constantly learning Models are updated whenever new transactions occur, agreements enter or exit the pipeline, or external factors (such as rates or macro indicators) change, rather than waiting for an annual budget refresh As conditions change, this enables finance teams to update forecasts in hours rather than weeks and retain accuracy Case studies show that AIassisted systems automatically compare actuals to plans and adjust variables to better each new estimate
Lastly, AI is better at scenario ranges than single-point forecasts. Increasingly, time-series and generative models emphasize segments or items with greater fluctuations and provide probability bands (such as a 70% chance of falling within a revenue range) For Canadian CFOs and treasurers, this provides improved insight into the upside/downside tails that are important for covenant planning and liquidity, as well as more precise midpoints
When linkages are complex, and data are abundant, as in multi-channel revenue forecasting, granular demand planning, and cash-flow estimates that rely on numerous behavioural factors, AI's accuracy advantage is most noticeable Research and case studies demonstrate that AI is especially adept at identifying weak signals (such as minute variations in pipeline velocity or seasonal patterns) and making rapid adjustments as new data becomes available Traditional models in certain situations either miss inflection points or necessitate laborious manual overrides, which adds bias and delays
Experts warn that AI is not a panacea, though Forecasts are only as good as their inputs; AI might confidently produce false outputs if the underlying data is inconsistent, inadequate, or poorly controlled These outputs can occasionally be more difficult to reject since they appear sophisticated Leading experts in AI forecasting stress that human evaluation, scenario framing, and clear assumptions remain crucial, particularly for rare events, structural breaks, or regions with sparse data. High-performing finance teams in Canada and other countries are really combining the two: AI is used to create baselines and ranges, and before figures are presented to the board or the market, judgment, business context, and governance controls are applied
The evidence indicates that, when combined with reliable data and sound procedures, AI may significantly increase forecast accuracy for Canadian financial teams. While possible, benchmarks of 20–50% error reductions require human-in-the-loop evaluation, oversight, and disciplined deployment
Practitioners advise beginning with a side-byside comparison: run AI models concurrently with conventional forecasts for several quarters, assess accuracy and stability, and use the findings to improve both approaches and foster stakeholder confidence
The ultimate objective is to develop a forecasting system that is more accurate, quicker to refresh, and richer in scenarios giving Canadian CFOs a better understanding of risk and opportunity as they make capital, price, and growth decisions rather than to "beat" traditional forecasting for its own sake
Your role in staying informed is essential to our mission of building a strong community of AIdriven 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
By Varun K Sirohi
Governance is becoming a board-level concern as Canadian businesses rush to implement AI in their finance departments, but only a small proportion are witnessing tangible benefits. According to KPMG's most recent survey of 753 Canadian company executives, 93% say their companies use AI in some capacity, up from 61% a year ago However, only 2% say that investments in generative AI are yielding a noticeable return on investment
Simultaneously, another KPMG study on AI in finance finds that over 80% of Canadian companies are already using or testing AI in their finance departments, with local businesses reporting higher-than-average returns when adoption is mature and well managed
Governance structures are now crucial because of this gap between enthusiasm and achieved value Before approving new AI spending, boards and auditors are requesting greater controls, more transparent business cases, and reliable post-implementation evaluations Canadian regulators and supervisors have started outlining expectations for model risk, data, and ethics.
Businesses in Canada that see the potential of AI in finance approach each project as a performance investment rather than a technological test According to KPMG's AI-infinance research, when AI is closely associated with forecasting, decision-making, and productivity gains rather than general automation, Canadian respondents exceed their international counterparts on ROI A clear business case owned by finance, not just IT, is the first step in governance It should outline the problem being addressed (forecast accuracy, closing time, fraud losses, working capital), what the baseline indicators look like, and the anticipated uplift over a specified time horizon.
Documenting the justification for modelling is emphasized by industry and regulatory guidelines; this requirement is reflected in OSFI's model-risk expectations and in the growing AI-governance playbooks that Canadian financial institutions have adopted According to practical frameworks, business cases should have specified owners and timetables and quantify both hard advantages (such as cost savings, error reduction, and capital efficiency) and soft benefits (such as faster insight, auditability, and resilience) Crucially, amid AI hype, governance committees are requesting explicit "kill criteria" conditions under which a pilot would be discontinued or altered if claimed advantages do not materialize to reinforce capital discipline
Model validation is the cornerstone of AI investment governance for financial institutions and, increasingly, for major corporations. All models, including AI and vendor systems, must adhere to OSFI's Guideline E-23 on model risk management, which sets out requirements for the entire lifecycle, including development, validation, approval, deployment, and continuous monitoring Comments on E-23 from Canada emphasize that governance needs to be comprehensive, with distinct roles, a specified risk appetite, and interdisciplinary oversight covering risk, finance, data, IT, and compliance
The "EDGE" principles explainability, data quality, and ethics are likewise emphasized by OSFI and FCAC as essential cornerstones of safe AI For finance services, this means that significant AI-informed decisions (such as credit limits, provisions, or liquidity buffers) must be explicable to internal and external stakeholders, and the control environment must include documentation of overrides and model modifications
A practical point that needs to be addressed in a Canada-specific AI governance strategy is: who makes the decisions? Many Canadian businesses still view AI as a side project with scattered ownership and uncertain accountability for value and risk, according to surveys and market opinion AI-in-finance executives are more likely to have central AI teams integrated into or closely aligned with the finance function, supported by corporate values on responsible AI and digital procedures to stay abreast of regulatory change, according to KPMG's research
Three levels of governance are included in Canada's evolving best-practice
blueprints:
An executive or board-level AI steering group that establishes investment criteria, company values, and risk tolerance for AI deployment.
An investment committee for finance AI that evaluates business cases, ranks use cases, and ensures capital budgets and strategy are aligned
Validation, monitoring, and remediation are managed by a model-risk and data-governance forum that is frequently shared with risk/IT.
