Author: Jay Wang

  • How to Set Up Role-Based Access Controls for Multi-Entity Financial Reporting

    How to Set Up Role-Based Access Controls for Multi-Entity Financial Reporting

    Six entities. Three currencies. One consolidation spreadsheet. And a shared drive link half the company can open.

    Most finance controllers have lived this.

    The second your financial data leaves the accounting system and lands in Excel, the controls vanish. No role restrictions. No segregation of duties. No audit trail showing who changed cell F47 at 2 a.m. on the Sunday before the board pack went out.

    This is not a hypothetical. In 2024, 83% of organizations reported at least one insider attack, and financially motivated privilege misuse drove 89% of those cases (IBM, 2024; Verizon, 2025). Financial services pays the most, at $20.68 million a year in insider threat costs (Syteca, 2025).

    So if you run multi-entity consolidation through spreadsheets, the question is not whether ungoverned access creates risk. It is how long until that risk shows up.

    Setting up role-based access for financial reporting is not an IT project. It is a finance operations priority. Here is how to do it right.

    What RBAC Is, and Why Multi-Entity Teams Need It

    Role-based access control (RBAC) restricts system access by a person’s role, not person by person. In multi-entity reporting, it decides who can view, edit, approve, or export financial data for each entity in the group.

    Why does it matter more with multiple entities? Because every entity widens the attack surface. New ledgers. New bank accounts. New intercompany transactions. New users who need some level of access. Without RBAC, permissions sprawl.

    Teams that consolidate manually spend over 15 days on month-end close, and every manual handoff is a point where access governance breaks (Phoenix Strategy Group, n.d.).

    The goal is simple. Every person sees exactly what they need, does exactly what their role requires, and nothing more.

    Step 1: Inventory Your Entities, Users, and Data

    Before you configure anything, map where you stand. You need three lists.

    Entities and their data. Write down every legal entity, subsidiary, and branch that feeds consolidation. Note the jurisdiction, the currency, and any local rules. Singapore’s PDPA, for instance, carries penalties up to S$1 million or 10% of annual turnover, and enforcement looks hard at whether your controls match the sensitivity of the data (DPO Consulting, 2025). For multi-currency groups, currency jurisdiction adds one more layer to scope.

    Users and their jobs. List everyone who touches financial data. The FC running consolidation. The external auditor at year-end. Contractors. Board members with reporting access. Anyone in FP&A pulling data for the budget.

    Current access. For each system you run (Xero, QuickBooks, NetSuite, Excel, Google Sheets, reporting tools), write down who has access and at what level. This step is usually the most revealing. Most teams find permissions piled up over years with no cleanup.

    Step 2: Build Roles Around Function, Not People

    The most common RBAC mistake is one role per person. That creates “role explosion,” where you end up with so many roles that access reviews become impossible. Permify (n.d.) does the math: 50 job functions times 20 locations times 10 projects is 10,000 roles.

    Build roles around function instead:

    • Entity Preparer. Enters and edits transactions in one entity. Cannot approve journals or touch other entities.
    • Entity Reviewer. Views and comments on assigned entities. Cannot edit.
    • Consolidation Manager. Accesses all entities to consolidate. Runs eliminations and adjustments. Cannot change source entity data.
    • Approver. Approves journals, intercompany transactions, and consolidated reports. Cannot approve what they initiated.
    • Read-Only Analyst. Views reports and dashboards. No access to underlying transactions.
    • External Auditor. Read-only, time-limited, scoped to set entities and periods.

    Keep roles broad enough to avoid explosion, tight enough to mean something. For most mid-market multi-entity setups, 6 to 10 core roles is the sweet spot.

    Step 3: Bake Segregation of Duties Into the Roles

    Segregation of duties (SoD) means no one person controls every stage of a transaction. Whoever starts a payment should not be the one who approves it.

    In small teams, this is hard. A solo FC or a two-person team often initiates, approves, and records the same transaction. That is the exact setup SOX and audit best practice forbid (Numeric, n.d.).

    Build SoD into the framework, not into goodwill. Pathlock (n.d.), an ERP security specialist, recommends naming the “toxic combinations” of permissions and blocking them before they ever reach a user. The system should never let one role both “create vendor” and “approve payment” for the same entity.

    When the team is too small for clean separation, write down your compensating controls. Supervisor review above a threshold. Quarterly access certification. Automated alerts on odd patterns. These are weaker than real separation, but far better than nothing, and the auditor will ask.

    Step 4: Scope Permissions to the Entity

    This is where many cloud accounting tools fall short. QuickBooks lacks the enterprise audit trails and role controls that multi-entity governance needs (EagleRock CFO, n.d.). Xero has no native multi-entity consolidation, so you cannot scope access per entity inside the platform. Even NetSuite, which offers role-based dashboards, takes real configuration to enforce entity-level permissions across consolidated reporting.

    Your design has to account for this data governance gap. If your accounting platform cannot scope access by entity, you need an orchestration layer that sits above your source systems and governs the consolidated view. This is where governed data infrastructure earns its place.

    It is also where purpose-built multi-entity platforms pay off. Planir, for example, connects straight to your source accounting systems and enforces entity-level access inside its reporting and budgeting workflows. Every action is logged. Every output is auditable. The FC keeps approval authority over what gets consolidated. The spreadsheet gap closes, and the FC does not have to become a systems administrator to close it (Planir, 2026).

    Step 5: Put It All on One Access Dashboard

    The most stubborn problem in multi-entity access is the lack of a single view of who can see what. Picus Capital (n.d.) flagged this as a real hole in mid-market finance tooling: run a different accounting system per entity and there is no central way to audit permissions.

    Your dashboard should answer four questions at a glance:

    1. Who has access to each entity? By role, not just by name.
    2. What can they do? View, edit, approve, export.
    3. When was access last reviewed? Stale permissions are a top risk.
    4. What changed recently? New users, changed roles, escalated permissions.

    If your tools cannot produce this, build it by hand in a controlled document and review it quarterly at minimum. The average insider threat takes 81 days to detect and contain (Ponemon Institute, 2025). A quarterly review shrinks that window fast.

    Step 6: Automate the Reviews

    RBAC is not a one-time setup. Roles drift. People move. Entities get acquired or wound down. Skip recertification and your framework rots within months.

    Set a fixed cadence:

    • Monthly. Review new access grants and role changes.
    • Quarterly. Recertify every active role across every entity.
    • Annually. Audit role definitions, SoD rules, and compensating controls.

    Automate what you can. Flag dormant accounts (no login in 60 days). Alert on permission escalations. Generate auditor-ready access reports. The less manual work it takes to maintain RBAC, the more likely it gets maintained.

    Why This Is a Finance Problem, Not an IT One

    Multi-entity reporting without access controls is a liability dressed up as a workflow. Every shared spreadsheet, every ungoverned export, every role that mixes incompatible permissions is a line on a risk register no one wrote.

    The fix is structural, not cultural. Define roles by function. Enforce segregation of duties in the system. Scope permissions to the entity. Make access visible. Review it on a cadence that catches drift before it turns into a breach.

    55% of insider incidents come from negligence, not malice (StationX, 2025). The right RBAC framework turns that number from a threat into a problem you have already solved.

    References

    DPO Consulting. (2025). PDPA obligations Singapore. DPO Consulting. https://www.dpo-consulting.com/blog/pdpa-obligations-singapore

    EagleRock CFO. (n.d.). QuickBooks vs Xero vs NetSuite. EagleRock CFO. https://www.eaglerockcfo.com/blog/startup-financial-tools/quickbooks-vs-xero-vs-netsuite

    IBM. (2024). 83 percent of organizations reported insider threats in 2024. IBM Think. https://www.ibm.com/think/insights/83-percent-organizations-reported-insider-threats-2024

    Numeric. (n.d.). Segregation of duties in accounting. Numeric. https://www.numeric.io/blog/segregation-of-duties-accounting

    Pathlock. (n.d.). Role-based access control (RBAC). Pathlock. https://pathlock.com/blog/role-based-access-control-rbac/

    Permify. (n.d.). Role explosion. Permify. https://permify.co/post/role-explosion/

    Phoenix Strategy Group. (n.d.). Challenges of multi-entity reporting automation. Phoenix Strategy Group. https://www.phoenixstrategy.group/blog/challenges-multi-entity-reporting-automation

    Picus Capital. (n.d.). Beyond borders: The current problem with finance tools for multi-entity companies. Medium. https://picus-capital.medium.com/beyond-borders-the-current-problem-with-finance-tools-for-multi-entity-companies-5cfd2d2ad08d

    Planir. (2026). Planir: The complete FP&A platform for mid-market finance teams. Planir. https://planir.app/

    Ponemon Institute. (2025). 2025 cost of insider risks global report. Ponemon Institute.

    StationX. (2025). Insider threat statistics, facts, and figures. StationX. https://www.stationx.net/insider-threat-statistics/

    Syteca. (2025). Insider threat statistics, facts, and figures. Syteca. https://www.syteca.com/en/blog/insider-threat-statistics-facts-and-figures

    Verizon. (2025). 2025 data breach investigations report. Verizon.

  • 8 Best Financial Reporting Software in Australia for ASX Small-Caps (2026)

    8 Best Financial Reporting Software in Australia for ASX Small-Caps (2026)

    Why ASX Small-Caps Need Purpose-Built Financial Reporting Software

    Most ASX small-cap finance teams run lean, often just one to three people, yet face the same continuous disclosure obligations as companies with 50-person finance departments. Quarterly activity reports, half-yearly financials, Appendix 4C cash flow statements, and corporate governance disclosures all land on the same desk.

    The numbers tell the story. Only 18% of finance teams close their books in three days or less, while 50% need six or more business days (Ledge, 2025). Meanwhile, 94% of finance teams still use Excel in their close process, and half say it is the primary bottleneck (Ledge, 2025). For ASX-listed companies, spreadsheet errors in public filings carry regulatory risk and reputational damage that goes far beyond a bad month-end.

    The gap is clear: basic accounting software like Xero or MYOB handles day-to-day bookkeeping but falls short on the reporting depth ASX compliance demands. Enterprise platforms like Workiva or Anaplan deliver the depth but at price points that start in the thousands per month. ASX small-caps need financial reporting software in Australia that sits between these extremes.

    What Is the Real Cost of Manual Reporting for Small-Caps?

    Financial reporting inefficiency costs ASX small-caps far more than time. PrimaryMarkets (2024) highlights that ASX reporting obligations impose a disproportionate burden on small-cap companies. The cumulative cost of quarterly activity reports, audit and remuneration disclosures, and governance requirements strains boardrooms and limited resources. When your finance controller spends half the month closing the previous month, real-time visibility disappears, and agile decision-making becomes impossible.

    This matters more than ever in 2026. The ASX Small Ordinaries Index has declined roughly 12% year-to-date compared to approximately 3% for the All Ordinaries, putting small-caps under intense macro pressure (Australian Securities Exchange [ASX], 2026). Efficient financial operations are not a nice-to-have. They are survival infrastructure.

    Cross-departmental dependencies compound the problem. According to Ledge (2025), 56% of finance teams cite waiting on data from sales, HR, or operations as the primary obstacle to faster closes. When that data arrives late or in inconsistent formats, manual reconciliation across three to five systems becomes the single most time-consuming close activity. The shift from Excel to automated reporting addresses this bottleneck directly.

    What to Look for in Financial Reporting Software in Australia

    Before evaluating specific tools, ASX small-cap finance teams should prioritize five capabilities.

    ASX compliance support. The tool should handle or streamline Appendix 4C, 4D, and 4E reporting, not just produce generic P&L statements. With AASB 18 replacing AASB 101 for financial statement presentation from January 2027, your reporting system needs to adapt to new presentation requirements without a full rebuild (Australian Accounting Standards Board, 2024).

    Accounting platform integration. Seamless connection to Xero or MYOB is non-negotiable for Australian small-caps. Manual data exports and CSV uploads introduce errors and waste time. If you are running multi-entity structures, consolidation support is equally critical.

    Investor-grade analysis. Basic statutory reports satisfy the ASX, but boards and investors demand variance commentary, forward-looking projections, and driver-based analysis. Your tool should bridge that gap.

    Automation depth. Modern FP&A tools can automate up to 75% of routine closing tasks (Ledge, 2025). Look for automated reconciliation, consolidation, and report generation rather than just dashboards that still require manual data wrangling.

    Scalable pricing. The Australian reporting overlay market (Fathom, Spotlight, Calxa) runs at roughly A$35 per month per entity (Bentleys, 2024). Enterprise tools start at 10 to 50 times that. Find the right tier for your revenue and team size.

