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Orgs going full steam ahead on AI, regardless of economy

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4 months ago

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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

Global ESG Reporting Standards and Double Materiality Compliance

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5 days ago

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August 9, 2026

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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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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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1 week ago

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August 5, 2026

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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.

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Accounting

Automated Tax Compliance and Global Regulatory Harmonization in 2026

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1 week ago

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August 5, 2026

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Corporate tax accounting departments are navigating an era of unprecedented regulatory complexity as global tax harmonization frameworks take full effect alongside real-time digital tax reporting mandates. Tax directors and accounting teams are adopting cloud-based tax compliance automation tools to manage multi-jurisdictional tax liabilities and satisfy stringent reporting rules across international jurisdictions.

Implementation of Global Minimum Tax Provisions
The implementation of international tax reform agreements—notably the Pillar Two global minimum tax framework—has reshaped multinational corporate tax planning. Multinational enterprises with consolidated revenues exceeding established thresholds must ensure an effective tax rate of at least 15% across every jurisdiction in which they operate.

Accounting teams are implementing specialized tax calculation modules integrated directly into enterprise resource planning (ERP) platforms. These automated tools calculate effective tax rates per country, identify top-up tax liabilities, and generate standardized compliance documentation required by national tax authorities.

Real-Time Digital Invoicing and E-Reporting Mandates
Tax authorities across Europe, Latin America, and Asia-Pacific have enacted mandatory electronic invoicing (e-invoicing) and continuous transaction controls (CTC). Under these systems, corporate transaction data must be submitted electronically to government portals in real time at the point of sale or invoice issuance.

This shift toward continuous digital tax reporting eliminates traditional annual tax audits in favor of ongoing automated compliance monitoring. Accounting departments are upgrading invoicing software to ensure seamless XML data formatting, digital signature authentication, and real-time validation against tax authority databases.

Automation and Data Analytics in Corporate Tax Strategy
To keep pace with dynamic tax legislation, tax departments are transitioning from reactive compliance teams to proactive strategic advisors. Machine learning algorithms analyze corporate transactional data to identify tax credits, research and development (R&D) incentives, and cross-border transfer pricing adjustments.

By automating routine tax return filings and calculations, corporate tax directors can focus on long-term capital structuring, evaluating the tax implications of corporate mergers, and optimizing international supply chain networks.

Strategic Priorities for Tax Executives
1. ERP System Upgrades: Ensure enterprise software is capable of generating real-time, granular tax data required for global minimum tax compliance.
2. E-Invoicing Integration: Implement scalable e-invoicing platforms to satisfy regional continuous transaction control regulations.
3. Strategic Tax Analytics: Utilize predictive tax modeling tools to evaluate structural changes in corporate operations and cross-border trade.

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