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In this week’s edition (est. 5 min. read):

• Main Article

• Quick Hits

• Number to Know

And we’re back for everyone’s favorite time of the year 🥳 More meetings than we all would like to nail down next year’s plan and goals, endless number of spreadsheets, so much fun! Meanwhile, it’s the closing blitz for the CRO with large accounts negotiations, discount approvals, legal/procurement cycles, and exec escalations to land 12/31.

Similar story for the CPO/CTO (peak traffic, high-stakes releases, budget-impacting architectural calls before year-end), CHRO (carrier decisions, new payroll system roll-out), and the rest of the C-Suite.

Everyone knows the importance of it, but it pulls leaders into spreadsheets and meetings exactly when they should be doubling down on year-end execution.

Annual planning has long been the backbone of organizational alignment. It’s a chance to set strategy, allocate resources efficiently, and align the company around a shared mission. But in the age of AI, where forecasting, scenario analysis, and data refreshes can happen in real time, many leaders are asking:

Do we still need an annual plan?

The answer is both yes and no. Annual planning isn’t disappearing, but AI is fundamentally reshaping its role, cadence, and purpose. What’s dying is the rigid, spreadsheet-driven ritual of old. What’s emerging is a hybrid model: a baseline annual plan, continuously updated through AI-powered insights.

For the purpose of this newsletter, AI will be short for AI applications and augmented workflows used during the planning process. Let’s dive in!

🧐 Why Annual Planning Still Matters

Despite AI’s disruptive potential, several core reasons keep annual planning relevant:

Investor & Board Expectations

• Boards, investors, and lenders still demand an annual financial roadmap. Compensation structures, capital raises, and governance models depend on having a baseline operating plan. Without one, exec teams lose a shared definition of success.

Compensation & People Programs

• Salary adjustments, equity refreshes, and bonus programs are tied to annual cycles. Employees expect predictability and stability, something AI can’t replace.

Strategic Alignment & Tradeoffs

• Annual planning forces cross-functional leaders to pause, align, and negotiate. It’s the one moment where the C-Suite sits down to reconcile competing priorities. This is less about numbers, more about building accountability and a “North Star” for the year.

Regulatory & Compliance Requirements

• In regulated industries and public company contexts, annual forecasts and board-approved budgets remain non-negotiable.

🤖 Why AI Challenges the Annual Planning Cycle

AI doesn’t eliminate planning; it removes the need for static, one-time planning:

From Static to Dynamic

• AI enables rolling forecasts and continuous recalibration based on live market signals. Plans are no longer “set in stone”; they are “living documents.”

Speed & Accuracy

• AI automates variance analysis, reconciles data across systems, and runs dozens of “what-if” scenarios in minutes. This shortens cycle times from months to weeks, while improving forecast precision.

Business Conditions Shift Faster

• In markets disrupted by AI, customer behavior, pricing, and competitive dynamics evolve quarterly, sometimes monthly. A 12-month budget drafted in Q4 may already feel outdated by Q2.

Cost of Iteration Collapses

• What once took analysts countless hours, nights and weekends (data gathering, spreadsheet work, reconciliations) can now be automated. This makes real-time course correction practical, not aspirational.

🙆 The Human Element AI Cannot Replace

While AI handles the tactical and analytical heavy lifting, the human role grows more strategic:

Asking the Right Questions

• AI generates answers; leaders must frame the right questions, tradeoffs, and risk tolerances.

Interpreting Insights in Context

• AI spots patterns; executives must interpret what they mean for competitive strategy, customer experience, and long-term bets.

Building Culture & Trust

• AI can recommend reallocations, but only leaders can manage the human implications, from morale to compensation to cultural resilience.

Decision-Making Under Uncertainty

• Machines model probabilities; humans decide which risks are worth taking.

🚀 The New Planning Paradigm: Hybrid & Continuous

The future is neither “annual planning only” nor “continuous planning only.” It’s a synergistic model:

Annual Plan as the Strategic Launchpad

• Sets the high-level strategy, capital envelopes, and people programs. The plan functions as the “baseline contract” with investors, employees, and the board.

AI-Powered Rolling Forecasts

• Monthly or quarterly refreshes that update assumptions dynamically. Continuous monitoring of revenue, spend, and headcount variances.

AI-Enabled Assumption Tracking

• Automated alerts when key inputs (pipeline health, attrition, cloud costs) deviate far enough to trigger a re-plan.

Human + AI Collaboration

• AI accelerates scenario modeling, benchmarking, and anomaly detection. Executives focus on judgment, strategy, and storytelling, the irreplaceable leadership layer.

Implementation Catch Up for Leaders

• Not using AI in your planning process yet? Don’t worry, I’ve got you covered 😉

• Follow me on LinkedIn, and I’ll be posting examples of quick-win implementations for the most functional leaders over the next few days and weeks.

♟️ One Last Thought

As the saying goes, “garbage in, garbage out”. It’s even more true in the age of AI. Take an honest look at the quality and reliability of your data and don’t force AI onto your planning process for the sake of following a trend. Your team and the board are depending on you to put together a plan that sets the company up for success, and everyone needs the confidence to own it. Prioritize that outcome, with or without AI.

Quick Hits: What’s On My Radar

• Recent research, including the Lancet Gastroenterology & Hepatology one is showing that doctors can lose critical medical skills with routine AI use, particularly in diagnostic settings such as colonoscopy and cancer detection. These findings highlight a growing consensus that routine dependence on AI can erode doctors’ critical skills in diagnosis, motivation, and independent decision-making, with leading researchers and journals calling for deliberate strategies to maintain human expertise and safeguard patient care quality. The same can apply to the business world.

• Microsoft just released the first version of its Copilot Analyst agent, an AI-powered assistant for data analysis. The agent is like having a skilled data analyst on hand to help you make sense of data quickly without requiring advanced expertise. Still early, uses OpenAI’s o3-mini and usage caps will limit adoption in my humble opinion.

Number to Know

58%

AI investments account for 58 cents of every VC dollar deployed in 2025 so far. It’s a massive concentration of capital in a single sector.

If you’re in AI: You’re competing in an incredibly well-funded but crowded space where AI companies are also operating with higher burn rates. This means you need to be especially disciplined about capital efficiency and differentiation.

If you’re not in AI: You’re fighting for the remaining 42% of VC dollars alongside every other non-AI startup. This makes fundraising significantly more challenging and means you need to be even more compelling in your value proposition and unit economics.

Thanks for reading. Don’t forget to subscribe!

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Until next time, 👋

JP

The concept was made popular by John Chambers, the former CEO of Cisco, who delivered an astonishing 3,800% revenue growth during his tenure, but it is often forgotten or underutilized by business leaders. It may now take a new life thanks to AI.

Most tech businesses were started by building an MVP, so the natural progression from there is to continue to build as the company grows and investors are putting money in for you to build even more and deepen your moat. Makes total sense. But you may reach a point where there are faster and more cost-effective ways to scale your business than just building it all in-house. This does not only apply to the company product by the way, it’s applicable to all functions across the business.

Let me walk you through a simple but real example from one of the startups I was part of:

The company website had been developed in-house, and the marketing team basically depended on already limited engineering resources to make changes to their copies and creative assets. To make matters worse, the original developer who created the website had left the company. Needless to say, that did not scale 🙂 We made the decision to switch from a Build to a Buy strategy and migrated to Webflow. That put the Marketing team in the driver seat and unlocked their ability to be agile and drive more leads for the business. It also freed up engineering resources that could then be reallocated towards higher value deliverables on our roadmap.

Assessments and decisions like this should happen regularly as businesses scale, and your Finance Business Partner is best suited to help teams understand the financial trade-offs and ROI. In the case above, we understood that there would be temporary overage for hosting two websites during the build and migration phase, but the ROI was clear.

I also think that the pace of innovation in AI is shifting and accelerating how leaders should weight such options.

🧐 What do we mean buy Build vs. Buy vs. Partner?

➡️ Build

When you’re choosing to develop new capabilities entirely in-house, from ideation to development, deployment and on-going maintenance.

• Pros:

  • Complete control over strategic direction, short-term and long-term.

  • Strengthens business resilience by building internal capabilities and expertise, fostering a culture of innovation.

  • Flexibility to adapt and pivot rapidly as market and company priorities evolve, without external constraints or dependencies.

  • Enhanced security and confidentiality of proprietary technologies, processes and data.

• Cons:

  • Usually requires higher upfront investment in infrastructure, headcount, and training, often leading to extended development cycles and delayed market entry.

  • Significant ongoing commitment to maintaining and updating technology, which can divert resources from other critical strategic initiatives.

  • Potential challenges in talent acquisition, retention, and motivation, particularly in competitive labor markets, increasing operational risks related to talent gaps.

  • Risks of internal inefficiencies, siloed decision-making, and limited external market perspective potentially hindering optimal solution development.

How is AI impacting this investment strategy?

On one hand, agentic AI is starting to lower the costs and delivery timelines of projects. Developers who are embracing AI are able to ship a lot more code and with higher quality, making a fully owned tech less onerous. On the other hand, because AI is making it easier to build, it doesn’t mean that you’re building a moat, if it’s easier for competitors to do the same. I always advise that developers be focusing their efforts on what the company has identified as core IP and ruthlessly deprioritize or reassign what is not.

➡️ Buy

When you acquire external companies or license specific technologies to rapidly integrate pre-existing capabilities, accelerating strategic initiatives without the extended timelines of internal development.

• Pros:

  • Immediate integration of proven technologies and established market solutions, significantly reducing the time to market compared to internal development.

  • Rapid expansion of market share and capabilities, particularly valuable in highly competitive or fast-moving markets where speed and agility are critical.

  • Acquihires to get quick access to talent and operational expertise that might otherwise take significant time and resources to develop internally.

