Connect with us

Accounting

Gen AI will upgrade you not replace you: KPMG

Published

on

As the AI revolution continues apace, data has confirmed that generative AI is making some workers more productive and some companies more profitable, though this does not mean it’s a good idea to start cutting staff. 

During a virtual roundtable hosted by KPMG last week, Pär Edin, the US AI go-to-market leader for the Big Four firm, said that there is hard data showing that, for at least some workers, generative AI has been paying dividends in terms of productivity, referencing research from last year finding that, on average, the technology has introduced productivity gains of about 14%. He noted this is based on not some ideal future state but what can be done with the technology today, with solutions that are already out in the market. He added that, in conversations with AI researchers, there is confidence this figure will hold as a realistic expectation. 

He referenced KPMG’s own research on top of this, which found that—after analyzing 10,000 companies—generative AI has a EBITA impact ranging from 3 to 17%, which is calculated as time freed up multiplied by the labor cost of that time, which he felt was a highly significant impact. Effectively, he said, generative AI has created an entirely new driver for productivity. 

Cyborg

Kitreel – stock.adobe.com

“It varies by sector and company, but those are really, really huge numbers. This is an additional lever that didn’t exist 18 months ago. Now any company can pursue single-digit or low double-digit percentage points of improvement. Not overnight, but within a 12-36 month period using existing tools,” he said. 

While all this does mean companies can do more with less, Edin warned that this does not mean companies should start reducing headcount. In fact, he said, generative AI is pretty terrible at fully replacing people, at least right now. While AI is often touted for its automation capabilities, he said over the past few years companies have found this was a flawed conception. The promise of generative AI, he said, isn’t so much in replacing people but augmenting them. 

“It’s not a headcount-reduction tool in the sense some may have thought about. [Instead, it’s] really a task augmentation tool. We talked about how to get those numbers–you need to break down the entire workforce. I don’t mean headcount but tasks and activities. For every one of those, there are some pretty interesting benchmarks on how much time could be freed up by using better tools. Think of it more as a power tool for the mind than an automation factory,” he said. 

He understands that this might not be what certain business leaders want to hear. Edin noted that he has had many conversations with finance and accounting leaders that basically come down to ROI. This isn’t always the easiest to measure, especially when it comes to AI tools, and so sometimes it can be difficult to communicate the benefits. If it’s not reducing the cost of labor, some wonder, what’s the point? Edin, though, felt that focusing on the cost of labor was missing the point entirely. 

“The most likely case we discussed was not labor cost or headcount reduction but gradual market expansion. So, think of it as companies continuing to grow at the same or greater pace on the top line while not growing labor costs and headcount at the same rate—or even keeping them steady,” he said. 

Given that, by definition, this is more about supporting future growth than directly creating it, he conceded it can be difficult to quickly make back the investment. This has led to a push and pull for accounting and finance leaders between wanting to implement AI for its productivity benefits while, at the same time, wanting to spend only on that which has a direct business case. 

“There is a tug-of-war between wanting to fund this as much as possible, because it does drive productivity, but at the same time not being too overblown about what it will do when explaining this to the board or an investor. This is a balancing act between wanting to do it and being fiscally responsible,” he said. 

It may be easier to directly communicate the need to adopt AI in the future. Edin broke AI development down into three phases: retooling, reengineering and reimagining. The first phase, retooling, is about doing the same job with the same person and role but just more efficiently than before. He noted most companies are in this phase, rolling out pilots and training their staff. The second phase, reengineering, is where workflows themselves are changed to include AI, which he said serves to free up time and enhance efficiency by not just doing the same job but faster but doing a better job overall. Some companies, he said, are just entering this phase. Finally, reimagining is something few to no companies are doing now: thinking about AI as it applies to the entire business model.

“This is when you think about disruption. Will your entire business model be wiped out? Or will you disrupt others? You might go lower in the value stack, or even enter a different market entirely using this technology,” he said. “These phases are somewhat sequential but are happening in parallel depending on the company. Most companies sit somewhere between the first two phases.” 