Similar emphasis is placed on strong governance frameworks, role clarity, and openness in the federal government's AI strategy for the public service patterns that private-sector finance teams might modify for their own AI investments
The last pillar of an AI governance roadmap tailored to Canada is demonstrating value and learning when it didn't More than half of Canadian CEOs cite "knowing how to capture value" as one of their major concerns, and fewer than four in ten say they have a clear plan for extracting value from generative AI, according to KPMG's ROIfocused surveys Leaders in the finance function are increasingly expecting auditors to be involved Most want thorough evaluations of the control environments surrounding AI for financial reporting, and many want official assessments of the maturity of AI governance or third-party attestations of AI use
Effective ROI audits evaluate model performance and overrides, compare promised and actual benefits, and determine whether process and behaviour changes truly took place. Additionally, they influence investment and risk appetite choices, raising the bar for new offers. Closing this cycle is crucial to transforming "AI in finance" from hype to long-term competitive advantage in a Canadian setting of low productivity and growing AI spending
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Canadian CFOsUsingAI toStressTest Financial Strategiesin VolatileTimes
By Varun K Sirohi
Volatility is now the standard for planning for Canadian CFOs Housing markets remain stretched and susceptible to policy changes; interest rates have risen sharply from the extremely low levels of the 2010s; and commodity and foreign-exchange fluctuations directly affect margins in the energy, mining, manufacturing, and export-oriented service industries AI is simultaneously changing the macroenvironment and the tools used to manage it The Bank of Canada has emphasized AI as a structural force that will complicate conventional policy and planning regulations and change productivity, inflation dynamics, and how businesses set prices over the next ten years
To transition from sporadic "what if" exercises to ongoing stress testing, Canadian financial officials are using AI-driven scenario planning AI solutions assist CFOs in measuring risk, testing strategic choices, and hardwiring resilience into capital allocation decisions by consuming real-time macro, market, and business data
In Canada, traditional scenario planning typically involved manually building a few static case bases upside and downside once or twice a year The Bank of Canada's policy statements, inflation statistics, housing market indicators, yield curves, FX rates for CAD vs key currencies, and commodity prices pertinent to Canadas export mix are just a few of the external signals that AI continuously pulls in to alter this Machine-learning models map these signals to a company ' s unique value drivers, including funding costs, wage pressures, input costs, and sales volumes.
The pricing of commodities, which are crucial to many Canadian balance sheets, including oil, gas, metals, and agricultural products, is similarly affected by commodity-driven factors A living scenario library, updated as circumstances change, is the end result
Creating scenarios is just the beginning; systematic stress testing is what gives Canadian CFOs their power Using integrated P&L, balance sheet, and cash flow models, AI-enabled solutions run hundreds or thousands of macroeconomic pathways to assess the impact of significant yet realistic shocks on sales, margins, liquidity, and covenant headroom For instance, it is possible to model a combined shock of a 150 basis point rate increase, a 10% correction in house prices in important cities, and a 15% depreciation of the Canadian dollar relative to the US dollar in a matter of minutes, with distinct results on interest expenses, loan losses, and import costs
To monitor portfolios and capital positions more precisely, Canadian financial institutions are already investigating AI-layered stress testing on top of their current risk engines Similar strategies are being used by non-financial corporations, especially those with leveraged balance sheets or significant exposure to commodity prices and cyclical demand
AI-driven stress tests can provide insights that were challenging to achieve with conventional approaches, such as when refinancing windows might expire, when leverage could violate internal guardrails, and how long organizations can sustain targeted shareholder-return plans under various macro regimes
Whether AI-driven scenario planning truly alters where Canadian CFOs allocate resources is the primary test AI insights are increasingly influencing choices on capital pacing, M&A, buybacks and dividends, and balance-sheet de-risking, according to advisory work with Canadian finance leaders In actuality, CFOs use scenario outputs to rank investment opportunities by risk-adjusted return, determining which projects will still meet the hurdle rate if rates continue to rise, housing slows, or commodity prices stagnate
AI models enable more dynamic hedging plans for enterprises exposed to foreign exchange and commodities, suggesting the best instruments and timing based on ongoing evaluation of market conditions and probability-weighted scenarios This ultimately influences the amount of capital set aside for liquidity buffers, collateral, or margin calls AI-informed macro perspectives assist boards in determining how aggressively to finance AI and automation projects within their industries undergoing technological disruption, weighing short-term profitability pressures against long-term competitiveness
Governance and judgment are becoming just as crucial as models as Canadian organizations scale AI-driven scenario planning According to KPMG's GenAI survey of Canadian financial services companies, regulators and boards demand strong risk-management frameworks that include human-in-the-loop oversight, controls on model use, and transparent documentation of assumptions. Leading CFOs perceive AI as a co-pilot rather than an oracle They examine scenario outputs, contrast them with expert opinions, and provide clear guidelines for when to disregard model advice, especially in tail-risk scenarios
AI itself is a cause of structural change, which means that long-standing relationships may disintegrate, according to macro research from the Bank of Canada and others The winning strategy for Canadian finance executives is practical: use AI to see around obstacles more clearly, and combine that visibility with sound governance and strategic discipline in the final allocation of funds
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For many years, AI in finance seemed like a luxury for large banks, not something that a startup in Waterloo or a little store in Saskatoon could afford That is rapidly changing Analysts predict that between 2026 and 2031, Canadas AI-in-finance industry will almost treble, mostly due to SMB adoption of off-the-shelf technologies for forecasting, cash flow management, and payments At the same time, cash errors are more painful than ever due to increased borrowing costs and restricted credit
Canadian recommendations now emphasize how contemporary cash-flow platforms, forecasting apps, and AI-enhanced accounting software connect directly to small businesses' current tools, such as QuickBooks, Xero, Wave, Shopify, and Stripe, enabling owners to obtain rolling forecasts and scenario planning without the need to hire data scientists Even very small businesses are embracing AI to minimize manual bookkeeping, foresee financial constraints, and make more informed recruiting, marketing, and inventory decisions, according to surveys and case studies In 2026, real automation that sustains small firms will be more important to AI finance than futuristic algorithms
For Canadian SMBs, the majority of AI finance products fit into a few useful categories. Real-time data from QuickBooks, Xero, or FreeAgent is immediately synced by cash-flow forecasting platforms like Float, which then convert past inflows and outflows into daily, weekly, and monthly estimates without the need for spreadsheet tricks Owners can identify problems weeks before cash runs out thanks to their ability to forecast various scenarios best case, base case, and worst case and update them automatically when invoices, bills, and payroll run
In addition to operations, more generic SMB AI systems provide finance functionality Shopify Magic (sales and revenue analytics for ecommerce), Zoho or HubSpot AI (sales forecasting and pipeline visibility), and QuickBooks AI and FreshBooks AI (automatic invoicing, categorization, and insight dashboards) are highlighted in tool round-ups targeted at Canadian SMBs Although they don't take the position of accountants, these tools lessen manual labor, highlight trends, and provide non-financial entrepreneurs with a better understanding of recurring revenue, seasonality, and spending habits. Instead of asking people to "create a model," the AI frequently works in the background, automatically generating forecasts, identifying anomalies, or recommending changes to the budget
Instead of purchasing an all-in-one enterprise suite, it is more practical for a small Canadian company to put together a lightweight stack A frequent pattern is suggested by Canadian AI-tool guides:
Invoices, expenses, and basic reporting are handled by core accounting with AI features (QuickBooks, Xero, or a Canadian provider).