  • Provides competitive advantages by strengthening market positioning through integrating complementary technologies and customer bases.

• Cons:

  • High initial capital outlay, including possible acquisition premiums, legal fees, and integration costs.

  • Integrations are hard, posing significant risk and potentially disruptive to ongoing operations.

  • Potential for cultural clashes, employee turnover, and morale issues due to the challenges inherent in merging different organizational identities and management styles.

  • Sometimes 1+1 ≤ 2, not 3, leading to diminished returns and increased operational complexity if integration is poorly executed.

How is AI impacting this investment strategy?

The rapid advancements in agentic AI are rapidly lowering barriers of entry for competitors across all industries. Companies will need to be super crisp on their core IP and defend it through accelerated innovation. For what is not core IP, you may get a better ROI and time-to-market by licensing the technology. When targeting a company for acquisition, however, valuation multiples for AI startups are at record levels. As such, mostly deep pocketed and larger companies have been able to afford such acquisitions.

➡️ Partner

When you establish strategic collaborations or alliances with third parties, leveraging mutual strengths without requiring full ownership or extensive resource allocation.

• Pros:

  • Quick access to advanced capabilities, technologies, and established market positions without substantial upfront investment, enabling rapid strategic expansion.

  • Reduced financial and operational risk through shared investments, responsibilities, and mutual accountability, allowing flexible responses to market changes.

  • Provides opportunities for experiential learning, innovation, and knowledge transfer, enhancing internal competencies without major capital commitment.

  • Facilitates scalability and agility, enabling companies to pilot new technologies and market segments rapidly, validating strategic assumptions before deeper investments.

• Cons:

  • Limited direct control over strategic direction, product development, and quality assurance, potentially resulting in misaligned expectations or compromised outcomes.

  • Increased vulnerability due to dependency on partner performance, reliability, and strategic priorities, creating risks if partners pivot or underperform.

  • Intellectual property risks, including potential exposure or loss of sensitive proprietary information shared within partnerships.

  • Potentially complex and resource-intensive partnership management, including governance structures, contract negotiation, and ongoing performance monitoring, requiring sustained attention and resources to maintain successful collaborations.

How is AI impacting this investment strategy?

While new, MCP and A2A frameworks are making it easier to integrate with other AI providers and leverage their capabilities. This is a area to watch for learnings from early success and setback use cases. More to come in future posts.

📶 How do these materialize in the real world?

Below are a few public cases from industry leaders to illustrate who each strategic decision can play out in complex business environments:

Build: Stripe’s Infrastructure Investment

Stripe’s strategic choice to build its payment infrastructure internally was instrumental in its global success. By opting for internal development, Stripe ensured maximum flexibility, customization, and operational control over its critical payment processing systems. This internal build strategy allowed Stripe to uniquely cater to the specific needs of developers and enterprises, rapidly iterating on features tailored to its target market. The investment in proprietary infrastructure also provided Stripe with unmatched reliability and scalability, directly contributing to its market dominance and consistent growth in transaction volumes and client base.

Stripe Case Study Source

Buy: Snowflake’s Acquisition of Crunchy Data

Snowflake’s recent strategic acquisition of Crunchy Data was a significant turning point, greatly enhancing its capabilities in AI-driven database management. Recognizing the urgent need for advanced, AI-ready data platforms, Snowflake swiftly acquired Crunchy Data to leverage its expertise in PostgreSQL database solutions. This acquisition immediately boosted Snowflake’s technical capabilities, accelerated market entry, and significantly improved competitive positioning. Snowflake’s proactive buy strategy allowed it to integrate proven technologies, leverage Crunchy Data’s established client relationships, and rapidly scale operations, demonstrating the strategic effectiveness of acquisitions in swiftly evolving tech landscapes.

Snowflake Case Study Source

Partner: Slack’s Growth through Strategic Alliances

Slack’s meteoric rise can largely be attributed to its strategic approach of forming alliances with widely used software platforms such as Google Drive, Trello, GitHub, and Salesforce. Instead of building extensive functionalities in-house, Slack prioritized integration capabilities, significantly accelerating user adoption. These integrations not only broadened Slack’s appeal but also allowed it to rapidly scale its market presence and user base. Slack’s emphasis on partnerships minimized initial capital investments and technical risks, positioning it as a powerful and indispensable collaboration tool in the enterprise communication market.

Slack Case Study Source

🚀 A Step-by-Step Guide for Practical Decision-Making

Note that this framework should only really be used for sizable investments and when the answer is not obvious.

1️⃣ Frame the Decision in One Sentence

“We need [capability] by [when] to unlock [growth / savings / risk-reduction].”

Locking the objective first keeps later scoring anchored to outcomes, not preferences.

2️⃣ Assign Strategic + Financial Weights (100 pts total)

Tip: Make weights editable—different initiatives may shift emphasis (e.g., regulated FinTech raises Control to 20 pts).

3️⃣ Score Each Option (0–5) Against Every Criterion

Multiply score × weight to get a weighted subtotal.

Example:

Add subtotals; the highest total suggests the front-runner but go/no-go still gets a qualitative sanity check.

4️⃣ Run the Financial Drill-Down

Model three scenarios (base, upside, downside) over 3–5 years to expose hidden landmines—e.g., renewal price-uplifts beating self-hosting costs by Y3.

👍 Quick Rules of Thumb for Tech Startups

🤖 Leveraging AI for Enhanced Decision-Making

This space is evolving quickly, but modern CFOs can already significantly amplify their strategic decision-making capabilities through practical AI-driven tools and methods, including:

• Advanced Scenario Planning: Utilizing AI-driven predictive analytics tools (such as Pigment) to perform sophisticated scenario modeling. This helps CFOs anticipate financial outcomes under varying market conditions and strategic scenarios, reducing uncertainty.

• Dynamic Forecasting: Employing real-time AI forecasting tools like Planful Predict, DataRails Predict or Float to continuously update financial and operational forecasts. These tools leverage historical and real-time data to provide highly accurate and timely financial insights.

• Resource Optimization Algorithms: AI algorithms, such as optimization tools provided by platforms like Mosaic or mondayAI, can dynamically reallocate resources across projects, departments, and strategic initiatives, ensuring maximum efficiency and alignment with strategic goals.

• Enhanced Due Diligence: AI-driven analytics can swiftly evaluate target companies in acquisition scenarios, assessing factors like market positioning, financial stability, technology compatibility, and operational alignment, greatly reducing the time and risk associated with due diligence processes.

• AI-Enhanced Risk Assessment: AI solutions like UpGuard or Vouch enable CFOs to proactively identify and manage potential risks, offering advanced insights into market volatility, regulatory compliance issues, and operational vulnerabilities.

♟️ Final Thought

The best frameworks turn debates into numbers without letting numbers replace judgment. By pairing weighted scoring with runway-aware financial modeling and explicit kill-criteria, CFOs give startups the discipline of an enterprise while preserving the agility that wins markets.

In this week's edition (est. 5 min. read):

• Main Article

• Quick Hits

• Number to Know

Whether it’s your first product or your next ones, knowing where you stand on your product-market fit (PMF) journey is essential to making the right investment decisions.

In this edition, I share strategies for achieving and maintaining PMF, leveraging quantitative and qualitative metrics, scalable acquisition strategies, and maintaining operational excellence. I’ll organize the content across the five most frequent questions I came across when scaling startups.

🧐 What metrics should be prioritized to objectively assess PMF?

Contrary to popular belief, having PMF is rarely binary. You are on a journey where you reach a certain degree of “fit”. Markets are dynamic and that fit could be lost after some time, and regained again later. So, I’ve found that establishing a measurement process to objectively assess PMF allows you to stay ahead of the curve. It’s a balance of quantitative and qualitative metrics. Here are a few examples:

1️⃣ Starting with my favorite: Customer Retention Rate. That’s a key early indicator that shows that customers find ongoing value, signaling that the product does solve a meaningful problem. Expand that to a Net Revenue Retention (NRR) above 100%, then you have a clear signal.

2️⃣ Followed by my second favorite: Net Promoter Score (NPS). This metric measures your customers’ willingness to recommend your product, which is a reflection of customer satisfaction and market reception.

3️⃣ Growth Rate (both in terms of users and revenue). Strong consistent growth in active users and sales demonstrates that there is market demand and scaling potential.

4️⃣ Customer Lifetime Value to Customer Acquisition Cost, or LTV:CAC. An improving ratio, combined with consistent conversion rates, demonstrates efficient growth and sustainable unit economics, confirming that there is a viable desire for the product.

5️⃣ Direct customer feedback about the value and usability of your product can provide deeper context behind your quantitative data.

6️⃣ Competitors changing strategies or features in response to your product indicates it is impacting the market landscape.

Question: What else would you look at?

🧭 How to benchmark customer satisfaction against industry standards to confirm PMF?

I would start with the American Customer Satisfaction Index (ACSI), a great resource providing access to valuable insights trends.

How is customer satisfaction (CSAT) calculated?

Broadly speaking, a CSAT score above 80% is generally considered excellent, while scores between 60% and 80% indicate room for improvement. Scores above 50% are typically seen as decent, with anything around 60% to 70% being fairly common for many industries. For instance, a 2022 study from Klaus reported that B2B tech companies with less than 1,000 employees had an average CSAT of 89%.

Perhaps as important as the score you’re currently at, is it improving?

Strategic Value and Risks

Market Positioning: Benchmarking helps companies understand their market position relative to competitors, enabling them to make informed decisions about product improvements and investments.

Resource Allocation: By identifying areas where customer satisfaction is lagging, companies can allocate resources more effectively to address these gaps, which can lead to increased customer loyalty and profitability.

Investor Confidence: Demonstrating strong customer satisfaction can enhance investor confidence, as it indicates a company’s ability to generate revenue and sustain growth.

💖 How to interpret referrals or word-of-mouth in the context of PMF validation?