    The 8 Best Financial Reporting Tools for ASX Small-Caps in 2026

    1. Xero with Reporting Add-Ons

    Xero remains the dominant cloud accounting platform for Australian SMEs. On its own, Xero handles core bookkeeping, bank reconciliation, and basic financial statements. Its real power for ASX small-caps comes from its ecosystem. Paired with a reporting overlay like Fathom or Spotlight, it becomes a workable reporting stack.

    Best for: Companies already on Xero that need incremental reporting upgrades without a platform migration.

    Limitation: Xero does not natively produce ASX-compliant filings or investor-grade analysis. You will need at least one add-on.

    2. Fathom

    Fathom connects directly to Xero, MYOB, and QuickBooks Online to deliver management reporting, KPI tracking, and visual dashboards. It is one of the top three reporting apps recommended by Australian accounting firms (Bentleys, 2024) and has been in market for over a decade.

    Best for: Finance teams that need board-ready management reports and trend analysis without learning a complex platform.

    Limitation: Limited budget modeling and forecasting depth. It reports on what happened but does not help you plan what comes next.

    3. Spotlight Reporting

    Spotlight Reporting offers consolidated reporting, cash flow forecasting, and three-way financial modeling. It integrates with Xero and MYOB and is widely used by Australian accounting practices and their SME clients.

    Best for: Multi-entity ASX small-caps that need consolidation and cash flow forecasting at an affordable price point.

    Limitation: The interface can feel dated compared to newer platforms. Advanced scenario modeling is limited.

    4. Calxa

    Calxa focuses on budgeting, cash flow forecasting, and KPI reporting for Australian SMEs. It pulls data from Xero, MYOB, and QuickBooks and produces automated report packs on a schedule.

    Best for: Finance teams that want automated, scheduled report generation without manual intervention each month.

    Limitation: Primarily a budgeting and reporting tool. It does not handle ASX-specific filings or deep variance analysis.

    5. MYOB Advanced (Business)

    MYOB Advanced is MYOB’s cloud ERP offering for mid-market businesses. It includes more robust financial management, multi-entity support, and customizable reporting compared to MYOB’s SME products.

    Best for: Companies that have outgrown Xero or MYOB Essentials and need ERP-grade financial management without jumping to NetSuite or SAP.

    Limitation: Higher price point and implementation complexity. Reporting customization often requires consultant support.

    6. Workiva

    Workiva is a compliance and reporting platform used by listed companies globally, including Australian enterprises like Challenger and Coles (SatoriFP&A, 2025). It supports XBRL tagging, Appendix 4E preparation, and ESG reporting.

    Best for: ASX small-caps approaching mid-cap status that need a platform to grow into, particularly those preparing for AASB 18 and sustainability reporting requirements.

    Limitation: Enterprise pricing and complexity. This is overkill for most small-caps today but worth evaluating if you are scaling fast.

    7. Jirav

    Jirav is a cloud-based FP&A platform that integrates with Xero and QuickBooks for driver-based planning, workforce modeling, and scenario analysis. Its visual dashboards and collaborative planning features are designed for growing finance teams.

    Best for: Finance teams that need sophisticated forecasting and scenario planning without spreadsheet dependency.

    Limitation: U.S.-headquartered with less Australian-specific compliance awareness. ASX filing support is limited.

    8. Planir

    Planir takes a different approach to financial reporting software in Australia by deploying AI agents that connect to your accounting platform (Xero, MYOB, or ERP) and generate the financial core of board packs, investor updates, variance analyses, and budgets. Rather than building dashboards you still have to interpret, Planir agents produce draft financial sections with every assumption documented and reasoning visible. The finance controller reviews, overrides where business context dictates, and approves. It is designed for the gap between affordable but shallow overlay tools and unaffordable enterprise platforms, giving ASX small-caps investor-grade reporting depth with an auditable, governed data pipeline.

    Best for: ASX small-cap finance controllers who want AI-generated financial analysis and reporting they can review and approve, not more dashboards to build manually.

    How to Prepare for AASB 18 and Sustainability Reporting

    Two regulatory shifts should influence your financial reporting software decision in 2026.

    First, AASB 18 replaces AASB 101 for financial statement presentation, effective for reporting periods beginning on or after 1 January 2027 (Australian Accounting Standards Board, 2024). This changes how companies classify and present income and expenses. Any reporting tool with rigid, hardcoded statement formats will require significant rework. Prioritize tools that allow flexible report customization or that actively update their templates for new standards.

    Second, sustainability reporting requirements under Australia’s climate-related financial disclosure framework begin phasing in for Group 2 entities (those with 250 or more employees, $200 million or more in revenue, or $500 million or more in assets) from 1 July 2026 (Australian Treasury, 2025). Most ASX small-caps sit below these thresholds today, but the direction is clear. Choosing a platform with ESG reporting capabilities, or at least the architecture to support them, avoids a forced migration in two to three years.

    How to Choose the Right Financial Reporting Tool for Your Team

    The decision comes down to three questions.

    Where are you today? If you are on Xero with no reporting overlay, start with Fathom or Spotlight. The marginal improvement per dollar is enormous. If you already have an overlay but still spend a week on month-end, the problem is likely automation depth, not reporting format.

    What does your board actually need? If they want statutory compliance and basic KPIs, an overlay tool covers it. If they want investor-grade variance commentary, forward-looking projections, and scenario analysis, you need an FP&A platform or an AI-powered reporting tool like Planir that generates that analysis from your source data.

    What is coming? AASB 18, sustainability reporting, and the broader shift toward real-time financial visibility all point in the same direction: flexible, automated, and auditable. The tool you choose today should not need replacing in 18 months.

    The global FP&A software market is projected to grow from $5.82 billion in 2024 to $13.91 billion by 2033, reflecting a 10.2% compound annual growth rate (Verified Market Research, 2024). That growth is driven by exactly the pain points ASX small-caps face today: manual processes, fragmented systems, and compliance complexity that outstrips team capacity.

    The right financial reporting software in Australia will not just save your finance team hours each month. It will turn your reporting from a compliance obligation into a strategic asset, giving your board and investors the clarity they need while your finance controller focuses on judgment and narrative instead of data wrangling.

    References

    Australian Accounting Standards Board. (2024). AASB 18 Presentation and Disclosure in Financial Statements: Transition guidance for Australian reporters. AASB.

    Australian Securities Exchange. (2026). S&P/ASX Small Ordinaries Index year-to-date performance. ASX.

    Australian Treasury. (2025). Climate-related financial disclosure framework: Phased implementation timetable. Commonwealth of Australia.

    Bentleys. (2024). Australian reporting overlay market review: Cloud-based financial reporting tools for SMEs. Bentleys.

    Ledge. (2025). The 2025 financial close benchmark report. Ledge.

    PrimaryMarkets. (2024). ASX reporting obligations and the small-cap compliance burden. PrimaryMarkets.

    SatoriFP&A. (2025). Enterprise FP&A platform users in Australia: Workiva, Anaplan, and OneStream client landscape. SatoriFP&A.

    Verified Market Research. (2024). Global FP&A software market forecast 2024-2033. Verified Market Research.

  • The 8 Best Financial Reporting Tools for Growing Southeast Asian Companies (2026)

    The 8 Best Financial Reporting Tools for Growing Southeast Asian Companies (2026)

    It’s the Tuesday of close week. You’re in Singapore. Your Malaysia controller is in KL. Your Indonesia GM is somewhere between Jakarta and a flight delay. The consolidation file your team built in 2022 is open in seven different windows across three time zones.

    You’ve got one tab for the SGD entity. One for MYR. One for IDR. One for VND. One for the consolidated USD view. Three “FX adjustments” tabs that nobody can fully explain. A sheet called “DO NOT TOUCH – intercompany eliminations” that everyone touches anyway. And a board pack template that has not been refreshed since the last fundraise.

    The board meeting is Thursday. The numbers are tied.

    Maybe.

    Welcome to financial reporting in Southeast Asia.

    Let me say what every finance leader operating across ASEAN already knows but nobody puts in a vendor pitch deck. The tools sold to you as “regional ready” are usually neither. They were built for one jurisdiction (usually the US or UK) and bolted onto Singapore as a market expansion play. The Malaysia tax engine was added in v3. The Indonesia compliance pack is “on the roadmap.” The Vietnam entity? Hope you like manual journals.

    This is the actual market. Let me walk you through it.

    The numbers that should make every ASEAN CFO uncomfortable

    A few stats to set the scene.

    Only 18% of companies globally close their books in three days or less (Ventana Research, 2024). Growing companies running multiple ASEAN entities take far longer. Seven days is normal. Ten is common. I have seen fifteen.

    58% of APAC SMEs plan to upgrade their legacy financial systems within the next two years (ACCA, 2024). The APAC accounting software market is growing at 10.9% per year and will hit $8.6 billion by 2030 (Mordor Intelligence, 2025). That growth is not vendor PR. It is finance teams collectively hitting the wall.

    Nearly all multinationals report struggles with intercompany reconciliation (Deloitte, 2024). For a company with three entities and a five-person finance team, that “struggle” is the difference between closing on WD5 and closing on WD12.

    If your reporting stack is breaking, you are not behind. You are in the majority. The question is what to do about it.

    What “ASEAN ready” actually means

    Before I walk through the tools, let me list what your reporting platform actually has to do. Vendors will tell you all of these are “supported.” Most of them are not. Test before you buy.

    Multi-entity consolidation with intercompany eliminations. Not aggregation. Elimination. Your Singapore HQ books revenue from your Malaysia subsidiary. That intercompany line has to disappear in consolidation, not double up. You would be amazed how many tools quietly leave that to your finance controller.

    Multi-currency with rates that are not lying to you. SGD, MYR, IDR, VND, and USD all flowing through the same financials. Stale rates, rounding errors, and FX gain/loss treatments that do not match local GAAP will eat your margin in ways the board will absolutely notice.

    Jurisdiction-aware compliance. Singapore is on SFRS (broadly converged with IFRS). Malaysia uses MFRS. Indonesia uses SAK. Vietnam uses VAS. They are not the same. Over half of finance pros cite regulatory change as their single biggest operational headache (Thomson Reuters, 2024), and four overlapping standards is a special kind of headache.

    InvoiceNow readiness. If you have a Singapore entity, this is not optional. Phase one of the IRAS mandate begins April 2026 for new voluntary GST registrants and expands to cover all GST-registered businesses by 2031 (Inland Revenue Authority of Singapore [IRAS], 2025). The Singapore government has committed SGD 1 billion to digitization through SME Go Digital (Infocomm Media Development Authority [IMDA], 2025). Pick a tool that is on the right side of this. (And while you are at it, fix the gap between your accounting system and your board pack. That stack matters as much as the GL itself.)

    Investor-grade output without a manual rebuild. Board packs, variance commentary, KPI dashboards. Generated from your data, not from a weekend of copy-paste. If your CFO is rebuilding the same pack every month, you have already failed this test.

    OK. Tools.

    The eight tools, ranked the way I’d actually deploy them

    Standard SEO listicles do this in alphabetical order. I will do it the way an actual CFO thinks about it: from “single Singapore entity, just need better reports” up to “complex multi-jurisdiction enterprise.”

    1. Zoho Books and Zoho Finance Plus. Your starting point if you are early stage. Zoho opened a Singapore office, which is more commitment than most US-based vendors have shown. GST handled. InvoiceNow on the roadmap. Starts around USD 15 per organization per month, which is essentially free. The catch: multi-entity consolidation is basic. If you have three Zoho organizations stitched together, you are doing the consolidation by hand. Fine for a single entity under SGD 5M revenue. Stops scaling fast.

    2. Fathom (with Xero underneath). If you are already on Xero (and most Singapore SMEs are), Fathom is the easiest reporting upgrade in the market. USD 49 to 399 per month. Decent management reports. KPI dashboards that do not look like Excel from 2008. The limitation: Fathom is a reporting layer, not a planning tool, and it does not do real multi-entity consolidation. If you have one entity or two simple ones, Fathom is fine. If you are running Xero across three or more entities, the consolidation gymnastics get ugly fast.

    3. Xero with add-ons. Some companies are not ready to leave Xero entirely, and that is a defensible position. The pattern is: Xero for the GL, Fathom for reporting, Dext for documents, ApprovalMax for AP workflow. Modular. Familiar. (Same pattern works for QuickBooks teams using QuickBooks reporting add-ons.) The downside: every add-on adds an integration. Every integration adds a failure point. And you still do not have a single source of truth for consolidated reporting. Worth reading our take on what to do when you have outgrown Xero but your reporting has not kept up.

