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Accounting

Finance-grade GPT-5? Not yet, but get ready

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Leading AI models, like ChatGPT-5, continue to get faster and better. But finance teams still cannot — and should not — trust it to close the books. 

This is not because the models lack intelligence, it’s because they lack the needed context and integrity to truly be “finance-grade.” 

For instance, an AI model may not know that debits must equal credits — always. Or that cash flow from operations has to tie back to net income and working capital. Today’s AI models don’t have finance-native guardrails that recognize journal entries that violate cash flow identities. They lack verifiable finance reasoning graphs that reveal a number’s origination and the logic used to put it there. There are not yet external assurance standards for auditors to meet to sign off on AI-generated narratives. The list goes on.

Sure, AI models can draft plausible entries and smart reports, but they have no inherent sense of whether it broke accounting logic. As such, it can look good, but still be wrong.

In finance, there’s no place for wrong. Every action must be explainable, auditable and defensible. That’s how I define “finance-grade.” And while AI tools are getting faster and better at pieces of the finance team’s work, it still doesn’t make a finance system safe enough for go-it-alone AI.

Pillars of finance

We’ve rebuilt systems before. Think about it: pilots didn’t disappear from cockpits once autopilot arrived. Instead, cockpits were redesigned and the role of the pilot was redefined. Trust in autopilot rose because the entire system — of autopilot and pilot — proved trustworthy. Finance is at that moment now with AI. 

To get to finance-grade AI, I break it down to four key buckets:

  • Control: This entails traceable outputs, enforceable constraints and systems that can be audited. When things can be verified, they can be trusted.
  • Integration: Many companies face fragmented data, disconnected analysis tools, too many spreadsheets and too many manual workflows. AI was not built to jump over such crevasses and work its machine reasoning. “Garbage in equals garbage out” remains true even now that AI is on the scene. You need data that is clean, correctly curated and explainable so AI can integrate with it. 
  • Reliability: The world changes all the time, but so do policies, interest rates, exchange rates and so on. If AI models don’t keep up — and they won’t — the work it did an hour or day ago will no longer be optimal when you pull a trigger. Any automated workflow needs guardrails to allow human intervention. This means stop rules and other red flags that signal need for human oversight. You want intervention before payments are wrongly made or outdated forecasts infuse sales teams’ targets — not just after.
  • Accountability: It needs to be clear who owns decisions. Finance teams, like teams in all industries, are starting to use more AI agents to work autonomously. As they do this in finance, roles for human workers change too. Controllers become control architects. Reviewers look for exceptions. Auditors check systems, not just outputs. Still, it needs to be clear who owns every decision so, if one goes off track, there’s a way to accountability and correction.

 

Planning for the inevitable

While AI is not yet, on its own, “finance-ready,” it will get there. Increased capabilities are already in motion. They’ll arrive even faster once the infrastructure is in place to house them.

In the meantime, finance leaders need to take steps for the short and long term. 

For the quarter ahead, if you want to prove that AI belongs in your finance team, try it on something that causes your team pain and offer relief that scales.

Start with the mundane: chasing receipts, approvals, last-minute clarifications. These are simple tasks that suck time and energy out of highly skilled finance people. Give these tasks to AI agents trained to understand urgency, context and policy. They won’t ask, “Is this right?” but they will ask, “Is this overdue or out of policy?” AI is great at taking action on domains where it can propose before a human approves and domains where logs track every message, action and verification.

Procurement is another likely target for an AI pilot. There’s often a lot of rules around procurement — and a lot of grief for employees to know and follow them. Imagine an intelligent assistant that starts where the employee is — with a natural-language request — and guides them through the procurement process. It figures out whether to raise a purchase order or fund a card. It collects approvals based on pre-set logic. It gives finance visibility before the money moves. The end result is that something gets correctly procured and purchased within policy rules.

