AI in Finance: Why Adoption Is Outpacing Value

The executive committee asks about the status of the finance function’s AI transformation. The answer is reassuring: licenses are deployed, and teams are using them every week. Then the controller asks the tough question: what has improved, in numbers, over the last twelve months?

The silence that follows has a name. KPMG calls it the value gap.

According to KPMG Canada (September 2026), 83% of Canadian companies surveyed have moved past the AI planning phase in finance, but only 36% are seeing more accurate forecasts, compared to 64% globally.

Why is AI in the finance function producing so little measurable value?

Because most organizations have deployed tools without redesigning the processes, roles, and controls that turn a prediction into a result. KPMG’s global survey of 1,013 finance leaders across 20 countries (March 2026) reaches a blunt conclusion: using an AI tool does not create value on its own.

Value emerges when an AI-generated forecast is vetted by the judgment of a finance professional, supported by reliable data and clear governance. It disappears when ownership of the file is fragmented between finance, IT, and data, or when pilots pile up without a path to a business outcome.

Canadian respondents are experiencing this firsthand. 74% use AI in financial reporting and analysis, 68% in financial planning, and 64% in risk management (KPMG Canada, 2026). The return barely follows: 57% feel their ROI meets expectations at best, and only 19% say it exceeds them.

What do the 2026 figures reveal about the gap between adoption and value?

They show that adoption has become mainstream among leaders, gains are concentrated in a minority of organizations, and frontline teams are still largely left behind. The benchmarks published this year can be summarized in a short list.

• 83% of Canadian respondents have moved past planning for AI in finance, compared to 92% globally (KPMG, 2026).
• 36% report more accurate forecasts in Canada, compared to 64% globally; for decision-making speed, the ratio is 45% versus 71% (KPMG, 2026).
• 77% of Canadian respondents have moved past planning for agentic AI (KPMG Canada, 2026).
• 80% of accounts receivable teams use no AI in their processes, even though more than eight out of ten expect more automation (NACM and BlackLine, 2026).
• 12.7% of Quebec businesses were using AI for production purposes in the second quarter of 2025 (Institut de la statistique du Québec, November 2025).

A note on methodology: these figures come from public surveys with differing samples and definitions. They provide orders of magnitude, not a portrait of your specific organization.

The cross-analysis is unsettling. Leaders report record adoption while the teams processing payments are still working manually.

Where does AI create measurable value in your financial processes?

In repetitive, high-volume processes that can be measured in dollars and days. Cash application is the clearest example. KPMG confirms this: quick wins in reconciliations, variance analysis, and financial reporting fund and de-risk more strategic use cases.

The NACM and BlackLine (2026) survey shows the scale of the opportunity: half of accounts receivable professionals name the reduction of repetitive manual tasks as their number one priority for the next 18 months. Not an aspiration. Their most urgent need.

The same survey reveals the constraint: no respondents feel very comfortable entrusting customer interactions to an AI. Trust is earned first through internal tasks—matching payments before sending collection emails. Quadient (2026) observes the same caution: agentic automation progresses where rules are clearly defined.

In the accounts receivable mandates PlanAxion leads for B2B distributors, it’s the manual sorting of bulk, partial, or unremitted payments that eats up the team’s hours. The cash application solutions PlanAxion implements target this specific pain point, and the question of who approves what the agent decides is resolved during the design phase.

The value gap isn’t closed by adding another tool. It’s closed one process at a time, with a baseline measurement and a person accountable for the results.

How can you close the value gap in four steps?

By documenting the baseline, choosing a bounded process, establishing guardrails for automated decisions, and measuring quarterly. The sequence consists of four actions.

1. Document the baseline. Hours spent on the process, exception rates, and cycle times. Without a documented baseline, no ROI calculation is credible.
2. Choose a bounded process. One process, one business unit, one result measurable in dollars and days. Cash application checks all three boxes.
3. Establish decision guardrails. A written scope, human approval thresholds, and an audit trail. KPMG notes that 66% of Canadian respondents can effectively produce evidence related to AI, compared to 82% globally. This is a direct barrier to scaling.
4. Measure and decide. After one quarter, the same indicators will dictate the path forward: scale or stop.

This is exactly the approach of our AI quick-win solutions workshop: 4 weeks, 5 steps (prepare, identify, prioritize, validate data, decide and deliver), with the same filter applied to every idea: problem to solve, expected value, available data, and effort required. The investment varies based on scope and is confirmed during a short discovery call.

Where should you start before the end of the quarter?

Take a single high-friction financial process—often cash application—and document its baseline: hours, exceptions, and delays. This two-page document will be worth more than your next tool because it changes the conversation with your executive committee: you’ll stop talking about adoption and start talking about value.

FAQ: AI in the Finance Function

What is the AI value gap in finance?

The value gap refers to the distance between reported AI adoption and the measurable results it produces. In Canada, 83% of surveyed companies use AI in their finance function, but only 36% see more accurate forecasting, according to KPMG (2026). The gap is closed by redesigning processes, not by purchasing tools.

Why are Canadian companies deriving less value from AI than their global peers?

According to KPMG Canada (2026), value is hindered by fragmented ownership of files, pilots with no clear path to business outcomes, and incomplete measurement. Only 64% of Canadian respondents track meaningful AI indicators, compared to 85% globally. Organizational capacity matters more than technology.

Which financial processes offer the fastest AI gains?

Repetitive, high-volume, and measurable processes: reconciliations, cash application, variance analysis, and reporting. KPMG (2026) observes that these quick wins fund more strategic use cases. In accounts receivable, half of all teams cite reducing manual tasks as their priority for the next 18 months (NACM and BlackLine, 2026).

How do you measure the value of an AI project in the finance function?

Document the baseline before deployment: hours spent, exception rates, and cycle times. Then, compare the same indicators after one quarter and quantify the recovered hours and accelerated cash flow. Without a baseline documented before the project, no ROI calculation is credible.