The Challenge: Weak Scenario Planning

Most finance teams know they should model more than a base case and a simple best/worst case. But building multiple financial scenarios is slow, manual work. Data sits in scattered spreadsheets, assumptions are locked in people’s heads, and every new scenario means copying tabs, updating drivers, and checking formulas. Under time pressure, teams end up testing only a few simplistic cases.

Traditional approaches to scenario planning in finance were built for a world with stable markets and annual planning cycles. In that world, a static model and a one-off budgeting exercise could be “good enough”. Today, demand, prices and supply conditions shift too quickly. Manual Excel gymnastics and one-size-fits-all planning tools cannot keep up with constantly changing assumptions, external data, and management questions like “What if we cut this product?” or “What if energy prices spike 20%?”

The business impact is significant. Weak scenario planning leaves companies poorly prepared for shocks in demand, prices, or supply. Strategic decisions are made without robust downside views or credible upside cases. Finance teams spend their time fixing links and reconciling versions instead of challenging assumptions or quantifying strategic options. The result: higher risk, slower decisions, and a competitive disadvantage against organisations that can translate new information into updated financial scenarios in days, not months.

The good news: this is a solvable problem. Modern AI tools like Claude can digest large planning files, understand narrative assumptions, and help you build, compare, and stress-test many more scenarios with far less manual work. At Reruption, we’ve seen how AI-first thinking can replace fragile spreadsheet chains with robust, dynamic planning workflows. In the rest of this page, you’ll find concrete, finance-specific guidance to turn scenario planning from a bottleneck into a strength.

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Our Assessment

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

From Reruption’s work building AI solutions for finance and strategy teams, we’ve learned that tools like Claude are most powerful when they augment — not replace — your existing financial models. Claude’s long-context capabilities allow it to read complex budget workbooks, planning decks, and assumption documents in one go, then help finance teams design richer scenarios, highlight risks, and explain sensitivities in clear business language.

Think in Drivers and Narratives, Not Just in Excel Tabs

Weak scenario planning often starts with the wrong mental model: copying the base case spreadsheet and tweaking a few percentage points. To use Claude for financial planning effectively, shift your focus to business drivers (volume, price, mix, FX, headcount, capacity, churn, etc.) and the narratives behind them. Claude is extremely good at turning narrative assumptions into structured sets of drivers and scenarios.

Begin by documenting the stories your management team actually worries about: “rapid demand drop in key region”, “input cost spike”, “slower hiring”, “aggressive expansion”. Feed these narratives plus your existing planning model structure into Claude and ask it to suggest which drivers to flex and what ranges are realistic. This moves your planning from cosmetic tweaks to scenario narratives tied to clear financial levers.

Use Claude as a Scenario Architect, Not a Black-Box Forecaster

Claude should not be treated as an oracle that spits out a perfect forecast. Instead, use it as a scenario architect that helps finance teams design, organise, and interrogate scenarios at scale. Claude can propose scenario frameworks (e.g. macro, operational, strategic), cluster assumptions, and define consistent naming and documentation conventions across scenarios.

This mindset keeps ownership of numbers and critical assumptions with Finance, while Claude does the heavy lifting around structure, documentation, and comparison. By clearly separating “AI helps us design and evaluate scenarios” from “humans sign off on the numbers”, you reduce model risk and increase trust in AI-supported planning.

Assess Data and Model Readiness Before Scaling AI

Before you roll out Claude broadly, evaluate how ready your planning models and data are. Claude can work with messy spreadsheets, but you’ll get far more value if core structures are stable: clear revenue and cost bridges, consistent account mapping, and a clean separation between input assumptions and calculation logic.

At Reruption, we often start by analysing a client’s existing budget and forecast workbooks with Claude itself: loading sample files, asking it to map the model structure, identify key drivers, and spot inconsistencies or circularities. This quick health check gives you a realistic view of what Claude can safely automate today, and what needs to be tidied up first to avoid scaling spreadsheet chaos.

