AI in accounting and reporting

How many of you have imagined having a J.A.R.V.I.S. (Just A Rather Very Intelligent System) like Tony Stark has? How would you use it? It could read the newspaper for you and summarize the key information in five minutes based on your preferences. It could help you prepare for the day's meetings with all data points taken from your mails or simply finish up daily routine activities like voucher posting, reconciliations, financial statement preparation and keeping them ready for your review.

In today’s digital playground, Agentic AI use cases are plentiful. It may not be long before every CFO has their own version of J.A.R.V.I.S., helping manage routine finance activities, connect information across systems and support faster, more informed decision-making. What feels futuristic today could soon become a normal part of how finance teams operate.

Finance organizations are now in the process of scaling AI from point use cases to autonomous workflows. There is pressure from the board around investing in and deploying AI on a scale and bringing in efficiencies.

As accountants, for someone who is very used to a very structured and methodical way of working, such as checklists and documentation, the pivot to AI, especially Agentic AI, where there could be an agent doing the accounting and produce the first pass of financial statements, could mean a lot of change management from the inside and the outside.

How can accountants transition from filing up checklists, documentation, performing reconciliations, posting journal entries to managing agents and reviewing the agent output and signing off as agent boss (human in the loop) …. this is going to be an interesting journey.

Let’s deep dive into reimagining accounting and reporting through AI

According to EY 2026 Global DNA of the CFO Survey, only 21% of CFOs describe their finance function's AI preparedness as leading or advanced. EY also highlights an example where an organization implemented an end-to-end Agentic AI solution for purchase-to-pay, reducing the capacity required for those activities by approximately 85%.

The role of AI has significantly reshaped how finance function operates, enabling faster and smarter business decisions. To achieve that, there must be a fundamental shift in how AI can be used in accounting and reporting processes to be AI ready in order to rely on the numbers being reported. A successful AI implementation is more about getting the fundamental framework and data architecture right rather than the AI model itself being used for implementation.

The accounting and reporting landscape has transformed significantly over the last decade. Finance functions have progressed from manual spreadsheets to ERP-enabled processes, from rule-based automation to advanced analytics, and more recently, to GenAI-powered assistance.

Each stage of this evolution has helped address a different finance challenge. Automation has standardized repetitive processes and reduced manual effort. Traditional AI has enabled finance functions to identify anomalies, classify transactions and improve forecasting. GenAI has expanded these capabilities by supporting accounting research, drafting disclosures and producing management commentary.

However, most AI deployments today remain focused on individual activities. A solution may identify an exception, draft a narrative or perform a reconciliation, but the surrounding investigation, resolution, approval and reporting activities continue to require coordination across multiple teams and systems.

As organizations face greater reporting complexity, compressed timelines and rising stakeholder expectations, a new paradigm has emerged: Agentic AI.

What makes Agentic AI different?

The distinction between traditional AI, GenAI and Agentic AI can be understood through the way each responds to a reporting issue.

• Traditional AI may identify an inconsistency in the reporting data.
• GenAI may summarize the inconsistency and draft an explanation.
• Agentic AI can potentially identify the issue, determine which reporting activities may be affected, retrieve supporting information, route the exception to the appropriate owner, monitor its resolution, refresh permitted reporting outputs following approval and retain evidence of the actions performed.

This ability to coordinate a sequence of activities is particularly relevant to accounting and reporting because the reporting lifecycle is highly interconnected. Data readiness affects reconciliations and close timelines. Adjustments affect financial statements, disclosures and management reporting. Accounting conclusions influence compliance assessments and stakeholder communication.

The real potential of Agentic AI therefore lies not in deploying one autonomous agent. It lies in creating a governed network of specialized agents that can exchange information and coordinate activities across the accounting and reporting journey.

Agentic AI across the reporting journey

Agentic AI can enable an integrated reporting journey — from data preparation and financial close to statements, disclosures, stakeholder reporting and assurance. Each agent performs a defined role, with governed outputs feeding subsequent stages while human judgment remains central to material decisions.

1. Preparing reporting-ready data

Reporting depends on complete, accurate and traceable data drawn from ERPs, subledgers, operational systems and supporting schedules. Data-readiness agents can retrieve and standardize information, map it to approved reporting hierarchies, reconcile it to source systems and preserve provenance.

When exceptions arise, agents can route them to process owners, obtain supporting information, monitor remediation and update the dataset after approval. The validated information then feeds reconciliation, close and reporting activities, allowing finance teams to focus on accounting and business implications.

