BDC LIFT: your first AI project in finance

BDC's LIFT program opens the door to AI advisory and financing to help Canadian SMBs move from intent to action. But faced with the breadth of possibilities, many finance teams hesitate on the first step. Our take: start with a single use case where AI delivers measurable value quickly, namely accounts receivable.

LIFT has two components: AI advisory at a preferred rate and financing for software projects of $25,000 to $2M. For a finance team that wants one concrete first project, accounts receivable checks every box: direct cash impact, real day-to-day friction, return on investment legible in a few months.

What is BDC's LIFT program, concretely?

LIFT, which stands for Leverage Innovation and Focus on Technology, was launched by the Business Development Bank of Canada in April 2026. The program is open to any Canadian SMB with at least $1M in annual revenue.

It has two pillars.

• Subsidized AI advisory. BDC matches you with AI consulting experts at roughly half the usual cost. The mandate covers needs assessment, selection of the highest-impact solutions, and implementation oversight. If you're looking for where to start, Finaxis can also refer you to qualified consultants in its network.
• BDC financing. Loans of $25,000 to $2M for software-based AI projects, and up to $5M for projects involving robotics or physical automation.

The program prioritizes AI tools developed in Canada. That's excellent news for Canadian SMBs: the mandate and the resources are there, without having to argue for every dollar.

Why start with a single AI use case rather than transforming everything?

When you start exploring "what AI can do for my business," you quickly fall into the noise. Hundreds of tools, demos that all look alike, inflated promises, jargon everywhere. It's dizzying, especially for a finance team looking for a concrete result, not an 18-month pilot.

The pragmatic rule: don't try to transform the entire company at once. Pick a use case where the value delivered is clear, measurable, and limited to a single workflow you can isolate.

A good first AI use case in an SMB has three characteristics.

• It touches a real friction point. Something your team does every week that eats up time.
• The outcome is measurable. You know before, you know after, no debate.
• It doesn't force you to rebuild everything. You add it to your current environment, you don't replace it.

If the first project succeeds, it funds the confidence for the next one. If you start with a project that's too broad, you get bogged down. You end up concluding "AI is too complicated for us," which is rarely the real answer.

Why are accounts receivable a good first AI use case?

Accounts receivable check all three criteria.

The friction point is real. Tracking late payments, drafting follow-ups, keeping a thread on payment commitments, managing disputes: it's repetitive work that consumes qualified time. Every hour mis-invested costs directly in days of cash lag.

The result is measurable. DSO (days sales outstanding) is a number you already track. You can compare before and after within a few weeks. No need for qualitative studies or control groups.

The project is isolated. Your accounting tools don't change. Your invoicing process doesn't change. You add a layer of intelligence to the follow-up work, that's it.

It's also a domain where the value delivered is felt quickly. On average, teams that automate their follow-ups reduce DSO within a few weeks, freeing up time the finance team can redeploy to higher-value work: analysis, forecasting, planning.

Why is Finaxis a strong candidate for this first LIFT project?

Once accounts receivable are chosen as the first use case and LIFT is on the table, the choice of tool remains. Three things set Finaxis apart in this specific context.

• LIFT-grade metrics, already in the dashboards. A LIFT file needs before/after numbers: DSO, response rate, automated cases, time freed. Finaxis tracks these out of the box and presents them in dashboards ready to export for your LIFT consultant or internal committee. You don't write the measurement report, you read it.
• Complementary to the LIFT consultant, not redundant. The LIFT consultant helps you diagnose, measure, and scope. The tool executes. Finaxis fits this two-step logic naturally: the diagnostic phase can rely on real data from a short trial, rather than theoretical estimates. Each plays its role without stepping on the other.
• Quantified validation before the next investment decision. You have measurable results before committing to the next phase of your AI roadmap: time freed, cases off the finance team's desk, days gained on DSO. The opposite of an AI project that takes two years before anyone can say whether it works.

What does Finaxis actually look like in practice?

The effectiveness of an AI tool isn't measured by the length of its feature list, but by what happens in the first hours of use. Two key moments capture our product philosophy.

AI activates with a single click, with no upfront configuration. Finaxis was designed so AI adoption happens without friction. Some agents activate with a single click. Deep customer research is one example: you see a case slipping, you click. The button passes the agent the context it needs (payment history, past communications, open cases), and the agent produces its analysis. No form, no upfront configuration, no migration path. The result lands in the same flow as the rest of the work.

Statements prepare themselves, with two agents validating in the background. Sending statements at month-end is one of the most-used features in Finaxis. Done by hand, it's a full day's work for an accounts receivable lead: pulling balances, checking recent payments, identifying problem invoices, formatting the send, chasing bounces. Finaxis built a feature that gathers all this information at the right moment, automatically.

Behind the scenes, two AI agents collaborate. The first, the reconciliation agent, validates that payment data is current before the send goes out, so you don't ship a statement that ignores a payment already received but not yet posted. The second, the analysis agent, checks there's no missed invoice or in-flight issue that would make the send inappropriate, such as an open service ticket or a late product delivery. If a case warrants attention, the agent alerts the customer's internal owner before the statement goes out. All of this runs in parallel, whether you're sending to one customer or two hundred.