An add-on specifically designed for cash flow forecasting, like Float, reads all bills and invoices and creates visual projections that can be shared with investors or banks
Stripe, Square, Shopify, or FreshBooks are examples of AI-enabled payment or invoicing layers that can speed up collections and maintain clean revenue data
Without requiring extensive IT projects, this combination provides founders with near-real-time financial insight and forward-looking estimates According to The Globe and Mail, Canadian SMBs that employ AI-driven marketing and finance technologies have witnessed increased online sales and improved "portfolio resilience," partly due to their ability to quickly reallocate investment when data indicates a downturn or an underperforming channel
You don't have to "switch on " every AI function at once, according to guides targeted at Canadian firms Instead, start with one or two high-impact areas, usually cash-flow forecasting and invoicing, then add others after the fundamentals are operating properly
Instead of eye-catching "AI ROI" figures, the primary benefits of AI forecasting for small enterprises are time saved and costly errors avoided Within six months, Canadian SMBs that used a combination of Shopify Magic, Jasper AI, and QuickBooks AI reported a 25% increase in online sales, a 30% decrease in manual data entry, and improved cash flow due to automated invoicing and payment reminders According to Float's own materials, automating scenario planning and cash-flow forecasting significantly reduces the amount of time founders and bookkeepers spend juggling spreadsheets while boosting investor and lender confidence
Realistic near-term finance ROI targets, such as recovering five to ten hours of founder or controller work every month, minimizing latepayment surprises, and identifying cash shortages four to eight weeks ahead of schedule, are recommended by generic SMB AI surveys. The Globe and Mail's article about Canadian SMBs using AI-powered platforms to expand internationally highlights the fact that smaller businesses rarely begin with exact ROI models; instead, they conduct tiny pilots, monitor a few indicators (such as time saved, error rates, and collections), and only grow technologies that demonstrably improve those statistics This practical approach is becoming more and more common in Canada
Canadian experts caution that jumbled books or confusing business models cannot be fixed by AI techniques, despite the fanfare Before adding automation, cash flow guides for Canadian firms emphasize the importance of starting with clean, current accounting data, straightforward categories, and a basic manual forecast Owners should refrain from signing up for too many platforms at once or pursuing tools that don't work with their current systems, according to AI-tool roundups
The largest obstacle, according to commentary on AI in Canadian firms, is frequently skills and change management teaching teams how to analyze forecasts, challenge presumptions, and act upon findings rather than merely admiring dashboards The good news is that small firms may now access inexpensive training and "AI basics" information from big vendors and Canadian initiatives. The winning recipe for innovators in 2026 is straightforward: begin with one or two AI products that clarify finances and budgets, measure impact brutally, and then expand from there
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
Generative AI has transitioned from innovation labs to the center of treasury and finance planning and analysis (FP&A) for Canadian banks According to KPMG Canada's 2025 GenAI Business Survey, more than 90% of Canadian financial services executives now view GenAI as essential to competitive advantage, and a sizable majority are investing despite economic uncertainties However, the main concern has changed from "Can we do this?" to "What are we receiving back?" as boards and regulators demand discipline
It is no longer a theoretical task to calculate the return on investment for GenAI in forecasting, fraud detection, and planning It is evolving into a boardlevel competency that influences risk tolerance, budget approvals, and the pace at which pilots expand across Canada's largest financial institutions.
In addition to innovation narratives, Canada's Big Five banks are beginning to discuss AI in concrete financial terms publicly Some significant Canadian banks have revealed clear AI return targets, according to industry opinion One investor report mentions hundreds of millions in anticipated added value over a multiyear period.
Treasury and FP&A teams must link model performance to revenue growth, cost reductions, capital efficiency, and risk-adjusted returns once explicit ROI targets are in place
Analysts and rating agencies are also inquiring into how AI investments affect the financial statements of Canadian banks, particularly in terms of productivity, credit losses, fraud write-offs, and operating leverage In this environment, GenAI projects in finance functions are evaluated like any other performance investment, with hurdle rates, payback periods, and sensitivity analysis
ROI modelling for GenAI in Canadian finance functions now begins with a methodical focus on use cases Global research on AI in the finance function reveals that, compared with general back-office automation, the highest-ROI applications are concentrated in risk, forecasting, and statutory reporting This trend is mirrored by Canadian CFOs and treasurers, who prioritize use cases such as cash-flow forecasting, stress-testing liquidity under various rate scenarios, automated variance analysis, payment anomaly detection, and narrative generation for management discussion and analysis (MD&A)
Usually, the ROI model incorporates:
Hard financial criteria include reduced fraud and credit-loss expenses, improved working-capital turns, lower funding costs, less manual labour in reporting and planning, and increased fee or interest income
Improved forecast accuracy, quicker close cycles, scenario coverage, model explainability, and auditability are strategic and risk measures that are vital in Canada's regulated environment.
