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AI dreams running into AI realities says Gartner survey

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AI dreams are running into AI realities as companies that have adopted the technology report uneven gains, even in areas where there is improvement, the results are less radical and dramatic than people may have initially thought. 

This is according to a recent survey from business technology consulting firm Gartner, which found teams that implement traditional AI and generative AI are not significantly more likely to report high productivity gains than teams that implement other technologies such as robotic process automation or blockchain. 

Among teams who primarily used traditional AI, 37% reported high productivity gains as a result of implementation, while gen AI-using teams fared marginally worse at 34%. In comparison, 34% of teams using other new technologies also reported high productivity gains, a level that is surprisingly not significantly different from teams using either type of AI. 

Gartner attributed this to several factors. For example, inflated expectations of AI’s capabilities lead to disillusionment. While AI can automate certain tasks and provide valuable insights, it does not automatically translate into substantial productivity improvements across the board. Additionally, measuring productivity gains can be challenging, and implementation lags often delay the realization of benefits.

Of note, though, is that these percentages are averages; some functions have actually benefited quite a lot from AI while others get very little. The biggest beneficiaries have been marketing professionals, with 59% reporting high productivity gains as a result of AI implementation, followed by supply chain specialists at 45%, and sourcing and procurement professionals at 44%. After this, those reporting high productivity gains drop dramatically: only 28% of those in manufacturing, production, quality and R&D functions report high productivity gains from AI; only 27% in the legal, risk and compliance areas; 26% in finance; and, last, IT, which only had 18% reporting high productivity gains. 

Marketing has taken well to AI, according to Gartner’s survey, because it can be used to analyze large customer datasets and pinpoint distinct segment-level buying characteristics, as well as quickly create highly targeted and personalized digital marketing content. By comparison, functions such as legal and HR teams have lots of opportunities for AI deployment, but have lagged, at least partly due to areas such as legal contract review or candidate screening processes, which require teams to invest in significant risk monitoring, governance and rework, effectively capping any time savings and productivity gains.

Similarly, while finance has a lot of opportunities for AI deployment—with most common use cases being things like intelligent process automation, error and anomaly detection, basic financial analysis and forecasting—it lags behind other functions, at least partly due to the culture of finance itself, according to Gartner. 

“Many finance leaders tend to be conservative in their AI deployment due to their high expectations for accuracy, desire to minimize data security risks, and need for auditable reporting and evidence. Coupled with AI-related data and skills gaps in finance and limited funding, most finance organizations are not yet at the point where they can roll out AI more broadly and capture big productivity gains. Only 20% of finance organizations are using AI in production, and only 6% are scaling AI to a larger group of users,” said the report. “The good news is that 66% of CFOs are more optimistic or much more optimistic about the value of AI in finance compared to a year ago.”

When it comes to the teams who do report high productivity gains because of AI, the most common positives have been significant cost savings on the enterprise level, improvements in the creation of more novel products and offerings, and significant improvements in the quality of their enterprise’s products and offerings. 

Gartner found individuals save 5.4 hours per week on average after implementing traditional AI, or 4.98 hours per week for those using generative AI. This is slightly less than what was found by a poll from business solutions provider Intapp, which said AI saves accountants about 31 hours a week; but roughly in line with the results of a Karbon survey which found AI solutions have saved accountants between 3.8 to 6.5 hours over a five-day work week. 

The Gartner poll asked what people were doing with the 5.4 hours saved per week. It found that 0.8 hours were devoted to reviewing and redoing work done by AI; 1.4 hours were devoted to taking on extra work that did not improve team outcomes; 0.8 hours were devoted to developing skills; 0.6 hours were dedicated to “reducing hours worked”; and 1.7 hours were devoted to taking on extra work that does improve team outcomes. A similar pattern emerges for the 4.98 hours saved by those who use gen AI. 

Staff inertia was named as a major factor in why AI has not been saving even more time. It noted, for instance, that 60% of finance staff have a tendency to perform manual work on processes that have been mostly or fully automated, either because they don’t trust the technology or because they have an affinity for legacy work. 

“Changes to ways of working will no doubt come with time, as workers begin to trust AI more and there is effective change management and oversight to reallocate time spend,” said the Gartner report. “Teams reporting higher productivity gains make more strategic use of this time by planning for it in advance.” 

Until that day comes, however, Gartner predicted is unlikely that we will see mass displacement of workers in the near-term future. Gartner found that, so far, the time savings from AI do not yet add up to a full-time employee’s time at the average organization. It’s likely that while AI helps an employee with singular tasks, it does not yet replace an entire employee. However, it did note that, given the uneven productivity gains from AI, this means that larger companies, departments and processes are more likely to quickly realize headcount reductions than smaller groups, making the productive reallocation of that time even more important. Overall the Gartner survey challenged widespread fears that AI is coming for people’s jobs already. 

“Anecdotal evidence abounds about AI-driven job displacement. For example, a technology CEO in a recent earnings call claimed that AI-based conversational agents enabled a 50% reduction in IT support headcount, in much the same way that word processors displaced floors’ worth of typists. To the contrary, Gartner’s AI in Finance Survey found that although 53% of surveyed finance leaders expect headcount reductions from AI, only 5% have actually made headcount reductions,” said the report. 

Leaders should instead think of AI not as a headcount reducer but as something that compresses experience in low-complexity roles and getting new workers up to speed quickly at delivering quality output. AI skills are now common among teams, but they are not a differentiating driver of productivity gains. Teams with the highest productivity gains from AI are better at reorganizing to optimize the impact of AI and taking an open and explorative approach to AI deployment. 

Gartner said that if leaders want to get the most out of AI, they need to adapt their operating model to the technology, not the other way around. Those who have seen high productivity gains adapted both internal structures as well as their team’s ways of working to take advantage of AI’s capabilities.

This includes redesigning structures and workflows to eliminate process bottlenecks and shifting time more quickly to value-added tasks. Leaders should also build AI communities that drive collaboration and knowledge sharing among users that can develop richer models than a siloed team of AI experts. Finally, they need to nurture a culture of AI acceptance through instilling an openness to learn and exploring new AI use cases without fear of AI replacing their jobs. Rather than asking, “Will AI replace us?” the mindset should change to “How can we be more effective at our jobs using AI?”

Overall, Gartner recommended that leaders set realistic expectations for productivity gains in AI investment business cases and drive manager accountability for effectively shifting their team’s time savings from AI use toward value-added activities that improve team outcomes. Beyond scaling back expectations for AI, they should also build a contingency plan for a possible increase in demand for knowledge workers. 

“Despite the excitement surrounding AI, its impact on productivity has been inconsistent, leading to what some describe as the AI productivity paradox,” said Randeep Rathindran, distinguished vice president at Gartner, speaking at its CFO & Finance Executive Conference in Sydney. “While AI has shown potential to boost productivity at the segment level, such as in call centers, broader organizational benefits have been harder to achieve. Therefore, CFOs should recalibrate expectations on how AI will truly impact worker productivity and headcount.”

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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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