Strong referral rates or word-of-mouth are among the most reliable qualitative signals of PMF. When a growing number of users actively recommend a product to peers, it demonstrates that the product is not only satisfying needs but also compelling enough to trigger advocacy. That’s a leading indicator, ahead of metrics like revenue or retention.

But you can take it further using Sean Ellis’ PMF Survey, which basically asks users “How would you feel if you could no longer use [ProductName]?” If a large portion (Ellis suggests at least 40%) say “very disappointed,” it signals strong PMF.

Slack is a great example of how word-of-mouth drove early adoption and rapid organic growth. Referral-driven growth lowers CAC and increases scalability of go-to-market strategies. Reducing reliance on paid channels and increasing organic adoption is critical for tech startups to achieve escape velocity.

For unlocking additional growth velocity, engage with the “very disappointed” users to deepen your understanding of product value and create new opportunities. So much more can be unlocked by understanding not only what’s working but also understanding what is not.

🎢 How to continuously evolve the product and value proposition to sustain PMF in a dynamic market?

Markets are dynamic and PMF should not be taken for granted. New entrants, moves by competitors or simply customer behaviors could change that. Product strategies must be fluid, evolving alongside company vision, market demands, and customer needs. Early-stage startups should prioritize discovery, growth-stage companies focus on scalability, and mature-stage organizations optimize for sustainability and relevance.

A product roadmap is not just a feature backlog but a strategic tool that aligns teams, prioritizes high-impact initiatives, and ensures resources are focused on what matters most. It should be based on user research, business objectives, market trends, and technical feasibility. It should be continuously updated to reflect changing priorities.

To operationalize this, you need a framework. Here’s a simple one: “impact vs. effort” and “strategic alignment” to ruthlessly prioritize those features. This ensures resources are allocated to initiatives that drive the most customer value and business growth, avoiding the trap of overloading the roadmap with every possible request.

🚀 How to structure cross-functional feedback loops to refine PMF?

Reaching and sustaining PMF requires cross-functional collaboration across the entire organization, as it not only directly correlates with the value delivered and perceived by customers across sales, onboarding, customer success and product, but also with how efficiently that value is being delivered in conjunction with operations and finance teams.

➡️ To establish the right feedback loops:

• Combine the above-mentioned metrics with detailed usage reports to align and focus your teams on improving them.

• By aggregating customer feedback from surveys, interviews and social media, you can get a comprehensive view of the product’s reception. By also integrating feedback from finance (e.g., ROI analyses), operations (efficiency metrics), and customer success (customer retention) you can get an even more holistic view of product performance.

• Publish and share reports regularly across the company. Involving finance, operations, and customer success in this process ensures alignment and support for product development decisions.

➡️ Turbocharge cross-functional collaboration by:

• Communicate back to stakeholders (including finance, operations, and customer success teams) how their feedback has been incorporated. This transparency builds trust and encourages further engagement, ensuring that feedback is continuously used to refine product-market fit metrics.

• Communicating back to stakeholders (including finance, operations, and customer success teams) on how their feedback has been incorporated. This transparency builds trust and encourages further engagement, ensuring that feedback is continuously used to refine product-market fit metrics.

♟️ Final Thought

Tracking everything in cohorts to see further confirmation that you’re headed in the right direction. Is each cohort curvature healthier than the previous one? Regardless of the answer, understanding why will help accelerate your journey towards a strong PMF.

Quick Hits: What's On My Radar

• A growing number of companies face significant hurdles with AI “hallucinations” with AI developers citing it as a top challenge.

• Prioritizing the right AI use case remains for a significant number of business (40%+ according to Glide)

Number to Know

That’s the percentage of entry-level white-collar jobs that AI could eliminate within five years, according to Anthropic’s CEO. He’s urging governments and companies to prepare for significant disruption.

We’ve already seeing some of the largest companies announcing significant rounds of layoffs. While there is correlation with those companies’ adoption of AI, not all have explicitly acknowledged causation.

Some companies have already started investing in upskilling their workforce in AI as developing more complex AI workflows still require human involvement.

Until next week, 👋

JP

In this week's edition (est. 8 min. read):

• Main Article

• Quick Hits

• Number to Know

AI Without Handcuffs: Unlocking Multi-Modal LLMs for Finance

I disagree with all the noise about Finance organizations being the last ones to adopt AI. The reality is that Finance teams are already using AI. It’s getting increasingly embedded in the tools they rely on daily, from ERP systems to forecasting software, billing, and spend management platforms. Those vendors have been innovating with AI for years, and it has made their offerings more competitive.

However, indirect usage isn’t the same as building an AI-native Finance function. To unlock real leverage, AI must be accessible and integrated into how teams work daily, directly, and safely. I’ve seen companies try to bridge the gap by letting employees expense tools like ChatGPT, Claude, or Perplexity, then pair that with rigid security policies that effectively muzzle meaningful use: don’t share confidential data, upload files, or trust outputs. It’s like handing someone a sports car and telling them not to go over 30 miles an hour. That approach might check a compliance box, but it doesn’t scale, and it certainly doesn’t build capability. Driving an AI-native culture means investing in secure, sanctioned tools that empower employees to actually use AI without handcuffs.

The space is evolving rapidly, and the number of solutions out there is growing exponentially with the promise of significant productivity gains. To build an AI-native organization, I’d recommend deploying multi-modal large language models (LLMs). Multi-modal refers to the model’s ability to process and interpret not only text but also numbers, images, and documents like PDFs and spreadsheets.

A “crawl, walk, run” approach to driving adoption:

1. A good first operational milestone would be for your teams to master prompting. I would also encourage forming community groups to share learnings and best practices. Consider inviting prompting experts from your or your investors’ networks to share their best practices and accelerate learning.

2. Your teams will then be able to identify repetition patterns when using the LLMs, know which ones work best for each use case, and explore automation solutions using AI agents for their workflows.

3. The third stage could be called the “copilot” stage, where the teams embed the AI agents as integral members of their teams, albeit at an intern level and needing supervision. That’s as advanced as it can pragmatically be at the time I’m writing this article.

In addition, data security and integrity of financial reporting are hard requirements for Controllers and CFOs, as they face serious personal consequences if the financials they publish are inaccurate. They can be held civilly or criminally liable,especially if the misstatements are deemed intentional, with U.S. laws like Sarbanes-Oxley carrying fines and prison time. Even unintentional errors can lead to SEC sanctions, shareholder lawsuits, and career-ending reputational damage.

We’re very much still in a “Trust but Verify” mode for the time being, but that doesn’t mean we can’t use technology to do it faster 🙂

In this edition of Profitable Growth, I get a bit more technical and explore the critical considerations and viable options for piloting and deploying secure, confidential multi-modal LLMs explicitly tailored for finance and accounting teams at tech startups. It could also serve as a blueprint for other functions.

🧐 Essential Criteria for Evaluating Solutions

Below, I evaluate four primary categories of solutions, assuming a cross-functional user base of about 25 team members:

1️⃣ Fully Self-hosted Open-source Solution

Deploying open-source LLMs on your infrastructure provides maximum control over data and operations.

Example Stack:

• Model: Mistral or Mixtral (open-source multi-modal models)

• Framework: LangChain or similar retrieval augmented generation (RAG) tools

• Storage: ChromaDB for vector search

• Hosting: AWS EC2 GPU instances (g5.xlarge)

• RAG: Documents stored on your own secure cloud storage (S3, Azure Blob Storage with server-side encryption).

Benefits:

• Complete data control and privacy; no external exposure.

• Customizable models to optimize specific finance use cases (report analysis, forecasting, document processing).

• Avoid ongoing vendor lock-in or usage-based licensing fees since those are open-source models.

Cost & Considerations:

• The initial setup is technically complex; dedicated engineering or external consultancy is likely required.

• AWS Infrastructure: EC2 g5.xlarge (GPU instances) typically range between $700-$1350/month.

• Additional operational costs (EFS, ECS Fargate): $50–$150/month.

• Ongoing operational overhead: regular updates, security patches, and resource monitoring require partial DevOps resources.

Who Should Consider:

Companies prioritizing absolute data sovereignty and willing to invest in upfront technical setup and ongoing management. As your business grows, you will need to provision additional hardware.

2️⃣ Managed Cloud Services with Dedicated Instances

Cloud providers offer managed LLM services with dedicated instances, ensuring data isolation.

Example Service:

Azure OpenAI Service Dedicated Instances or AWS Bedrock (Dedicated Instances). For RAG use the same setup as Option 1.

Benefits:

• Easier deployment compared to fully self-hosted solutions. It’s a quick setup with minimal in-house effort.

• Vendor-managed infrastructure reduces internal tech overhead.

• Providers offer strict data isolation guarantees. Your data isn’t used to train public models.

Cost & Considerations:

• Higher monthly costs ($1500–$3000/month). Azure OpenAI Service’s pricing varies based on model and usage. Provisioned Throughput Units (PTUs) offer predictable costs, with discounts available for reserved capacity.

• Per-seat expenses range significantly ($60–$120/month per user).

• Must trust vendor assurances about confidentiality and security (thorough SLA reviews required).

Who Should Consider:

Startups seeking to balance ease of use, rapid deployment, and robust confidentiality guarantees without internal infrastructure management complexity. The tradeoff would be the dependency on a single vendor, but you could A/B test with different vendors per team.

3️⃣ Commercial SaaS Solutions with Privacy Guarantees

Third-party SaaS providers offer LLM services with enterprise-grade security and confidentiality assurances.

Example Providers:

Anthropic Claude Enterprise and Cohere Enterprise. In this model, financial documents for RAG would typically be hosted on the provider’s cloud infrastructure.

Benefits:

• Rapid deployment; minimal technical oversight.

• Optimized user interfaces specifically tailored for finance and accounting tasks.

