The Challenge: Error‑Prone Excel Formulas

Most finance teams still run their monthly and quarterly reporting on sprawling Excel workbooks. Dozens of linked files, nested formulas, VLOOKUPs, INDEX/MATCH combinations and VBA macros sit behind every management report and board pack. Under time pressure, small structural changes, last-minute adjustments, or a broken link can silently corrupt key figures. The closer you get to deadlines, the more fragile the model becomes.

Traditional approaches to controlling this complexity no longer work. Manual spot checks, version control through file naming, and last-minute tie-outs are simply not enough when your models contain thousands of interdependent cells. Excel was never designed as a robust financial reporting platform pulling data from ERP systems, bank feeds, and operational tools. As your reporting logic grows, each additional formula and sheet increases the probability of hidden errors and broken links that no one sees until it’s too late.

The business impact is real. A single incorrect cell reference can distort EBITDA, misstate cash positions, or misallocate costs between business units. That leads to delayed board meetings, painful rework with auditors, and – most importantly – loss of confidence from executives in the finance function. Time that could be spent on scenario modelling, cash flow forecasting, or strategic analysis is instead burned in fire drills: fixing #REF! errors, reconciling unexplained variances and tracing formulas across tabs.

The good news: this problem is solvable. Modern AI tools can analyse formula logic, detect anomalies, and help you redesign fragile workbooks into robust, automated reporting flows. At Reruption, we’ve seen how AI-powered automation can turn manually stitched spreadsheets into reliable financial infrastructure. In the rest of this page, you’ll find practical guidance on how to use Gemini with Google Sheets to stabilise your reporting, reduce manual error risk, and build a finance function that can move at board speed with confidence.

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Our Assessment

A strategic assessment of the challenge and high-level tips how to tackle it.

From Reruption’s experience building and deploying AI automation in finance processes, error‑prone Excel is rarely just a tooling problem – it’s a structural one. Gemini’s deep integration with Google Sheets and its ability to analyse formulas, validate logic, and generate Apps Script make it a powerful way to transition from fragile spreadsheets to a more resilient reporting stack without having to rebuild everything in a new system on day one.

Think “Reporting System”, Not “Smarter Spreadsheet”

Before you start using Gemini to patch individual formulas, step back and define the target operating model for your financial reporting system. Decide which data should live in your ERP, which transformations belong in Sheets, and which checks need to be automatically enforced. Treat Gemini as an intelligence layer that helps you enforce this design, not as a quick fix for messy workbooks.

This mindset reduces the risk of simply adding another layer of complexity. By mapping your core financial statements, source systems, and required controls, you can guide Gemini to help you consolidate logic, propose ARRAYFORMULA-based structures, and move towards a more database-like design in Sheets. The goal is fewer manual touchpoints and clearer data flows, not just fewer formula errors.

Start with a High-Impact Pilot Report

Strategically, the best place to prove value is not your entire reporting universe but a single high-visibility report: for example, your monthly management P&L or cash flow overview. Select a report that regularly causes last-minute stress due to broken Excel links, hidden errors, or manual adjustments. This creates a clear success metric for Gemini: fewer errors, shorter cycle time, and higher trust.

In the pilot, use Gemini to audit formulas, propose simplifications, and generate validation checks. Document baseline metrics – like hours spent, number of corrections per cycle, and frequency of restatements – so you can communicate a concrete ROI. Once the team sees this working end-to-end, scaling to additional reports becomes a change management project, not a technology discussion.

Prepare the Finance Team for an AI‑Supported Workflow

Gemini will change how your finance team interacts with spreadsheets. Instead of manually tracing formulas, they will increasingly review AI‑suggested corrections, approve logic changes, and design new checks. That requires basic AI literacy in finance: understanding prompts, validating AI output, and knowing when to challenge Gemini’s suggestions.

Plan for short, targeted enablement sessions where controllers and analysts learn how to instruct Gemini effectively (“explain this formula chain”, “optimise this for maintainability”, “add consistency checks for these ranges”). Reruption’s experience shows that when finance owns the AI-assisted process – rather than handing it to IT – adoption and long‑term impact are much higher.

