Connect with us

Accounting

Sage Copilot AI aims to pair power and simplicity for small and medium businesses

Published

on

Small and mid-size business platform Sage is already well experienced with AI, having woven it throughout their products for years, but its new generative AI Sage Copilot solution has been touted as a dramatic step forward in the company’s long term vision of simplifying accounting to make it more accessible to all. 

Aaron Harris, Sage’s chief technology officer, said that the company has long had classic AI deep learning models that perform functions users have relied on for years. These tasks often require the use of many different models working in concert, with Harris noting that invoice processing alone requires the use of 27 different models: 15 to 20 are required to read the data alone, and more are needed to perform the calculations. 

Sage Copilot coordinates between these models and acts as an interpreter between them and the human users who are requesting they perform a task. Effectively a “mouth” attached to a larger whole, the LLM works by translating the user request, usually inputted through a conversational interface, into machine language. This then goes to the various AI models on Sage’s servers, which then get to work on whatever the user asked them for, eventually sending instructions back to the LLM. The LLM reads back the machine language and implements the command or, in the case of an informational query, translates it back to human language. Harris stressed that it is not the LLM itself that does this work, referencing their well-known difficulties with math, but the other AI models that the LLM interacts with. 

“We don’t trust them to do math. There’s much better ways to do math… We’re not using the AI to do the math on the results, we’re using AI to write the [structured query language] exactly as you described,” said Harris during an interview. 

He added that, rather than being a feature of any one particular solution, Sage Copilot can be used across its products through the use of specialized “agents.” The company creates AI “agents” purpose built to do one thing really well, such as interacting with certain types of data, executing specific queries, or engaging with specific software products. Sage Copilot has access to multiple agents, each built to communicate with a different product, whether Sage Intaact, Sage HR or something else.

“The intention is that Copilot can work across the portfolio of Sage products,” he said. 

Having Copilot act as a coordinator for all the other models also means its automation capacities go far past what it had previously accomplished. Harris raised the example of processing an invoice. This act alone requires multiple steps, but many of them have already been automated in Sage, taking out much of the work. Copilot goes a step further by allowing wholesale workflow automation through coordinating among several models that each enable a different automated process. “We can now really accelerate our ability to automate with large language models, and we can use AI to do more of the orchestration. So, giving it the ability to not just process the invoice but to move it on to the approval stage, to understand after it’s approved [it needs to] move it through the payment process,” he said. 

He added that, in the future, “that invoice will have been created for you, automatically.” His team had recently conducted a hackathon where it was found Copilot can automatically generate invoices based on events happening around the user, like if it knew they were going on a job using geofencing, and act proactively. 

Harris said that certain companies today will attach their solution to a ChatGPT account and call that generative AI functionality. In contrast, developing Copilot was a meticulous process that required a lot of trial and error even before its UK release earlier this year. During this time, the development team encountered many challenges that needed to be overcome, such as teaching the model that there can be more than one kind of cash balance. 

“I got in and I asked Copilot ‘what is my cash balance?’ And Copilot said it’s at zero. And I dug around and what I learned was that Copilot inferred from my question ‘what’s my petty cash?’ It didn’t actually understand that what I was asking for was the balance of cash across my bank account,” he said. “Fast forward a month. We’ve done a lot of work to train the model the way we wanted to when you ask that question. … [Now] it’ll give you a table with your bank accounts and your balances in total. If that is what you’re asking, this is the answer.” 

Getting these kinds of interactions right was vital to ensuring Copilot was easy to use and reliable in its outputs without having to possess a lot of arcane technical knowledge. Interacting with Copilot in plain language allows people to access accounting information and perform business tasks on their own, a major component of the wider goal of making accounting more accessible to a wider base of people. 

“Generative AI and copilots, and their natural conversational interface, enables us to bring accounting outside the finance team to the rest of the business. One of the biggest blocks is getting [accountants] to approve things or to answer questions, finance teams are spending time supporting me instead of getting the books closed. With Copilot having a conversational interface, it now becomes much more natural and easy for me to approve a purchase order or to ask a question like ‘how am I trending on my travel expenses?'” Harris said. 

While Sage wants to make accounting more accessible to the layperson, he added that professionals can be excited too. The big promise, he said, is that it will free them from things they don’t want to do. He compared it to having an army of interns at one’s disposal who can take care of the numerous mundane demands that pop up throughout the day. The result, according to Harris, will be “faster and smarter decisions.” 

“Unpacking that, what we’re really saying is [this can be] how you, across the whole of the business, understand the patterns of activity in that business in real time to discover when there is a change in performance—when there’s something that can indicate an opportunity or risk—that the more strategic specialists can address in real time… We’re enabling more decisions to be made confidently,” he said. 

Harris said the name “Copilot” represents what he felt was a good bet that the term copilot would become generic, versus being permanently associated with Microsoft’s product. He said that the term has, over time, emerged as standard in a similar manner as “band-aid,” “Xerox” and “Google.” 

The large language model was released in the UK in February and is set for release in the US at the end of this year. 

Part of a larger strategy

Copilot is one component of the company’s larger product strategy to promote continuous accounting, real time assurance and continuous insights. But this, itself, is part of Sage’s overall strategy, particularly for the North American market; Mark Hickman, the managing director of North America, said Copilot is “critical to our success.” 

“As we move forward, [we want] to really be that leader, we want to be ahead of the competition when it comes to AI and how we bring that to market, into that ecosystem of 2 million customers globally and hundreds and hundreds of thousands in North America,” said Hickman. 

To this end, Sage has been busy making new alliances and deepening current ones with companies like Microsoft, Amazon and PwC. They have collaborated on technology solutions with the aim of eventually driving integration into products like Office and other platforms, as well as on distribution and implementation of said solutions. With Microsoft and Amazon in particular, Hickman said they have whole partnerships where they go to market together and close new customers. Given these companies’ focus on large enterprises, Sage’s focus on small and medium businesses has acted as a bridge to this larger community. 

“What we’ve discovered here is that [Microsoft and Amazon], they don’t really play in the small to medium businesses with the cloud. So 90% of their new customers are net new customers so they’re actually getting into new customers because they’re working with us and closing new deals to get into these accounts and … using their amazing, world class platforms and their brands to work together,” he said. 

This is especially germane as the UK-based Sage expands further into the North American market, which makes up more than 44% of the company’s global revenues already. The company is making heavy investments in this region, which include technology but also additional staff as well as new facilities. Hickman said they will be building a whole new campus in Atlanta to serve as their new base for North American operations (in addition to the office they already maintain in that city), as well as new offices in Portland and Vancouver. 

Hickman said Sage’s thinking on these new locations came as the company emerged from the pandemic lockdowns, eventually settling on what he called a “hub strategy.” Previously, the company had over 80 offices around the world, but he said many of them were small and remote. The company chose a very deliberate strategy where they would instead have large offices in each of the major markets they really invest in, with fewer small satellite offices. This has allowed them to really focus their efforts around these flagship country “hubs.” He noted that the new offices in Canada is also a reflection of this hub strategy of “really increasing the investment in the offices where we want to concentrate our growth.” 

“So the North American businesses, the US businesses, are the fastest growing [sector] with great growth, and we hope to really accelerate that growth as we move forward,” he added. 

Continue Reading

Accounting

Continuous Auditing Transforms Corporate ERPs

Published

on

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.

Continue Reading

Accounting

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

Published

on

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.

Continue Reading

Accounting

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

Published

on

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.

Continue Reading

Trending