Before authorizing scale-up funding, leading institutions establish baseline performance (e g , forecast accuracy, fraud loss rates, days to close) and pledge to target improvements for each use case, such as five percentage points higher forecasting accuracy or a specific decrease in false positives in fraud alerts
To increase both detection and analyst efficiency in fraud and risk, Canadian institutions are integrating machine learning and GenAI-enabled investigation tools with conventional rule engines Using "championchallenger" techniques to test new models prior to full deployment, a Canadian bank that integrated machine learning models atop a rules-based system reported significantly faster detection and improved accuracy When legal transactions are not stopped, ROI is assessed by fewer fraud losses, fewer false positives, shorter investigation times per case, and better customer satisfaction
To anticipate cash holdings across various scenarios for Canadian interest rates, housing markets, and commodity-linked sectors, the Treasury's GenAI-supported forecasting models rely on richer internal and external data Banks monitor improvements in prediction error, the quantity of manual analyst modifications needed, and the effects on funding costs and liquidity buffers. At scale, even modest increases in forecast accuracy can result in significant gains in capital and liquidity
Not all of the Canadian Treasury's and FP&A's GenAI experiments have produced the desired results Many financial services companies are increasing productivity, according to survey data, but they still lack robust frameworks to translate these improvements into quantifiable growth and profitability Weak data foundations, unclear ownership among the finance, risk, and technology teams, and pilots that never scale because their benefits are not defined in terms that CFOs and treasury committees understand are common failure modes
Leading Canadian banks are currently tightening their governance Instead of treating GenAI projects as standalone proofs of concept, they are being included in larger financetransformation roadmaps, a strategy that global research links to higher ROI odds. To meet OSFI and internal model-risk requirements, they are also demanding explicit riskmanagement overlays, defined KPIs, and "gates" for the transition from pilot to production
Most significantly, banks are investing in change management and capabilities to help treasury personnel, risk officers, and FP&A analysts understand how to evaluate GenAI outputs and when to overrule them In Canada's highly regulated financial sector, this human-in-the-loop discipline is increasingly seen as essential for a defensible return on investment
Canadian banks are transitioning from testing to scaled deployment in areas most closely tied to financial performance as GenAI evolves Analysts predict that cloud spending and AI-related technologies will expand quickly through 2026, with financial services among the most active industries.
Institutions that approach GenAI as a performance investment, supported by rigorous ROI measurement, risk controls, and open communication with boards and regulators, will stand out more than those with the most ostentatious models For treasury and FP&A executives, this means any new GenAI project must be designed to demonstrate its value from the outset, not merely its novelty
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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
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However, moving away from Excel does not mean giving up spreadsheets completely. While managing data integration, model management, and cloud collaboration, many next-generation FP&A systems used in Canada integrate AI in the background while meeting users where they are inside recognizable grid interfaces and workflows As a result, Excel becomes the user interface rather than the system of record in the new planning stack
Cloud-based planning tools closely integrated with their ERP and HR systems are becoming increasingly popular among large Canadian businesses To enable projections to automatically update as new transactions and operational data come in, vendors such as Oracle, Workday, and SAP are integrating machine learning into their planning modules These platforms facilitate complex, multi-entity models for revenue, workforce, capital expenditures, and balance sheet planning, which are necessary for banks, insurers, and large corporations in Canada.
By creating rolling forecasts, identifying trends and anomalies in enormous data sets, and executing thousands of scenarios in the background capabilities well beyond what manual models can sustain AI adds value Integration is essential To connect FP&A systems with ERP, CRM, data warehouses, and operational apps while adhering to data-residency and compliance regulations, Canadian enterprise CIOs and CFOs are investing in iPaaS solutions and ERP connection platforms According to research on ERP integration in Canada, interconnected ecosystems can reduce manual processing by up to 50% and decrease closing cycles, setting the stage for more independent planning procedures
HowAIIsWrappingAroundExcelforSMEs
CFOs in Canada's upper-mid-market and SME sectors desire AI-powered planning without the expense and complexity of entire enterprise suites A new class of FP&A solutions bills itself as "AI wrapped around Excel," allowing finance teams to maintain spreadsheet-based templates for modelling, reporting, and budgeting while linking to well-known Canadian accounting and ERP systems These solutions transform static workbooks into a regulated planning environment by providing AIassisted forecasting, scenario planning, and automated data consolidation from many entities
To support sales, cash flow, and workforce planning, Canadian SMB-focused suppliers and advisers report high demand for products that integrate directly with cloud accounting, CRM, and billing systems Variance explanations, driver suggestions (such as connecting demand to macro or operational data), and automated narrative commentary for management packs are examples of AI capabilities. The business case for SMEs depends on speed and transparency: closing the books more quickly, testing more scenarios with less effort, and providing boards and owners with greater awareness of opportunities and risks over a 12- to 24month period
Integration and governance are critical to Canada's transition to AI-enabled FP&A, regardless of size The necessity of a clear data architecture which system is the master for GL, subledgers, operational KPIs, and non-financial drivers, and how that data flows into the planning tool is emphasized in ERP integration guides for Canadian companies Cloud-based middleware that offers prebuilt connectors, workflow orchestration, and monitoring dashboards is being used by Canadian businesses as iPaaS and API-led integration patterns gain popularity This lessens versioncontrol problems, spreadsheet linkages, and manual uploads that have historically compromised planning accuracy
Simultaneously, governance is changing FP&A thought leaders contend that, rather than focusing on discrete tooling trials, AI efforts should be rooted in core finance processes, such as closing, forecasting, planning, and performance assessment. Cross-functional steering groups, model-risk standards, and the ownership of data definitions and scenario narratives are all being established by Canadian organizations As AI becomes more prevalent in planning and capital allocation, accurate documentation of assumptions, overrides, and human interventions is crucial for auditability and regulatory comfort in regulated industries like financial services
Canadian finance teams must transition from spreadsheet mechanics to insight and storytelling roles as planning stacks become more independent According to a workforce study, many finance professionals are prepared to switch careers if they are not given the opportunity to meaningfully upskill in emerging technologies such as artificial intelligence Instead of expecting analysts to become data scientists, top Canadian firms are reacting by investing in training on data literacy, scenario design, and technologies that integrate AI into daily operations
In practice, this entails FP&A analysts learning to evaluate AI-generated forecasts, question underlying assumptions, and effectively convey uncertainty to boards and CEOs It also entails working more closely with the IT and data departments on model assumptions and performance monitoring The transition from Excel to autonomous FP&A is as much about people and culture as it is about platforms, as Canadian financial services professionals point out that talent and change management are among the largest obstacles to growing GenAI adoption.
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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.