Build in Governance and Auditability from Day One

Automating financial reporting with Gemini must not come at the cost of control. Strategically, you should define clear rules for who can change report logic, how those changes are documented, and how you evidence that controls are operating. Gemini’s Apps Script generation capability can be used to embed automated logs, approval flows, and exception reports directly in your Google Sheets environment.

Design this governance with your auditors and risk team in mind. For example, insist that all Gemini-assisted structural changes to key reports go through a documented review, and that critical consolidation logic is locked down and versioned. This way, you get the speed benefits of AI while still being able to demonstrate control and traceability when it matters.

Align IT, Data, and Finance on Integration Boundaries

Finally, using Gemini effectively for automated financial reporting is not just a finance initiative. You need agreement with IT and data teams on where Google Sheets sits in your architecture: which data is pulled via APIs from ERP and bank feeds, how often refreshes run, and where master data is maintained. Clear boundaries keep your Gemini implementation sustainable.

At a strategic level, define a light integration blueprint: sources (ERP, HR, banking), transformation layer (Sheets + Gemini logic), and outputs (board packs, dashboards, CSV exports). With that alignment, Gemini becomes a controlled part of your financial data pipeline instead of another undocumented shadow IT solution.

Used thoughtfully, Gemini can transform error‑prone Excel models into a governed, AI‑assisted reporting system built on Google Sheets. It helps finance leaders stabilise logic, automate checks, and compress closing timelines without a multi‑year system replacement. If you want to explore what this could look like in your environment, Reruption can co‑design and implement a focused Gemini‑powered reporting pilot – from architecture and Apps Script automation to team enablement – and validate the impact before you scale further.

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Real-World Case Studies

From Energy to Manufacturing: Learn how companies successfully use Gemini.

BP

Energy

BP, a global energy leader in oil, gas, and renewables, grappled with high energy costs during peak periods across its extensive assets. Volatile grid demands and price spikes during high-consumption times strained operations, exacerbating inefficiencies in energy production and consumption. Integrating intermittent renewable sources added forecasting challenges, while traditional management failed to dynamically respond to real-time market signals, leading to substantial financial losses and grid instability risks . Compounding this, BP's diverse portfolio—from offshore platforms to data-heavy exploration—faced data silos and legacy systems ill-equipped for predictive analytics. Peak energy expenses not only eroded margins but hindered the transition to sustainable operations amid rising regulatory pressures for emissions reduction. The company needed a solution to shift loads intelligently and monetize flexibility in energy markets .

Lösung

To tackle these issues, BP acquired Open Energi in 2021, gaining access to its flagship Plato AI platform, which employs machine learning for predictive analytics and real-time optimization. Plato analyzes vast datasets from assets, weather, and grid signals to forecast peaks and automate demand response, shifting non-critical loads to off-peak times while participating in frequency response services . Integrated into BP's operations, the AI enables dynamic containment and flexibility markets, optimizing consumption without disrupting production. Combined with BP's internal AI for exploration and simulation, it provides end-to-end visibility, reducing reliance on fossil fuels during peaks and enhancing renewable integration . This acquisition marked a strategic pivot, blending Open Energi's demand-side expertise with BP's supply-side scale.

Ergebnisse

  • $10 million in annual energy savings
  • 80+ MW of energy assets under flexible management
  • Strongest oil exploration performance in years via AI
  • Material boost in electricity demand optimization
  • Reduced peak grid costs through dynamic response
  • Enhanced asset efficiency across oil, gas, renewables
Read case study →

AstraZeneca

Healthcare

In the highly regulated pharmaceutical industry, AstraZeneca faced immense pressure to accelerate drug discovery and clinical trials, which traditionally take 10-15 years and cost billions, with low success rates of under 10%. Data silos, stringent compliance requirements (e.g., FDA regulations), and manual knowledge work hindered efficiency across R&D and business units. Researchers struggled with analyzing vast datasets from 3D imaging, literature reviews, and protocol drafting, leading to delays in bringing therapies to patients. Scaling AI was complicated by data privacy concerns, integration into legacy systems, and ensuring AI outputs were reliable in a high-stakes environment. Without rapid adoption, AstraZeneca risked falling behind competitors leveraging AI for faster innovation toward 2030 ambitions of novel medicines.