• Providers offer explicit legal agreements ensuring no usage of customer data in external training.

• Those are enterprise vendors and often include features like SSO, role-based access, and audit logs.

Cost & Considerations:

• Typical cost per user ranges from $30–$150/month.

• Costs scale linearly, making this expensive as teams grow.

• Data hosted externally, requiring strong trust and rigorous vendor assessment.

Who Should Consider:

Companies needing immediate, easy-to-use solutions, comfortable with managed confidentiality assurances from trusted vendors. Third-party servers likely process the data, and you should ensure you have contractual agreements to cover data usage and retention. You will have limited ability to customize models compared to self-hosted solutions.

4️⃣ Hybrid Solutions (Managed Hosting for Open-source Models)

Utilize services that host open-source models on dedicated infrastructure, combining control with reduced operational overhead.

Example Providers:

Hugging Face, Groq, RunPod, and Replicate. For RAG, you would have the option to choose your preferred secure storage method.

Benefits:

• Balances advantages of open-source customization with managed service convenience.

• Lower complexity in initial setup than fully self-hosted options.

• Providers offer isolated infrastructure and data handling guarantees.

Cost & Considerations:

• Hosting costs generally fall between $500–$1500/month. RunPod, for example, offers GPU instances starting at $0.17/hour for low-end GPUs and up to $3.99/hour for high-performance options.

• Lower operational overhead compared to self-hosted but still demands some ongoing management.

• External infrastructure hosting necessitates thorough data protection assurances.

Who Should Consider:

Startups that want control over model selection and transparency but prefer outsourcing infrastructure management. Here again, you should ensure you have contractual agreements covering data usage and retention.

💰 Summary of Cost Estimates

Note: Costs are approximate and based on available data as of May 2025. Actual costs may differ depending on your usage and choices of configurations.

🏆 Which Option is Best?

That will depend on your specific needs and operational context. I put together a few personas for you to consider:

• Budget-Conscious Startups: Hybrid solutions would offer a good balance between cost and control, making them suitable for startups with limited budgets.

• Startups with In-House Expertise: Fully self-hosted solutions provide maximum control and customization but require technical expertise.

• Rapid Deployment Needs: Managed cloud services offer quick setup with enterprise-grade security, which is suitable for startups needing immediate solutions.

• Non-Technical Teams: Commercial SaaS solutions provide user-friendly interfaces and support, which are ideal for teams without technical backgrounds.

Selecting the right solution depends on your startup’s specific needs, budget, and technical capabilities. It’s crucial to weigh the benefits and costs of each option to ensure secure, efficient, and effective access to multi-modal LLMs for your finance and accounting teams.

🙌 Cheat-Sheet for CFOs: How to Further Optimize Costs

♟️ Final Thought

Of course, Microsoft and Google are holding their annual AI conferences this week, and we should expect new announcements. Still, the four scenarios I laid out earlier in this post should remain valid.

You should also be wary of vendor lock-in strategies for this and your broader tech stack. Vendors like Microsoft, Amazon, and Google want you to buy more of their services, making it harder for you to switch down the line.

Quick Hits: What's On My Radar

• Trend: ARR/FTE is all the rage right now, with new cohorts of startups able to to reach multi-million dollars in ARR with small team, redefining what best-in-class looks like in terms of efficiency.

Number to Know

That’s how much weight revenue growth should have when you calculate Bessemer’s Rule of X. CEOs and CFOs are always trying to balance growth vs. profitability. I’ve struggled with it myself every startup I worked at. Bessemer’s analysis and subsequent recommendation is grounded in data.

So, your formula should look like this: (Growth Rate x 2) / FCF Margin)

It essentially challenges the Rule of 40, which in their view, gives equal weight to revenue growth and FCF. They have a very good argument that over time, ARR growth compounds, while FCF doesn’t. Also, when it comes to commanding a high multiple, we know the market continues to put more value on growth.

Until next week, 👋

JP

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In this week's edition (est. 7 min. read):

• Main Article

• Quick Hits

• Number to Know

Finance and People Functions Are Catalysts for Growth, Not Cost Centers

Under GAAP, we need to reflect Finance and People functions as G&A under OPEX (Operating Expenditures). That view is compliance-driven and not representative of modern versions of these functions’ full impact on business growth and innovation.

GAAP was created in 1930, almost a hundred years ago, and the business world has changed significantly since then.

When your startup is in growth mode, labeling those functions as “cost centers” invites misconceptions and potential risks for sustaining growth and innovation.

Some have argued that you’re either shipping or selling; nothing else really matters. That would be short-sighted and limit the company’s future prospects. After all, what are you selling if you don’t have the people to sell? What are you building if you don’t have the finances to invest?

In this edition, I explore how those functions play a critical role in startups’ success and how they can continue to elevate their contributions by embracing AI.

🧐 Strategic Advantages of Cross-Organizational Visibility

In addition to the CEO, the Finance and People functions, by the nature of their roles, have more holistic insights into operational dynamics, resource allocations, workforce productivity, and market responsiveness across the organization.

In short, they are best equipped to connect the dots and detect emerging trends, opportunities, or inefficiencies that could significantly impact organizational performance.

Of course, other functions will have a closer and more real-time pulse on their respective areas, but it will likely be limited to their focus and, therefore, more isolated.

Finance and people functions benefit from that unique perspective.

💪 Foundational Support Activities That Enable Other Functions to Create Value

It takes a village, as they say. Every function contributes to a startup’s success. Support functions like Finance and People play their part in providing the foundational layer, or platform, that enables other functions to be the “tip of the spear.”

For example:

• Having enough cash and a comfortable runway gives your company the flexibility to invest in growth without worrying about survival.

• When your financial planning is accurate, aligned with company strategy, and grounded in practical insights, your team can consistently make smarter decisions.

• Having the right talent on board who is engaged and empowered to perform its best work means the company will achieve its goals and objectives.

The CEO and the rest of the leadership team depend on these to have the necessary line of sight and get things done.

To keep this edition short, I chose to focus on Finance, but I’d be happy to share my thoughts on how similar concepts apply to People teams if that’s of interest.

🤖 Advancements in AI are Key to Increasing Finance’s Strategic Role

AI represents a fantastic opportunity in Finance, enabling teams to pivot from routine tasks to strategic problem-solving. But there isn’t a magic button to press. You need to invest in the AI stack and infrastructure that makes sense for your team and business model, and do it deliberately over time. You can’t go from “zero-to-hero” overnight here; it’s all too new for everyone.

For example, you could start by incorporating AI-driven predictive analytics to enhance your financial forecasting, like JPMorgan Chase did. They developed a proprietary AI tool called Coach AI to deliver personalized investment advice. The result? A 20% increase in sales while the rest of the market succumbed to volatility. It worked because it was a defined use case, allowing them to make proactive decisions.

So, where do you focus your efforts?

➡️ Automation and Efficiency

AI allows for automating monotonous tasks such as data entry and report generation, freeing FP&A and Accounting teams to focus on more value-added initiatives that drive growth.

For example, LiveRamp implemented Auditoria for collections dunning and month-end accrual workflows. Thanks to NLP bots handling collections touchpoints instead of analysts, they shortened the month-end close.

➡️ Improving Data Integrity

You know the saying: “Garbage in, garbage out.” Any robust financial strategy requires a solid data foundation. AI and automation now play an essential role in validating and spotting abnormalities in the data before you use it in your model, and it can do it at scale and get better over time.

A good example is Carta’s recent implementation of Pigment, which cut their data aggregation and manual calculation times by 80%!

➡️ Enhanced Predictive Analytics

AI-enhanced predictive analytics revolutionize financial planning and enable smarter budgeting, optimized capital allocation, and proactive identification of potential financial pitfalls, such as fraud or market volatility. These capabilities strengthen financial stability, improve investor confidence, and support data-driven investment decisions.

I’ve seen it first-hand at MarketShare. Our platform’s insights and predictive capabilities opened realms of possibilities that C-suite executives didn’t think were possible.

As you can see, leveraging AI in Finance or People functions doesn’t mean you have to build or code anything. Using the right AI-powered vendors to get the job done is totally fine as long as the ROI is there.

💡 New skills and competencies to become strategic growth enablers

The new tech is not enough. If anything, it further requires your teams to develop critical skills to realize the impact:

➡️ Ability to Deliver AI-Driven Insights

Finance teams need to become comfortable using AI tools like ChatGPT or whichever LLM their organization chooses, combined with automation applications like Zappier, Make, or n8n.

➡️ Strategic Partnership

That one needs to happen regardless of AI. Finance professionals must become strategic partners rather than back-office functions. This involves developing strong communication and influencing skills to guide cross-functional teams effectively, as suggested by Oliver Deacon Coaching.

Get inspired by industry CFO leaders. Microsoft’s CFO, for example, has been sharing how she’s transforming her organization to become AI-native.

🙋‍♂️ Organizational Design and Talent Management Strategies to Become Agile, Strategic Partners to Business Operators

Agile organizational designs and strategic talent management are crucial for Finance and People teams to become proactive partners with business operators in tech startup environments. Here are key strategies to achieve this:

➡️ Adopt Agile Methodologies

Finance teams can take a page from how engineering and product teams run, adopting agile methods to become quicker and more connected with the business. Instead of traditional setups, try small groups focused on specific goals, completing shorter work cycles. This approach helps people feel ownership over their projects and encourages them to collaborate naturally across FP&A, AP, AR, and beyond (Versapay, How To Structure a Modern Finance Department).

➡️ Implement Cross-Functional Teams

Cross-functional teams bring together diverse expertise towards a common goal, increasing collaboration and efficiency. This structure is particularly effective for addressing complex business challenges.

➡️ Rethink Hierarchical Structures

Agile organizations often adopt flatter structures, reducing bureaucracy and encouraging employee mobility and dynamic roles, leading to greater adaptability and innovation (McKinsey & Company, The five trademarks of agile organizations).