2. Orchestrating the financial close

Close-orchestration agents can monitor entity submissions, journals, reconciliations and consolidation activities; identify bottlenecks; and maintain visibility over the reporting calendar. They can prevent dependent tasks from advancing when issues remain unresolved and initiate the next authorized step once review and approval are complete.

The goal is a transparent, intelligently coordinated close; not speed at the expense of control. Routine follow-ups can be automated while human attention is directed to exceptions, significant adjustments and unresolved matters.

3. Preparing financial statements and disclosures

Using approved trial balances and governed mappings, financial-reporting agents can assemble primary statements, supporting schedules and standalone or consolidated reporting packs.

Disclosure and accounting-policy agents can draw on controlled guidance, policies, contracts, prior reports and approved precedents to support technical analysis and draft disclosures.

Agents can extract contract terms, identify potential disclosure gaps, compare drafts with reporting checklists and prepare initial accounting policies or technical memoranda. Structured outputs can move between contract-review, technical-accounting and disclosure agents after appropriate review and approval.

Material classifications, estimates and accounting conclusions remain subject to professional judgment. Agentic AI organizes evidence, coordinates workflow and provides a traceable starting point for human review.

4. Connecting financial results with business performance

Commentary agents can analyze actual budget, forecast and operational-driver data to draft period-on-period, budget and forecast variance explanations, identify material movements and support root-cause and segment analysis.

Where evidence is incomplete, an agent can route the variance to the relevant finance or business owner and refresh the narrative after an approved response. This creates a controlled link between reported results and business drivers, enabling finance leaders to focus on whether the message is balanced, relevant and appropriate for its audience.

5. Ensuring consistency and compliance

Tie-out agents can verify relationships across trial balances, statements, disclosures, narratives, KPIs, management reports and external communications. When discrepancies arise, they can trace the source, identify affected outputs, route resolution and revalidate dependent reports after approval.

Differences may reflect purpose, scope or reporting basis rather than error, so human review remains necessary. In parallel, regulatory and compliance agents can monitor obligations and rule changes, complete disclosure checklists, perform gap analyses, map requirements to affected reports and escalate unresolved matters — supporting more continuous monitoring of consistency and compliance.

6. Producing stakeholder-specific reporting

Reporting agents can use a common, controlled financial dataset to prepare audience-specific outputs, including CFO and CEO briefings, board and audit committee packs, controller reviews, investor presentations and regulatory submissions. Approved templates and communication parameters tailor purpose, detail and presentation while keeping every output connected to the governed reporting foundation. This reduces duplication, version-control challenges and inconsistent messaging without forcing a one-size-fits-all report.

7. Embedding assurance throughout the journey

Assurance and controls can be embedded throughout the lifecycle rather than added at the end. Control agents can monitor journals and adjustments, test defined controls, identify unusual transactions or rule breaches, log exceptions, notify owners, track remediation and maintain evidence histories for traceability and audit readiness.

For reporting adjustments, agents can link supporting documentation and approvals, identify affected statements or disclosures and confirm that relevant tie-outs are repeated. This provides earlier visibility of risk and reduces late-cycle surprises.

Automated monitoring is not assurance in itself. Management and assurance professionals remain responsible for evaluating evidence, challenging conclusions and determining whether controls operated effectively and issues were appropriately resolved.

How should organizations get started with Agentic AI?

For many CFOs, the challenge is not in imagining a future powered by intelligent agents but in deciding where to begin. After all, even Tony Stark did not start with a fully capable J.A.R.V.I.S.; it evolved over time, taking on greater responsibilities as trust and capability increased.

Phase 1: Establish the foundations for an AI-ready finance function

The first step towards an autonomous finance function is creating a strong operational and data foundation. Agentic AI can only be effective when core finance processes, data structures and controls are standardized and reliable. CFOs should focus on harmonizing ERP platforms, improving master data quality, standardizing accounting processes and establishing Responsible AI governance. Rather than viewing Agentic AI as a technology deployment, leading organizations treat it as a finance transformation program.

Practical CFO actions:

Conduct an AI readiness assessment, establish a finance AI governance council, implement process mining, create enterprise data ownership models and define AI risk management policies.

Success metrics:

Finance data quality score, percentage of standardized processes, ERP harmonization levels, number of governance controls digitized and reduction in data-related reporting issues. This phase lays the foundation upon which all future AI agents will depend.

