The initial response to artificial intelligence from the accounting community was one of wonder and amazement and, for a while, it seemed like every firm from the smallest storefront to the largest network was eager to demonstrate its embrace of the technology.
But today, as more firms become familiar with AI — especially its generative and agentic variants — firms are focusing less on blanket adoption and more on governance, recognizing both its powerful potential and its very real limitations.
Virtually every firm included in this year’s list has a formal governance policy specifically governing AI use, and the few who do not have already found ways to work AI into already existing frameworks. Further, a rough consensus of what an AI policy should look like has begun to form. Firms in general have implemented strict prohibitions on unloading sensitive client data into public AI tools, require mandatory training on responsible AI use, and regularly monitor and assess their AI activities.
Many others have gone further, doing things like establishing cross-functional AI teams, addressing AI in their written information security plans, educating people on AI’s ethical challenges, or establishing private cloud environments specifically for AI.
Much of this has been done in recognition of AI’s risk and its limitations. It is not 2023 anymore and no one is thinking AI will solve all their problems. Yes, the technology has done many impressive things: Firm leaders report major time savings, deeper analytics, expanded automation, better brainstorming, and incredible efficiencies in software development. At the same time, nearly all the Best Firms for Technology have been frustrated by inaccurate, inconsistent or low-quality outputs, naming this as one of their biggest disappointments with the technology.
Other firms mentioned lengthy and difficult implementations, cybersecurity and data privacy problems, and difficulty working with tools like Excel.
“AI is far from a silver bullet. It’s easy to build something that works in the innovation lab, but it’s much more challenging to build something reliable and scalable across the firm. Even well-trained models can be inconsistent, and costs can rise quickly without a clear return,” said Jonathan Kraftchick, an assurance partner at Top 100 Firm Cherry Bekaert.
These firms’ approach toward AI has much in common with their technology stance as a whole, as every firm in this year’s list also said they have a written technology strategy. Each one is taking a deliberate and intentional approach to their technology infrastructure with the expectation that it will pay dividends in the future.
When asked what they hope AI would be able to do eventually, leaders mentioned not just these problems being solved, but also bots being able to better connect the dots and understand contexts better, which would enable them to become true assistants that can handle complex administrative tasks, as well as better data cleaning capacities and better interoperability with things like Excel or PowerPoint.
Yet these challenges are not preventing firms from continuing major investments in AI; they are, in fact, accelerating them, as tech spending is going up. Of the 10 firms in this list, four said their technology spending has increased significantly since last year, four said it increased slightly, one said it stayed about the same and one said that, while absolute costs have significantly grown, its per-user costs have shrunk. As for why so many firms are spending more on tech, AI was frequently cited, alongside rising service fees and the need to support additional staff.
“We are a digitally determined organization from the top down. We seek to invest in technologies that improve our capabilities, responsiveness, and our quality. Fiscal year 2024 and 2025 has us investing in AI-enabled solutions which support these attributes. Additionally, some core solutions under transition/transformation result in overlapping costs for the duration of the change,” said Peter Sebilian, chief technology officer and chief information security officer for Top 100 Firm AAFCPAs.
This focus on AI plays into other aspects of their technology. For instance, this year’s firms lean heavily on cloud computing, with the majority saying they have no physical servers on site. Even among those that do have at least one, the applications they use are almost entirely cloud-based. Similarly, cybersecurity is a big priority for each of these firms as well, as nearly all of them adhere to at least one recognized cybersecurity standard, such as SOC 2, ISO 27001 or NIST CSF.
Overall, these are firms that take AI seriously in terms of not only adoption but oversight and supporting infrastructure. Given the rapid pace of development in this sector, who knows what we’ll see next year?
With all that in mind, below are this year’s Best Firms for Technology.
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.
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.
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.