Lösung

AstraZeneca launched an enterprise-wide generative AI strategy, deploying ChatGPT Enterprise customized for pharma workflows. This included AI assistants for 3D molecular imaging analysis, automated clinical trial protocol drafting, and knowledge synthesis from scientific literature. They partnered with OpenAI for secure, scalable LLMs and invested in training: ~12,000 employees across R&D and functions completed GenAI programs by mid-2025. Infrastructure upgrades, like AMD Instinct MI300X GPUs, optimized model training. Governance frameworks ensured compliance, with human-in-loop validation for critical tasks. Rollout phased from pilots in 2023-2024 to full scaling in 2025, focusing on R&D acceleration via GenAI for molecule design and real-world evidence analysis.

Ergebnisse

  • ~12,000 employees trained on generative AI by mid-2025
  • 85-93% of staff reported productivity gains
  • 80% of medical writers found AI protocol drafts useful
  • Significant reduction in life sciences model training time via MI300X GPUs
  • High AI maturity ranking per IMD Index (top global)
  • GenAI enabling faster trial design and dose selection
Read case study →

Nubank

Fintech

Nubank, Latin America's largest digital bank serving 114 million customers across Brazil, Mexico, and Colombia, faced immense pressure to scale customer support amid explosive growth. Traditional systems struggled with high-volume Tier-1 inquiries, leading to longer wait times and inconsistent personalization, while fraud detection required real-time analysis of massive transaction data from over 100 million users. Balancing fee-free services, personalized experiences, and robust security was critical in a competitive fintech landscape plagued by sophisticated scams like spoofing and false central fraud. Internally, call centers and support teams needed tools to handle complex queries efficiently without compromising quality. Pre-AI, response times were bottlenecks, and manual fraud checks were resource-intensive, risking customer trust and regulatory compliance in dynamic LatAm markets.

Lösung

Nubank integrated OpenAI GPT-4 models into its ecosystem for a generative AI chat assistant, call center copilot, and advanced fraud detection combining NLP and computer vision. The chat assistant autonomously resolves Tier-1 issues, while the copilot aids human agents with real-time insights. For fraud, foundation model-based ML analyzes transaction patterns at scale. Implementation involved a phased approach: piloting GPT-4 for support in 2024, expanding to internal tools by early 2025, and enhancing fraud systems with multimodal AI. This AI-first strategy, rooted in machine learning, enabled seamless personalization and efficiency gains across operations.

Ergebnisse

  • 55% of Tier-1 support queries handled autonomously by AI
  • 70% reduction in chat response times
  • 5,000+ employees using internal AI tools by 2025
  • 114 million customers benefiting from personalized AI service
  • Real-time fraud detection for 100M+ transaction analyses
  • Significant boost in operational efficiency for call centers
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IBM

Technology

In a massive global workforce exceeding 280,000 employees, IBM grappled with high employee turnover rates, particularly among high-performing and top talent. The cost of replacing a single employee—including recruitment, onboarding, and lost productivity—can exceed $4,000-$10,000 per hire, amplifying losses in a competitive tech talent market. Manually identifying at-risk employees was nearly impossible amid vast HR data silos spanning demographics, performance reviews, compensation, job satisfaction surveys, and work-life balance metrics. Traditional HR approaches relied on exit interviews and anecdotal feedback, which were reactive and ineffective for prevention. With attrition rates hovering around industry averages of 10-20% annually, IBM faced annual costs in the hundreds of millions from rehiring and training, compounded by knowledge loss and morale dips in a tight labor market. The challenge intensified as retaining scarce AI and tech skills became critical for IBM's innovation edge.