Prepare the Finance Team to Work with AI, Not Around It

Even the best AI scenario planning setup fails if finance professionals don’t trust or know how to use it. Invest early in training that is practical and finance-specific: how to brief Claude, how to review its output, and how to translate management questions into structured prompts and scenario requests.

Clarify roles: who is responsible for scenario design, who validates key assumptions, who owns communication with stakeholders. Position Claude as a powerful assistant that extends the team’s capacity to explore more scenarios, not as a threat to expertise. When controllers, FP&A managers, and business partners see that Claude helps them deliver better insights faster, adoption follows naturally.

Manage Risk with Guardrails, Reviews, and Traceability

Using AI for financial planning introduces new risks: misinterpreted formulas, out-of-date files, or overconfident conclusions. Mitigate these with explicit governance for AI-supported planning. Define which tasks Claude may support (e.g. summarising scenarios, drafting risk analyses, proposing sensitivities) and which remain strictly under human control (final numbers, official forecasts, external guidance).

Implement a simple review workflow: Claude produces scenario documentation and comparisons; a designated finance lead reviews and signs off; changes are logged. Ensure all Claude interactions that influence planning decisions are captured (e.g. in a collaboration tool or version-controlled repository). These guardrails keep your auditors, CFO, and board comfortable while still unlocking the speed and depth benefits of AI.

Used with the right mindset, Claude turns weak, manual scenario planning into a structured, scalable process where finance teams can explore more scenarios, document assumptions clearly, and surface risks faster. Reruption combines this tool capability with deep implementation experience — from model assessment to workflow design and enablement — so your planning process becomes both more robust and more adaptable. If you want to test what Claude can do on your real budgeting files before a big rollout, our team can help you run a focused pilot and translate the results into a concrete roadmap.

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Real-World Case Studies

From Shipping to Automotive Manufacturing: Learn how companies successfully use Claude.

Maersk

Shipping

In the demanding world of maritime logistics, Maersk, the world's largest container shipping company, faced significant challenges from unexpected ship engine failures. These failures, often due to wear on critical components like two-stroke diesel engines under constant high-load operations, led to costly delays, emergency repairs, and multimillion-dollar losses in downtime. With a fleet of over 700 vessels traversing global routes, even a single failure could disrupt supply chains, increase fuel inefficiency, and elevate emissions . Suboptimal ship operations compounded the issue. Traditional fixed-speed routing ignored real-time factors like weather, currents, and engine health, resulting in excessive fuel consumption—which accounts for up to 50% of operating costs—and higher CO2 emissions. Delays from breakdowns averaged days per incident, amplifying logistical bottlenecks in an industry where reliability is paramount .

Lösung

Maersk tackled these issues with machine learning (ML) for predictive maintenance and optimization. By analyzing vast datasets from engine sensors, AIS (Automatic Identification System), and meteorological data, ML models predict failures days or weeks in advance, enabling proactive interventions. This integrates with route and speed optimization algorithms that dynamically adjust voyages for fuel efficiency . Implementation involved partnering with tech leaders like Wärtsilä for fleet solutions and internal digital transformation, using MLOps for scalable deployment across the fleet. AI dashboards provide real-time insights to crews and shore teams, shifting from reactive to predictive operations .

Ergebnisse

  • Fuel consumption reduced by 5-10% through AI route optimization
  • Unplanned engine downtime cut by 20-30%
  • Maintenance costs lowered by 15-25%
  • Operational efficiency improved by 10-15%
  • CO2 emissions decreased by up to 8%
  • Predictive accuracy for failures: 85-95%
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Forever 21

E-commerce

Forever 21, a leading fast-fashion retailer, faced significant hurdles in online product discovery. Customers struggled with text-based searches that couldn't capture subtle visual details like fabric textures, color variations, or exact styles amid a vast catalog of millions of SKUs. This led to high bounce rates exceeding 50% on search pages and frustrated shoppers abandoning carts. The fashion industry's visual-centric nature amplified these issues. Descriptive keywords often mismatched inventory due to subjective terms (e.g., 'boho dress' vs. specific patterns), resulting in poor user experiences and lost sales opportunities. Pre-AI, Forever 21's search relied on basic keyword matching, limiting personalization and efficiency in a competitive e-commerce landscape. Implementation challenges included scaling for high-traffic mobile users and handling diverse image inputs like user photos or screenshots.