➡️ Empower and Develop Talent

In agile environments, employees can step up, take initiative, and push key priorities forward. Leaders need to be coaches, helping people grow and creating a strong, collaborative team built to adapt and innovate.

➡️ Foster Role Mobility

This is not a big company move at all. Encourage role mobility to ensure employees gain diverse experiences and insights, enhancing their ability to contribute strategically across the organization.

➡️ Select Talent Aligned with Agile Culture

Recruit individuals who fit your agile culture by focusing on adaptable, collaborative, and innovative people. It helps maintain a cohesive team environment that drives strategic outcomes.

Final Thought

Finance and People leaders still need to measure and communicate the value of their teams’ contributions and earn a seat at the proverbial table through influence.

Savvy CEOs know these teams aren’t just keeping the lights on but actively pushing the business forward.

As business increasingly revolves around data, people, and tech innovation, putting real resources into your Finance and People teams can give you a crucial edge.

That doesn’t necessarily mean adding more people to those teams; it could just mean providing them with the means to invest in and build their own AI capabilities so they can make a greater impact.

So ask yourself: What specific steps are your Finance and People teams taking today to drive growth in your business?

Share

Quick Hits: What's On My Radar

• Trend: Edge computing isn't just for big tech anymore. Processing data on-device or at nearby micro-servers now comes in plug-and-play form from AWS, Azure, Google, Cloudflare, and Fastly. It's letting startups slash latency to single-digit milliseconds, trim bandwidth fees, boost uptime, and localize sensitive data for easier GDPR/HIPAA compliance.

  CFOs and CEOs can take advantage of this by pinpointing one latency-critical workflow (fraud checks, live collaboration, IoT analytics), piloting an edge service this quarter, quantifying the user-experience lift and cost savings, and baking the results into next year's budget and investor narrative before rivals raise the performance bar.

• New App: Stacks (seed stage AI-native app) removes the month-end grind: reconciliations, journal entries, variance checks, and reports run on autopilot and stay in sync with your ERP, freeing up your team's time for higher-impact work.

• New Feature: Canva just launched visual sheets. With built in live data connectors, could it be the new place where creative and finance teams collaborate? 💖

Number to Know

That’s the average churn rate for B2B SaaS companies in 2025 is 3.5%, according to Recurly, Vitally, and other sources. It breaks down as follow: 2.5% voluntary and 1% involuntary. Every company and business model is different, but that's a key metric to watch. Aside from impacting top-line revenue growth, it also directly impacts unit economics metrics like LTV:CAC.

Until next Tuesday, 👋

JP

In this week's edition (est. 6 min. read):

• Main Article

• Quick Hits

• Number to Know

Spot and Unlock Your Startup’s Next Inflection Point

An inflection point is when the scrappy, all‑hands‑on‑deck approach that once powered your startup begins to stall growth. Left unchecked, manual hustle mutates from super‑power to silent killer, consuming cash, talent, and time. The antidote is a deliberate shift to data‑driven systems and targeted automation. This guide breaks down:

• Early warning indicators that your hustle is now hurting you.

• Metrics that quantify strain before customers feel it.

• A three‑step Automation Playbook (Prioritize ▶ Pilot ▶ Scale).

• Cultural guardrails that keep tech and talent aligned.

Use this as a diagnostic tool and roadmap to turn looming bottlenecks into scalable advantages.

🧐 Why Inflection Points Matter

Even at tech startups, manual processes often stand as the company’s backbone at the onset. When the teams are small, you can get a lot done and quickly. However, as growth materializes and these companies expand, critical junctures start happening across the organization: maturation points where manual hustle becomes a bottleneck rather than a booster. Those are inflection points, and recognizing them early is essential for scaling your business.

• Shortcuts with manual work are fine early on, but as volume and complexity increase and you hire more people on the team, what your small founding team could handle reliably doesn’t scale, and the price of errors quickly outweighs any time you save.

• Automation gaps become a race gap: if you keep doing things manually while competitors get to market with more intelligent systems, they’ll ship faster and win customers you can’t.

• Investors pay a premium for startups that already run on scalable, repeatable processes and produce steady margins.

💡 Rule of Thumb: If headcount grows >30 % YoY while revenue per employee is flat or dropping, the hustle has hit diminishing returns.

🔎 How to Identify Inflection Points

Identifying inflection points requires a keen understanding of how growth alters organizational dynamics. For example, as your team grows from fewer than 50 employees to a more substantial number, individual initiative may give way to more collective decision-making, and reaching product-market fit is another. Assessing your company’s readiness and timing to embrace automation at this stage can make the difference between scaling gracefully and stagnating.

Check my blog post for the 4 signals indicating that you may have reached that point.

Here are sample metrics that spotlight bottlenecks:

• Capacity Utilization: % of max throughput you’re using. If consistently >85 %, you’re one surprise away from fire‑fighting.

• First Pass Yield (FPY): % of tasks finished right first time. Target ≥95 % for digital flows; anything lower screams for automation.

• Overall Equipment Effectiveness (OEE): Not just for hardware. Adapt the uptime × speed × quality formula to APIs or data pipelines.

• Time‑to‑Decision (TtD): Median hours from data availability to call made. Benchmark <24 h for tactical, <7 days for strategic.

• Employee NPS linked to process friction. Declining eNPS often precedes churn by 1–2 quarters.

🤖 The Automation Playbook

While we all agree automation is crucial for breaking the cycle of growing manual inefficiencies, the journey if rife with challenges.

Innotech has an interesting approach to systemizing and de-risking the process. I used a similar framework at ServiceTitan and MarketShare to help teams prioritize strategic innovation over mundane tasks.

1️⃣ Prioritize

• Build a scoring matrix. Info‑Tech and DXC document this “value‑first” lens to ensure automations attack the biggest pain first, not the easiest script.

• Map the outcome on a 2x2 grid. Plot each workflow as Quick Win (high value / low effort) or Strategic Bet (high value / high effort). Automate one of each to balance fast ROI with long‑term leverage.

2️⃣ Pilot

• Time-box experiments. Keep pilots under 90 days, long enough to gather data, short enough to abandon if ROI misses targets.

• Instrument KPIs: Cycle‑Time (minutes / task) and Margin Lift (% cost saved) are common yardsticks. Collect a baseline a month before go‑live; compare weekly during the pilot.

• Staff a nano team. A typical pilot team: one business owner, one automation ninja, one analyst, and a change champion from the front line — small enough to move fast, diverse enough to surface risks.

• Define “Kill or Scale” gates. Success means ≥ 30 % faster cycle‑time or ≥ 20 % margin lift. Missed the mark? Do a retrospective, share the learnings, retire the automation and avoids sunk‑cost bias.

3️⃣ Scale

• Fold into the core roadmap. Transition stable automations into the engineering / ops backlog, assign owners, and track uptime in the same dashboards as production code McKinsey & Company.

• Ruthlessly sunset legacy steps. Every automated task should delete or consolidate at least one manual touch point. Failure to retire old steps is a top source of automation technical debt.

• Layer in continuous process mining. Deploy tools like UIPath to watch live event logs, revealing new friction without manual discovery.

🙋‍♂️ Addressing Cultural and Organizational Barriers to Automation

For this to work and become second nature across the organization, you need cultural and organizational guardrails:

• Transparency by Default – Public dashboards over slide‑deck gatekeeping.

• Upskill, Don’t Replace – Pair automation roll‑outs with role redesign and micro‑learning paths.

• Decision Cadence – Adopt an 80% confidence, 100% commitment mantra to curb analysis paralysis.

• North‑Star Alignment – Tie every bot, script, or AI agent to a revenue or customer‑experience KPI.

Final Thought

This is a rite of passage. Treat each inflection point as a design review, not a crisis. Systemize what scales, automate what repeats, and elevate your employees’ quality of life. They’ll create more enterprise value as a result.

Last, it’s also an excellent opportunity to foster a Kaizen culture and encourage “Citizen Developers” to step up. But that’s probably for another post 🙂

Questions or war stories? Ping me on LinkedIn. Happy to help you think through how to turn your next bottleneck into your biggest growth lever.

Quick Hits: What's On My Radar

• Trend: The amount of early-stage (seed and Series A) capital raised worldwide in Q1 '25 was at the lowest level in over a year. Capital remains expensive everywhere, and mega deals are taking the lion's share of the dollars raised. Those and their press coverage are masking the actual scarcity.

• New App: Tabs is an AI‑powered revenue‑automation platform that ingests any contract or usage data to extract terms, generate invoices for any pricing model, and automate billing, revenue recognition, dunning, and cash application. Integrated with QuickBooks, NetSuite, tax and payment rails, it saves hundreds of hours and cuts days‑sales‑outstanding by ~20 days.

Number to Know

That's the customer lifetime value to customer acquisition cost ratio, or LTV:CAC for short. While a 3:1 LTV:CAC ratio is a solid benchmark and varies by industry, striving for higher ratios can indicate superior efficiency and profitability. That said, a too high ratio may suggest that you are underinvesting in growth.

Until next Tuesday, 👋

JP

In this week's edition (est. 7 min. read):

• Main Article

• Quick Hits

• Number to Know

The Advantages of Benchmarking to Scale Your Startup

Scaling a tech startup can feel like you're continuously changing the parts of a plane while it's flying. Founders, executives and board members are constantly navigating the tension between growth and sustainable operations.

Having been in the financial and operational trenches during hyper-growth phases at startups like ServiceTitan, DISQO, and MarketShare, I've learned that strategic benchmarking is far more than just comparing numbers. It's a critical navigational tool. But how, what, and why you benchmark makes all the difference.

Many leaders fall into traps: chasing vanity metrics, drowning in data (analysis paralysis!), or applying generic benchmarks that don't fit their unique context. The key isn't just data; it's the insights powered by a combining operational rigor and financial acumen, grounded in first principles.

