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Orgs going full steam ahead on AI, regardless of economy

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Companies seem to be going all in on AI, not only planning huge investments in it this year but making these investments central to their growth strategy, even amid other economic headwinds. 

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A number of studies and surveys has found that businesses are planning major spending on AI technology this year. For instance, a recent survey from Big Four firm KPMG found that the average projected investment over the next 12 months has nearly doubled since last year, going from $124 million at the end of 2025 to $207 million now. Similarly, another survey conducted by Top 10 Firm Grant Thornton said that 68% of CFOs expect IT and digital transformation spending to increase over the next year, marking the highest level recorded in the 21 quarters the survey has been conducted.

Meanwhile, finance and procurement solutions platform Coupa found in its own survey that finance leaders were heavily prioritizing AI spending in the coming year: 49% cited increasing their AI investments as a top strategic priority and 42% cited training and upskilling staff to use AI. 

And while economic headwinds are a reality throughout the business world, organizations do not appear to be letting that stop them. The KPMG survey said that 79% said AI will continue to be a top investment priority even if a recession occurs in the next 12 months. This is despite mixed feelings about the future of the economy, as cited by the GT survey: Optimism dropped from 52% to 46%, but pessimism also fell from 31% to 25%; overall, more have a neutral view of the economy, going from 17% to 29%. 

This planned spending, however, might be because of, not despite, these perceived headwinds, as businesses seem to be placing a lot of hope in AI to carry them through these troubling economic times. The Coupa survey, for instance, asked leaders about their top profitability strategies going forward, and a clear majority, 60%, cited increasing their investment in AI. Meanwhile, asking about their top growth strategies, the most common answer, at 58%, was once again to increase AI technology investments. And finally, 85% said AI was central to their financial strategy this year, and a whopping 100% are planning AI investments over the next six to 12 months. 

Other surveys also show confidence that organizations’ AI bets will pay off. The Coupa survey said 19% expect return on investment within six to 12 months, 52% expect it within 13-24 months, and 26% think it will take two to three years. Only 3% thought it would take longer than that. They might be getting these expectations from their peers: The KPMG survey found 62% of finance leaders saying they have either achieved measurable ROI or expect to sometime in the next year, up from 59% the last time they asked this question. 

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In general, leaders appear to be counting on gains from AI, according to the GT survey. It noted that while CFOs are investing in technology at record levels, they’re not cutting elsewhere to fund it. The survey revealed that 72% expect their net profit to grow over this year, up from 68% last quarter, which the report said could indicate faith that AI will expand revenue and increase productivity. 

Organizations also might be planning spending increases because their technology costs have gone up. A report from Big Four firm Deloitte found that AI consumption and spending have exploded to the point where usage has dramatically outpaced cost reductions, which has led some to look for more economical options like on-premise hosting for the high-volume workloads that AI requires. The report noted that large language model tools can become cost-prohibitive when deployed across an enterprise, and some organizations are starting to see monthly bills for AI use in the tens of millions of dollars. In particular, agentic AI can spike token costs. 

Regardless of motivation, though, companies also seem well aware of the challenges of AI implementation. The KPMG survey showed that leaders did a lot of learning over last year about them: those citing difficulty scaling use cases as an ROI barrier went from 33% in Q1 last year to 65% now; similarly, those citing skills gaps went from 25% to 62%, those naming difficulty quantifying indirect or long-term benefits went from 34% to 59%. The only thing that went down were those talking about risk considerations like data privacy and cybersecurity, going from 74% to 58%. Yet, at the same time the KPMG survey found they’re eager to address these issues, as 91% of leaders named data security, privacy and risk concerns as the top factor influencing AI strategy for the next six months. 

Similarly, the Deloitte report noted that, over and over, people have named three fundamental infrastructure obstacles that prevent organizations from fully realizing the potential of agentic AI: legacy system integration, data architecture constraints and governance/control frameworks. Deloitte said that, right now, most enterprises are not set up to take advantage of the opportunities agents represent. 

This is quite similar to what leaders cited in the Coupa survey. When asked about the largest constraints to integrating AI into daily workflows, 72% said data quality and readiness, 65% cited integration complexity, and 70% said data security and compliance. However, Coupa also noted another issue is that organizations may have trouble actually determining whether their AI investments were worth it in the first place, as 76% said that difficulty actually quantifying ROI was hindering further implementation. 

However, Twisha Sharma, senior research principal for Gartner Finance practice, said companies may not be realizing their goals because of the way they’re viewing AI. Many talk about AI investments as a big broad category, but Sharma, in a recent talk, noted that the economics of AI differ sharply from one use case to another, which makes developing a standard approach difficult, as it likely won’t be able to capture the full picture. Each use case, she said, has different timelines, different ambitions, different risk profiles and different ongoing costs. Finance teams need to dissect cost models more precisely if they want to benefit from AI. 

“AI does not follow one cost curve, and it does not produce one uniform type of value,” said Sharma. “CFOs need to stop looking for a single ROI formula and instead build a balanced portfolio that includes productivity use cases, targeted process improvements, and selective transformational bets.”

Sharma warned that CFOs risk undervaluing AI if they focus too narrowly on immediate financial returns, such as revenue growth, cost reduction or cash flow improvement alone. She said many AI initiatives create important nonfinancial value first — including better decision support, stronger business agility, wider organizational reach, innovation capacity and even a shift in finance’s role within the enterprise — long before those benefits are fully visible in the P&L.

“The value of AI is not always captured first in traditional financial metrics. In many cases, it appears earlier in better decisions, faster adaptation and stronger organizational capability. CFOs need to account for that if they want a complete picture of what AI is really delivering,” said Sharma.

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Accounting

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

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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.

 

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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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.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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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.

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