Canadian CFOs are under pressure to use AI to modernize treasury and FP&A, but the most cutting-edge companies are rethinking how humans and AI interact rather than attempting to replace people According to surveys of Canadian financial services executives, more than 90% prioritize GenAI for productivity and decision support over full automation, indicating that it is now viewed as a crucial competitive advantage However, the same study identifies talent and change management as the biggest obstacles: while half of businesses are reassigning employees to AI-enabled roles, there remain significant gaps in reskilling and employee buy-in
This conflict is echoed in Can studies. The future of finance led, technology-enabled, as e by many finance professional they would consider quitting i employers did not provide lea opportunities in emerging tec such as artificial intelligence setting, "human-in-the-loop f emerging as the new operatin which FP&A and treasury team interpretation, challenge, and while AI systems handle datatasks and generate projection
Repetitive, rule-based tasks such as data extraction, reconciliations, anomaly identification, and first-draft projections or narrative reports are increasingly being replaced by AI-driven FP&A and treasury solutions Tasks such as matching transactions, identifying anomalies, and creating baseline predictions are now routinely handled by automation in Canadian financial departments, relieving analysts of laborious data collection and verification
Treasury positions that "promote ongoing innovation by employing AI agents to automate workflows, liquidity management, and forecasting" are described as an essential component of daily tasks in job descriptions from multinational corporations with significant Canadian hubs
Human-in-the-loop models give people a strong sense of ownership at crucial phases, such as
creating scenarios and drivers verifying and challenging AI outputs making decisions based on macro assumptions or isolated incidents
converting insights into actions and communications for boards and executives
Treasury teams are increasingly focused on strategic liquidity, capital markets, and risk decisions, while FP&A analysts become "storytellers and challenges" rather than spreadsheet builders The need for hybrid positions finance data analysts, FP&A systems analysts, AI model auditors that combine financial literacy with comfort with cutting-edge tools is growing, according to Canadian recruiters, indicating a rewriting of job content.
Employers in Canada are finding that AI literacy knowing what AI tools can do, how to challenge them, and how to integrate them into finance processes is the primary talent requirement rather than "data science." According to Randstad, three out of ten financial professionals would quit if they weren't given the chance to learn about cutting-edge technologies such as artificial intelligence, underscoring the need for structured upskilling Data analytics, scenario modelling, and ESG fluency are identified as highpriority competencies in skills studies for Canadian financial professions; routine reporting is increasingly being handled by automation and artificial intelligence
In response, top Canadian financial services companies are combining reskilling with talent acquisition. Nearly half of banks and insurers, according to KPMG, are investing in both new recruits and cutting-edge AI systems, as well as upskilling current staff for AI-enabled roles Data literacy, using AI-infused planning and treasury systems, and dealing with IT and data teams are the main topics of training Organizations are presenting FP&A analysts as translators between machine outputs and corporate strategy, accountable for ensuring AI recommendations are ethical, understandable, and aligned with risk appetite, rather than expecting them to become programmers
ImageCourtesy:Canva
Despite the availability of tools, Canadian surveys repeatedly indicate that culture and change management are among the most significant obstacles to scaling GenAI While CEOs struggle to define new methods of working, finance experts worry about losing their jobs or having their trade damaged Human-inthe-loop finance provides a framework and vocabulary to address these concerns: make it clear that AI is meant to supplement, not replace, the jobs that will be automated, and outline how the freed-up time will be put back into analysis, stakeholder support, and strategic projects
In Canadian institutions, practical change initiatives include pilot projects with welldefined KPIs; Workflows are co-designed with frontline analysts; "AI champions" are integrated into treasury and FP&A; and accomplishments and lessons learned are regularly communicated. According to outside research, teams' confidence in AI forecasts increases when they can observe how models perform over time and when overrides are monitored and debated rather than avoided HR-focused papers also caution that high-potential finance professionals would gravitate toward the software and consulting industries in the absence of clear career pathways into AIenabled professions As a result, successful Canadian businesses are combining AI implementation with updated job descriptions, career paths, and performance indicators
According to Canadian commentators, finance positions will continue to shift from being "operators" to "orchestrators" of human-AI systems CFOs and their FP&A and treasury leaders will be evaluated more on how successfully they coordinate cross-functional teams, develop AI-enhanced procedures, and guarantee ethical, well-governed use of data than on how many reports they generate
Automation will continue to grow, but so will the demands on finance to manage uncertainty, understand complex situations, and effectively communicate with regulators and boards In that scenario, human-in-theloop finance is essential to how Canadian businesses transform AI's raw potential into resilient performance, not merely a safety feature
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.
Top AITools Financia Forecast inCanad for2026
By SK Uddin
In Canada, annual budgeting processes have given way to ongoing, AI-driven planning that informs strategy, financial management, and board reporting The adoption of AI technologies that combine governed data, machinelearning models, and self-service analytics has risen, according to analysts, since just 40% of executives worldwide can forecast with 10% accuracy. Practicality is the top concern for Canadian businesses: technologies must integrate with existing ERPs and accounting systems, comply with data residency and governance regulations, and deliver more accurate projections without requiring a data science team
Anaplan (with PlanIQ) is an enterpriseconnected planning platform that generates demand, revenue, and cost projections across massive multidimensional models using embedded machine learning (ML), incorporating native algorithms and Amazon Forecast Widely used by global corporates, including Canadian subsidiaries, for integrated P&L, balance sheet, and workforce planning
Planful (Predict Suite): This Cloud FP&A suite helps financial teams expedite budgeting and increase forecast accuracy by using AI to identify anomalies, recommend predictions, and uncover data signals
Pigment is a cutting-edge planning platform that is becoming popular in techsavvy finance departments It employs AIassisted forecasting and in-memory modelling, and it has robust support for multi-scenario modelling and driver-based planning.
Prophix: Originally established in Canada, this multinational company offers corporate performance management and planning with AI tools for anomaly identification and prediction support, targeting mid-market and enterprise finance departments.
These solutions can support structured implementations with IT and data governance assistance and are most appropriate for Canadian businesses that require complex, multi-entity, multicurrency planning They usually occupy the center of the forecasting stack, feeding board-level dashboards and BI tools with summarized outputs
Cube is a spreadsheet-native FP&A platform featuring an "AI Analyst" agent that prebuilds variance analyses, automatically generates intelligent predictions, and responds to natural-language inquiries about performance and budgets. created for lean finance teams who wish to use contemporary planning without giving up on Sheets or Excel.
Abacum is an AI-native FP&A platform designed for mid-market IT and SaaS enterprises. It handles complex source data with a robust modelling layer and employs built-in AI to deliver intelligent forecasts, variance explanations, and proactive performance warnings.
With Vena Copilot and Insights offering AIassisted analysis, narrative commentary, and dashboarding linked with Excel and Power BI, Vena is a popular Excel-first planning and reporting tool in North America
Drivetrain is a planning and forecasting platform for high-growth companies, focusing on revenue forecasting and scenario modelling. It has AI features to expedite data aggregation and driver-based modelling.