🤔 Why Most Benchmarking Falls Short

Before diving into the "how," let's address the "how not to." Benchmarking fails when:

1. It's untethered from your strategy: measuring things just because you can, without linking them directly to your core strategic priorities (your North Star, OKRs).

2. It's a "vanity" metric: focusing solely on metrics that look good on the surface (e.g., raw user growth) without scrutinizing the underlying health (e.g., engagement, retention, unit economics).

3. It lacks context: applying generic industry averages without considering your specific stage, business model, market dynamics, or funding environment.

4. It creates fear, not focus: using benchmarks punitively rather than as tools for learning, improvement, and celebrating progress. In people-first cultures, integrity and trustworthiness are key – benchmarks should illuminate, not intimidate.

5. It ignores the operational plumbing: Focusing only on high-level financial outcomes without benchmarking the operational processes that drive them. At MarketShare, for example, scaling hosting costs required a deep dive into operational analytics, not just the final bill.

📈 The Role of Benchmarking Across Growth Stages

The metrics that matter most change as your company grows. Applying late-stage metrics too early burns cash and focus; sticking with early-stage metrics too long invites inefficiency.

Early Stage (Seed / Series A)

At this stage, your focus is product-market fit validation and survival (Gilion, TaxFyle).

Sample Key Metrics:

• User Growth & Engagement: Are people using the product? Are they sticking around? (e.g., DAU/MAU, session length, feature adoption).

• Customer Acquisition Rate: How quickly are you adding initial customers? How viral is your app?

• Early Churn Indicators: Why are the first users leaving?

• Burn Rate & Runway: The non-negotiable reality check. How long can you sustain operations?

• Qualitative Feedback: What are customers saying? This is crucial data!

At Overture, in the pioneering days of paid search, understanding early user adoption and feedback loops was paramount for survival and informing rapid international expansion.

Are your early metrics telling a coherent story about market reception and operational viability?

Growth Stage (Series B/C)

To fuel the growth engine, you must scale your acquisition channels and unit economics (Crazy Egg).

Sample Key Metrics:

• Customer Acquisition Cost (CAC) vs. Lifetime Value (LTV): The golden ratio. Is your growth profitable and sustainable? Aim for LTV > 3x CAC.

• Net Revenue Retention (NRR): Are you growing with your existing customers? Crucial for SaaS. Aim for >100%.

• Sales Cycle Length & Conversion Rates: How efficient is your GTM engine?

• Gross Margin: Are you building a fundamentally profitable business? How are your growing AI costs impacting your plans? That's a new expensive that is getting more prevalent.

• Burn Multiple: How efficiently are you converting invested capital into net new ARR? (Net Burn / Net New ARR). Lower is better.

• Team Velocity & Output (R&D): Are you shipping valuable features efficiently?

ServiceTitan's journey past $100M ARR was driven by a relentless focus on SaaS metrics like NRR and disciplined financial management, which underpinned our growth.

Are your unit economics scaling positively alongside your top-line growth? Where are the bottlenecks in your growth engine? Are you keeping an eye on your AI expense line?

Later Stage (Pre-IPO / Acquisition)

At this stage, what matters most is predictable growth, profitability, and operational excellence.

Sample Key Metrics:

• Sustainable Revenue Growth Rate: Demonstrating predictable, efficient expansion.

• Rule of 40: A cornerstone metric combining your growth rate with your profit margin (EBITDA or FCF Margin). If greater than 40%, you’re considered a good investment.

• Operational Efficiency Ratios: Sales & Marketing efficiency, R&D leverage, G&A cost control.

• Working Capital Velocity: How efficiently are you converting cash?

• Market Share & Competitive Positioning: Validating leadership and defensibility.

Achieving SaaS-grade gross margins and preparing MarketShare for our acquisition by Neustar required meticulous attention to operational efficiency and robust financial reporting.

Does your financial and operational performance tell a compelling story of durable value creation for potential investors or acquirers?

👩‍🎨 Avoiding Analysis Paralysis - The Art of Focused Execution

A common trap is getting lost in the data ocean. Benchmarking should catalyze action, not stall it.

• Focus on the "Vital Few": Don't track 100 metrics poorly. Identify your current stage's 3-5 critical metrics and strategic priorities. Once you're consistently reporting and effectively acting on them, expand to the next batch. Use frameworks like the Balanced Scorecard, but keep it lean (Builtin.com, BCSDesigner.com).

• Establish clear ownership: who is responsible for tracking, reporting, and acting upon each key benchmark? Accountability drives progress.

• Integrate into your operating cadence: don't treat benchmarking as a separate task. Embed metric reviews into your existing operating cadence (e.g., weekly leadership meetings, monthly business reviews, quarterly board updates) (Comidor.com, EsInli.com).

At MarketShare, focusing on metrics that our teams could impact turned strategy into results, fueling growth and, ultimately, enterprise value.

🔗 Linking Operational Discipline to Efficient Scale

A good measurement process will reveal what's happening; operational discipline will explain why and drive improvement.

• Codify your culture: Frameworks like the DuPont Bradley Curve help visualize how safety and operational maturity are linked. Apply this thinking: a culture of discipline, Accountability, and continuous improvement directly impacts performance benchmarks (DuPont Sustainable Solutions).

• Standardize where it counts: Implement clear processes (e.g., Conduct of Operations principles) for critical workflows like sales, onboarding, support, and development releases. Standardization reduces errors, improves predictability, and makes scaling smoother. Leadership involvement is key here (Milliken & Company).

• Build feedback loops: Implement closed-loop processes. When a benchmark flags an issue (e.g., rising churn), have a defined process to diagnose the root cause, implement corrective actions, and monitor the impact. Regular internal audits or process reviews are crucial.

Operational excellence is a mindset that needs to get embedded in your company culture. Understanding performance against benchmarks will help.

🙋‍♂️ Benchmarking can rally your troops to the cause

Numbers don't lead people; people lead people. How does benchmarking fit into your operating model?

• Metrics as tools for empowerment: Benchmarks should provide teams with the information they need to understand their impact, make better decisions, and see progress. They are tools for enablement, not judgment.

• Transparency builds trust: Share relevant benchmarks openly (within reason) and explain the why behind them. When people understand the goals and how their work contributes, engagement increases.

• Data-informed coaching: Use performance data not for criticism, but as a starting point for supportive coaching conversations.

• Celebrate progress, not just perfection: Recognize improvements and milestones achieved based on benchmarked data. This will reinforce positive behaviors and build momentum.

How can you use benchmarking data to better serve, support, and develop your team members?

💪 Benchmarking helps demonstrating value

Effective benchmarking isn't just an internal tool; it's also crucial for external communication, particularly with investors and board members.

• Translate Operations into Value: Show how operational improvements and strong benchmarks (NRR, CAC:LTV, Gross Margin) directly translate into increased enterprise value, lower risk, and a more compelling investment thesis.

• Build Credibility: Consistently hitting or thoughtfully explaining deviations from benchmarked targets builds immense credibility. It shows you understand your business levers. My experience helping ServiceTitan elevate board meetings focused on strategic topics, backed by solid data from our newly created audit committee, built significant trust.

• Demonstrate Scalability: Use benchmarks to prove your business model is scalable and your unit economics hold up (or improve) with growth. This is critical for securing funding at favorable valuations.

Final Thought

Are the benchmarks for your metrics available all in one place? Probably not. You will need to piece them together from multiple sources, but with today's advancement with AI agents, it's easier than ever, but triple check your sources to ensure there aren't any hallucinations!

That said, while I’m big advocate of AI in the workplace, in this case, I would actually recommend you err on this side of caution and use curated data sources. Here are a few places to get you started:

• High Alpha

• Meritech

• KeyBanc

Quick Hits: What's On My Radar

• Trend: Open source continues to make AI more affordable and accessible. Meta recently released Llama 4 and Alibaba just announced Qwen 3. They continue to challenge the closed models and I may write a post on how to use them at work.

• New App: Gamma. Gamma is a browser-based “AI design partner” that turns a short prompt, pasted outline, file, or URL into fully formatted presentations, one-page websites, social posts, and documents in seconds. Under the hood, it taps a stack of more than 20 LLM and image models to draft copy, choose layouts, pull or generate visuals, and keep everything on-brand, so you start with a finished first draft instead of a blank slide..

Number to Know

That’s the percentage of times unassisted LLMs still produce inaccurate results when analyzing complex financial data. Despite improvements in tests and benchmarks, we’re still in a “Trust but Verify” period.

Until next Tuesday, 👋

JP

In this week's edition (est. 5 min. read):

• Main Article

• Quick Hits

• Number to Know

What's the fastest path to earning a seat at the strategic table?

Start by putting yourself in the shoes of your fellow C-suite leaders. Understand what's keeping them up at night. When you align your work with their goals and challenges, not just your own, you stop being a functional leader and start becoming a trusted partner.

Empathy isn't a "soft skill" in this context. It's a business imperative.

Why Empathy is a Strategic Superpower

Empathy builds high-performing executive teams. It enables you to:

• Break silos: Cross-functional collaboration starts with mutual understanding.

• Make better decisions: Consider long-term impacts on all departments.

• Drive alignment: Shared goals are easier to build when you know what matters to your peers.

Empathy also raises the bar. When you genuinely understand a leader's constraints and pressures, you earn the right to hold them, and yourself, to a higher standard.

Inside the C‑Suite: 2025 Priorities & Pressures

Below is a quick snapshot of what your peers are up against this year and how you can show up as a real partner.

🎯 Chief Marketing Officer (CMO)

• Top Challenge: Proving ROI, especially for brand building, amidst a constantly changing, AI-influenced marketing mix. They need cost-effective lead generation and long-term brand equity.