Lösung

To address this, Forever 21 deployed an AI-powered visual search feature across its app and website, enabling users to upload images for similar item matching. Leveraging computer vision techniques, the system extracts features using pre-trained CNN models like VGG16, computes embeddings, and ranks products via cosine similarity or Euclidean distance metrics. The solution integrated seamlessly with existing infrastructure, processing queries in real-time. Forever 21 likely partnered with providers like ViSenze or built in-house, training on proprietary catalog data for fashion-specific accuracy. This overcame text limitations by focusing on visual semantics, supporting features like style, color, and pattern matching. Overcoming challenges involved fine-tuning models for diverse lighting/user images and A/B testing for UX optimization.

Ergebnisse

  • 25% increase in conversion rates from visual searches
  • 35% reduction in average search time
  • 40% higher engagement (pages per session)
  • 18% growth in average order value
  • 92% matching accuracy for similar items
  • 50% decrease in bounce rate on search pages
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Visa

Payments

The payments industry faced a surge in online fraud, particularly enumeration attacks where threat actors use automated scripts and botnets to test stolen card details at scale. These attacks exploit vulnerabilities in card-not-present transactions, causing $1.1 billion in annual fraud losses globally and significant operational expenses for issuers. Visa needed real-time detection to combat this without generating high false positives that block legitimate customers, especially amid rising e-commerce volumes like Cyber Monday spikes. Traditional fraud systems struggled with the speed and sophistication of these attacks, amplified by AI-driven bots. Visa's challenge was to analyze vast transaction data in milliseconds, identifying anomalous patterns while maintaining seamless user experiences. This required advanced AI and machine learning to predict and score risks accurately.

Lösung

Visa developed the Visa Account Attack Intelligence (VAAI) Score, a generative AI-powered tool that scores the likelihood of enumeration attacks in real-time for card-not-present transactions. By leveraging generative AI components alongside machine learning models, VAAI detects sophisticated patterns from botnets and scripts that evade legacy rules-based systems. Integrated into Visa's broader AI-driven fraud ecosystem, including Identity Behavior Analysis, the solution enhances risk scoring with behavioral insights. Rolled out first to U.S. issuers in 2024, it reduces both fraud and false declines, optimizing operations. This approach allows issuers to proactively mitigate threats at unprecedented scale.

Ergebnisse

  • $40 billion in fraud prevented (Oct 2022-Sep 2023)
  • Nearly 2x increase YoY in fraud prevention
  • $1.1 billion annual global losses from enumeration attacks targeted
  • 85% more fraudulent transactions blocked on Cyber Monday 2024 YoY
  • Handled 200% spike in fraud attempts without service disruption
  • Enhanced risk scoring accuracy via ML and Identity Behavior Analysis
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Mayo Clinic

Healthcare

As a leading academic medical center, Mayo Clinic manages millions of patient records annually, but early detection of heart failure remains elusive. Traditional echocardiography detects low left ventricular ejection fraction (LVEF <50%) only when symptomatic, missing asymptomatic cases that account for up to 50% of heart failure risks. Clinicians struggle with vast unstructured data, slowing retrieval of patient-specific insights and delaying decisions in high-stakes cardiology. Additionally, workforce shortages and rising costs exacerbate challenges, with cardiovascular diseases causing 17.9M deaths yearly globally. Manual ECG interpretation misses subtle patterns predictive of low EF, and sifting through electronic health records (EHRs) takes hours, hindering personalized medicine. Mayo needed scalable AI to transform reactive care into proactive prediction.

