Top 25 firm Armanino plans to launch a data warehouse service for smaller organizations later this year and has been using AI to develop some of its rollout strategies.
Carmel Wynkoop—Armanino’s partner in charge of AI, automation and analytics—explained that big organizations can store and manage large amounts of data in order to analyze trends, optimize processes, fuel AI solutions and much more. However, building and maintaining the infrastructure for this can be very expensive as well as technically complex, so smaller organizations typically lack these capacities.
The idea behind the new service is to level the playing field somewhat by providing these smaller organizations the resources and expertise necessary for a data warehouse of their own. The client would either send Armanino their financial data or integrate it with the firm’s directly; from there, professionals would gather the information into a centralized data warehouse—hosted on Armanino’s infrastructure—that could be interacted with via a built-in chatbot.
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“We are creating a data warehouse as a service… [We are] providing them the reporting and KPIs that larger, more sophisticated organizations—that have a larger IT department and larger revenue—can do but the smaller ones still sort of struggle with a little bit,” she said in an interview. “So it’s a way for them to get that level of reporting and AI without building their own and having to hire a bunch of people.”
The motivation to launch this service line came from observing smaller clients who struggled with getting data-driven insights into their own organizations, which means they tend to ask a lot of questions. Many of them involve the kind of modeling that larger organizations with data warehouse infrastructure can do themselves, such as “if I change this, what will happen?” Wynkoop wondered whether she could empower clients to do this themselves, which then led to the idea for managed data warehouse services.
“We will host it and manage it. Not that other organizations won’t do that… but we’re basically saying we’re going after a smaller footprint of clients here. What we’re trying to do is enable firms that want to grow and see their data as a differentiator and competitive disruptor. How do they use that data to make themselves more competitive? Because they would not be able to do that today unless they built their own AI model or loaded their data into an existing public model,” said Wynkoop.
She said the firm has people who already know how to build data warehouses, typically for ERP implementations. These would likely be the professionals who would be responsible for the new service line. Asked about the timeline she is working with, Wynkoop anticipated a late Q3, early Q4 rollout.
AI as business developer
One notable thing about this new service line is the role AI played in developing it. AI did not come up with the idea and did not lay out the finer points of how the service would work. Further, AI was not used to develop the data warehouses themselves. Wynkoop said it was mostly used for researching things like the market landscape as well as general brainstorming, making extensive use of the technology in both cases.
“I used Google Deep Research… Then I used ChatGPT to make a marketing plan and had Copilot build me a PowerPoint presentation, then I put that [presentation] back into ChatGPT and Claude. Once I sort of had the business plan and the go-to-market strategy and the PowerPoint, I loaded them [into the models] and asked the three of them to poke holes in it and compare it to other service offerings,” she said.
She said that, without AI, this process likely would have taken months. Using AI reduced it to about two weeks.
The go-to-market plan is where the AI fingerprints would be most apparent. Its proposed strategy covered types of clients and industries that would need these services, market share calculations and revenue projections, and possible drivers of interest such as partnering with Microsoft and promoting case studies.
“The biggest piece with use of AI in this scenario was the research, [particularly on the] competitive landscape and in thinking about what features we have to have and what other organizations have that show up in a similar data warehouse offering,” she said.
Beyond AI, developing the new service line also required collaboration with the other humans in the firm, particularly IT and legal. No one objected to Wynkoop using AI in her process in and of itself. She said leaders were more concerned about how to make the service safe and secure.
“We’re all on board with using AI for research and development of new ideas. I think, because we are going to be hosting client data, one of the things we had to consider and ensure is that we’re doing it in a way that is smart and safe, so that is an area we spent a lot of time and focus on and had several discussions with our IT and legal and the whole gamut to make sure all the safeguards are in place for client data,” she said.
The service is currently in beta testing. Wynkoop said Armanino is currently searching for clients to help vet the service and make sure the strategy the firm has is viable and valuable.
“II think I think it could be a real game changer for businesses that don’t have sophisticated reporting today and hopefully helps them move the needle on the next step in their own evolution,” she said.
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.
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.
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.