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

AI-Driven Automation and Continuous Accounting Frameworks

Published

on

The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

Continue Reading

Accounting

Global ESG Reporting Standards and Double Materiality Compliance

Published

on

Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

Continue Reading

Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

Published

on

Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

Continue Reading

Trending