Lösung

IBM developed a predictive attrition ML model using its Watson AI platform, analyzing 34+ HR variables like age, salary, overtime, job role, performance ratings, and distance from home from an anonymized dataset of 1,470 employees. Algorithms such as logistic regression, decision trees, random forests, and gradient boosting were trained to flag employees with high flight risk, achieving 95% accuracy in identifying those likely to leave within six months. The model integrated with HR systems for real-time scoring, triggering personalized interventions like career coaching, salary adjustments, or flexible work options. This data-driven shift empowered CHROs and managers to act proactively, prioritizing top performers at risk.

Ergebnisse

  • 95% accuracy in predicting employee turnover
  • Processed 1,470+ employee records with 34 variables
  • 93% accuracy benchmark in optimized Extra Trees model
  • Reduced hiring costs by averting high-value attrition
  • Potential annual savings exceeding $300M in retention (reported)
Read case study →

NatWest

Banking

NatWest Group, a leading UK bank serving over 19 million customers, grappled with escalating demands for digital customer service. Traditional systems like the original Cora chatbot handled routine queries effectively but struggled with complex, nuanced interactions, often escalating 80-90% of cases to human agents. This led to delays, higher operational costs, and risks to customer satisfaction amid rising expectations for instant, personalized support . Simultaneously, the surge in financial fraud posed a critical threat, requiring seamless fraud reporting and detection within chat interfaces without compromising security or user trust. Regulatory compliance, data privacy under UK GDPR, and ethical AI deployment added layers of complexity, as the bank aimed to scale support while minimizing errors in high-stakes banking scenarios . Balancing innovation with reliability was paramount; poor AI performance could erode trust in a sector where customer satisfaction directly impacts retention and revenue .

Lösung

Cora+, launched in June 2024, marked NatWest's first major upgrade using generative AI to enable proactive, intuitive responses for complex queries, reducing escalations and enhancing self-service . This built on Cora's established platform, which already managed millions of interactions monthly. In a pioneering move, NatWest partnered with OpenAI in March 2025—becoming the first UK-headquartered bank to do so—integrating LLMs into both customer-facing Cora and internal tool Ask Archie. This allowed natural language processing for fraud reports, personalized advice, and process simplification while embedding safeguards for compliance and bias mitigation . The approach emphasized ethical AI, with rigorous testing, human oversight, and continuous monitoring to ensure safe, accurate interactions in fraud detection and service delivery .

Ergebnisse

  • 150% increase in Cora customer satisfaction scores (2024)
  • Proactive resolution of complex queries without human intervention
  • First UK bank OpenAI partnership, accelerating AI adoption
  • Enhanced fraud detection via real-time chat analysis
  • Millions of monthly interactions handled autonomously
  • Significant reduction in agent escalation rates
Read case study →

Best Practices

Successful implementations follow proven patterns. Have a look at our tactical advice to get started.

Use Gemini to Audit and Simplify Existing Formula Logic

Start by moving one critical Excel report into Google Sheets or by opening the existing Sheet version. Use Gemini to analyse complex formulas, identify redundant constructs, and highlight inconsistent references (e.g. mixing absolute and relative ranges, mismatched period ranges across tabs). The goal is to stabilise what you already have before you add automation.

Within Sheets, select a problematic range and prompt Gemini to explain and optimise the logic. For example:

Prompt to Gemini in Google Sheets:

"You are reviewing a complex financial reporting sheet.
1. Explain in simple terms what the formulas in range 'P&L!D8:D200' do.
2. Identify potential risks (hard-coded values, inconsistent references, volatile functions).
3. Propose a simplified, more robust formula structure, preferably using ARRAYFORMULA where appropriate.
Return your answer with 'Current logic', 'Risks', and 'Proposed logic' sections."

Apply Gemini’s recommendations incrementally, testing each change with known historical datasets. Always preserve a versioned backup before refactoring, and document major formula changes in a dedicated “Logic_Documentation” tab.

Redesign Repetitive Calculations with ARRAYFORMULA Patterns

Many finance models copy the same formula down thousands of rows. This is brittle and slow. Use Gemini to convert row-by-row logic into ARRAYFORMULA‑based designs, reducing the risk of missing rows or partial updates. Identify areas where the same formula is dragged down a column and ask Gemini to consolidate.