Lösung

Mayo Clinic deployed a deep learning ECG algorithm trained on over 1 million ECGs, identifying low LVEF from routine 10-second traces with high accuracy. This ML model extracts features invisible to humans, validated internally and externally. In parallel, a generative AI search tool via Google Cloud partnership accelerates EHR queries. Launched in 2023, it uses large language models (LLMs) for natural language searches, surfacing clinical insights instantly. Integrated into Mayo Clinic Platform, it supports 200+ AI initiatives. These solutions overcome data silos through federated learning and secure cloud infrastructure.

Ergebnisse

  • ECG AI AUC: 0.93 (internal), 0.92 (external validation)
  • Low EF detection sensitivity: 82% at 90% specificity
  • Asymptomatic low EF identified: 1.5% prevalence in screened population
  • GenAI search speed: 40% reduction in query time for clinicians
  • Model trained on: 1.1M ECGs from 44K patients
  • Deployment reach: Integrated in Mayo cardiology workflows since 2021
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Mass General Brigham

Healthcare

Mass General Brigham, one of the largest healthcare systems in the U.S., faced a deluge of medical imaging data from radiology, pathology, and surgical procedures. With millions of scans annually across its 12 hospitals, clinicians struggled with analysis overload, leading to delays in diagnosis and increased burnout rates among radiologists and surgeons. The need for precise, rapid interpretation was critical, as manual reviews limited throughput and risked errors in complex cases like tumor detection or surgical risk assessment. Additionally, operative workflows required better predictive tools. Surgeons needed models to forecast complications, optimize scheduling, and personalize interventions, but fragmented data silos and regulatory hurdles impeded progress. Staff shortages exacerbated these issues, demanding decision support systems to alleviate cognitive load and improve patient outcomes.

Lösung

To address these, Mass General Brigham established a dedicated Artificial Intelligence Center, centralizing research, development, and deployment of hundreds of AI models focused on computer vision for imaging and predictive analytics for surgery. This enterprise-wide initiative integrates ML into clinical workflows, partnering with tech giants like Microsoft for foundation models in medical imaging. Key solutions include deep learning algorithms for automated anomaly detection in X-rays, MRIs, and CTs, reducing radiologist review time. For surgery, predictive models analyze patient data to predict post-op risks, enhancing planning. Robust governance frameworks ensure ethical deployment, addressing bias and explainability.

Ergebnisse

  • $30 million AI investment fund established
  • Hundreds of AI models managed for radiology and pathology
  • Improved diagnostic throughput via AI-assisted radiology
  • AI foundation models developed through Microsoft partnership
  • Initiatives for AI governance in medical imaging deployed
  • Reduced clinician workload and burnout through decision support
Read case study →

Best Practices

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

Let Claude Map Your Existing Planning Model Before Changing Anything

Start by having Claude build a mental model of your current budgeting and forecasting setup. Upload a representative planning workbook (or share anonymised structures), plus any documentation or slide decks you use in the annual budget process. Ask Claude to identify key tabs, link structures, and driver relationships.

This gives you an immediate x-ray of how your model actually works versus how people think it works. Claude can point out where assumptions are hard-coded, where growth rates are inconsistent, and which inputs are reused across many calculations. Use this as the foundation for cleaner scenario design.

Example prompt to map your planning file:

You are assisting the FP&A team with understanding their planning model.

1) Read the attached Excel file(s) and describe:
- Main sheets and their purpose
- Key input assumptions and where they are entered
- Main calculation flows (e.g. revenue build-up, staff costs, capex & depreciation)
- Links between sheets that are critical for the P&L, balance sheet, and cash flow

2) Identify potential risks for scenario planning:
- Hard-coded assumptions instead of driver-based inputs
- Inconsistent formulas across years/business units
- Circular references or complex dependencies

3) Provide a concise summary we can share with the CFO.

Expected outcome: a structured overview of your planning model in 30–60 minutes instead of days of manual documentation, and a clear list of weaknesses to fix before scaling AI-driven scenarios.

