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What accounting firms miss when they rush into AI

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Firm leaders are getting contradictory messages right now. Every conference, every vendor, every LinkedIn post says artificial intelligence is about to transform accounting. The implication: If you’re not implementing it yesterday, you’re already behind.

But the companies actually building AI tools for accounting are working on a different timeline than the one being marketed.

When I talked with Mike Cieri, executive vice president of software at Bill, about implementation timelines, he was direct: “We’ll see rapid change over five years, but I don’t think in six months you’re going to have a complete turnaround on how the accounting industry works.”

Five years of steady change. Not a six-month revolution.

That gap between the hype and the reality is creating unnecessary anxiety. Firms are making decisions about AI adoption while feeling panicked about being left behind, when the actual timeline gives them room to learn and test properly.

What ‘experimenting’ actually means

Most firms fall into one of two camps: diving into AI implementations without testing, or freezing because they don’t know where to start. There’s a middle path.

“Take a couple of associates and say, ‘We’re going to try this in your area. We’re going to try this with a few clients and experiment with it until we feel good that there was value added, and we have the controls we value as a firm in place,'” Cieri suggested.

The word “experiment” matters here. An experiment has defined parameters, a limited scope, and permission to produce unexpected results. A firm-wide rollout has none of those things.

This approach addresses what firms actually worry about: What if this doesn’t work the way the vendor says? What if it creates more problems than it solves? What if we spend money and time and see no real benefit?

Testing small answers those questions with data instead of assumptions.

Where the value actually shows up

During my years in public accounting and later in C-level roles at companies, the frustration I saw repeatedly was talented people spending hours on work that didn’t require their expertise: Transaction coding. Data entry. Repetitive reconciliations.

AI should solve that problem — not by replacing accountants, but by handling the work that doesn’t need human judgment.

In our conversation, Cieri framed it this way: “Human involvement will be high leverage at key moments, where creativity is needed, judgment is needed, advisory is needed. We’re trying to amplify the value of human intervention in those moments.”

This changes what entry-level work looks like. Instead of spending two years learning to code transactions before getting to do analysis, new staff can move into interpretation and pattern recognition faster. The technical skills still matter, but they’re not the bottleneck anymore.

For experienced professionals, it creates bandwidth for work that keeps getting postponed: Strategic client conversations. Team mentoring. The advisory work that actually requires expertise.

From the client perspective: “I should be happier that we’re showing up to meetings and getting a higher-order thinker on the other side, showcasing for me a better picture of my business than I was getting before.”

That’s the actual value — not efficiency metrics on internal processes, but better outcomes for clients because the firm has capacity for meaningful work.

How review processes build trust

The question I hear most often: How do I know the AI did it correctly?

Same way you know a new staff person did it correctly: You review their work.

“We think of AI as just another actor in the system. We record those actions, there’s clear auditability, there’s clear transparency,” Cieri explained. “You’re building trust over time through verification. You see what the AI did, you check its work, you override when needed. The same process firms already use for training staff.”

The difference between firms that scale AI successfully and firms that pull back after problems: The successful ones built review processes from day one. They didn’t assume it would “just work.”

Starting point

If you’re somewhere between “We should do something about AI” and “I don’t know what to do,” here’s a practical framework:

  1. Spend time learning what AI actually does. “People tend to fear things they don’t understand, so just spending some time on that alone … to separate some of the fact from fiction in your own mind” makes the difference between decisions driven by anxiety and decisions driven by clarity, Cieri suggested.
  2. Pick one workflow that’s causing pain. Not the most complex one — the most consistently annoying one. Test with a limited scope. One person, one set of clients, defined timeframe.
  3. Define success before you start. What are you measuring? Time saved? Error reduction? Less end-of-month stress?
  4. Then pause before scaling. What worked? What didn’t? What surprised you? Most firms skip this reflection and miss the learning.

“Firms have a choice. If you want to turn on agents to do some of this work for you, that’s a choice you’re going to be able to make,” Cieri said. “It’s not just going to happen overnight.”
You control the pace of adoption.

What this actually requires

When I work with executives and partners on transformation — technology, workplace culture, business process — the pattern is consistent: The ones who succeed don’t move fastest; they move with intention. They test, reflect, adjust.

The ones who struggle try to do everything simultaneously because someone told them urgency equals importance.

AI creates opportunities for firms to reclaim time for work that matters: Strategic advising. Client relationships. Team development. But only if the adoption process itself doesn’t create the burnout and overwhelm that AI is supposed to solve. AI trends come and go. Fulfillment is evergreen.

Cieri said it best: “This is about supporting, not replacing, human connection.

The technology will wait. The question is whether you’re making decisions from clarity or from the pressure to keep up with what everyone else seems to be doing.

Take a beat. Test small. Build trust through verification. Scale when you understand what you’re scaling.

The firms that will succeed with AI are the ones who test first and scale deliberately.

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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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