For example, where you currently have a per‑row margin calculation like =IF(B8="","",C8-D8) dragged down, prompt Gemini:

Prompt to Gemini in Google Sheets:

"We calculate gross margin in column E as =IF(B8="","",C8-D8) and drag it down.
1. Propose an equivalent ARRAYFORMULA that works for all data rows.
2. Ensure it ignores blank rows and is efficient for 10,000+ rows.
3. Provide the final formula and a short explanation suitable for our finance team documentation."

Replace repetitive formulas with the ARRAYFORMULA solution Gemini proposes, and add a short explanation in your documentation tab. This not only reduces error surface but also makes structural changes (new rows, imported data) less risky.

Automate Data Refresh and Validation with Gemini‑Generated Apps Script

Move beyond manual copy‑paste from ERP exports and bank statements. Use Gemini to generate Google Apps Script that pulls data from APIs, refreshes imports on a schedule, and runs validation checks before anyone touches the file. This is where you start turning spreadsheets into a lightweight reporting application.

Describe your desired workflow to Gemini in natural language and let it draft the script skeleton. For example:

Prompt to Gemini for Apps Script:

"Create a Google Apps Script for this spreadsheet that:
1. Every weekday at 6am pulls the latest 'GL export' from a connected Google Drive folder and pastes it into the 'Raw_GL' tab, replacing old data.
2. Refreshes the 'Bank_Feed' tab from a CSV file in the 'Bank_Exports' folder.
3. Runs a validation: check that total debits equal total credits in 'Raw_GL' and that cash per bank feed matches the 'Cash' account in GL within 1 EUR.
4. If validation fails, send an email to finance@company.com with a summary of the issue and do NOT update the consolidated report tab.
5. If validation passes, recalculate and timestamp cell A1 in the 'Summary' tab with "Last refreshed: [datetime]".
Output: full Apps Script code with comments for a finance-literate but non-developer user."

Review the generated script with IT or a technically inclined team member, test it in a copy of your file, then deploy. This reduces manual work and prevents wrong or incomplete data from flowing into board materials.

Embed Anomaly Detection and Consistency Checks Directly in Sheets

Use Gemini to design and implement automated anomaly checks inside your reporting workbook. Instead of manually scanning for strange variances each month, have Gemini help you build rules that flag outliers, unexpected negative values, or sudden changes in key ratios.

For example, instruct Gemini to create a series of helper formulas or a script that compares current month figures to a rolling 12‑month average and flags anything above a defined threshold:

Prompt to Gemini in Google Sheets:

"We want automatic anomaly flags for our monthly P&L.
1. In tab 'P&L_Monthly', column H contains current month values, rows 8:200.
2. Columns B:G contain the previous 6 months for the same accounts.
Design formulas (or a simple Apps Script) that:
- Calculate a 6‑month average per row.
- Flag any current month where the deviation is > 30%.
- Output a 'OK' / 'Check' status in column I and a short reason in column J.
Return: formulas or script, plus instructions where to place them."

Integrate these flags into your standard review workflow so that each close cycle starts with a structured list of issues to investigate rather than manual hunting through the numbers.

Standardise Narrative Reporting with Gemini‑Drafted Commentaries

Once your numbers are more reliable, use Gemini to produce first‑draft narrative explanations of performance. Pull your P&L, cash flow, and key KPIs into a “Narrative_Source” tab and let Gemini turn variances and trends into structured comments that your finance team can refine.

For example:

Prompt to Gemini in Google Sheets:

"You are a financial controller.
Use data from the 'Summary' tab:
- Current vs prior month revenue, gross margin, OPEX, EBITDA
- Key variances in major cost lines
Create a concise monthly management commentary (max 400 words) that:
1. Explains the main drivers of change vs last month and vs budget.
2. Highlights any anomalies flagged in 'P&L_Monthly' column I.
3. Uses clear, non-technical language for executives.
Return: Intro paragraph, 'Performance Highlights', 'Risks & Watchpoints'."

Paste Gemini’s output into your board pack template and adjust as needed. This can save hours each close cycle and ensures consistent, structured communication around the numbers.