• Key Focus: Championing the brand, unifying messaging, aligning customer-facing teams, and crafting compelling customer experiences.

• Pressure Point: Demonstrating measurable results for marketing spend while navigating algorithm changes and budget shifts that don't yield instant results.

Strategic Partner Takeaway: CMOs need agile yet stable strategies. Support them by connecting operational efficiencies to innovative marketing programs and understanding that quick pivots don't always yield instant results.

📈 Chief Revenue Officer (CRO)

• Top Challenge: The relentless pressure of hitting revenue targets month after month ("starting from zero"), improving sales rep performance and efficiency, and expanding revenue streams (upsell, cross-sell, new markets).

• Key Focus: Driving revenue growth, optimizing the sales process, increasing conversions and Average Revenue Per User (ARPU), and ensuring strong alignment with Marketing, Product, and Success teams.

• Pressure Point: Constantly needing to adapt to market shifts, which may require experimentation and unplanned resources.

Strategic Partner Takeaway: CROs need room to experiment. Partner creatively on funding pilot initiatives, understanding that a shared focus on ROI means embracing "fail fast" learning when necessary.

🙋‍♂️ Chief Customer Officer (CCO)

• Top Challenge: Championing customer-centricity often with underinvested internal tools, while reducing cost-to-serve and churn, increasingly leveraging AI responsibly.

• Key Focus: Advocating for the customer, gathering post-purchase feedback, pushing for a customer-centric culture, and leveraging data for customer insights.

• Pressure Point: Balancing the need for resources to enhance customer experience and support internal teams against pressures to reduce costs and improve margins, especially as organizational complexity grows.

Strategic Partner Takeaway: Help the CCO by advocating for integrated solutions where every function contributes to a seamless customer journey and experience, fostering a true 360-degree view of the customer.

🧠 Chief Product Officer (CPO)

• Top Challenge: Translating AI investments—especially generative AI—into measurable outcomes like greater efficiency and personalized services. Balancing long-term product vision with current market demands is a constant tension.

• Key Focus: Harmonizing innovation with customer needs, influencing internal stakeholders, and aligning cross-functional teams to drive product development that builds long-term enterprise value.

• Pressure Point: 35% of product leaders cite stakeholder alignment as a primary challenge. 60% of CPOs report feeling lonely in their role, and nearly half say they lack organizational empowerment to experiment or take risks.

Strategic Partner Takeaway: CPOs thrive with robust data and true cross-functional collaboration. Support them by advocating for experimentation, backing long-term bets, and reducing organizational friction that blocks product-led growth.

💻 Chief Technology Officer (CTO)

• Top Challenge: Scaling infrastructure while addressing tech debt. What got the company to its current stage (monolithic architecture, rapid feature shipping) may now be holding it back.

• Key Focus: Transitioning to scalable systems, reducing bugs and outages, mentoring engineering talent, and protecting long-term code quality amid shifting priorities.

• Pressure Point: Engineering teams often have to deal with context switching, leading to burnout, growing technical debt, and frustration. Junior engineers need mentorship, not just pressure to deliver fast.

Strategic Partner Takeaway: CTOs often need to balance scale innovation with maintaining stability. Be a true ally by aligning technology investments with business priorities and reinforcing best practices in quality, risk, and team development.

👥 Chief People Officer (CPO/CHRO)

• Top Challenge: Building leadership capabilities at every level. 51% cite manager development as a top priority, especially as orgs scale quickly.

• Key Focus: Embedding a strong culture, addressing wage inflation, and managing HR issues that often stay behind closed doors but consume significant time and energy.

• Pressure Point: People teams face economic pressures (61% cite wage inflation, 45% cite cost management) yet often lack analytics support to justify strategic programs.

Strategic Partner Takeaway: Support the People function by aligning budget decisions with investments in culture, leadership, and wellness --key levers for long-term performance and retention.

💰 Chief Financial Officer (CFO)

• Top Challenge: Balancing growth and efficiency. 70% of CFOs report difficulty reducing costs without hurting performance, while still being expected to fund new initiatives.

• Key Focus: Financial forecasting, enterprise-wide data ownership, and serving as the organization's "single source of truth" for performance and decision-making.

• Pressure Point: The pressure to adopt AI is rising, but finance is more cautious due to risk and compliance implications. Add to that market volatility, which makes forecasting even more difficult.

Strategic Partner Takeaway: CFOs prioritize data, risk, and accountability. Partner by bringing forward cost-aware strategies that are aligned with performance goals, while enabling selective, smart innovation.

Final Thought: Collective Leadership Wins

Becoming a true strategic partner to your fellow execs starts with empathy. Step into their shoes. Earned the right to hold others and yourself to a higher standard, because you've taken the time to really understand what's on the line for them. That's how great teams align faster and push each other to be better.

What has been your experience? Anything else you'd add or change?

Share Profitable Growth

Quick Hits: What's On My Radar

• Trend: Significant FOREX swings against the US dollar. Consider hedging using forwards. You bank may offer those services.

• New App: Zeck. Zeck offers a modern approach to streamline preparing and running efficient board meetings, so you can focus on what really matters: making strategic decisions.

Number to Know

That’s the percentage of Internet traffic that was automated in 2024, projected to be as high at 80% in 2025.

That’s it for this week, short and sweet! Hope you found this content helpful.

If you wouldn’t mind sharing your feedback here, that would be great! 🙏

Until next Tuesday, 👋

JP

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In this week's edition (est. 5 min. read):

• Main Article

• Quick Hits

• Number to Know

How to make decisions with limited data?

80% of organizations struggle with decision-making, mainly with the speed and the quality of those decisions.

There are already a lot of great advice and frameworks on WHO should make decisions, like Guru's SPADE, McKinsey's DARE, or even Eric Pliner's 4Vs, but what about HOW to make decisions when you only have partial or insufficient information?

It is a more likely scenario in a startup environment where you're breaking new ground daily. In this week's edition, I'm sharing a few suggestions to help you navigate such situations.

1️⃣ Prioritize the decisions you have to make

To help me with this, I use a simple mental 2x2 prioritization matrix, inspired by Eisenhower's own, to quickly assess in which bucket the decision coming to you is falling.

Share

The first column speaks to the magnitude of the decision's impact on the business.

If the decision's impact is low, you should delegate. Who is the DRI (directly responsible individual)? Do you trust them to make the right call? If not, why should they still be on your team?

The second column speaks to the urgency of the decision. When the decision is urgent and essential, you are most needed in the top right quadrant.

Needless to say, you and your team need to strive to make decisions without the pressure of urgency. That's the bottom right quadrant, and that's where you’d prefer to be.

It is important to know if the lack of data is a result of urgency, i.e., if the team didn't have time to research. Unless there is an impending deadline, the decision should be postponed until proper research is done.

Now that you are solely focused on the decisions you are truly required to make, what options do you have?

2️⃣ Leverage your experience and intuition

Have you been in a similar situation before? Were the conditions somewhat familiar? What did you or someone else do?

Combined with that experience, what are your guts telling you?

What would you have to believe to be true to choose one option over another? Are your assumptions reasonable?

3️⃣ Value qualitative feedback

A school of thought advocates that data is always there, not just in the form you initially sought. You have ways to get signals through alternative data sources to inform your decision through inference. Consider the following example:

Situation: your startup is trying to expand into a new market, but it's a smaller market, and there isn't enough market research data available.

AI Application: Using AI, you can analyze unstructured data sources like social media, online forums, and news articles to gauge consumer sentiment and the needs of a market.

Outcome: this approach can support your decision-making process without the need for extensive traditional market research.

4️⃣ Grab the phone

Ask professional peers and mentors if they have been in a similar situation before and what their approach/learnings were. If they have not, what would they consider in your situation, knowing you're lacking data?

5️⃣ Be inclusive

While you want to avoid the trap of the democratic vote, what does your team think? What do they recommend and why? Are they ready to commit?

6️⃣ Do the right thing

Does the decision you're leaning toward align with the company's core values, mission, and vision statements?

Sometimes, making the right decision means taking one small step backward to make two steps forward.

Core values and compliance are foundational elements in building a culture of accountability. By fostering a work environment where ethical conduct is prioritized, you cultivate trust and integrity across the organization.

7️⃣ Make the decision

Personally, the more of the above boxes I check when making a critical decision with limited data, the more comfortable I am with it, even in the face of uncertainty.

No decision, or delaying the decision, can also lead to disaster. Avoiding decisions is not the way to avoid the consequences. As a leader, your teams count on you to make the tough calls, and you must own them.

But it doesn't stop there

Perhaps as important as making that difficult decision is what you do after you've made it:

• Years ago, a brilliant colleague said something that resonated and stuck with me:

  "You lay a brick"

  If you were missing data, are you now instrumenting to ensure it doesn't happen again?

  Share

• Remain flexible and ready to adapt your strategy as new information emerges. Allowing room for tweaks makes taking calculated risks easier.

• Keeping a positive outlook and fostering continuous learning within your organization supports resilience.

A few parting thoughts…

• Get used to it. It's going to happen again.

• Develop your own decision-making framework to help you analyze options efficiently. Your framework should be closely tied to your business model and company values.

What do you think? What has been your experience? Anything you would do differently?

Quick Hits: What's On My Radar

• Article/Video: a glimpse into the future when AI agents will use their own language to communicate more efficiently in real life scenarios. Have you been in a situation when two people start speaking in a foreign language and you’re standing there without a clue of what they’re saying? Watching the staged demo felt like that. 😳 For more context, this feat came out of a recent Eleven Labs hackathon -- fascinating!

• Trend: Wikipedia raised awareness on an emerging trend that should be underestimated: 65% of their most expensive traffic now comes from AI scraping. Your business is likely impacted as well. It’s not just hosting but also advertising costs.

• New AI model: Open AI just released GPT-4.1 in their API. The cost of “older” models has been lowered - more arbitrage opportunities!

