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🎯 The Complete Walk-Through: How to Build Professional Financial Models in Minutes Using Shortcut

Ever needed to quickly model the revenue impact of a new feature but felt it was overkill or you just don’t have the time? Or wanted to create growth forecasts for your product roadmap without bothering the finance team? Shortcut could be the answer for you. I featured it a month or so ago back on the Time Saving Tools section so thought I’d cover the recent Greg Eisenberg Podcast that dives deeper into how Shortcut transforms anyone into an Excel wizard.

In the Pod he chats to Nico, the co-founder of Shortcut, who doesn’t just talk about the product—he builds real financial models live on screen, showing exactly how to prompt the AI, handle errors, and create professional dashboards. From updating DCF models with fresh 10-K data to building custom utilisation trackers, this is your complete playbook for AI-powered spreadsheet mastery that every product manager can apply to feature impact analysis, growth forecasting, and business case development.

🎥 Watch the full episode here
📆 Published: 29th July 2025
🕒 Estimated Reading Time: 4 mins. Time saved: 40+ mins! 🔥

🏗️ Step 1: Understanding What You're Actually Getting

Shortcut isn't another Excel add-on—it's Excel completely rebuilt for the AI era. The interface looks identical to Excel, but underneath lies an AI agent that can handle 90% of your spreadsheet work autonomously. Instead of helping you write formulas, it writes entire financial models.

The smartest starting approach isn't building from scratch. Import your existing Excel files directly into Shortcut and let the AI manipulate them. During the demo, Nico opens a complex Microsoft DCF model that would typically require half a day to build manually, then delegates the entire update process to the AI.

The time-saving reality is dramatic. Tasks that consume hours of manual work get completed in 10-15 minutes. But this requires a mindset shift from seeking assistance to full delegation—you're not getting help with Excel, you're supervising an AI that's doing Excel for you.

Key Takeaways:

• Import existing Excel files rather than starting from scratch

• Think delegation, not assistance—let AI do 90% of the work

• Complex models that take hours complete in 10-15 minutes

• The interface remains familiar while capabilities transform completely

🧠 Step 2: The Counter-Intuitive Prompting Strategy

Most people assume AI requires detailed, specific prompts. Nico demonstrates the opposite approach works better. Deliberately vague prompts force the AI to get creative and ask clarifying questions, leading to better results than trying to specify everything upfront.

When updating a DCF model with Google's financial data, Nico uses a simple prompt asking the AI to update the template using Google data and pull 10-K filings from 2022-2024 for forward projections through 2029. No complex instructions about data sources, formatting, or calculations.

The magic happens through Shortcut's clarifying questions feature. Instead of guessing what you want, the system asks intelligent follow-ups about growth assumptions, time periods, visualisation preferences, and data sources. This conversational refinement produces more sophisticated results than traditional detailed prompting.

This approach makes AI accessible to non-technical users while teaching better prompting through guided interaction. Users who struggle with traditional AI prompting find success through this question-and-answer flow.

Key Takeaways:

• Vague prompts work better than detailed specifications

• Let the AI ask clarifying questions rather than guessing requirements

• Conversational refinement produces more sophisticated results

• The system teaches better prompting through guided interaction

🚀 Step 3: Advanced Document Processing - The DCF Update Walkthrough

That same demo is definitely the most impressive.

The AI automatically locates Google's 10-K filings in SEC databases—100-page PDF documents that are notoriously difficult to parse. When hitting context limits due to document size, the system intelligently chunks the information, processing sections systematically while maintaining coherence across the entire analysis.

Throughout the process, the AI provides a clear task plan: reading current models, downloading 10-K filings, extracting relevant data, and updating historical information and assumptions. This transparency allows users to understand and verify the work being performed.

Real-time error detection sets Shortcut apart from traditional tools. During the live demo, the AI identifies income calculation errors, incorrect cell references, and circular dependencies—then fixes them automatically while explaining its reasoning. These are among Excel's most challenging problems, typically requiring expert-level knowledge to resolve.

Key Takeaways:

• Complex document processing happens automatically with intelligent chunking

• The AI creates and follows detailed task plans for transparency

• Real-time error detection handles Excel's most difficult problems

• Circular references and formula errors are identified and fixed automatically

💡 Step 4: Building Custom Business Dashboards

The agency utilisation dashboard demo shows how Shortcut handles real business problems. Greg describes needing visibility into employee utilisation rates, team profitability, and performance benchmarks—a complex analytical challenge that would typically require significant Excel expertise.

The AI breaks this down through clarifying questions about time periods, metrics, visualisation preferences, and benchmarking requirements. When Greg mentions his company name, the system researches the actual business to understand context and industry positioning.

The build process involves 13 separate tasks, including creating utilisation calculations, revenue analysis, industry benchmark research, and strategic recommendations. The AI sources benchmark data from web research, comparing Greg's hypothetical utilisation rates against industry standards for design agencies.