Use Claude to Generate a Scenario Library Aligned to Your Strategy

Instead of inventing scenarios ad-hoc, build a reusable scenario library with Claude. Provide your strategic plan, risk register, and market outlook reports, then ask Claude to propose a structured set of scenarios: macro cases, operational disruptions, strategic choices, and regulatory shocks.

For each scenario, have Claude define the narrative, affected drivers, and quantitative ranges (which you will later validate). This creates a repeatable catalogue that you can reuse every planning cycle and adjust as your strategy evolves.

Example prompt for building a scenario library:

You are an FP&A co-pilot helping us design a scenario library.

Input:
- Strategic plan deck (attached)
- Latest risk register (attached)
- Current base-case financial forecast (attached)

Tasks:
1) Propose 8–12 distinct scenarios grouped into:
   - Macro/market
   - Operational/supply
   - Strategic decisions
2) For each scenario, define:
   - 3–5 sentence narrative
   - Key financial drivers impacted (volume, price, mix, FX, headcount, capex, etc.)
   - Suggested quantitative ranges for each driver (we will validate)
3) Output in a structured table we can paste into Excel.

Expected outcome: a consistent, strategy-aligned scenario set that moves you beyond simplistic “+/-10% revenue” thinking.

Automate Scenario Documentation and Management Updates

One of Claude’s biggest wins is eliminating the PowerPoint treadmill. After Finance has run numbers in Excel or your planning system, use Claude to turn raw outputs into clear, narrative scenario summaries for management.

Export key tables (P&L, cash flow, KPIs) for each scenario and paste them into Claude with a short description of the assumptions. Ask Claude to highlight key differences vs. base case, quantify impact on critical metrics (EBIT, FCF, leverage), and phrase it in language your CEO and business leaders actually use.

Example prompt for management-ready scenario summaries:

You are helping the CFO prepare a scenario planning update for the executive team.

Input:
- Base case P&L and cash flow tables (FY+3 years)
- Two alternative scenarios with key assumptions

Tasks:
1) Summarise each scenario in max. 5 bullet points.
2) For each scenario, explain the financial impact vs. base case on:
   - Revenue
   - EBIT
   - Free cash flow
   - Net debt / EBITDA
3) Highlight top 3 risks and top 3 opportunities per scenario.
4) Propose one slide outline per scenario for our management deck.

Expected outcome: management-ready scenario narratives in under an hour, freeing finance capacity for deeper analysis.

Have Claude Run Sensitivity Analyses and Spot Hidden Exposures

Beyond discrete scenarios, use Claude to explore sensitivities and hidden exposures. Provide your base case plus several scenario outputs and ask Claude to identify which drivers the financials are most sensitive to and where downside risk is concentrated.

You can also ask Claude to propose additional stress cases that specifically test these sensitive drivers: for example, combining a modest volume drop with a specific cost increase to reflect more realistic risk clusters rather than extreme, unlikely shocks.

Example prompt for sensitivity analysis:

You are an FP&A analyst reviewing scenario outputs.

Input:
- Base case and 5 scenario outputs (P&L, cash flow, key driver values)

Tasks:
1) Identify which financial KPIs are most sensitive to changes in:
   - Volume, price, and mix
   - FX rates
   - Personnel costs
   - Capex
2) Rank the drivers by impact on EBIT and free cash flow.
3) Suggest 3 additional stress-test scenarios that combine the most impactful drivers
   in a realistic but adverse way.
4) Summarise findings for the CFO in one short paragraph.

Expected outcome: a clear view of which levers matter most, enabling targeted management attention and better risk conversations.

Standardise Prompts and Templates for the Finance Team

To make Claude part of your finance workflow, don’t rely on everyone inventing their own prompts every time. Create a set of standard prompt templates for recurring tasks: model mapping, scenario creation, management summaries, and risk analyses. Store them in your collaboration tools or finance playbook so they can be reused and improved.