    4. Cube. For finance teams that live in Excel and refuse to leave. Cube puts a centralized data layer underneath your existing spreadsheets so your models keep working but the underlying data is governed and current. Multi-entity and multi-currency support included. Strong on budgeting, forecasting, variance analysis. (Our Cube review has the full breakdown.) Pricing starts around USD 30K per year, denominated in USD, which adds awkward FX exposure for SEA buyers. Limited regional presence. No SFRS-specific templates.

    5. Datarails. Same general thesis as Cube. Excel-native FP&A platform, consolidates data from your ERP, accounting software, and spreadsheets. Strong on variance analysis. (Datarails review, and Datarails alternatives if you have already evaluated it and want options.) Enterprise pricing (USD 25K+ per year). Limited SEA-specific compliance features. Buy if your bottleneck is Excel-based FP&A modeling, not jurisdictional compliance.

    6. Sage Intacct. This is the one most growing ASEAN companies should be looking at and are not. Purpose-built multi-entity consolidation. Dimensional reporting that does not require you to bloat your chart of accounts to track project, department, and entity. Automated intercompany eliminations that save days each month. Mid-market pricing starts around USD 20K per year. Less brand recognition in SEA than Xero or NetSuite, which is partly why it is underused. If you have three or more entities and you have outgrown Xero but find NetSuite excessive, this is the obvious step.

    7. NetSuite OneWorld. The incumbent for upper mid-market. 27 languages, 200+ tax jurisdictions, AWS data center in Singapore. If you are at 200+ employees and SGD 30M+ revenue with entities across four ASEAN countries plus probably a US or AU entity for good measure, NetSuite OneWorld earns its keep. The cost: USD 50K+ per year, 3 to 6 month implementation, dedicated admin staff required. And if you are already on NetSuite, you have probably noticed your finance controller still exports everything to Excel for reporting. The board pack pain does not go away just because you have an enterprise ERP. Worth reading how to actually automate the board pack from NetSuite before you blame the tool.

    8. Planir. Different model entirely. Instead of replacing your accounting system, Planir connects to it (Xero, QuickBooks, NetSuite, or your ERP) and deploys AI agents that draft the financial sections of your board pack, investor update, variance commentary, and budget directly from source data. The agents document every assumption. Every number traces back through an auditable pipeline. Your finance controller stops being the assembler and becomes the editor: review the draft, override where business context demands, approve, move on. The 69% of time finance teams currently spend on manual data aggregation (BlackLine, 2024) shifts to analysis and strategic narrative. (Planir vs Datarails for a direct comparison.) Best fit for growing ASEAN companies with two to five entities and SGD 5M to 50M revenue.

    How I’d actually choose

    Forget the feature matrix. Three questions.

    Where are you today? Single entity Singapore on Xero with no overlay? Add Fathom or move to Zoho. Marginal lift per dollar is enormous. Two to three entities and your consolidation file has crossed the “no one fully understands all the tabs” threshold? You have outgrown overlays. You need either Sage Intacct (traditional cloud financials) or Planir (AI-driven automation of the reporting workflow itself).

    What is your real bottleneck? Mechanical consolidation work? Sage Intacct or NetSuite OneWorld. Excel-based FP&A modeling that will not die? Cube or Datarails. Drafting the actual board pack and variance commentary every month? Planir. These are different problems with different fixes. Pick the one that matches your actual pain.

    What is coming in 18 months? InvoiceNow phase one in April 2026. Probably another entity in Vietnam or the Philippines. Possibly a Series B or trade sale. Pick a tool that does not need replacing the day after any of those happen.

    The bottom line

    Most “best ASEAN financial reporting software” lists are written by people who have never actually closed books across SGD, MYR, IDR, and VND in the same week. The actual job is not picking the tool with the most logos on its homepage. It is picking the tool that lets your finance team stop being the integration layer between systems and start being the analytical layer your board actually needs.

    Your finance controller did not join your company to spend 70% of the month on data aggregation. They joined to do the work only a human can do: judgment, narrative, the hard conversation about which entity to wind down.

    If your reporting stack is preventing that, fix it. The tool you pick matters less than the fact that you actually pick one.

    Everything else is dashboards.

     

    References

    ACCA. (2024). Digital transformation in APAC finance functions: 2024 survey report. https://www.accaglobal.com/gb/en/professional-insights/technology/digital-transformation-apac.html

    BlackLine. (2024). The state of modern finance: Closing the books in 2024. https://www.blackline.com/resources/state-of-modern-finance

    Deloitte. (2024). Global intercompany survey: Challenges and opportunities in cross-border transactions. https://www2.deloitte.com/global/en/pages/tax/articles/global-intercompany-survey.html

    Infocomm Media Development Authority. (2025). SMEs Go Digital programme. https://www.imda.gov.sg/how-we-can-help/smes-go-digital

    Inland Revenue Authority of Singapore. (2025). InvoiceNow implementation timeline for GST-registered businesses. https://www.iras.gov.sg/taxes/goods-services-tax/general-gst-schemes/invoicenow

    Mordor Intelligence. (2025). Asia-Pacific accounting software market size and share analysis: Growth trends and forecasts (2025-2030). https://www.mordorintelligence.com/industry-reports/asia-pacific-accounting-software-market

    Thomson Reuters. (2024). Cost of compliance report 2024. https://www.thomsonreuters.com/en/reports/cost-of-compliance.html

    Ventana Research. (2024). Benchmark study: The state of the financial close. https://www.ventanaresearch.com/benchmark/financial-close

  • NetSuite Reporting: Why Most FCs Still Export to Excel

    NetSuite Reporting: Why Most FCs Still Export to Excel

    It’s the Wednesday after WD3. You’ve walked past the FC’s desk for the third time today. She’s still got NetSuite open in one window, Excel in another, and a coffee that went cold around 4pm.

    You ask how it’s going.

    “Good, just rebuilding the OPEX pack.”

    Rebuilding.

    The pack she rebuilt last month. And the month before. And every month for the last four years. Since the day your business signed the NetSuite contract, in fact. The contract that came with a slide deck claiming “real-time financial visibility” and “single source of truth.”

    Lol.

    Let me tell you what’s actually happening on her screen.

    She runs the consolidated P&L. She hits Export. Excel throws a security warning about the XML Spreadsheet 2003 format, because it’s 2026 and NetSuite is still serving up a file format from when George W. Bush was in office. She clicks through. She reformats columns. She rebuilds subtotals. She pastes into last month’s pack template. She starts checking numbers.

    Then someone posts a late journal entry.

    She does it all again.

    This is not a workflow anyone designed. This is a workaround that calcified into process. And there is one of these in every NetSuite-implemented finance team I have ever seen.

    “Just train her on the report builder”

    Every CFO has, at some point, said the words: “Surely we can get this out of NetSuite directly?”

    You bring in the NetSuite admin. You bring in the implementation partner. You spend $40K on a “reporting optimization” engagement.

    Six weeks later you are back where you started. Why?

    Because the FCs aren’t dumb. They knew about the report builder. They knew about saved searches. They knew about Financial Report Builder. They tried all of it. They went back to Excel because the export-rebuild cycle is faster than fighting the native tools.

    That should make you uncomfortable. It means the problem is not your team.

    It is the tool.

    Why this is structural, not configuration

    NetSuite serves more than 40,000 customers across 219 countries, with the bulk of them sitting in the 100-249 employee range (Anchor Group, 2025). Mid-market.

    Mid-market FCs need very specific things from their reporting layer. Board packs. Lender covenant calcs. Departmental P&Ls with custom KPIs. Investor updates. Budget-vs-actual with commentary. The kind of output a board chair will actually look at.

    The native NetSuite report builder was not built for any of that.

    I’ll let NetSuite consultancy Coras Consulting (2025) say it for me: “the system cannot automatically generate a complete monthly reporting pack without external tools.”

    Translation: even the consultants who make a living configuring this thing admit it can’t do the job. (Same dynamic plays out across other ERPs and tools like QuickBooks, by the way. NetSuite is just where the problem is most expensive.)

    You can’t group accounts outside your COA structure without workarounds. You can’t blend financial and non-financial data in one view. You can’t produce presentation-quality formatting that won’t embarrass your CFO in front of the board. Want a Web Query connection to Excel or Power BI? Available for transactional data. Not available for financial reports. The single feature that would bridge NetSuite to a usable reporting front end is missing exactly where the problem lives.

    These aren’t bugs. These are architectural decisions baked in a decade ago.

    The real cost

    Let me put a number on this.

    Each export-rebuild cycle is 10-15 minutes per report. Sounds like nothing. Now count the reports in a typical month-end pack.

    Consolidated P&L. Balance sheet. Cash flow. Departmental breakdowns (multiply by however many cost centers you have). Budget-vs-actual at total and department level. KPI summary. Cash position. Headcount. Top customers. Top vendors. Aged debtors. Aged creditors.

    Twelve, fifteen, twenty reports. Each a 10-15 minute export-rebuild. Each vulnerable to version drift.

    Finance pros burn up to 25% of their time on manual data extraction and prep (Zone & Co, 2025), and half of finance teams take six business days or more to close their books (Ledge, 2025). A quarter of one of your most expensive employees is being torched on work a machine should be doing.

    Then the late journal lands and you do it all again.

    This is the real cost of manual reporting, and most CFOs run it without ever measuring it.

    The bit nobody talks about: governance

    Here’s the part that should give every CFO of an audited company a cold sweat.

    The moment a NetSuite report leaves the system as an export, it exits NetSuite’s security model. No access controls. No audit trail. No version log.

    Five stakeholders ask for the same report at five different times in the close cycle. They get five Excel files. Different cuts. Different timestamps. Different numbers.

    Which one did the board actually review? Which one did the auditor sign off on? Which one is “the truth”?

    I’ve sat in audit committee meetings where this exact question landed and nobody could answer it. Just a long silence and an FC suddenly searching her downloads folder.

    96% of FP&A teams use spreadsheets daily (Association for Financial Professionals [AFP], 2025), not because they prefer it, but because native ERP reporting doesn’t meet the bar. For a private company that’s a process problem. For a listed company, it’s a continuous disclosure problem dressed up as one.

    The “have you tried a saved search?” answer

    Whenever I raise NetSuite reporting limitations with an admin, the first response is always the same: “have you tried a saved search?”

    Yes. Everyone has tried a saved search. Saved searches are great for transactional data. They fall over fast on financial reporting.

    Period comparisons need scripting. Budget overlays need scripting. Hierarchical account groupings need scripting. Large datasets hit the 5,000-row CSV export ceiling or time out completely. Building a complete financial picture means stitching together multiple saved searches by hand. Which is just the spreadsheet problem in a NetSuite skin.

    The saved search is the ERP equivalent of “have you tried turning it off and on again.” Costs nothing to suggest. Almost never solves anything.

    “Well, finance is cross-functional”

    Yes. Finance is cross-functional. And 56% of teams cite cross-departmental dependencies as a close blocker (Ledge, 2025). Worth understanding how to structure dimensional variance analysis across departments to address this.

    But the real reason your OPEX variance commentary is so thin is not because Sales and Ops are slow. It’s because pulling a single coherent view across NetSuite GL, AP, AR, and the CRM module requires either custom development or external tools. Your FC has spent the close cycle being the integration layer herself.

    When “why did OPEX increase 12%” lands in the board meeting, the answer lives across three NetSuite modules and two spreadsheets that someone manually reconciled last Tuesday. By the time it’s pulled together, the conversation has moved on.

    What actually fixes this

    There are two real categories of fix. Stop wishing for a third.

    1) Live data layers. Tools like Zone & Co’s Solution 7 (and a handful of others) create a two-way live sync between NetSuite and Excel. Your FC keeps her spreadsheet workflow, but the data is live. No more export. No more rebuild. Refresh the file, the numbers are current. Cube and Planful do something similar with a more structured planning layer on top. These are not bad answers. They eliminate the mechanical work without forcing your team to learn a new front-end.

    2) AI agents that draft the work. Newer model. Tools like Planir connect to NetSuite, look at the data, and draft the financial sections of your board pack, variance analysis, and budget-vs-actual directly. The FC stops being the assembler and becomes the editor. She reviews the AI’s draft, overrides where business context demands it, and writes the strategic narrative on top. Every number traces back to source data through an auditable pipeline, which closes the governance gap that AI in financial reporting was supposed to introduce but actually fixes when implemented properly.

    The dividing line: live data layers automate the export. AI agents automate the analysis. Both beat what you have. The right one depends on whether your bottleneck is mechanical work or analytical capacity. (Reporting automation vs the Excel-first approach covers the trade-off in more depth.)

    What is not on this list: “more NetSuite training.” “A better admin.” “A new report bundle from your implementation partner.” “Move it to Power BI.” All of these have been tried. None of them have closed the gap.