By addressing your finance team’s pain points, you’ll engage human employees in the value of having automation make their lives easier and their jobs more fulfilling. Your finance team will love an AI agent that nudges employees for receipts instead of having to do it themselves. As you amass ROI, you’ll also amass employee belief that AI is a worthy colleague. That’s how trust scales.

For the year ahead

Plan bigger and go wider as you consider the year ahead. Be ready for a scenario in which trust in AI builds steadily along with the tool’s capabilities and one in which AI moves really fast and you need to keep up.

With the first one, assume AI adoption will mirror other enterprise technologies. Take the time now to design control environments. Ask audit and risk to give feedback so that, when automation scales, trust does, too. Document everything so you know how to tweak as you go.

With the second one, assume AI reliability leaps ahead of the controls you’ve built into your infrastructure. Prepare now. Get guardrails approved before you need them. This way, when the tech is ready, your system and team will be, too.

Scaling trust, not AI

No AI will ever remove the need for trust. In fact, as machines do more tasks, the trust bar goes even higher. Invest now in things that will build that trust: provenance, constraints, clean data, clear roles, human-in-the-loop intervention. AI will then be finance-ready.

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Accounting

Continuous Auditing Transforms Corporate ERPs

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continuous auditing transforms corporate erps

As corporate accounting departments cross the threshold into late July 2026, the adoption of continuous, automated auditing systems has reached a definitive turning point. Driven by advances in artificial intelligence and deep integration with modern Enterprise Resource Planning (ERP) platforms, leading finance organizations are moving away from traditional, periodic post-hoc audits in favor of real-time, 100% transactional verification. This technological transition is redefining internal control environments, reducing compliance costs, and eliminating the structural delays inherent in legacy quarterly closing processes.

Unlike traditional auditing frameworks that rely on statistical sampling—a process that inevitably leaves operational blind spots—continuous auditing software monitors operational data feeds continuously. Every purchase order, electronic invoice, payroll disbursement, and cross-border wire transfer is automatically cross-referenced against established corporate governance parameters, regulatory tax schedules, and anti-fraud algorithms in real time. Anomalies or unauthorized ledger entries are flagged instantly, allowing internal audit teams to investigate and remediate compliance gaps immediately rather than months after the close of a financial period.

The implications for executive financial management are far-reaching. By embedding continuous verification directly into daily transaction workflows, chief financial officers gain uninterrupted visibility into the organization’s true financial standing. Real-time balance sheet auditing eliminates the severe operational bottlenecks associated with month-end and quarter-end financial reconciliations, freeing accounting professionals to focus on strategic financial modeling, tax planning, and capital allocation rather than manual data entry and spreadsheet consolidation.

However, implementing continuous auditing requires accounting leadership to invest heavily in data governance and technical upskilling. Internal audit teams must evolve from manual ledger reviewers into system architects capable of auditing complex algorithms and validating automated data pipelines. Accounting firms and corporate controllers that master continuous auditing will establish a resilient compliance framework capable of meeting stringent international regulatory standards with total transparency.

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Accounting

U.S. Imposes New 50% Tariffs on Canadian Imports Under Rare Legal Provision

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U.S. Imposes New 50% Tariffs on Canadian Imports Under Rare Legal Provision

WASHINGTON — In a major escalation of cross-border trade friction, U.S. President Donald Trump has signed executive orders imposing new 50% tariffs on a wide selection of Canadian exports, citing discriminatory practices by Ottawa targeting American auto, dairy, and beverage industries.

The new duties, announced Monday, will take effect in 30 days. They target a broad spectrum of consumer and industrial goods—ranging from wine, liquor, and milk products to commercial cement, furniture, clothing, and hockey equipment.

Untested Legal Mechanism

To enact the sweeping measures, the administration invoked Section 338 of the Tariff Act of 1930—a rarely used legal provision allowing the executive branch to levy additional tariffs of up to 50% on foreign nations deemed to discriminate against U.S. commerce.