Phase 2: Deploy AI copilots to augment finance professionals

Before introducing autonomous agents, finance teams should become comfortable working alongside AI. AI copilots can assist with accounting research, disclosure drafting, management commentary, variance analysis and forecasting while ensuring professionals retain decision-making authority. The objective is to build trust in AI-generated outputs, improve productivity and create a culture that embraces AI-enabled ways of working.

Practical CFO actions:

Deploy finance copilots, train finance teams on AI usage, build approved accounting knowledge repositories, measure productivity improvements and define review controls for AI-generated content.

Success metrics:

Copilot adoption rates, reduction in manual research effort, time saved in reporting and disclosure preparation, employee satisfaction scores and percentage improvement in finance productivity. This stage enables CFOs to transition from experimentation to measurable AI-driven value creation.

Phase 3: Introduce domain-specific finance agents

Once confidence in AI is established, organizations can deploy specialized agents within high-volume finance processes. Examples include reconciliation agents, journal entry agents, close management agents and reporting agents. These agents operate under defined governance and focus on specific objectives such as exception resolution, validation and workflow monitoring.

Practical CFO actions:

Deploy finance copilots, train finance teams on AI usage, build approved accounting knowledge repositories, measure productivity improvements and define review controls for AI-generated content.

Success metrics:

Copilot adoption rates, reduction in manual research effort, time saved in reporting and disclosure preparation, employee satisfaction scores and percentage improvement in finance productivity. This stage enables CFOs to transition from experimentation to measurable AI-driven value creation.

Phase 4: Orchestrate multi-agent finance workflows

The real transformation occurs when agents collaborate across the end-to-end finance lifecycle. Reconciliation, compliance, accounting policy, control and reporting agents can work together to investigate anomalies, resolve issues, obtain approvals and update reporting outputs. Finance shifts from managing activities to managing exceptions.

Practical CFO actions:

Deploy an enterprise agent orchestration platform, define inter-agent communication protocols, establish materiality thresholds requiring human intervention and embed controls directly within AI workflows.

Success metrics:

Percentage of finance workflows executed autonomously, reduction in close duration, decrease in manual hand-offs, exception auto-resolution rates and improvements in workflow cycle times. This phase represents the transition from automation to true process autonomy.

Phase 5: Build the autonomous finance control tower

The final stage is to create a finance control tower powered by interconnected AI agents. This environment continuously monitors transactions, controls, compliance requirements and emerging risks while generating real-time insights for finance leadership. Controllers oversee exceptions while CFOs focus on strategic decisions supported by predictive intelligence.

Practical CFO actions:

Establish a finance AI control tower, implement continuous monitoring, deploy predictive risk agents, create real-time executive dashboards and redesign finance roles towards oversight and business partnering.

Success metrics:

Touchless transaction rates, continuous close maturity, forecast accuracy improvements, reduction in control failures, faster anomaly detection and percentage of decisions supported by AI-driven insights. At this stage, finance evolves into a self-monitoring, self-learning and strategically focused function.

A structured Agentic AI journey empowers finance organizations to transition from process execution to intelligent orchestration. By combining autonomous agents, trusted data and embedded controls, finance can operate as a continuously learning ecosystem that anticipates risks, accelerates decision-making and delivers real-time insights, allowing CFOs and Controllers to focus on stewardship, strategy and sustainable enterprise value.

How can accountants own AI

As Alvin Toffler famously observed, "The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn and relearn." Nowhere is this more relevant than in the age of AI. To truly harness the potential of Agentic AI, accountants must move beyond viewing AI as a tool and start embracing it as a digital colleague. The future finance professional will spend less time performing reconciliations, posting journal entries and compiling reports, and more time designing controls, governing AI agents, validating outputs and exercising professional judgment. Success will require a combination of accounting expertise, data literacy, AI fluency and a willingness to continuously adapt. Rather than being displaced by AI, accountants have a unique opportunity to own its deployment, ensuring that autonomous systems operate within a framework of trust, compliance and accountability. In the emerging finance function, the most valuable professionals will not be those who perform the work themselves, but those who can effectively orchestrate and supervise teams of intelligent agents to deliver better, faster and more reliable outcomes.

Just as Tony Stark had J.A.R.V.I.S., tomorrow's CFO will have a network of intelligent agents working seamlessly across the finance function. The real transformation, however, is not technological but human. Accountants will evolve from process executors to AI orchestrators, from checklist managers to judgment-driven supervisors, ensuring that autonomous systems remain accurate, compliant and trustworthy. The organizations that learn, unlearn and relearn fastest will redefine finance, creating a future where reporting is faster, smarter, more transparent and increasingly touchless, while human expertise remains at the heart of every critical decision.