Canadian mid-market and high-growth businesses that require more control than simple cash-flow apps but do not believe full enterprise suites are necessary would find these tools appealing They integrate with popular ERPs, CRMs, and data warehouses used in Canada, and implementations typically take weeks rather than months
Popular among Canadian SMBs, Float (cashflow forecasting) links to QuickBooks, Xero, and FreeAgent to create visual runway dashboards, various scenarios, and daily and weekly cash-flow projections without requiring intricate modelling. frequently suggested as a starting point for forecasting automation in Canadian small businesses and AI product manuals
QuickBooks AI and FreshBooks AI are accounting programs with integrated AI that automate classification, identify irregularities, and produce simple cash flow and budget forecasts for small businesses. Their ability to provide straightforward forward views straight from accounting data and minimize human labour are their main advantages.
Monarch and consumer-grade forecasting apps: These tools use AI to anticipate cash flow by category, aggregate accounts, and create personal and microbusiness budgets They can be helpful for very tiny ownermanaged enterprises or founders managing both personal and business liquidity simultaneously, even though they are not enterprise-grade.
These products are a good fit for Canadian SMBs that prioritize excellent integrations, price, and ease of setup over sophisticated modelling features
Kaelio is an AI analytics platform for finance that offers forecasting assistance, root-cause investigation, and conversational analysis over governed data It has been praised for its high extraction accuracy and its ability to help teams gain near-real-time financial insights
ThoughtSpot and related NLQ solutions are searchdriven analytics platforms that sit atop data warehouses and planning systems and enable executives and non-technical users to ask naturallanguage queries about revenue, costs, and projections
These "last-mile" technologies are important, according to expert guides, because forecasts only add value when multiple stakeholders can comprehend and analyze them. Adding conversational analytics to FP&A platforms and data lakes can democratize forecasting and reduce ad hoc reporting requests for Canadian firms, particularly those with dispersed leadership teams This will free up finance to concentrate on higher-value analysis
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
WhatAIMeansfor FinancialPlanning inCanada
By Varun K Sirohi
AI will transition from "nice-to-have" technologies at the periphery of Canadian financial planning over the next five years to becoming central to the creation, administration, and delivery of advice According to market projections, the use of AI in Canadian finance is expected to increase at an annual pace of about 22–27% By 2032, the total amount spent will have increased as institutions integrate AI into forecasting, advising, risk management, and customer experience The demand for automation, risk management, customization, and regulatory compliance is predicted to propel Canada's AI-in-finance sector alone from slightly over USD 1 billion in 2023 to around USD 8 8–9 0 billion by the early 2030s
Simultaneously, Canada's Pan-Canadian AI Strategy and other public-sector programs are intensifying their focus on "safe and responsible" AI, suggesting that those who can combine innovation with robust governance and customer trust will be the victors in financial planning. AI is expected to become both the planning engine and the regulatory focal point for planners, wealth businesses, and corporate finance teams in the years 2026–2030
Canadian households and investors can anticipate far more automated and customized financial plans by 2030, with human planners emphasizing empathy and judgment According to FP Canada's 2025–2030 Strategic Plan, AI-powered tools will be used to automate tasks such as risk analysis, portfolio management, and data entry, increasing productivity and lowering the cost and improving the accessibility of advice The plan specifically pledges to use AI to improve the accessibility of advice while "raising the relevance of the human touch" in providing sophisticated planning.
Simultaneously, wealth-management trend articles predict that AI will drive hyperpersonalization by analyzing spending trends, tax brackets, risk tolerance, and life events in real time to recommend portfolio adjustments and planned actions much more quickly than conventional review cycles allow According to the industry analyses cited there, over 70% of wealth managers worldwide believe AI will be the biggest disruptive factor in the next few years, and early adopters may see revenue increases of several hundred basis points as customer engagement and productivity rise This suggests hybrid advisory models in Canada, where human CFPs and portfolio managers focus on difficult trade-offs and significant life decisions, while robo-style engines handle monitoring and nudging
The emergence of financial AI agents specialized systems capable of monitoring, simulating, and acting within predetermined boundaries will be a significant change between 2026 and 2030 According to strategic AI overviews, autonomous agents are one of the major trends of the coming ten years They contend that we are heading toward "autonomous enterprise" patterns, in which agents oversee complicated processes and recurrent duties This translates into agents in the finance industry that can optimize cash and debt positions aligned with individual or business policies, run tax-efficient withdrawal simulations, and continuously rebalance plans
According to emerging commentary on the development of financial AI agents, these systems will become more adept at handling complicated financial contexts, such as corporate treasury landscapes and multi-account households, while increasingly outperforming humans The realistic future for Canadian financial planners and corporate FP&A teams looks like this: agents that monitor plan deviations (such as a spike in expenditure or a decline in revenue), rerun multi-scenario forecasts, and suggest corrective measures within minutes As a result, planners' responsibilities will shift from manually calculating figures to creating guidelines, goals, and communication plans
By 2030, sustainability will be a key factor in financial planning AI, especially in Canada's highly regulated wealth and pension industries According to Finastras forecast on AI in banking and financial services, machine learning will be used more frequently to assess carbon footprints, validate ESG data, and produce tailored recommendations for sustainable investments, such as automated carbon tracking and green bond analysis It points out that natural language processing can assist in identifying potential greenwashing and in promoting adherence to stricter sustainability regulations by evaluating risks and impacts in social and green bonds
For Canadian planners and institutional investors, this means AI engines that automatically classify portfolios against client preferences and regulatory criteria by integrating traditional riskreturn measurements with climate and social impact dimensions. AI-supported verification and audit trails will probably become commonplace in planning workflows as domestic and international scrutiny of ESG claims increases This is consistent with the larger push by Canadian regulators for ethical, transparent AI in the financial sector, where data lineage and explainability are just as crucial as optimization
ImageCourtesy:Canva
The policy landscape in Canada will significantly affect how quickly and to what extent AI transforms financial planning A unique environment in which ethics and innovation are closely intertwined is being created by the Pan-Canadian AI Strategy, large public investments in research centers such as the Vector Institute, and clear government objectives to enable "safe and responsible" deployment The "AI in Finance 2030" evidencegathering program, which intends to identify realworld use cases, hazards, and supervisory actions, is one of the global initiatives on AI in finance that Canadian regulators and industry bodies are contributing to
According to market projections, risk management, compliance tools, and AI-driven personalized services will be the main drivers of AI-in-finance growth in Canada through 2030 This will increase the need for planners and finance professionals who can work with confidence in AI-rich environments For businesses, this entails creating governance frameworks and planning stacks that can adapt to changing regulations while still reaping the benefits of quick AI innovation
When considered collectively, these developments suggest that by 2030, artificial intelligence will have a significant role in Canadian financial planning, but people will still be in charge Data-intensive forecasting, ESG analysis, and scenario planning will be handled by generative and predictive models; autonomous agents will monitor and modify plans within well-defined boundaries; and regulators will demand strong governance, documentation, and consumer protection
The strategic challenge for Canadian CFOs, wealth managers, and planners is to invest in AI-ready data, tools, and expertise now while maintaining the client relationships, trust, and judgment that are still at the core of financial planning
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Earlier this year, Mina Johl attended the World AI Creativity Forum in Cannes as a Canadian delegate sponsored by the Ontario Ministry of Economic Development The forum brought together enterprise leaders, government officials, researchers, and vendors from across the world. She came back with one observation she could not set aside: 99% of what filled the rooms had no connection to operational outcomes Session after session adoption stories, vendor frameworks, ethics panels almost none of it answered the question that every leader in every organisation actually faces: what does AI success look like inside a real business, and how will we know when we ' ve reached it?