White House officials noted that Section 338 addresses trade discrimination rather than national security or economic emergencies. The move comes months after prior global emergency tariffs faced legal challenges in domestic courts, signaling Washington’s pivot toward alternate statutory authorities to maintain import duties.

Senior administration officials briefed reporters that the measure directly responds to Canadian provincial bans on U.S. alcohol, restrictions on American vehicle exports, and import quota disparities affecting U.S. dairy and cheese producers relative to third-party trading partners.

“While the administration continues to secure reciprocal trade agreements globally, Canada retaliated against efforts to protect domestic industry,” U.S. Trade Representative Jamieson Greer stated.

USMCA Impact and Carve-Outs

Significantly, the newly ordered 50% duties will apply to designated items even if they otherwise comply with the United States-Mexico-Canada Agreement (USMCA).

However, the administration confirmed key targeted exemptions:

  • Energy products (including oil and natural gas)
  • Potash and critical minerals
  • Fish and seafood
  • Goods already governed by sector-specific duties (such as existing steel and aluminum tariffs)

Administration representatives emphasized that the tariffs do not stem from recent disputes concerning drifting Canadian wildfire smoke, noting that policy options regarding environmental spillover remain under separate review.

Canadian Response and Market Reaction

Following the White House announcement, the Canadian dollar experienced a sharp decline against the U.S. dollar, falling approximately 0.4% during evening trading.

Canadian Prime Minister Mark Carney issued a statement emphasizing that Canada’s earlier counter-duties had merely matched previous U.S. trade actions. “Canada stands ready to engage intensively to address outstanding issues with the U.S. to the mutual benefit of our citizens,” Carney stated, pointing to detailed proposals Ottawa submitted to modernize the USMCA framework.

Ontario Premier Doug Ford took a firmer stance, urging a “dollar-for-dollar” reciprocal response if the measures go into effect on August 19.

With a 30-day implementation window before the duties officially lock in, industry associations and trade groups on both sides of the border are calling for urgent bilateral negotiations to avert further supply chain disruption across North America.

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Accounting

Automated Continuous Auditing: Transforming Compliance and Real-Time Financial Oversight

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Transforming Compliance and Real-Time Financial Oversight

The traditional accounting paradigm—defined by periodic monthly closures and post-hoc annual audits—is rapidly giving way to continuous, automated financial oversight. As of July 2026, forward-thinking accounting practices and multinational corporate finance departments are leveraging continuous auditing systems powered by advanced machine learning models. These systems monitor operational transactions in real time, shifting audit methodologies from sample-based post-analysis to absolute, 100% transaction-level verification.

The operational advantages of continuous auditing are transformative. Standard auditing procedures historically relied on statistical sampling, which, despite rigorous methodology, inherently left gaps where anomalies or fraudulent transactions could go undetected for months. Modern continuous auditing platforms integrate directly with enterprise resource planning (ERP) databases, instantly cross-referencing purchase orders, invoices, bank feeds, and tax records. Any deviation from established control parameters or unusual transaction behavior triggers immediate flags for internal audit teams, dramatically reducing detection lag from quarters to seconds.

Beyond fraud prevention, continuous auditing fundamentally alters internal reporting and decision-making. Executive leadership no longer has to wait weeks after the close of a quarter to evaluate precise financial standing; real-time verified ledger data provides an uninterrupted view of operating margins, tax liabilities, and cash flow dynamics. This real-time visibility enables corporate controllers to adjust capital allocation strategies dynamically, mitigating liquidity constraints and capitalizing on emerging commercial opportunities far more efficiently than competitors bound to legacy reporting cycles.

However, implementing continuous auditing requires accounting professionals to acquire new analytical capabilities. The role of the auditor is evolving from manual data reconciliation toward system validation, algorithmic model governance, and strategic risk interpretation. Accounting firms and corporate finance departments must invest in continuous technical education, ensuring that audit staff possess the data engineering skills necessary to design, maintain, and evaluate complex automated compliance systems.

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