Agentic AI—where bots are given limited autonomy and initiative—may place companies between the second and third phase, but even then he said it will not mean the end of human involvement. 

“There will be many types of tools. Even in an automated factory, you still have wrenches and screwdrivers. It will be an ecosystem. We’ll continue to use many different tools. The AIs are great because they’re flexible—they can do things they weren’t originally designed to do, and they can get better,” he said. 

Continue Reading

Accounting

SEC’s Semiannual Reporting Proposal Faces Investor Pushback: What CFOs Need to Know

Published

on

U.S. Securities and Exchange Commission (SEC)

A proposal from the U.S. Securities and Exchange Commission to potentially shift some public companies away from quarterly financial reporting toward a semiannual model is drawing significant pushback from investors, even as it continues moving through the regulatory process. The debate has direct implications for corporate finance teams, auditors, and the broader transparency of U.S. capital markets.

What the SEC Proposed

According to a summary published by accounting advisory firm Cohen & Co., the SEC issued a proposed rule on May 19, 2026, aimed at simplifying financial reporting requirements for many U.S. public companies. The proposal would potentially reduce the frequency of certain mandatory disclosures from quarterly to semiannual, a structural change that has not been made to core U.S. reporting requirements in decades.

The proposal follows an extended debate within U.S. policy circles, with proponents arguing that reduced reporting frequency could lower compliance costs and free up management time for longer-term strategic planning rather than quarter-to-quarter results management.

Why Investors Are Pushing Back

Comment letters submitted in response to the proposal have been extensive, and according to Cohen & Co.’s review of the public record, investors “appear to be largely opposed” to the shift, viewing frequent interim reporting as a core benefit of U.S. capital markets relative to other jurisdictions.

Accounting and law firms have taken a more measured position, generally urging any changes to remain aligned with the Financial Accounting Standards Board (FASB), whose existing disclosure requirements and guidance are built around a quarterly reporting cadence. A shift to semiannual reporting without corresponding changes to FASB guidance could create friction between SEC filing requirements and GAAP-based disclosure expectations.

Lessons From the U.K. Experience

The debate is not without precedent. The United Kingdom moved away from mandatory quarterly reporting for listed companies in 2014, returning to a semiannual disclosure requirement. According to Cohen & Co.’s analysis, that experience offers a cautionary data point: there was no measurable increase in capital expenditure or R&D investment following the change, while analyst coverage of affected companies declined as reliable interim information became less available — a particular risk for smaller and newly public companies that rely on analyst coverage to maintain investor visibility.

Practical Implications for Finance Teams

Beyond the debate over disclosure philosophy, the proposal carries practical complications. Many companies have debt covenants and credit agreements structured around quarterly financial delivery; a shift to semiannual reporting could require renegotiating those terms. Reduced reporting frequency would also extend the “window of market silence” between disclosures, a factor that governance and investor-relations teams would need to manage carefully to avoid information asymmetry.

Separately, and unrelated to the reporting-frequency debate, the SEC and FASB have continued finalizing more routine updates this year. New Accounting Standards Updates are taking effect for December 31, 2026, fiscal year-ends covering income tax disclosures, credit loss measurement, induced debt conversions, and stock compensation, according to Eide Bailly’s review of 2026 ASU activity. Additional guidance on paid-in-kind dividends and environmental credits is also on the near-term horizon.

What to Watch Next

The semiannual reporting proposal remains in the comment and review phase, and no final rule has been adopted as of this writing. Finance leaders should monitor the SEC’s regulatory agenda for further movement, while treating the current quarterly reporting requirement as the operative standard until any final rule is issued and an effective date is set.

Given the extent of investor opposition documented in the comment file, a full shift to mandatory semiannual reporting appears more likely to result in either a scaled-back compromise or continued study rather than swift adoption — though the SEC’s ultimate direction remains uncertain.

 

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

Trending