The field of AI governance is not young The OECD established its AI principles in 2019 The NIST AI Risk Management Framework arrived in 2023 ISO 42001 formalised AI as a governance and risk discipline The EU AI Act is now moving from policy to enforcement These are serious, credible bodies of work
They also share a common boundary Every one of them governs the AI system how it is built, how it is tested, what risks it carries, how it is audited, what it is permitted to do The governance controls stop at the policy and model layer They do not reach inside the organisation.
The NIST AI RMF explicitly acknowledges that AI risks emerge from how people build, deploy, and use systems not from the systems alone But it provides no instrument for measuring whether an organisation's people, structures, decision-making, and knowledge flows are capable of doing any of those things well That is assumed It is almost never verified
Source:DeloitteGlobalHumanCapitalTrends2026
Deloitte's 2026 Global Human Capital Trends research drawing on responses from 100 Csuite leaders finds that 59% of organisations are taking a tech-focused approach to AI, and those organisations are 1 6 times more likely to not realise AI investment returns that exceed expectations, compared to those taking a human-centric approach Technology first Organisation second The sequence is the problem
The insight that execution drag was being treated as a soft issue rather than a structural governance problem came from two careers that were never in the business of soft issues
MinaJohl
Founder&CEO, PurpleWins
Mina spent nearly twenty years in operations, sales leadership, HR strategy, and C-suite consultancy across North America, the Gulf, and Asia including roles at Schlumberger, Hilti Group, and Bruce Power She holds an MBA from Ivey Business School and credentials from Cornell ILR That cross-functional, cross-regional career gave her a front-row seat to the same pattern repeated across industries and sectors: strategy rarely fails because the plan is wrong It fails because the organisation's actual conditions in alignment, capability, knowledge retention, and accountability cannot deliver what the strategy requires AI does not change that pattern It accelerates it
MiraJohl
Co-Founder&IPArchitect
Mira brings forty years of experience as a practising architect In the 1980s, she became the first woman architect from her diaspora at a time when womenled professional practices were rare in any field, let alone one as capital-intensive as architecture That career forged the instincts that now run through every layer of NAVETRA™: an owner-operator's discipline for outcomes over activity, structural precision over abstraction, and a refusal to separate design from delivery Mira is the co-creator of NAVETRA's intellectual property and the origin of the platform's outcome-first design philosophy Where others see a people problem or a technology problem, Mira sees a structural one and structures can be measured, governed, and fixed.
Together they translate between plant managers and board directors across the execution layer that sits between the two That capability is not common In the age of AI, it is becoming essential
Direction asks whether the organisation is pointed the right way measured through Executive Alignment, Leadership Capability, and Strategic Communication Capacity asks whether the organisation can actually deliver through Organisational Alignment, Team Effectiveness, Talent and Hiring Alignment, and Technology Capabilities Conversion asks whether effort is reaching the intended outcome through Sales Readiness, External Risk Management, and Knowledge Retention and Transfer
Each domain represents an organisational condition that AI deployment will immediately test Knowledge Retention and Transfer determines whether the institutional context that makes AI outputs interpretable stays inside the business when experienced people leave. Team Effectiveness determines whether the humans working alongside AI tools are aligned enough to act coherently on what those tools surface Executive Alignment determines whether the strategy that justified the AI investment is genuinely shared at the top or fractures between the offsite and Monday morning
NAVETRA™ is an execution risk governance platform built for industrial operators It does not assess models, audit training data, or evaluate algorithmic bias It assesses the organisation across three proprietary pillars and ten execution domains that map precisely to where AI will either succeed or expose a structural gap.
The platform is AI-assisted, not AI-decided A human is in the loop at every governance decision point The methodology and scoring architecture are the subject of a USPTO provisional patent filing
Canada's Artificial Intelligence and Data Act died on the order paper when Parliament was prorogued in January 2025 Canada's first-ever Minister of Artificial Intelligence and Digital Innovation Evan Solomon was sworn in on May 13, 2025, under PM Mark Carney's cabinet The intent is clear The operational standard has not yet been set
NAVETRA's delegation to the World AI Creativity Forum in Cannes sponsored by the Ontario Ministry of Economic Development offered a direct view of where the global conversation stands The policy frameworks are being built The ethics panels are running What is conspicuously absent, at every level and in every session, is the operational layer: the governance that runs inside organisations, where AI either delivers or destroys value
Canada has the research authority: Bengio, Hinton, and Mila are globally recognised as the intellectual foundation of ethical AI The EU has set the policy standard Canada is positioned to set the operational standard the governance that runs at the business level, where AI either works or it doesn't That is a different category And it is currently empty
The organisations that move first on governing the organisation not just the model will define what that category looks like. For a country with Canada's AI research credibility and a government already investing in the sector, the opportunity to own the operational standard is real But the window is not permanent
Mina Johl describes the gap in straightforward terms: every board is now asking how to govern their AI. The prior question which no framework currently answers is how the organisation governs itself in the presence of AI The two questions are not the same And until now, only one of them has had a governance instrument
Mina Johl is Founder & CEO of Purple Wins and the creator of NAVETRA™. She brings nearly twenty years of cross-sector operating experience across energy, manufacturing, construction, and C-suite consultancy, an Ivey MBA, and Cornell ILR credentials. She is a Canadian delegate at the World AI Creativity Forum and a published contributor to Industry Today
Mira Johl is Co-Founder and IP Architect of NAVETRA™. A forty-year practitioner and the first woman architect from her diaspora, she is the cocreator of NAVETRA's intellectual property and the origin of its outcome-first design philosophy.