    How to know if you have this problem

    Three questions. Be honest.

    How many hours per month does your FC spend on mechanical reporting work? Exporting, reformatting, reconciling, version-managing. Most CFOs underestimate by 50% because the work is distributed across many small tasks. Sit with your FC for one close and clock it.

    How many versions of the truth exist at any one time? Count the Excel files generated during your last close. If it’s more than one per report, you have a version-control problem that grows every cycle.

    What percentage of your close is automated? If it’s under 40%, you’re in the majority. You’re also leaving days of FC capacity on the table every month. The 18% of teams hitting a three-day close (Ledge, 2025) didn’t find a secret NetSuite setting. They layered something on top. Our guide on cutting your close from 6 days to 3 walks through the specifics.

    The bottom line

    NetSuite is a fine ERP. It is also a structurally limited reporting platform. Those two things are allowed to be true at the same time, and pretending otherwise has cost the mid-market a decade of finance team productivity.

    Telling your team to “use it better” is the same thing as telling them to drive nails with a screwdriver more efficiently. The tool is wrong for the job. They are doing what every other FC in your peer group is doing: routing around it.

    Your FC is not the problem. The export button is the problem.

    Stop treating it as the final step in your reporting workflow.

    Start treating it as the symptom of a workflow that needs fixing.

    Because the next time you walk past her desk at 9pm and she’s “just rebuilding the OPEX pack,” that one is on you.

    References

    Anchor Group. (2025). NetSuite customer base and market segmentation analysis. Anchor Group Consulting.

    Association for Financial Professionals. (2025). 2025 AFP FP&A benchmarking survey: Technology and spreadsheet usage. AFP.

    Coras Consulting. (2025). NetSuite financial reporting: Limitations and workarounds for mid-market finance teams. Coras Consulting.

    Ledge. (2025). The 2025 financial close benchmark report. Ledge.

    Zone & Co. (2025). The state of NetSuite reporting: Manual work in mid-market finance teams. Zone & Co.

  • The FC’s Guide to KPI Dashboards That Actually Get Used

    The FC’s Guide to KPI Dashboards That Actually Get Used

    You know that KPI dashboard your company spent six figures on?

    The one the implementation team built with 40 metrics, animated charts and a colour palette that matched the brand guidelines? The one that got a 30-minute standing ovation at the leadership offsite?

    Three months later, you are back in Excel.

    You are not alone, and it is not your fault. 78% of enterprises have at least one BI platform, but overall KPI dashboard adoption sits at around 20% (DataStackHub, 2025). Executive usage has climbed from 48% to 67% over two years, but the people who run day-to-day finance, the FCs and senior accountants, are still on spreadsheets. 100% of FP&A professionals continue to use spreadsheets for planning and reporting at least quarterly (AFP, 2025).

    Here is the short version: most dashboards fail because they track too many metrics, sit outside the FC’s daily workflow, and cannot answer “why”. Dashboards that get used keep visible KPIs at five or fewer, live inside existing tools, assign clear ownership for every metric, and surface action, not raw numbers.

    The problem is not the technology. The problem is that most dashboards are built for presentations, not decisions.

    Why Do Finance KPI Dashboards Fail? Five Root Causes

    KPI dashboard failure in finance is predictable. The same five patterns repeat across organisations of every size, and they have nothing to do with which BI vendor you picked.

    1. KPI Overload Paralyses Instead of Empowers

    The average financial KPI dashboard ships with 30 to 50 metrics on a single screen. Revenue, EBITDA, cash, AR aging, AP aging, headcount, burn, runway, gross margin, net margin, and on it goes. The Finance Weekly put it plainly: “when you give an executive 50 KPIs to track, you aren’t empowering them. You’re paralysing them” (The Finance Weekly, 2025).

    This is the paradox of choice applied to finance data. More metrics do not produce better decisions. They produce tab-switching back to the spreadsheet where you already know where everything lives.

    2. Dashboards Show What Happened, Never Why

    A KPI dashboard can tell you OPEX increased 12% month-over-month. It cannot tell you the increase came from three unplanned contractor hires in engineering, signed off by the CTO outside the normal requisition process, and that the variance self-corrects next quarter.

    That context lives in your head, in an email thread, and in the notes column of somebody’s spreadsheet.

    When the board asks “what is driving the OPEX increase?”, you do not open the dashboard. You open Excel. Every time.

    3. The Dashboard Lives Outside the Workflow

    This is the most underestimated failure mode of the lot.

    Most KPI dashboards live in standalone BI tools like Tableau or Power BI, fully outside the FC’s real working environment. Your finance team actually works in Excel, the ERP, and the accounting platform. So the dashboard becomes cosmetic. Something you update the night before a board meeting, not something you use on a Tuesday afternoon.

    Gartner reported that over 60% of organisations now embed analytics directly into business applications rather than maintaining standalone dashboards (Gartner, 2025). The direction is clear. If the insight does not appear where the work happens, it does not get used.

    4. Nobody Trusts the Numbers

    Only 39% of organisations report high confidence in their BI data quality (DataStackHub, 2025). When the dashboard says one revenue number and the ERP says another, the FC trusts the ERP. Every time.

    Conflicting numbers across systems do not just reduce usage. They actively destroy it. When a meeting turns into a 20-minute debate about whose number is right, the dashboard has failed its only job.

    5. No Owner, No Accountability

    A KPI without an owner is a number without a purpose.

    When no single person is responsible for interpreting a metric, communicating what it means, and acting on it, dashboards become what Randstad’s research team calls “digital wallpaper” (Randstad, 2026). They exist. They update. Nobody looks at them.

    Five FC Dashboard Best Practices That Actually Drive Daily Adoption

    Building a KPI dashboard that survives first contact with reality needs a different design philosophy. These five principles separate the dashboards that get opened every morning from the ones that get abandoned by month three.

    Principle 1: Cap Visible KPIs at Five

    Randstad’s dashboard research recommends limiting executive-level views to three to five high-level KPIs, in three layers. Headline metrics at the top. Contextual drivers one click deeper. Full detail available on demand. A consistent visual language across those layers cuts time spent on report analysis by up to 61% (IBCS, as cited in Randstad, 2026).

    For an FC at a growing SME, those five headline KPIs might be:

    • Cash runway (months at current burn)
    • Revenue vs. forecast (current month, with variance)
    • OPEX vs. budget (current month, with top three drivers)
    • AR aging (overdue balance and trend)
    • Month-end close progress (days elapsed vs. target)

    Everything else belongs in the second or third layer. Gross margin by product line should be available. It should not compete for attention with cash runway.

    Principle 2: Put a Name Next to Every KPI

    Each of those five KPIs needs an owner. Not a department. A person.

    That person is responsible for three things: interpreting the number in context, communicating what it means to stakeholders, and flagging when action is needed. Capitalize Analytics made the same point: “clear ownership and data discipline matter more than features” (Capitalize Analytics, 2026).

    In practice, the FC owns the overall dashboard and delegates individual metrics. AR aging sits with the credit controller. OPEX variance sits with the management accountant. The FC reviews the whole picture, and each owner is accountable for their number being accurate, current, and explained.

    No owner, no number.

    Principle 3: Embed Analytics Where the Work Happens

    Stop asking your team to leave Excel or the ERP to check a dashboard. The data needs to surface inside the tools they already use.

    This does not mean building heroic integrations from scratch. It means choosing platforms that push insights into existing workflows rather than pulling users into a separate screen.

    58% of finance leaders still use spreadsheets as their primary tool, and 26% use no automation at all (The CFO, 2024). Fighting that reality is futile. Working with it is strategic.

    Principle 4: Show the “Why”, Not Just the “What”

    Every metric should be one click away from its variance explanation.

    Revenue down 8%? The dashboard should already surface the three biggest contributing factors. Delayed contract. Seasonal pattern. Lost customer. Then the FC adds the strategic context that only the FC has. The analytical groundwork should be done before you open the file.

    This is where AI-powered platforms are changing the game. Instead of an analyst manually stitching together a variance bridge in Excel, agent-based systems generate driver analysis automatically from source data. Planir uses AI agents to build financial reports and variance commentary directly from connected accounting data. The FC reviews the reasoning, overrides where business judgement dictates, and focuses on the narrative rather than the number-crunching.

    If you are evaluating dashboard tools, prioritise the ones that automate the “why”, not just the “what”.

    Principle 5: Build the Dashboard With the FC, Not For the FC

    Dashboards designed by BI teams or external consultants without FC involvement end up tracking metrics the FC does not care about, in formats that do not match how an FC thinks.

    The Personiv controller dashboard guide made the point that there is no universal KPI set for controllers, because the FC role varies dramatically based on organisational stage and industry (Personiv, 2025).

    The fix is simple and rarely followed. Sit with the FC for an hour before building anything. Ask what three questions they need answered every Monday morning. Build the dashboard around those questions. Iterate weekly for the first month.

    A dashboard built with the user gets used by the user. Every time.

    The Failure Mode Everybody Forgets: Scenario Planning

    There is one more failure mode worth calling out on its own, because it explains why dashboards get abandoned at exactly the moment they should matter most.

    KPI dashboards cannot run scenarios.

    When the board asks “what happens to runway if revenue drops 15%?”, you cannot answer that from a dashboard. You go back to Excel, rebuild the model, stress-test the assumptions, and come back two days later. The Finance Weekly observed that dashboards are unused “during critical moments, exactly when finance insight is needed most” (The Finance Weekly, 2025).

    This is not a minor gap. Scenario modelling is the highest-value activity an FC performs, and it is the one activity traditional dashboards cannot support. Any dashboard strategy that ignores this is a tool for calm days, and a betrayal on the days that actually count.

    The answer is not to bolt scenario capability onto your reporting dashboard. It is to make sure your planning stack, whether that is a dedicated FP&A tool, an AI agent platform like Planir, or a well-structured spreadsheet model, is tightly connected to your dashboard layer so that actuals and projections live in the same ecosystem.

    How to Roll Out a Financial KPI Dashboard That Gets Used

    If you are building or rebuilding a dashboard that your team will actually open, here is a sequence that works.

    1. Audit the current state. List every metric currently tracked. Identify which ones drove a decision in the last 90 days. The rest are candidates for removal or demotion to a detail layer.
    2. Pick five headline KPIs. Choose metrics that reflect your organisation’s current priorities, not a generic best-practice list. A pre-revenue startup and a profitable 200-person company need different dashboards.
    3. Assign owners. Put a name against every KPI. Define what ownership means: accuracy, interpretation, escalation.
    4. Choose tools that meet you where you work. Embedded analytics in the ERP, an AI agent platform that pushes insights into your existing workflow, or a well-configured Power BI instance your team actually opens. The tool matters less than the workflow fit.
    5. Iterate in public. Share the dashboard with stakeholders in week one, not month three. Collect feedback. Adjust. A dashboard is a living product, not a project with a launch date.

    The Real Reason Dashboards Reduce Reporting Time

    KPI dashboard adoption is not a technology problem. It’s a trust problem.

    FCs use a dashboard when it shows the right metrics, explains why those metrics moved, lives where they already work, and can be trusted to match the source system. Everything else is decoration.

    The shift from manual reporting to automated, insight-driven dashboards typically reduces reporting time by 60 to 70% (Phoenix Strategy Group, 2025). But that reduction only materialises if the dashboard is actually used.

    Start with five KPIs. Assign owners. Embed the analytics in your workflow. Build the “why” into every number.

    That is how you build a dashboard that survives past the first board meeting.

    References

    AFP. (2025). AFP FP&A benchmarking survey. Association for Financial Professionals. https://www.afponline.org

    Capitalize Analytics. (2026). Data discipline over dashboard features: A 2026 analytics report. Capitalize Analytics. https://www.capitalizeanalytics.com

    DataStackHub. (2025). The state of business intelligence adoption: 2025 enterprise benchmark. DataStackHub. https://www.datastackhub.com

    Gartner. (2025). Embedded analytics and the future of BI. Gartner, Inc. https://www.gartner.com

    Personiv. (2025). The controller dashboard guide: Metrics that matter for modern FCs. Personiv. https://insights.personiv.com

    Phoenix Strategy Group. (2025). The financial reporting automation report. Phoenix Strategy Group. https://www.phoenixstrategy.com

    Randstad. (2026). Dashboard design principles for finance leaders. Randstad Research. https://www.randstad.com

    The CFO. (2024). The state of finance automation: Spreadsheets, systems and the gap in between. The CFO. https://the-cfo.io

    The Finance Weekly. (2025). Why executive KPI dashboards fail (and how to fix them). The Finance Weekly. https://www.thefinanceweekly.com

  • How AI Agents Actually Build a 3-Way Budget (Without the Spreadsheet Nonsense)

    How AI Agents Actually Build a 3-Way Budget (Without the Spreadsheet Nonsense)

    Most finance teams don’t budget the balance sheet or cash flow. Not really. They budget the P&L, stick a balance sheet together at month 3, and pray the cash line doesn’t embarrass anyone at the next board meeting.

