How Finance Leaders Make Decisions | Michael Miao and Adam Swiecicki

In the high-stakes world of hyper-growth startups, the role of the finance leader has fundamentally shifted. No longer just the custodians of the balance sheet or the "department of no," modern CFOs are expected to be strategic architects who drive growth and connect the dots across the entire organization. In a candid conversation from Kleiner Perkins’ Builders series, Michael Miao (CFO of Glean) and Adam Swiecicki (CFO of Rippling) pull back the curtain on how they navigate decision-making, scale complex organizations, and leverage AI to modernize the finance stack. Their insights offer a blueprint for founders and operators looking to build a finance function that acts as a catalyst for innovation rather than a bottleneck.

Key Takeaways

• Shift the mindset from gatekeeper to partner: The modern finance leader’s goal is to "get to a yes" by using data to validate investment opportunities rather than simply cutting costs.
• Apply "Maslow’s Hierarchy" to finance scaling: Prioritize accurate accounting (the foundation) and forecasting before attempting advanced strategic finance.
• Focus on "Growth Endurance" over "Rule of 40": Long-term value is often better predicted by how well a company sustains its growth rate year-over-year rather than a simple balance of growth and profitability.
• Utilize zero-based budgeting: When evaluating headcount, analyze the total allocation of resources rather than just incremental requests to prevent organizational inertia.
• Leverage AI for operational rigor: Automation in data cleaning and reconciliation allows finance teams to focus on high-value analysis rather than manual reporting.

From Gatekeeper to Growth Driver

The stereotype of the finance department as the corporate "bad guys" is fading. For leaders like Miao and Swiecicki, the transition from investment banking and venture capital into operational roles required a fundamental shift in perspective. While investors operate in the theoretical realm of market analysis, operators must deal with the messy reality of personnel, execution, and cross-functional alignment.

However, the core skill set of an investor—pattern matching and data-driven conviction—remains vital. The difference lies in the application. Instead of acting as a barrier to spending, the finance function should serve as a strategic partner that helps other departments articulate the business case for their initiatives.

You can and you should outsource accounting, but the thing you can't outsource in the early days is critical thinking. Help me get to a yes. Partner with me to better understand the problem.

This "help me get to a yes" philosophy requires empathy. Finance leaders must understand the unique languages of engineering, sales, and marketing. By translating these disparate operational goals into the universal language of dollars and cents, finance becomes the great equalizer that aligns the company toward a shared objective.

Maslow’s Hierarchy of Finance Needs

Building a finance team is not a linear process of adding bodies; it is a structural evolution. Swiecicki proposes a framework akin to Maslow’s Hierarchy of Needs for scaling a finance organization. Attempting to reach the top of the pyramid without securing the base leads to unstable decision-making.

1. The Foundation: Accounting

At the base lies accurate historical data. If you cannot trust your historical financials, you cannot forecast the future. In the early stages (Seed to Series A), this function can often be outsourced, but the data must be clean and reliable.

2. The Middle Layer: FP&A

Once historicals are accurate, the focus shifts to Financial Planning and Analysis (FP&A). This involves using past data to infer future performance. This "forecasting muscle" is critical for managing burn rates and understanding the trajectory of the business.

3. The Apex: Strategic Finance

Only when the data is clean and the forecast is reliable can a team engage in true strategic finance. This is where the function becomes exciting—moving from reporting on the business to helping run the business. This involves allocating capital to high-ROI initiatives and making data-driven pivots.

For early-stage startups, the first finance hire should likely be a generalist—someone with a banking or private equity background who can handle ambiguity and strategic thinking. Specialized roles, such as dedicated controllers or R&D finance partners, come later as the organization matures into the Series C or D stages.

Redefining Success Metrics: Growth Endurance vs. Rule of 40

While standard SaaS metrics like the "Rule of 40" (where growth rate + profit margin should exceed 40%) are popular in public markets, they can be misleading for high-growth private companies. The Rule of 40 treats all burn equally and can incentivize slower growth for the sake of short-term profitability.

Instead, Swiecicki advocates for a metric known as Growth Endurance. This measures the durability of a company's growth rate year-over-year. For example, if a company grows 100% in year one and 80% in year two, it has 80% growth endurance.

If you're a 100% grower with 70% growth endurance, you're worth 50x on a revenue multiple... And if you're a 100% grower with 90% growth endurance, you're worth a 1,000x.

Maintaining high growth endurance requires long-term planning—often looking 3 to 5 years out. Companies must plant seeds today (new products, new geographies) to ensure they harvest growth years down the line.

Leading vs. Lagging Indicators

To influence these outcomes, finance leaders must look beyond lagging indicators like churn or revenue. Miao emphasizes the importance of leading indicators:

• Pipeline Composition: Not just the volume of deals, but the mix of late-stage vs. early-stage and the presence of large enterprise deals.
• Product Engagement: Metrics like "Daily Active Users / Monthly Active Users" (DAU/MAU) and seat utilization rates often predict retention better than contract terms.
• Token Consumption: For AI companies specifically, tracking how much the AI features are actually being used can signal future expansion revenue.

Operational Rigor and Zero-Based Budgeting

As companies scale from hundreds to thousands of employees, entropy sets in. A high-functioning finance team combats this through rigorous operating cadences and disciplined budgeting.

One of the most effective tools for managing headcount and resources is zero-based budgeting. In annual planning, it is common for department heads to ask for "incremental" headcount (e.g., "I need 5 more engineers"). This approach assumes that the existing team is fully optimized and working on the right things.

A more disciplined approach involves reviewing the total headcount. Leaders should ask: "If you have 100 people today, are they all working on the highest ROI initiatives?" Often, the resources for a new project can be found by reallocating existing staff from legacy projects that no longer drive growth. This forces leaders to force-rank their priorities and combat organizational inertia.

The Board Meeting as a Forcing Function

Board meetings should not be viewed merely as governance obligations but as operational forcing functions. The preparation of a board deck compels the executive team to align on strategy, measure fidelity to the plan, and confront intellectual dishonesty regarding what is and isn't working.

AI as the New Financial Analyst

The integration of Artificial Intelligence into finance is no longer theoretical. Both Glean and Rippling utilize AI to automate the "drudgery" of finance, allowing human talent to focus on analysis and strategy.

Miao notes that processes like reconciling Salesforce data against signed order forms—previously a manual task requiring days of effort—can now be handled by AI agents that OCR documents and validate fields automatically. Similarly, Rippling uses AI to draft flux analyses (explanations for variances in financial accounts) during the monthly close.

It turns out it's just an AI problem... you can use Glean to build an agent to check the order form, OCR it, make sure it matches all the correct fields within Salesforce.

However, both CFOs advise against over-engineering internal AI solutions. Finance teams should not necessarily hire their own dedicated engineers, which creates maintenance debt. Instead, they should leverage "AI-native" features embedded in modern software platforms or use low-code tools to build simple automations.

Conclusion

The convergence of software and AI means that the finance function must evolve faster than ever. Whether it is adopting "Growth Endurance" as a north star, enforcing zero-based budgeting to maintain agility, or deploying AI to automate the close process, the objective remains the same: driving responsible, enduring growth. By mastering the data and fostering cross-functional empathy, finance leaders do not just report on the future—they help build it.