Encourage team members to log which prompts work best and where Claude struggled, then refine templates centrally. Over time, this evolves into a tailored “AI handbook for FP&A” that reflects your specific business model, KPIs, and planning cadence.

Example standard prompt for recurring quarterly scenario updates:

Quarterly Scenario Refresh – Standard Prompt

Context:
- We update base case and scenarios every quarter.

Tasks for Claude:
1) Compare the new actuals and updated base case to last quarter's forecast.
2) Identify which assumptions behind our existing scenarios are now outdated.
3) Propose updated parameter ranges for affected drivers.
4) Suggest which scenarios to retire, keep, or add.
5) Draft a short note for business unit leaders explaining the key changes.

Expected outcome: faster, more consistent quarterly updates with less variation in quality between team members.

Connect Claude to Your Planning Stack Gradually

In the longer term, you may want to integrate Claude more tightly with your planning environment (Excel-based workflows, planning tools, or data warehouse). Start simple: use exports and structured CSVs that Claude can read, then progressively move to API-driven workflows where outputs are written back into templates or reporting layers.

Work with IT and security to define where Claude can access real data vs. anonymised extracts, and ensure compliance requirements are met. A clear integration roadmap prevents ad-hoc experiments from turning into shadow IT, while still letting your team experiment and learn quickly.

Expected outcomes when these best practices are implemented: finance teams can typically cut scenario preparation time by 30–50%, expand from 2–3 simplistic cases to 8–15 well-structured scenarios per cycle, and provide management with clearer, faster insight into risks and options. The result is not just efficiency, but materially better financial decisions.

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Frequently Asked Questions

Claude improves scenario planning in finance by handling the high-effort, low-value parts of the work. It can read large Excel workbooks, map your model structure, propose scenario sets based on your strategy, and generate clear summaries comparing each case to the base plan.

Instead of building every scenario from scratch, your team defines the key drivers and assumptions, and Claude helps with structuring, documentation, and narrative output. This allows you to explore more scenarios in the same time, focus human judgment on validating assumptions, and give management a clearer view of risks and options.

You don’t need a data science team to start. The essential ingredients are: a reasonably structured planning model (usually in Excel or a planning tool), finance professionals who understand your business drivers, and access to Claude in a compliant environment.

Helpful skills include comfort with Excel exports, basic data hygiene, and the ability to describe planning assumptions in clear language. Reruption typically supports clients by setting up the first prompts, workflows, and templates so controllers and FP&A managers can work with Claude without needing to become AI experts.

In our experience, you can see tangible benefits within one planning cycle if you start focused. A targeted pilot on a specific use case — for example, documenting and comparing 4–6 scenarios for the next budget round — can usually be set up in a few weeks.

In the first 2–4 weeks, Claude typically helps you map your model, define a scenario library, and generate better management summaries. Over 1–3 subsequent cycles, you can standardise prompts, refine workflows, and gradually expand usage to more business units, making AI-supported planning part of your normal finance rhythm.

The direct usage cost of Claude is typically modest compared to overall finance budgets; the main investment is in setup, governance, and training. Most ROI comes from time saved in preparing scenarios, more informed strategic decisions, and better risk management.

Realistically, finance teams can often reduce manual scenario preparation time by 30–50%, avoid expensive last-minute rework when assumptions change, and support better capital allocation by quantifying more strategic options. The combination of efficiency gains and better decision quality usually makes the business case compelling, even in conservative environments.

Reruption works as a Co-Preneur alongside your finance and IT teams to turn Claude from an interesting tool into a working solution. We start with a 9.900€ AI PoC that uses your real planning files and processes to prove what’s technically and operationally feasible: model assessment, first workflows, and measurable impact.

From there, we help design and build the concrete solution: Claude prompts and templates tailored to your business, secure integration into your planning stack, and enablement for controllers and FP&A teams. With our Co-Preneur approach, we don’t stop at slides — we embed with your team, iterate quickly, and ship working automations and internal tools that strengthen your financial planning capabilities.

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