    If that sounds harsh, it’s because I’ve lived it. And if you’re a finance controller at a growing SME, so have you.

    Here’s the short version of what follows: AI agents can now build and maintain a live 3-way budget where every assumption flows through the P&L, balance sheet and cash flow at the same time. No circular references. No plug cells. No “I’ll send you the updated version by Friday” emails. You get an auditable, integrated budget in minutes, not weeks.

    Let’s talk about why that matters.

    The Dirty Secret of SME Budgeting

    Most companies only budget the P&L. The balance sheet and cash flow get cobbled together offline, usually by one or two people in finance, disconnected from the revenue and cost assumptions that drive them (ACGI, 2024).

    The result is a budget that answers “Will we be profitable?” but never “Will we have the cash to fund it?”

    For a finance controller at a growing SME, that gap is not theoretical. It’s the difference between confidently telling the board your expansion plan is fundable, and discovering three months in that your working capital cycle cannot support the growth you just committed to.

    The integrated 3-way budget fixes this. P&L flows into the balance sheet. Balance sheet flows into cash flow. Everything ties. Every FC knows this is the right way. Nobody has the time.

    Why Excel Keeps Losing This Fight

    Between 88% and 94% of spreadsheets contain formula errors (Panko, 2008; Poon, 2024). In a single-tab model, one error affects one output. In a 3-way integrated model, one broken link cascades silently across all three statements.

    The root cause is structural. A proper 3-way budget has circular dependencies baked in. Interest expense depends on debt balance. Debt balance depends on cash. Cash depends on net income. Net income depends on interest expense. Welcome to the circle.

    In Excel, you have two choices: enable iterative calculations (fragile), or build manual plug cells with convergence checks (ugly). Either way, the model is one “insert row” away from death.

    Every FC knows the workflow. The model works. Somebody adds a row. A named range breaks. Consolidation overwrites a formula. You spend the next day debugging and never really trust it was fixed.

    And that’s just the mechanical risk. The deeper problem is assumption transparency. Your revenue growth rate, your DSO assumption, your capex phasing. All of it lives in individual cells. Undocumented. Invisible to anyone reviewing the model. When the CEO asks “What happens if revenue grows at 8% instead of 12%?”, you don’t answer. You rebuild.

    This is why finance controllers are moving off Excel for budget construction.

    How an AI Agent Solves the Circular Reference Problem

    An AI agent does not think about financial models the way a spreadsheet does.

    It doesn’t store formulas in a grid of cells. It maintains a structured model where the relationships between statements are defined as rules. The agent resolves those rules programmatically, in the correct order, and iterates where it needs to. No circular reference prompts. No iteration settings to fiddle with.

    Here is what the workflow actually looks like when an agent builds a 3-way budget.

    Step 1: Pull in the actuals and the assumptions

    The agent plugs into your accounting system (Xero, QuickBooks, NetSuite, pick your poison) and pulls the historicals. Then it takes your assumptions: revenue growth, hiring plan, payment terms, capex phasing. Each one is stored as a named, documented parameter. Not a cell reference. Not a colour-coded cell with a comment from three FCs ago.

    Step 2: Build the P&L from drivers

    Revenue grows off a rate. COGS falls out of margin. Headcount cost follows the hiring plan. Every line traces back to a specific, visible driver you defined.

    Step 3: Let the balance sheet fall out of the P&L

    This is where the agent earns its keep. Receivables come from revenue and DSO. Payables come from COGS and DPO. Inventory from inventory days. Capex feeds fixed assets, offset by depreciation. Debt drawdowns and repayments follow your financing plan. The agent sequences the dependencies the right way round. No circular references, because the agent understands the dependency graph.

    Step 4: Derive cash flow from balance sheet movements

    The cash flow statement is not typed in. It’s computed. Operating cash flow from net income plus non-cash adjustments plus working capital movements. Investing cash flow from capex. Financing cash flow from debt and equity. Closing cash feeds back into the balance sheet. If interest expense depends on average debt, the agent iterates until it converges. In milliseconds.

    Step 5: Hand it back to the FC

    You get a complete 3-way budget. Every assumption documented. Every linkage intact. Ask “What if revenue grows 8% instead of 12%?” and you get the answer in seconds, not a rebuild.

    Budgeting shifts from a build-from-scratch job to a review-and-approve job. That is the real change.

    What This Does to the FC’s Week

    This is not about taking the FC out of the process. It’s about changing what the FC spends time on.

    In a spreadsheet workflow, you are the architect and the builder. You design the logic. You write the formulas. You test the linkages. Then, if there is any time left, you analyse the output and write the narrative.

    Most FCs I know spend 80% of budget season on construction and 20% on judgement. That ratio is upside down.

    With an AI budget agent, you define the assumptions and review the output. The agent handles the build, the linkage integrity, and the mechanical flow through all three statements. Your time shifts to where it should have been all along. Are these assumptions reasonable? Does the cash position support the plan? What do I tell the board?

    This is also why the trust question gets answered on the way. Survey data shows 70% of FP&A professionals trust AI only for low-risk tasks, and just 3% trust AI outputs near-completely (Drivetrain, 2025). The agent model works because it does not ask you to trust a black box. Every number traces back to a specific assumption and a specific rule. You audit the reasoning, not just the result.

    Why Transparency Is the Whole Ball Game

    FP&A Trends called 2024 the year of AI hype and 2025 the year of AI noise, with generic AI tools falling short on consistent financial workflows (FP&A Trends, 2025). The teams making actual progress shared one thing. They treated AI as a workflow participant with visible reasoning, not an oracle.

    For 3-way budget AI, that transparency has three features.

    Every number traces to a source. Revenue in the P&L connects to a growth assumption. Receivables on the balance sheet connect to that revenue and a DSO assumption. Collections in the cash flow connect to the receivables movement. You can follow any number back to its origin in one click.

    Assumptions are first-class objects. They are not buried in cell B47 of the “Inputs v3 FINAL_FINAL” tab. They are named, documented and changed in one place, with impact flowing through all three statements automatically.

    Every change is logged. When you override an assumption, the change is recorded. The board pack shows the agent’s construction and your judgement on top of it. Both layers are visible.

    This is the difference between “the AI gave me a number” and “the agent built a model using these specific assumptions, and here is exactly how each number was derived.”

    The first erodes trust. The second builds it.

    Where the Market Is Actually Heading

    The adoption curve is steep but uneven. KPMG found that 78% of US companies are piloting or using AI for financial planning, higher than any other finance function (KPMG, 2024). But only 12% of finance teams are actively using AI tools today. 63% are still in evaluation or planning (Cube Software, 2025).

    The gap between pilot and production is mostly a data problem. Inconsistent definitions. No agreed source of truth. Poor system integration. The 3-way budget is the perfect example. You cannot automate P&L-to-balance-sheet linkages if your revenue data lives in a CRM, your cost data lives in an ERP, and your capex approvals live in a spreadsheet that gets emailed around every Tuesday.

    For SMEs, the opportunity is meaningful. Singapore’s IMDA reported that AI adoption among SMEs tripled in one year, from 4.2% in 2023 to 14.5% in 2024, with companies using AI-enabled solutions achieving average cost savings of 52% (IMDA, 2024). The pattern is consistent globally. Once you solve the data connection problem, automating structured financial workflows like 3-way budgeting pays back quickly.

    Where Planir Fits

    Planir is an AI-powered financial intelligence platform built for this exact job. It uses purpose-built agents to construct integrated P&L, balance sheet and cash flow budgets directly from source accounting data.

    It connects to systems like Xero and NetSuite. The Budget Agent builds a full 3-way model with every assumption documented and every linkage maintained programmatically. You review, override where your business context tells you to, and approve.

    The agents handle the construction. You own the judgement and the narrative.

    The Takeaway

    The 3-way budget is not a new idea. Every FC knows it’s the right way to plan. The problem has always been that building and maintaining one in spreadsheets costs more time than a growing finance team can spare, especially when the model has to change every quarter.

    3-way budget AI does not replace your expertise in how these models work. It replaces the mechanical work. The formula chains. The circular reference management. The assumption documentation. The scenario rebuilding. What’s left is the work that actually needs a finance controller. Reviewing the numbers. Applying business context. Telling the board what it means.

    This is not a future state. The tools exist today.

    The only question is whether your next budget cycle starts with a blank spreadsheet or a connected agent.

    References

    ACGI. (2024). Why most companies fail at integrated financial planning. ACGI Software. https://www.acgisoftware.com

    Cube Software. (2025). The state of AI in FP&A: 2025 benchmark report. https://www.cubesoftware.com

    Drivetrain. (2025). 2025 FP&A benchmark report: AI adoption in financial planning. https://www.drivetrain.ai

    FP&A Trends. (2025). AI in FP&A: Lessons from the hype cycle. FP&A Trends Group. https://fpatrends.com

    IMDA. (2024). Annual survey on infocomm usage by enterprises. Infocomm Media Development Authority of Singapore. https://www.imda.gov.sg

    KPMG. (2024). Global AI in finance report 2024. KPMG International. https://kpmg.com/xx/en/home/insights/2024/ai-in-finance.html

    Panko, R. R. (2008). What we know about spreadsheet errors. Journal of End User Computing’s Special Issue on Scaling Up End User Development, 10(2), 15-21. https://doi.org/10.4018/joeuc.1998040102

    Poon, P.-L. (2024). Spreadsheet errors in practice: An updated analysis. Journal of Organizational and End User Computing, 36(1), 1-18. https://doi.org/10.4018/JOEUC

  • Automation Didn’t Replace Accountants. It Changed What They’re Valued For.

    Automation Didn’t Replace Accountants. It Changed What They’re Valued For.

    Compliance Is Necessary, but No Longer Sufficient. Why many accountants feel busy, but constrained

    Most accountants did not enter the profession to simply produce reports. They did it because they wanted to bring order to complexity, help businesses make sense of their numbers, and support better decisions. Over time, however, much of that work has settled into a familiar rhythm: closing the books, reconciling accounts, preparing reports, and moving on to the next deadline.

    That rhythm exists for good reason. Compliance work is critical. It creates trust in financial information and forms the foundation of every credible advisory conversation. Clients depend on it, regulators require it, and firms take pride in doing it well. Yet for many finance professionals, there is a growing sense that this work, while essential, no longer reflects the full value they bring to their clients.

    As accounting has moved toward cloud accounting applications and more connected systems, expectations have shifted alongside it. Clients now have access to real-time data through bookkeeping cloud software and modern business accounting software, but access alone does not create understanding. What they increasingly look to their accountants for is interpretation, context, and guidance on what the numbers mean for future decisions.

    This shift is reflected across the industry. Research shows that accounting firms are moving away from a purely compliance-led model toward strategic advisory services, driven by client demand for empathetic, insight-led support rather than transactional outputs (Diaz, 2025). In this environment, compliance remains the baseline, but advisory becomes the differentiator.

    At the same time, the structure of the traditional business model for accounting firms naturally limits how value can be delivered and scaled. Compliance services tend to be concentrated around statutory and reporting cycles, which makes revenue seasonal and client engagement intermittent rather than continuous (Kelleher, 2025). This results in accounting firms staying stuck in a cycle of trying to improve their margins while remaining in a loop juggling rising workloads and client expectations (Kelleher, 2025).

    For accountants, this tension often shows up without them even realizing. The data is there. The understanding is there. Increasingly, firms are adopting AI for accounting and use of AI accounting software to reduce manual effort. Yet for some, insight still arrives late, conversations still happen after the fact, and opportunities to influence decisions pass without being surfaced in time. This begs the question of where the issue stems from?

    Well, compliance, by design, looks backward. Advisory looks forward. The challenge facing the profession is not whether compliance is still important, but whether it can continue to carry the weight of modern client expectations on its own. Increasingly, it is shown that it is not possible.

    Advisory Was Always the Destination

    Long before phrases like ai for accounting or accounting automation applications entered everyday conversation, advisory work was already part of the profession. It simply showed up inconsistently. Often it lived in conversations after meetings, in margin notes on reports, or in phone calls prompted by a client’s sudden concern. Advisory existed, but it depended heavily on individual experience and availability rather than structure.