The final dashboard includes professional formatting with conditional formatting that highlights concerning utilisation rates, departmental summaries, and strategic recommendations based on the analysis. Greg's reaction confirms the output matches his mental model despite starting with a vague description.

Key Takeaways:

• Start with business problems rather than technical specifications

• The AI researches industry benchmarks automatically from web sources

• Complex multi-sheet analyses are planned and executed systematically

• Results match user expectations even from vague initial descriptions

🔄 Step 5: Refinement and Error Handling Techniques

Professional Excel work requires iterative refinement, and Shortcut handles this through targeted editing capabilities. When formula errors appear, users can highlight specific ranges and request fixes without affecting the broader model.

This approach mirrors Cursor's Command-K feature for code editing. The AI restricts changes to selected areas while intelligently gathering necessary context from across the spreadsheet. This prevents unintended modifications while ensuring fixes address root causes rather than symptoms.

The system handles Excel's most complex problems, including circular references where formulas depend on each other in ways that create calculation loops. During the demo, the AI identifies these dependencies, locates the specific formulas causing issues, and resolves them while maintaining model integrity.

Iterative refinement builds on previous work rather than starting over. When Greg suggests adding conditional formatting for utilisation rates above 70%, the system can incorporate these changes while preserving existing analysis and formatting.

Key Takeaways:

• Highlight specific ranges for targeted fixes rather than broad requests

• The AI handles Excel's most complex problems including circular references

• Iterative refinement builds on previous work rather than restarting

• Context is maintained across multiple refinement requests

📈 Step 6: Transparency and Professional Output

Trust becomes critical when AI handles financial analysis. Shortcut addresses this through comprehensive observability features that show exactly how results were generated. Users can distinguish between hardcoded values and formula-driven calculations, with full traceability to source documents.

Every data point can be traced back to its origin. Click on any figure and see the exact page from the source PDF that provided that information. This level of transparency enables proper review and verification of AI-generated models.

Professional formatting happens automatically, including conditional formatting based on business logic, appropriate colour schemes, chart generation, and dashboard layouts with key performance indicators. The system applies industry-standard formatting conventions without requiring user specification.

Export functionality maintains full Excel compatibility. Users can create shareable links that include edit history and collaboration features, or export directly to Excel format. Recipients would never know the analysis was created using AI rather than traditional Excel methods.

Key Takeaways:

• Full transparency into data sources and calculation methods builds trust

• Professional formatting and conditional logic are applied automatically

• Complete edit history and collaboration features support team workflows

• Seamless Excel export maintains compatibility with existing processes

🎯 Getting Started: Your Immediate Action Plan

Access Shortcut at https://www.tryshortcut.ai where you can try prompts for free before committing to a subscription. Think about starting with a task that directly impacts your daily work: feature impact analysis, growth forecasting, or competitive benchmarking (rather than complex financial models!).

The platform serves product people pretty well I think. Need to model how a new feature might impact monthly recurring revenue? Want to forecast user growth across different scenarios? Or create a business case showing the ROI of a product initiative? These become 10-minute tasks rather than day-long projects.

Consider these product-specific starting use cases:

• Feature Impact Analysis: Model how conversion rate improvements translate to revenue growth

• Growth Forecasting: Create scenario planning for user acquisition and retention metrics

• Competitive Analysis: Build dashboards comparing your product metrics against industry benchmarks

• Business Case Development: Generate ROI calculations for product initiatives with professional formatting

• Cohort Analysis: Transform user behaviour data into retention and lifetime value models

Pricing reflects the platform's positioning as a professional tool: $40 monthly for the pro plan, or $200 monthly for the max plan that includes "analyst beta" functionality—essentially 10 parallel AI analysts working simultaneously on different analyses.

The most effective starting approach is probably to bring your actual product data—user metrics, conversion rates, revenue figures—and asking Shortcut to help you understand relationships and forecast scenarios. These represent high-value activities where AI capabilities provide immediate, measurable benefits to product strategy and planning.

"The good way to think about it is not as like a co-pilot for one or two steps. It's like it will do 90% of your entire job and then you get to do other things as that happens," Nico explains, capturing the fundamental shift from assistance to delegation that defines this new category of AI-powered productivity tools.

Key Takeaways:

• Start with product-specific analyses like feature impact and growth forecasting

• Eliminate dependency on finance teams for basic business case development

• Early adoption provides competitive advantages in data-driven product decisions

• Integration requires minimal learning curve while dramatically expanding analytical capabilities

Sophisticated product analytics is no longer limited to data scientists or finance teams (or long-since retired Strategy Consultants….). With proper understanding of AI prompting and Shortcut's capabilities, anyone can produce professional-grade analyses and forecasts in minutes rather than hours or days.