In an exclusive interview with AI Business Review Magazine, Brandon Mina, CEO of BrandPilot AI, breaks down a growing challenge in digital advertising that many businesses overlook: not all clicks are created equal. As AI reshapes the advertising landscape, Brandon Mina shares how brands can identify hidden inefficiencies, detect non-human traffic, and make smarter decisions with their media budgets.
Interview By SK Uddin
Brandon Mina is the CEO of BrandPilot AI, a leading adtech company specializing in AIdriven solutions that optimize paid media performance and combat ad fraud in regulated markets With a deep background in marketing technology and enterprise growth, Brandon has spearheaded the development of innovative platforms that empower B2B and enterprise brands to achieve higher ROI through datadriven insights and precision targeting.
AI now powers both sophisticated ad fraud and the tools fighting it. How do you explain BrandPilot AI’s approach to detecting “non human” traffic in real time?
The uncomfortable truth about the internet today is that not every click comes from a person A large portion of web traffic is automated through bots, scrapers, click farms, and increasingly AI driven agents Research from companies such as Cloudflare suggests that automated traffic can represent more than forty percent of activity on the internet
At BrandPilot AI we treat advertising performance as a forensic problem Instead of relying only on platform dashboards, our systems analyze traffic at the landing page level where real user behavior actually happens
Our technology looks for signals that indicate whether a visitor behaves like a human or a script. These include abnormal click velocity, session irregularities, network fingerprints, geographic inconsistencies, and machine generated interaction patterns
Artificial intelligence allows us to process large volumes of these signals in real time The objective is not only to block invalid traffic but to help advertisers optimize campaigns toward verified human engagement and recover wasted spend when fraud occurs
In simple terms, we help marketers make decisions based on people rather than bots
Your platforms help reclaim wasted ad spend for global brands. What kinds of AI signals or patterns most often reveal hidden fraud, waste, or inefficiency?
Fraud and waste rarely appear as one obvious problem They usually reveal themselves through patterns that initially look normal
One common signal is abnormal click behavior Some campaigns generate large volumes of clicks but show almost no engagement after the visitor reaches the website This can indicate automated traffic, competitor click activity, or scraping tools
Another signal is conversion imbalance When large numbers of paid clicks occur without meaningful downstream behavior such as time on site, scrolling, or navigation, the traffic is often not coming from real people
We also identify inefficiencies inside advertising platforms themselves One of the most overlooked examples is uncontested branded search advertising Many companies pay for clicks on their own brand terms even when no competitors are bidding on those keywords
Artificial intelligence allows us to analyze these signals across thousands of campaigns and identify where budget leakage occurs
Once these inefficiencies are discovered they can often be corrected quickly, allowing brands to redirect budget toward campaigns that actually generate growth
Many marketers feel overwhelmed by AI jargon. When you meet a new CMO or performance lead, how do you help them separate real AI value from buzzwords?
I usually start with a very simple question Does the technology produce measurable results
Artificial intelligence has become one of the most overused terms in marketing. But senior marketing leaders do not buy technology They buy outcomes
If an AI solution cannot demonstrate clear improvement in metrics such as cost per click, cost per acquisition, or incremental revenue, then it is likely more marketing language than meaningful innovation
The most effective AI solutions in marketing tend to do three things well First, they process large volumes of data that humans cannot realistically analyze on their own Second, they identify patterns and inefficiencies that would otherwise remain hidden Third, they automate decisions that improve campaign performance
When we work with brands we focus less on explaining the algorithm and more on proving the outcome If the technology reduces wasted spend and improves efficiency then the AI is doing its job
The most impressive AI is not the most complicated It is the most accountable
You’ve reported dramatic CPC reductions and budget recovery with AdAi. What early results should a brand look for in the first 30–60 days of using AI for media optimization?
The first thirty to sixty days usually reveal where the largest inefficiencies exist within a campaign
One of the earliest indicators is improvement in cost per click When unnecessary spend is removed from campaigns whether through invalid traffic, redundant bidding, or inefficient keyword coverage the cost of acquiring each click often declines quickly.
Another early signal is improved data quality When campaigns stop optimizing against automated traffic or nonincremental clicks the signals feeding the advertising platform become more accurate This allows automated bidding systems to make better decisions
We also look closely at how budgets shift Once wasted spend is identified those dollars can be redirected toward campaigns that produce meaningful engagement and revenue
Many brands are surprised by how quickly these improvements appear The goal is not to reduce marketing investment The goal is to make the same budget work harder
When waste disappears performance often improves without increasing spend.
What practical first steps would you recommend to SMB and mid market marketers who suspect ad waste but aren’t sure how to use AI to prove and fix it?
The first step is to question a long standing assumption in digital advertising that every click represents a real person
That assumption may have been reasonable in the early days of the internet Today automated traffic and bots represent a significant portion of online activity
For SMB and mid market marketers the best starting point is measurement transparency Install independent tracking tools that allow you to observe what actually happens after a click reaches your website Look at engagement behavior, session activity, and traffic sources rather than relying exclusively on advertising platform dashboards
Next conduct a periodic audit of paid campaigns Many marketers discover inefficiencies such as unnecessary branded keyword spending, suspicious click patterns, or campaigns that generate traffic without engagement.
Finally start small Eliminating even one clear source of waste can materially improve performance
Artificial intelligence becomes powerful when it helps marketers answer a simple question Are we paying for real customers or simply paying for clicks
Disclaimer: The views and opinions expressed in this interview are those of the guest and do not necessarily reflect the views of The CanadianSME AI Business Review Magazine This content is for informational and inspirational purposes only and is not intended as professional business, legal, or wellness advice