    In practical terms, advisory is the work of helping a client understand what their numbers mean and what they should do next. It is not about producing more reports or adopting a consulting label. It shows up when clients ask why profit has changed despite steady revenue, where cash is being absorbed even though the business is growing, or which costs are starting to constrain performance. In those moments, the accountant moves beyond recording outcomes and into interpretation, trade-offs, and direction. That is advisory: translating financial information into implications for decisions, timing, and action.

    For many accountants, this is familiar territory. They understand their clients’ businesses deeply. They know which numbers matter, where risks tend to emerge, and how small operational changes can have outsized financial impact. What has historically been missing is not insight, but the ability to deliver it reliably and at scale.

    Research supports this view. As automation takes over repetitive and procedural tasks, the accountant’s role naturally shifts toward judgment, interpretation, and strategic thinking rather than execution (Murray, 2025). In this sense, advisory is not a new direction for the profession, but a re-emergence of its most valuable contribution.

    Productivity gains in finance do not primarily come from doing the same work faster, but from reallocating professional time toward higher-order activities such as explaining variance, evaluating trade-offs, and supporting decision-making (Church, 2025). This aligns closely with how advisory work has always functioned at its best: grounded in context, interpretation, and trust.

    The challenge, historically, was that advisory could not scale. It relied on senior professionals, manual analysis, and after-the-fact reflection. Even as cloud accounting applications and modern business accounting software improved access to data, turning that data into timely insight remained labour-intensive. Advisory conversations happened when time allowed, not when they were most needed.

    What is changing now is not the nature of advisory itself, but the conditions around it. As bookkeeping cloud software and ai accounting software reduces effort required to produce accurate numbers, they create space for thinking accountants have always been capable of, but rarely had time to deliver consistently.

    Advisory, in other words, was never an add-on. It was always the destination. Automation is simply making it reachable.

    Why Advisory Has Been Difficult to Deliver Consistently

    For many accountants, advisory has never been absent. It has simply been uneven. These moments often surface in conversations about falling profit, tightening cash, or margins that no longer behave as expected signals that advisory work is already taking place, just without the structure to make it consistent.

    It appears in moments of reflection, in conversations sparked by concern, or in recommendations offered once the numbers are already final. The challenge has never been knowing what to say. It has been finding the time and structure to say it when it matters most.

    Historically, the bulk of professional effort has been absorbed by the mechanics of producing reliable financial information. Before the rise of cloud accounting applications and integrated systems, even basic reporting required extensive manual work. Reconciliation, validation, and formatting were not peripheral tasks; they were the work itself. Advisory thinking had to be layered on top of this workload rather than embedded within it.

    Finance teams are often overwhelmed not by a lack of data, but by the effort required to prepare, integrate, and analyze it in a meaningful way (Harvard Business Review Analytic Services, 2021). When large portions of time are spent assembling information, little capacity remains for interpretation or forward-looking analysis.

    This structural imbalance affects how advisory shows up in practice. Insights tend to emerge after reporting cycles close, when outcomes are already locked in. As a result, advisory becomes explanatory rather than preventative. It helps clients understand what happened but rarely shapes what happens next.

    Even as accounting automation applications and ai for accounting tools began to reduce manual effort, many firms experienced efficiency gains without a corresponding shift in how insight was delivered. Automation made reporting faster, but it did not automatically make it more strategic. Without systems designed to surface patterns, explain drivers, and prompt timely questions, advisory remained dependent on individual review and professional intuition.

    Many organizations adopt automation to accelerate existing processes but fail to redesign workflows around decision-making itself (Sukharevsky et al., 2025). In those cases, technology improves speed without changing outcomes. Advisory remains possible, but not predictable or scalable.

    For smaller firms and lean finance teams, this challenge is even more pronounced. Advisory often relies on the attention of senior professionals who already carry significant client and compliance responsibilities. That makes advisory valuable, but scarce. It happens when time allows, not when conditions demand it.

    Seen this way, advisory did not struggle because accountants resisted it or lacked the necessary skills. It struggled because the infrastructure of the work was never built to support it consistently. Until insight could be generated continuously and communicated clearly, advisory would remain episodic by design.

    Why Automation Changed How Finance Teams Work

    When automation first entered mainstream accounting workflows, its promise was largely framed in terms of efficiency. Faster closes. Fewer manual reconciliations. Reduced errors. For many firms, these gains were real and welcome. But efficiency alone did not fundamentally change how finance teams contributed to decision-making.

    What has become clearer over time is that automation only becomes transformative when it changes the shape of the work, not just the speed of it.

    How finance teams are currently using AI shows that many organizations initially deploy automation to accelerate existing processes rather than redesign them (Sukharevsky et al., 2025). In those cases, reporting happens faster, but insight still follows the same cadence. Decisions are informed more quickly, but not necessarily earlier. The workflow improves, yet the outcome remains largely unchanged.

    The structural breakpoint occurs when automation is applied upstream of analysis, not downstream of reporting. Instead of treating automation to finish reports sooner, leading finance teams use it to continuously surface signals, explain drivers, and highlight emerging risks as they develop. This shifts finance from a periodic reporting function to an ongoing interpretive role.

    The most significant productivity gains come not from compressing existing tasks, but from reallocating professional effort toward sense-making, explanation, and judgment (Church, 2025). In practice, this means less time spent assembling information and more time spent interpreting what that information implies.

    This is where ai for accounting and modern accounting automation applications begin to matter in a deeper way. When automation handles classification, aggregation, and basic variance detection, it removes the need for accountants to search for issues manually. Instead, attention can shift toward understanding why something changed and what should be done next.

    Crucially, this does not eliminate the human role. It sharpens it. Automation creates the conditions for advisory by ensuring that insight is available early, consistently, and in a form that invites interpretation. Judgment, context, and accountability remain firmly human responsibilities.

    In this sense, automation is not the end of compliance work. It is the moment when compliance stops consuming most of the professional attention. Once that constraint is lifted, advisory no longer depends on individual heroics or spare capacity. It becomes a predictable, repeatable part of how finance teams operate.

    From Outputs to Outcomes: How Advisory Work Changes

    Once automation begins to change how accounting work is organized, the nature of advisory shifts with it. The most visible change is not in the tools being used, but in what finance teams spend their time discussing.

    In a compliance-led model, the primary output is a report. Conversations tend to revolve around what happened during a period and whether results aligned with expectations. These discussions are valuable, but they are inherently retrospective. Insight arrives after performance is already locked in.

    As automation reduces the effort required to produce and validate numbers, the focus moves upstream. Instead of asking whether the reports are correct, finance teams can ask why performance is changing and what that implies for upcoming decisions. In practice, this means conversations about what is driving changes in the P&L, where cash is being absorbed as the business grows, or which costs are beginning to pressure margins.

    As automation and advanced analytics mature, leading finance teams spend less time compiling information and more time identifying drivers, testing scenarios, and informing decisions before they are made (Yee, 2024). In this model, finance becomes a contributor to outcomes rather than a commentator on results.

    Generative AI accelerates this shift by making analysis more interpretable and accessible. AI can support narrative explanation, variance interpretation, and scenario evaluation, enabling finance professionals to communicate insight more clearly to non-financial stakeholders (Yee, 2024). This matters because advisory only creates value when insight is understood and acted upon.

    The result is a different cadence of engagement. Advisory becomes more continuous and less event-driven. Instead of waiting for month-end or quarter-end reviews, finance teams can surface emerging issues, highlight early signals, and support trade-offs as they arise. The conversation moves from “what happened” to “what should we do next.”

    Importantly, this does not diminish the importance of professional judgment. If anything, it increases it. Automation can surface patterns and signals, but it cannot assess context, weigh competing priorities, or account for organizational nuance. Those responsibilities remain firmly with the accountant or finance leader. What changes is that judgment is applied earlier and more consistently.

    In this way, advisory work evolves from a reactive service into a core operating capability. It becomes less dependent on individual effort and more embedded in how finance supports the business. Outputs still matter, but outcomes become the measure of value.

    Finance Moves Closer to the Decisions That Matter

    As advisory becomes more continuous and forward-looking, the role of finance inside organizations begins to change in subtle but important ways. Finance is no longer engaged only after decisions are made, or at predefined reporting moments. Instead, it becomes involved earlier, when options are still open and trade-offs can still be shaped.

    This shift has been widely observed in organizations that have invested seriously in data, analytics, and automation. Finance teams increasingly draw on operational, people, and external data to support decision-making across the enterprise, rather than limiting their remit to financial reporting alone (Harvard Business Review Analytic Services, 2021). In these environments, finance acts less as a control function and more as an integrator of insight.

    This repositioning has practical consequences. When finance is embedded earlier in decision cycles, conversations change. Discussions focus less on whether results met expectations and more on how assumptions are evolving. Scenario testing becomes a shared activity rather than a specialist exercise. Risk is surfaced earlier, not after it has already materialised.

    Importantly, this does not require finance teams to become strategists in name or to abandon their technical discipline. What changes is the timing and framing of their contribution. Automation reduces the effort required to maintain accuracy and control, creating space for finance professionals to engage where judgment, context, and financial literacy add the most value.

    As generative AI and advanced analytics improve access to insight, the differentiator for finance professionals becomes their ability to interpret signals, challenge assumptions, and communicate implications clearly to decision-makers (Church, 2023). In other words, finance’s influence grows not by owning more data, but by helping others make better use of it.

    Seen this way, the shift toward advisory is not about expanding scope for its own sake. It is about alignment. Finance moves closer to the decisions it was always meant to inform, supported by automation that makes this involvement sustainable rather than episodic.

    From Automation to Advisory, Made Practical

    The shift from compliance to advisory did not happen because accountants suddenly wanted to change roles. It happened because the nature of the work made that shift unavoidable. As reporting became faster and data more accessible, the real constraint moved upstream to interpretation, judgment, and timing.

    Automation did not replace accountants. It removed the friction that kept their most valuable contributions locked behind reporting cycles and manual effort. When insight arrives earlier, more consistently, and in a form that supports conversation, advisory stops being an exception and becomes part of everyday work, not because accountants have changed what they do, but because insight can now arrive in time to support it.

    This is where the difference between tools that automate tasks and systems that support advisory becomes clear. Automation alone improves efficiency. But advisory requires structure: continuous insight, clear explanations, and a workflow that supports discussion before decisions are made.

    Platforms like Planir are built with this distinction in mind. By connecting directly to accounting systems and using AI to surface insights, explain changes, and highlight implications, Planir is designed to support the kind of forward-looking conversations finance teams already want to have. The accountant remains firmly in control, applying judgment, context, and professional expertise. The technology simply ensures that insight arrives in time to matter.

    The future of accounting is not defined by how quickly reports can be produced, but by how effectively financial insight shapes decisions. Automation made that future possible. Advisory makes it valuable.

    Reference

    Diaz, H. (2025, October 17). The industry shift: From compliance to strategic advisory services. Wolterskluwer.com. https://www.wolterskluwer.com/en/expert-insights/the-industry-shift-from-compliance-to-strategic-advisory-services

    Harvard Business Review Analytic Services. (2021). Finance’s key role in building the Data-Driven enterprise. In Pulse Survey [Report]. https://forms.workday.com/content/dam/web/en-us/documents/reports/hbr-finances-key-role-in-building-the-data-driven-enterprise-final.pdf?refCamp=7014X000001yvgK.html

    How generative AI can make accountants more productive | MIT Sloan. (2025, August 5). MIT Sloan. https://mitsloan.mit.edu/ideas-made-to-matter/how-generative-ai-can-make-accountants-more-productive

    Kelleher, M. (2025, May 29). From compliance to consulting: Year-round revenue. Tax & Accounting Blog Posts by Thomson Reuters. https://tax.thomsonreuters.com/blog/from-compliance-to-consultancy-your-answer-to-year-round-revenue/

    ‌Murray, S. (2025, June 26). AI Is Reshaping Accounting Jobs by Doing the “Boring” Stuff. Stanford Graduate School of Business; Stanford University. https://www.gsb.stanford.edu/insights/ai-reshaping-accounting-jobs-doing-boring-stuff

    Sukharevsky, A., West, A., Catania, C., & Grande, D. (2025, November 3). How finance teams are putting AI to work today. McKinsey & Company. https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/how-finance-teams-are-putting-ai-to-work-today

    Yee, L. (2024, November 4). What an AI-powered finance function of the future looks like [Review of What an AI-powered finance function of the future looks like]. McKinsey & Company. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/what-an-ai-powered-finance-function-of-the-future-looks-like

  • Fathom Review 2026: Features, Pricing & Limitations

    Fathom Review 2026: Features, Pricing & Limitations

    Quick answer: Fathom is a well-regarded financial reporting tool used by 99,000+ companies, rated 4.8/5 on Capterra. It excels at visual report building and KPI tracking but lacks AI-driven automation for variance commentary, budgeting, and deep forecasting. Pricing starts at $65/month for a single company. Finance teams needing automated analysis beyond manual report construction should evaluate alternatives.