Create a Controlled “Gold Copy” of Key Reporting Sheets

To prevent logic drift, separate “playground” models from your production reporting. Use Gemini to help you document and lock down a gold copy of your most important sheets, with clear inputs, transformation logic, and outputs. Apply protection to formula ranges and use Apps Script to log any structural changes.

You can ask Gemini to generate a simple change log mechanism:

Prompt to Gemini for Apps Script:

"Write a Google Apps Script for this spreadsheet that:
1. Monitors structural changes (inserts/deletes columns or rows, edits to formulas) in the 'Production_P&L' tab.
2. When a change is detected, logs: user email, timestamp, cell range, old value, new value into a 'Change_Log' tab.
3. Sends a brief summary email to finance-owners@company.com for any change in the range B8:Z200.
Provide commented code and brief instructions for enabling the trigger."

This gives you traceability without heavy tooling and reassures auditors and executives that your Gemini‑supported reporting environment remains under control.

Implemented step by step, these practices can realistically cut manual reporting effort by 30–50%, reduce formula-related errors dramatically, and shave one to two days off your monthly close. The exact metrics depend on your starting point, but a well-scoped Gemini deployment in Google Sheets typically pays back within a few reporting cycles through fewer restatements, faster turnaround, and higher confidence in the numbers.

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Frequently Asked Questions

Gemini helps on three levels. First, it analyses and explains existing formulas, making it easier to spot broken references, inconsistent ranges, and risky constructs like hard-coded values or volatile functions. Second, it proposes simpler, more robust designs, for example by converting repeated row formulas into ARRAYFORMULA-based logic in Google Sheets. Third, it can generate Apps Script automations that enforce validation checks (e.g. debits=credits, variance thresholds) before numbers are used in reporting, reducing the chance that incorrect data makes it into executive packs.

At minimum, you need a finance power user comfortable with spreadsheets and willing to learn how to interact with Gemini inside Google Sheets. For more advanced automation (APIs, scheduled refreshes, change logs), it helps to have access to someone with basic scripting or engineering experience, or a partner like Reruption. You do not need a full data science team; most of the value comes from augmenting existing finance skills with AI, not from building a new technical organisation.

Practically, we recommend forming a small project squad: one finance owner (controller or head of reporting), one IT/data contact for access and security questions, and optionally an AI engineer to productionise the most useful Gemini-generated scripts.

For a single high-impact report, you can see tangible improvements within 2–4 weeks. In the first days, Gemini can help you clean up formulas, add validation checks, and stabilise your core logic. Over the following cycles, you can introduce automated data refresh and anomaly detection. A broader rollout across multiple reports typically takes a few months, mainly driven by change management and testing, not by tool limitations.

Reruption’s approach is to deliver a focused proof of concept quickly (one reporting flow, one team), measure impact on error rates and cycle time, and then decide which reports to onboard next based on that evidence.

The ROI usually comes from reduced manual effort, fewer errors, and faster decision cycles. Finance teams often spend dozens of hours per month hunting formula issues, reconciling unexplained variances, and reworking board materials. By using Gemini to automate checks and simplify logic, you can free up a significant share of that time for analysis and planning.

On top of labour savings, there is a risk and reputation component: fewer restatements, higher confidence from executives, and smoother audits. While the exact numbers depend on your context, many organisations can justify the investment after just a few reporting cycles by quantifying avoided rework, overtime, and delayed decisions.

Reruption works with a Co‑Preneur approach: we embed alongside your finance and IT teams to build something that actually runs in your P&L, not just in slides. For this specific challenge, we typically start with our AI PoC offering (9,900€), defining one concrete reporting flow (e.g. monthly P&L) and proving that Gemini can stabilise the logic, automate data refresh, and reduce manual checks in a working prototype.

From there, we can help you prioritise additional reports, harden the Apps Script automations, implement governance (change logs, access controls), and train your finance team to work effectively with Gemini day to day. The goal is to leave you with a robust, AI‑assisted reporting system and a team that can extend it without depending on long consulting projects.

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