It's this invisible mechanic that turns a day's work into a few-minute send, while cutting out the statements that go out by mistake and have to be walked back afterwards.

Why does a tool built in the AI era see things differently?

Finaxis was designed after the start of the AI era. From our first lines of code, generative AI and AI agents were already part of the conversation, and already part of the solution we were building. It's neither a credit nor a slogan. It's just timing luck. And it changes a lot.

For vendors established before this wave, the AI turn is rarely neutral. It often disrupts their own business model.

Take per-user pricing, still widespread in B2B finance tools. When a platform charges per seat, its growth depends on the number of employees its customers plug in. But an AI agent does the work of several people. If the platform genuinely adopts AI, it reduces the number of seats its customers need, and therefore its own revenue. The conflict is structural.

That's one of the reasons so many tools inherit the "AI" label without rethinking their architecture, their user experience, or their pricing model. AI is added as a surface feature, because rethinking it in depth would mean breaking what pays the bills today.

Finaxis doesn't have that conflict. Our pricing is plan-based, not per-seat. Our architecture, our interface, and our workflows were designed for collaboration between you and AI agents, because there was nothing else to preserve. That changes three concrete things.

• Contextualization. A follow-up isn't just triggered by a delay. It takes into account the customer's payment behavior, the relationship history, recent signals. The same 15-day overdue invoice doesn't produce the same follow-up for a habitual payer as for a customer who's slipping.
• Interface designed for human-AI collaboration. You approve every action that touches money or a customer. The system suggests, you decide. The experience isn't a dashboard with an "AI" button hidden in a corner. It's a flow where suggestion and validation coexist naturally.
• Continuous improvement. Models learn from the broader market context, not just your team. Your data is never used to train the models.

It's not about who has the longest feature list. It's about coherence between what you're being sold and how it's built.

How does Finaxis connect to your stack to personalize each follow-up?

AI-native doesn't mean "all AI, all the time." It means AI is used where it brings real value (case prioritization, message contextualization, suggesting an optimal send time), and elsewhere, the system relies on solid, predictable features. The promise isn't AI for AI's sake. It's making fewer mistakes and saving time where it counts.

To deliver that value, Finaxis needs to see a case's full context. That's why the product plugs into your current stack, without replacing it.

• Accounting software (QuickBooks, Xero, Acomba) for invoicing and payment data.
• CRM for commercial history and contacts.
• ERP, project management, ticketing system: everything else that completes the picture of a customer relationship.

The broader the system's view, the sharper personalization becomes. And it's personalized follow-ups, not generic ones, that change the response rate.

Communications go out from your inbox, on the channel that works

Finaxis integrates with Office 365 and Google Workspace (Gmail) with mailbox delegation. When you approve a follow-up, the email leaves your own inbox, or that of the designated colleague, as if you had written it yourself. The customer sees your name, your address, your signature. They reply to you, not to a generic alias. That detail changes the quality of the relationship and the response rate.

For sectors where SMS outperforms email on response rate, particularly in construction, Finaxis supports SMS sending with the same approval rules. The tool suggests the channel most likely to land based on the customer's payment and communication behavior. You keep the final say on which channel goes to whom.

How does Finaxis fit into a LIFT initiative?

Finaxis is intelligent infrastructure for accounts receivable, designed in Canada, bilingual, hosted in-country. The product offers two operating modes: Copilot mode where every action is suggested then approved, and Autonomous mode bounded by rules you define.

Concretely, for a Canadian SMB starting a LIFT initiative:

• The expert advisory subsidized by LIFT can cover the diagnostic and tool-selection phase.
• The financing pillar can cover the setup and initial subscription to a software AI tool like Finaxis.
• Being developed in Canada, the tool aligns with the program's prioritization criteria.

We don't claim to be the universal AI solution for finance. We do one thing very well: turn follow-up and collection work into intelligent infrastructure.

Is it really AI, or just a label?

Before signing with a vendor, ask yourself a question that carries more weight than it seems: is what's being sold to you as "AI" actually AI?

The stakes are real. The U.S. Federal Trade Commission launched an enforcement operation in 2024 called AI Comply, targeting inflated AI claims in commercial products. The line between a true adaptive learning capability and a rule sequence dressed up with the word "AI" remains blurry for most buyers.

Here are three concrete questions to ask any vendor. The answers, or the absence of answers, will tell you a lot.

• What happens when the system meets an exception it has never seen? A rules-based system breaks or falls back to a human. A real AI system proposes a reasoned, traceable decision, which you can accept or reject.
• Can you explain how a specific decision was made? If the answer is vague ("our algorithm looks at several factors"), that's a signal. A real AI capability produces explainable decisions, even if the explanation isn't trivial.
• What performance indicators can you document? Not slogans ("80% gain"), but measurable data on real cohorts. The absence of documented indicators is a red flag.

Ask us these questions too. It's the minimum.

Next step

If you're weighing where to place your first AI project under LIFT, and accounts receivable are part of the conversation, let's talk. Fifteen minutes is enough to determine whether it's the right first step for your team.