    What Is Fathom and What Does It Do?

    Fathom is a cloud-based financial reporting and analysis platform that connects to Xero, QuickBooks, and MYOB. Founded over 13 years ago, it has grown into the most highly reviewed app of its kind in the Xero ecosystem, serving 99,000+ companies globally (Fathom HQ, 2026).

    At its core, Fathom is a reporting-first tool. It pulls your financial data from your accounting system and gives you a drag-and-drop editor to build visual reports, track KPIs, and present financial performance to stakeholders. It also offers basic forecasting and budgeting features, though these sit firmly in the secondary tier of its product.

    For Finance Controllers at growing SMEs, the question is not whether Fathom looks good on screen. It does. The question is whether it actually reduces the hours you spend each month constructing, analyzing, and narrating your financials. For a broader view of the landscape, see our guide to the 7 best financial reporting tools for SMEs in 2026.

    What Are Fathom’s Core Features?

    Fathom’s strongest capability is visual financial reporting, earning its 4.8/5 Capterra rating primarily on the strength of its report builder (Capterra, 2026). The platform lets you build branded, presentation-ready reports using a template library and a flexible layout editor. You can combine P&L summaries, balance sheet snapshots, cash flow charts, and custom KPIs into a single document. For firms that need to deliver polished client reports or internal board-ready financials, this is genuinely useful.

    Key Fathom features include:

    • Automated data syncing from Xero, QuickBooks, and MYOB
    • KPI tracking across financial and non-financial metrics
    • Benchmarking to compare performance across companies or periods
    • 3-way forecasting covering P&L, Balance Sheet, and Cash Flow
    • Consolidation for multi-entity reporting
    • A drag-and-drop report builder with custom branding options

    Users consistently praise the visual output quality and the ability to turn raw accounting data into something a board or investor can actually read. If you are preparing a board pack, Fathom handles the presentation layer well.

    How Much Does Fathom Cost in 2026?

    Fathom pricing in 2026 has three tiers, and the costs scale based on how many companies you manage.

    Plan Companies Monthly Price
    Pro Starter 1 company $65/month
    Pro Silver Up to 10 companies $390/month
    Pro Gold Up to 25 companies $540/month
    Pro Platinum Up to 50 companies $860/month
    Portfolio Up to 100 companies From $62/month

    (Fathom HQ, 2026)

    The Portfolio tier is a newer addition, priced from $62/month for up to 100 companies. But it comes with a significantly reduced feature set: you get an insights dashboard and simple summary reports, not the full Fathom financial reporting and forecasting suite. For accounting firms managing large client books with lightweight needs, Portfolio may work. For an FC who needs depth, it likely will not.

    For a single-company FC on the Pro Starter plan, $65/month is reasonable. But the value equation depends entirely on how much manual work the platform actually eliminates, not just how much data it displays.

    Where Does Fathom Fall Short? Five Limitations That Matter

    1. No AI-Driven Variance Commentary or Narrative Generation

    This is the gap that defines Fathom’s limitations in 2026. The platform syncs your data and lets you build reports, but you still write every word of analysis yourself. There is no AI agent generating variance commentary, flagging anomalies, or drafting the financial narrative for your board pack.

    In a year where 69% of CFOs say AI is integral to their finance transformation strategy (IBM, 2026), a reporting tool that automates the data pull but not the analysis leaves the most time-consuming part of the cycle untouched.

    2. Budget Upload Friction

    Fathom does not include an in-platform budget builder with driver-based logic. Instead, budgets must be uploaded via Excel. Users have explicitly noted they wish this process could be “automated a little bit more” (Capterra, 2026). For an FC building a budget from scratch each cycle, this means Fathom is a presentation layer for your budget, not a tool that helps you build it.

    3. Shallow Forecasting Capabilities

    Fathom offers 3-way forecasting, which sounds comprehensive on paper. In practice, users report that the forecasting module lacks custom formulas, scenario levers, and the depth needed for serious financial modeling. Competitor analysis from Clockwork AI describes Fathom’s forecasting as an area that “leaves room for improvement,” noting that the platform’s primary focus remains on financial reporting (Clockwork AI, 2025). Acuity Magazine’s comparison of the market similarly positions Futrli as the leader on predictive and driver-based forecasting, with Fathom’s strengths concentrated in KPI breadth and reporting (Acuity Magazine, 2024).

    4. No Daily or Weekly Cash Flow Monitoring

    Fathom supports monthly, quarterly, and annual forecast horizons only. It does not offer daily or weekly cash flow visibility. For a growing SME where cash position can shift meaningfully within a single week, this is a blind spot. The FC who needs to answer “Can we make payroll next Friday?” will not find that answer in Fathom.

    5. Scalability and Setup Challenges

    Multiple reviewers describe Fathom’s implementation as “a marathon, not a sprint,” particularly for businesses with complex financial structures or multi-entity setups (Capterra, 2026). Users also report that the platform “doesn’t always scale as smoothly as its rivals” for fast-growing or financially complex organizations. The customization Fathom offers is concentrated in presentation and formatting, not in financial modeling or custom KPI logic.

    Why Do Reporting Tools Still Leave FCs Doing Manual Work?

    According to CPA Practice Advisor, 66% of accountants in 2026 still cite time-consuming reporting and manual data entry as their biggest operational pain points, with manual work consuming up to 40% of staff time (CPA Practice Advisor, 2026).

    Fathom addresses one layer of this problem. It eliminates the need to manually export data from your accounting system and paste it into a spreadsheet. That is real value. But the hours an FC spends each month are not primarily in the data export. They are in the analysis, the commentary, the budget construction, and the narrative that turns numbers into decisions.

    The FP&A software market reflects this shift. Valued at approximately $4.38 billion in 2024, it is projected to reach $9.7 to $11.7 billion by 2032-2033 (Data Horizon Research, 2024; Verified Market Research, 2024). The growth is not being driven by better-looking reports. It is being driven by platforms that automate the analytical and planning work itself.

    CFO adoption of FP&A software jumped from 19% to 61% in 2024 alone (The Finance Weekly, 2024). That kind of acceleration signals a market that has moved past “do I need software?” and into “does this software actually do the work, or just display it?”

    Who Should Use Fathom?

    Fathom remains a strong choice for specific use cases:

    • Accounting firms that need to deliver visually polished client reports at scale
    • Small businesses with straightforward financials that need better-than-spreadsheet reporting
    • Teams that already have their analysis workflow and just need a better presentation layer
    • Xero-native firms that want a tightly integrated reporting add-on without a heavy implementation

    If your primary pain point is that your reports look unprofessional or that exporting data from Xero takes too long, Fathom solves that well.

    Who Should Consider a Fathom Alternative?

    If your pain points are deeper, Fathom may not go far enough. Specifically, if you are an FC at a growing SME and your month-end bottleneck is the 2 to 3 days spent writing variance commentary, building budgets from scratch, or constructing the financial section of your board pack, you need a tool that automates the analysis, not just the data display.

    Planir takes a fundamentally different approach. Rather than giving you a report builder and leaving the analytical work to you, Planir deploys AI agents that generate variance commentary, build budgets with documented assumptions, and construct the financial core of board packs and investor updates. The FC reviews the reasoning, overrides where business context dictates, and adds the strategic narrative that only a human can write. It connects to Xero and QuickBooks, but what it automates is not the data sync. It is the grunt work that sits between raw data and finished output.

    The Bottom Line on This Fathom Review

    Fathom is a mature, well-built reporting tool that delivers real value for its core use case: turning accounting data into visual, branded financial reports. Its 4.8/5 rating on Capterra and its 99,000+ company user base are earned.

    But “reporting tool” and “FP&A platform” are not the same thing. In 2026, the gap between syncing data and automating analysis is where FCs lose their weekends. Fathom bridges the first half of that gap. The question for your team is whether you need a tool that bridges both.

  • How to Automate Investor Updates: A Step-by-Step Guide for Financial Controllers

    How to Automate Investor Updates: A Step-by-Step Guide for Financial Controllers

    Quick answer: Finance controllers can automate investor updates by connecting their accounting platform to a reporting tool, standardizing KPI templates, automating data pulls and variance commentary, and reserving their time for strategic narrative. Automation cuts consolidation workload by 50% and saves 15 to 20 hours per reporting cycle, turning a multi-day grind into a review-and-approve workflow.

    Why Manual Investor Updates Drain FC Productivity

    Finance teams spend 60% of their working hours compiling and verifying data, leaving just 40% for analysis and strategic support (EasyReports, 2026). Finance controllers know this ratio firsthand. Month-end close wraps on a Wednesday, maybe Thursday. Then the real scramble begins: pulling numbers from Xero or QuickBooks, copying them into a spreadsheet, cross-referencing bank statements, chasing department heads for operational metrics, writing variance commentary from scratch, and formatting everything into something professional enough to send to investors.

    For the FC responsible for the monthly investor report on top of day-to-day operations, that ratio is even more lopsided.

    The investor update itself is not complex. It follows roughly the same structure every month: executive summary, key metrics, financial snapshot, product or team updates, and asks. Yet producing it reliably on a set cadence, with accurate numbers and thoughtful commentary, consumes a disproportionate amount of time. The reason is not the report. It is everything upstream of the report — and that is exactly where investor reporting automation delivers the biggest gains.

    Why Manual Investor Reporting Breaks Down as You Scale

    The core issue is not a lack of effort. It is a workflow built on manual data collection, disconnected tools, and repeated grunt work.

    Disparate data sources create bottlenecks. The FC pulls revenue from the accounting platform, cash from the bank, headcount from HR, pipeline from the CRM, and burn rate from a spreadsheet model. Each source has its own format, its own update cadence, and its own margin for error. A single data entry mistake cascades through the entire report and triggers hours of rework.

    Version control compounds the risk. Multiple spreadsheet versions circulate between the FC, the CEO, and sometimes a fractional CFO. Without a single source of truth, no one is fully confident in the final numbers. This is a common challenge when assembling board packs and investor updates alike.

    Narrative writing is deceptively time-consuming. Variance commentary follows a predictable structure, yet FCs rewrite it from scratch every month. Revenue is up or down relative to forecast, and the explanation usually falls into a handful of recurring categories. Despite this repetition, the manual effort remains constant.

    Only 18% of finance teams complete month-end close in three days or less, and half take longer than five business days (G-Accon, 2026). When the close itself runs long, the investor update gets pushed back, and the FC misses the 8-to-10-day reporting cadence that Forecastr recommends as best practice (Forecastr, 2025).

    What Does an Investor Reporting Automation Stack Look Like?

    Automating investor updates is not about buying a single tool. It is about restructuring the workflow into layers, where data flows automatically and the FC’s time is reserved for judgment, context, and narrative.

    Abacum’s framework is useful here: investor reporting is the “output layer” of a broader automation stack (Abacum, 2025). You cannot automate the update without first automating consolidation, variance analysis, and budget-vs-actual workflows underneath it.

    Think of it as three layers:

    1. Data layer: Automated connections to your accounting platform, bank feeds, and operational tools
    2. Analysis layer: Automated consolidation, variance calculations, and KPI tracking
    3. Reporting layer: Templated output that pulls from the analysis layer and generates the investor-ready report

    When all three layers are connected, reconciliation and reporting tasks that previously took two weeks can drop to 25 minutes (G-Accon, 2026). Choosing the right financial reporting tools for each layer is critical to making the stack work.

    How to Automate Your Investor Update Workflow Step by Step

    Step 1: Audit Your Current Reporting Process

    Cube Software recommends starting any automation initiative by assessing current workflows before researching solutions (Cube Software, 2025). Before selecting tools, document exactly how your investor update gets built today. Map every data source, every manual step, every handoff. Identify where time is lost. For most FCs, the biggest time sinks are data collection (pulling numbers from multiple systems), reconciliation (verifying those numbers match), and formatting (making the output look consistent).

    This prevents the common mistake of automating a broken process rather than fixing it first.

    Step 2: Standardize Your Monthly Investor Report Template

    Investor updates should follow a consistent structure every month. Forecastr’s recommended format is a solid starting point: executive summary, five to seven KPIs, financial snapshot covering revenue, expenses, cash balance, and burn rate, team and product updates, and clear asks (Forecastr, 2025).

    Lock this template down. When the structure stays constant, investors can quickly scan and compare across months, and your automation tools have a predictable output format to target. Inconsistent formatting wastes the FC’s time and undermines credibility with investors who review dozens of portfolio updates each month.

    Step 3: Connect Your Data Sources to Automate Investor Update Inputs

    This is where the actual automation begins. Connect your accounting platform (Xero, QuickBooks, or equivalent) directly to your reporting tool so financial data flows automatically. No more copying numbers into a spreadsheet.

    The goal is a single source of truth that updates when your books update. Finance teams save 15 to 20 hours per reporting cycle using automated consolidation versus manual methods (Fuel Finance, 2025). Most of that savings comes from eliminating the copy-paste-verify loop between systems.

    Step 4: Automate Variance Analysis and Commentary

    This is the step most FCs skip, and it is the one that saves the most time. Variance commentary follows predictable patterns. Revenue beat forecast because of a large deal closing early. OPEX exceeded budget due to unplanned hiring. Cash burn accelerated because of a one-time infrastructure cost.

    An AI-powered system can generate first-draft variance commentary by comparing actuals to forecast, identifying material variances, and drafting explanations based on the underlying data. The FC then reviews, edits, and adds strategic context that only they can provide. This flips the workflow from “build from scratch” to “review and approve.”

    Step 5: Build the Review-and-Approve Cadence

    With data flowing automatically and commentary pre-drafted, the FC’s role shifts. Instead of spending days building the report, the FC spends an hour reviewing it: checking the numbers look right, refining the narrative, adding operational context the CEO needs to include, and flagging anything that needs a conversation before the update goes out.

    Forecastr’s recommended cadence works well here (Forecastr, 2025):

    • Days 1-3: Close financials
    • Days 4-5: Automated data pull and variance analysis generate the draft update
    • Days 6-7: FC reviews and adds strategic narrative
    • Days 8-10: Final review and send

    The difference is that days four through seven now require hours, not days.

    Step 6: Add Investor Engagement Feedback Loops

    Most investor updates are one-directional PDFs emailed into the void. The FC has no idea whether investors actually read them, which sections they focused on, or what questions the update raised.

    Modern investor reporting platforms like Visible.vc offer engagement tracking, so you can see open rates and section-level attention. This feedback loop lets you refine future updates based on what investors actually care about, rather than guessing (Visible.vc, 2025).

    Step 7: Iterate and Expand Your Reporting Automation

    Start with the financial section of your monthly investor report. Once that workflow is stable, expand automation to include operational KPIs, hiring updates, and product milestones. The principle remains the same: automate the data collection and first-draft generation, reserve the FC’s time for review and strategic context.

    How Planir Automates Investor Reporting for FCs

    Planir is an AI-powered financial intelligence platform that automates the financial core of investor updates and board packs. It connects directly to accounting platforms like Xero and QuickBooks, and its AI agents handle consolidation, variance analysis, and report generation. The FC reviews the output, sees the reasoning behind every number and variance explanation, overrides where their business context dictates, and adds the strategic narrative that only a human can write. For FCs at growing SMEs who need to automate investor updates without a large finance team, Planir turns a multi-day manual process into a review-and-approve workflow.

    What Changes When You Automate Investor Updates?

    Automation cuts consolidation workload by 50% every single close cycle (G-Accon, 2026). The shift is not just about saving time, though that matters. The deeper change is in what the FC spends their time on.

    Robert Half found that 83% of FCs dedicate the bulk of their time to operational tasks, leaving almost no bandwidth for strategic analysis (Robert Half, 2024). When the grunt work of investor reporting is automated, the FC can redirect that time toward the work that actually moves the business forward: analyzing trends, advising on cash runway decisions, preparing for board questions, and shaping the financial strategy.

    Meanwhile, 86% of controllers expect their role to change significantly over the next five years (EY, 2024). The FCs who build automated reporting workflows now are positioning themselves for that shift, moving from data compilers to strategic finance leaders.

    How to Start Automating Your Investor Update Today

    You do not need to automate everything at once. Start with one investor update cycle. Connect your accounting platform to a financial reporting tool. Standardize your template. Let the system generate the first draft of the financial section. Review it, fix what needs fixing, and send it.

    Measure how long it took versus your previous manual process. That delta is your business case for expanding investor reporting automation across the rest of your reporting workflow.

    The monthly investor report is not the hard part. The hard part is everything you do to produce it. Automate the upstream work, and the update practically writes itself.

  • AI Budget Workflow: Review and Approve vs Build from Scratch

    AI Budget Workflow: Review and Approve vs Build from Scratch

    Quick answer: AI agents shift the budget workflow from a weeks-long, manual build-from-scratch process to a review-and-approve model. Finance controllers connect their accounting data, agents generate a complete draft budget with documented assumptions, and the FC reviews, overrides, and approves. Platforms like Planir enable this AI budget workflow, cutting budget cycles by up to 75% while keeping human judgment at the center.

    Why the Budget Cycle Still Takes Nine Weeks

    The average budget cycle still takes roughly nine weeks, a number unchanged in three years (Association for Financial Professionals [AFP], 2024). Twenty-two percent of organizations need twelve weeks or more (AFP, 2024). And yet, the tools available to finance teams have multiplied.

    So why hasn’t the cycle shortened?

    Because the bottleneck was never the tool. It was the workflow. Finance controllers and FP&A analysts still spend 46% of their time on data collection and validation rather than actual analysis (FP&A Trends, 2025). Two out of every three hours an FP&A analyst works are spent searching for data, not interpreting it (FTI Consulting, 2025). The budget is not slow because the spreadsheet is slow. The budget is slow because someone has to build it from scratch every single time.

    That is the AI budget workflow shift agents are finally designed to address.

    What the Build-from-Scratch Budget Actually Costs

    Every budget cycle begins the same way. Export data from the accounting system. Clean it. Restructure it into a planning format. Link revenue assumptions to headcount, headcount to payroll, payroll to cash flow. Format it for the board pack. Check for broken formulas. Reconcile against actuals. Repeat across entities if you run a multi-entity group.

    This is not strategic work. This is assembly.

    Only 2% of organizations consider their FP&A function optimized (FP&A Trends, 2025). Over 60% report being constrained by manual processes (FP&A Trends, 2025). The result is predictable: finance controllers who trained to be financial strategists spend their weeks as data janitors. Only 27% of CFOs actually spend half their time on strategy, even though 96% acknowledge that AI could free them to do so (Journal of Accountancy, 2026).

    The build-from-scratch workflow creates three specific problems that no amount of spreadsheet skill can solve:

    No Audit Trail for Budget Assumptions

    Excel does not track who changed what, when, or why. Assumptions live in someone’s head or in a tab no one reads. When the board asks “why did you model 12% revenue growth?”, the answer requires archaeology, not analysis.

    Silent Error Propagation Across the Model

    A broken link in row 47 cascades through the entire model. No alert fires. The error surfaces weeks later during reconciliation, or worse, in a board meeting.

    Repetition Without Institutional Learning

    Every cycle starts from zero. The model does not remember last quarter’s assumptions, what drove the variance, or which line items the FC overrode. Institutional knowledge evaporates between cycles.

    How the AI Budget Workflow Review-and-Approve Model Works

    The review-and-approve model is not “let AI build your budget and hope for the best.” Eighty-six percent of CFOs have encountered inaccurate or hallucinated data from AI systems (Journal of Accountancy, 2026). Trust is earned through transparency, not automation speed.

    The AI budget workflow flips the FC’s role from builder to reviewer:

    Step 1: Connect. The FC connects their accounting platform. Historical data, chart of accounts, and actuals flow in automatically.

    Step 2: Agents build. AI agents generate a complete agent-built budget. Revenue projections based on historical trends and stated assumptions. Expense forecasts linked to headcount plans. Cash flow modeled from the P&L and balance sheet. Every cell carries a documented assumption the FC can inspect.

    Step 3: FC reviews. The FC does not accept the output blindly. They see the agent’s reasoning for each line item. They override where their business context demands it. They know that the agent modeled a 10% rent increase because the lease renewal data showed it, but they also know the landlord agreed to hold rates. So they override. The agent logs the override and adjusts downstream projections.

    Step 4: Approve and iterate. The FC approves the budget, runs scenarios, stress-tests assumptions, and presents to the board with full confidence in the numbers, because they reviewed every material decision the agent made.

    This is not a black box. It is a draft that arrives with its reasoning visible.

    Why 97% of CFOs Require Human Oversight of AI Budgeting

    The Journal of Accountancy’s February 2026 survey found that 97% of CFOs view human oversight as critical to AI accuracy (Journal of Accountancy, 2026). That statistic is not a rejection of AI. It is a design specification.

    Finance controllers do not want to be removed from the budget process. They want to be removed from the assembly process. There is a meaningful difference between reviewing a budget an agent built with traceable logic and building that budget by hand from exported CSV files.

    The trust paradox in finance AI is real: 80% of CFOs report that agentic AI already handles at least 25% of their accounting and finance workload (Journal of Accountancy, 2026). Adoption is happening fast. But it is happening under a specific condition: the human stays in the loop.

    Gartner reinforces this framing. Their February 2026 research predicts that 90% of finance functions will deploy at least one AI-enabled technology by the end of 2026, but fewer than 10% will see headcount reductions (Gartner, 2026). AI agents in financial planning change what finance professionals do. They do not eliminate the need for them.

    Why Explainability Makes AI Budget Review Viable

    A budget number without a reason is just a guess. Finance controllers need to defend every line to the CFO, the board, and the auditors. That means AI-generated budgets must be explainable at the cell level.

    The CFA Institute published a dedicated report in 2025 urging the financial sector to prioritize explainable AI, arguing that finance professionals will not adopt systems they cannot audit (CFA Institute, 2025). This is not a theoretical concern. It is a practical one. When the board asks why OPEX increased 14%, “the AI said so” is not an acceptable answer.

    “The agent projected a 14% increase based on three new hires in Q2, a 6% SaaS cost escalation tied to user growth, and the lease renewal at current rates, which I overrode to reflect the renegotiated terms” is. That is the kind of variance analysis commentary boards actually read.

    Explainability is what makes the AI budget review model viable. Without it, you have automation. With it, you have a workflow.

    How Planir Enables the Review-and-Approve AI Budget Workflow

    Planir is an AI-powered financial intelligence platform that deploys agents to build budgets, generate reports, and produce dashboards from connected accounting data. The FC connects Xero or QuickBooks, and Planir’s agents generate a draft budget with cell-level assumptions, documented reasoning, and full audit trails. The FC reviews, overrides where business context requires it, and approves.

    The agents handle the analytical and planning grunt work. The FC focuses on judgment, strategy, and the narrative that only they can write. It is designed around the principle that agents propose and FCs approve, not the other way around.

    What AI Budget Workflows Mean for Growing SMEs

    For a finance controller at a growing SME, the review-and-approve model solves a resource problem, not just a process one. You do not have a team of analysts to delegate the data work to. You are the analyst, the modeler, the consolidator, and the presenter. When the budget takes nine weeks, those are nine weeks you are not spending on cash flow strategy, scenario planning, or advising the CEO.

    Early deployments of agentic AI in finance have shown budget cycle reductions of up to 75% and an 80% reduction in manual data work (ChatFin, 2025). Those numbers matter most at companies where one or two people own the entire finance function. Choosing the right financial reporting tools is critical for SMEs operating at this scale.

    In Southeast Asia specifically, Singapore’s Budget 2026 expanded the Productivity Solutions Grant to cover AI tools at up to 50% of qualifying costs, capped at S$30,000, alongside 400% tax deductions on AI-related expenditure (Ministry of Finance Singapore, 2026). The policy signal is clear: governments are actively incentivizing SMEs to adopt automated budgeting and AI in finance operations.

    How the Competitive Landscape Reflects the AI Budget Workflow Shift

    The competitive landscape confirms this transition is underway. Cube launched agentic AI for forecasting and variance analysis. Pigment introduced Planner agents that suggest revised plans from updated assumptions. Datarails added natural language querying to its Excel-native platform (Cube, 2025; Pigment, 2025; Datarails, 2025). The direction is uniform: every major FP&A platform is moving toward agent-built budget outputs that humans review.

    But the framing matters. This is not about replacing the finance controller. It is about changing their default action from “build” to “review.” The FC who once spent a week constructing a budget now spends a morning reviewing one. The expertise is the same. The time cost is not.

    Key Takeaway

    The AI budget workflow is splitting into two models. In one, the FC builds from scratch, spending weeks on assembly before getting to strategy. In the other, agents build a transparent, auditable draft, and the FC reviews, overrides, and approves. The second model is not hypothetical. It is how 80% of CFOs are already starting to work with AI.

    The question for finance controllers at growing SMEs is not whether AI will change budget workflows. It is whether you will spend your next budget cycle building from scratch or reviewing an agent’s work.

    The nine-week cycle does not have to be a permanent feature of your calendar.