Number to Know

That’s the median duration of recessions in the US since World War II, from peak to trough. The shortest was the 2020 COVID-19 recession, which lasted just two months. The longest was the Great Recession (subprime crisis) and lasted 18 months. It’s another data pointing influencing the cash runway startups should aim to maintain in uncertain times: 12-24 months.

That’s it for this week, short and sweet! Hope you found this content helpful.

If you wouldn’t mind sharing your feedback here, that would be great! 🙏

Leave a comment

Until next Tuesday, 👋

JP

I had initially prepared content focusing on decision-making. However, given last week's announcement and the subsequent markets' reactions, I decided to pivot and share some pointers on navigating the situation.

I'm also beyond grateful to everyone who believed in me and subscribed to this substack last week! We recorded almost 50 new subscriptions. To keep the momentum going, I'm granting the first 100 subscribers the special status of Founding Members, which will provide them unlimited access to all future content and tools.

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In this week's edition:

• Main Article

• Quick Hits

• Number to Know

I can appreciate how scary and daunting things can look at the moment, but for folks with a bit of gray hair like me, it's déjà vu. For context, I graduated from college right as the dot com bubble burst, as an international student with only a one-year practical training visa 😱, navigated the subprime recession, and lived through COVID like most of you... this situation, too, shall pass.

The best thing you can do is to quickly identify what you can control and focus on that, without losing sight of the broader picture so you won't get blind-sighted.

In this edition, I'm sharing eight steps your organization can take to help navigate the economic uncertainty. It's meant to be a starting point and is not comprehensive by any means. I'm also sharing a few examples of how AI can now play a transformative role in navigating tariff complexities and becoming a strategic advantage.

1️⃣ Refer to Sequoia's "R.I.P. Good Times" slides and cover your bases

When the 2007 subprime crisis happened, Sequoia famously shared this deck with its portfolio companies, which included instructions on navigating the long path to recovery. Since then, they've become startups' SOPs (standard operating procedures) for preserving cash and survival, including:

• Hiring freezes and zero-based re-budgeting

• Reviewing and cutting all unnecessary spending, including rent, vendor and software licenses, and travel

• Reviewing all salaries and moving towards more heavily commissioned sales structures

• Taking a more aggressive stance on A/R, A/P, renegotiating contracts, and bolstering your balance sheet

• Becoming cashflow positive ASAP and spending every dollar as if it were your last

Develop at least three scenarios (optimistic, moderate, pessimistic) for the next 24 months. Forecast P&L and cash flow under each to understand potential outcomes. For each scenario, define trigger points and corresponding actions. Document these in a contingency playbook. Revisit and revise scenarios as conditions change. Treat this as a living exercise – if new tariffs or economic data emerge, feed them into your models promptly.

2️⃣ Understand the Ripples of Tariffs' Direct and Indirect Impact on Costs and Demand

Meta and Nvidia, a good illustration here, discovered that 68% of their semiconductor suppliers relied on TSMC's Taiwan facilities, creating critical vulnerabilities given U.S.-China trade tensions.

So far, the tariffs have only been targeted at physical goods, not digital services (e.g., SaaS) or BPO (Business Process Outsourcing), so you may think you are insulated.

Think again!

The tariffs will impact hosting and AI servicing costs in these hardware-intensive industries. Who will absorb the incremental costs?

While not directly affected by tariffs, SaaS companies may also face reduced spending from clients whose costs have risen due to supply chain disruptions and higher import prices.

Similarly, the economic uncertainty will cause clients to delay purchasing decisions, resulting in longer sales cycles and your ability to meet your forecast.

On the other hand, the situation could also create opportunities to profit from a destabilized competitor's unfortunate situation.

Last, positioning your products as cost-saving essentials and being creative with contract terms could accelerate your pipeline.

3️⃣ Explore Strategic Partnerships and Collaborations

Your organization should actively pursue strategic partnerships and collaborations to navigate the tariff war effectively. These partnerships can offer alternative supply chains, access to new markets less impacted by tariffs, and shared resources and expertise.

Collaborating with companies in countries with lower tariff exposure, entering joint ventures, or forming co-development agreements can mitigate cost increases and foster innovation. Partnering with established corporations may also open acquisition opportunities or integration into established supply chains.

Engaging with local trade organizations provides startups valuable insights and advocacy on tariff-related issues. Successful collaboration strategies include "ally shoring," partnering with companies in allied nations and nearshoring to relocate production closer to key markets. Strategic partnerships enhance startups' agility and resilience, enabling access to crucial resources and capabilities unavailable internally.

4️⃣ Leverage AI for Tariff Engineering

What's tariff engineering? It's a legal strategy used in international trade to reduce the amount of duty (tariff) paid when importing goods into a country by modifying a product's design, materials, or components so that it falls into a more favorable (lower-tariff) classification under the Harmonized Tariff Schedule (HTS). It's about intelligent product design and supply chain planning, understanding tariff classifications, and proactively adjusting products to minimize import costs while staying within the law.

The HTS is a 4,400-page document governing 20,000+ product classifications to determine expected tariffs. It's also historically been a manual process, creating opportunities for AI-based automation.

For example, Mosaic Data Science achieved 94% accuracy in automated HTS classifications using TensorFlow models, slashing electronics importers' manual review time by 70%. This process proved critical when the US suspended $800 de minimis exemptions for Chinese goods this year.

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5️⃣ Establish a Tariffs Escrow Account

Take advantage of high interest rates and hold 3-6 months of projected duty payments in an escrow account. Using machine learning models to predict optimal cash reserves for duty payments, your organization could earn 3-5% APR on idle funds.

6️⃣ Start and/or Automate Hedging

We've already seen significant FOREX (foreign exchange) movement, which could impact your revenue and costs. It's easier to run your business when you've minimized the impact of fluctuating foreign currencies (one less moving part to worry about). Hedging is a service your bank may offer that allows you to guarantee a fixed currency exchange rate on a certain percentage of your revenue/costs through forwards.

There are AI-based alternatives to banks' traditional offerings. For example, Wise saved 89% of its customers from 2024 currency swings by executing 270,000+ micro-hedges daily, each calibrated to specific risks.

7️⃣ Adjust your Pricing Strategy

It's critical to adjust your pricing and revenue model deliberately. Analyze how tariffs impact your product margins and decide if, when, and how to pass those costs to customers.

Be transparent. People read the news like you do, and customers may be more willing to support you than you think, as long as you are honest.

While you don't need to broadcast all internal struggles, it's wise to communicate changes affecting customers. If tariffs force you to raise prices or cause shipping delays, craft a thoughtful message to customers explaining the situation. Emphasize that you value them and are doing everything possible to mitigate the impact (perhaps you've improved features or extended their subscription in exchange).

Honesty here can prevent frustration – customers hate surprise price hikes without explanation. For B2B clients, consider personal calls for significant changes. It's also an opportunity to reinforce the value you deliver so the customer sees the context of why you remain a worthwhile partner.

8️⃣ Communicate Transparently with Stakeholders

Transparent communication can be a superpower in times of crisis.

With your team (employees): Be candid about the challenges the company is facing and the steps you're taking. People appreciate honesty; it builds trust and galvanizes them to help. If you need to implement pay cuts, reduced hours, or a pivot in strategy, explain the reasoning and how it ties to ensuring the company's future. Reinforce the mission – why it's worth fighting for – and acknowledge the sacrifice you're asking of them.

With investors and board members: Don't go silent or sugarcoat issues. Share regular updates on how tariffs or economic shifts affect your metrics, and outline your response plan. If you need more capital or covenant waivers, flag them early. VCs want to help but need visibility. Bring data and scenarios to show you're in control. Honest communication can turn tough talks into collaborative problem-solving and may lead to unexpected support, like customer or lender introductions.

With key partners and suppliers: Similarly, communicate with major partners. If a channel partner relies on your product, assure them of your continuity and negotiate flexible arrangements that help both sides. For suppliers, if you need to delay orders or seek better pricing due to tariffs, discussing it openly can lead to compromises (they may prefer to keep you as a customer on adjusted terms than lose you entirely).

That's a starting point

There are obviously many more opportunities to prepare your organization best to navigate these uncertain economic times, but I had to cap my content to keep this newsletter manageable. Feel free to add your comments and suggestions on what else should be considered so the community can benefit!

If you need help, DM me, and I'll be happy to provide some guidance on how to structure the above.

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Quick Hits: What's On My Radar

• Article: CFO turnover reaches six-year high. As the "classically-trained" CFOs are retiring, a new generation of CFOs/COOs is emerging.

• Virtual Event: My former MBA professor of economics is holding a free virtual session to explore key economic trends shaping the U.S. and global landscape.

• Llama 4 Release: The disruption and moat discussion continue. With a 10 million token context window, can this LLM challenge specialized enterprise search and knowledge management platforms like Glean?

• New App: Runway is a modern FP&A (financial planning and analysis) platform with an intuitive interface. It enables teams to collaboratively forecast revenue, expenses, and cash flow, replacing traditional spreadsheets with a more dynamic and user-friendly experience.

Number to Know

That's the concentration of CoreWeave's revenue from a single customer (Microsoft). Typically, anything above 10% triggers questions from banks and potential investors. Note that they have multi-year contracts and a plan to de-risk over time and have demonstrated astonishing growth during the years leading to the IPO.

Hope you found this content helpful! Please drop a comment or DM me to share any feedback you have to improve my content. What would you like to see? I will read and respond to all messages.

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Until next Tuesday, 👋

JP

They say, you just don’t start a newsletter; you start a substack (thanks 😎) — but, what does it mean? For me, it’s an invitation. An invitation to leverage a CFO and COO’s years of experience helping tech startups scale, so you don’t have to reinvent the wheel. It’s also an invitation to jump in and contribute your ideas, requests for help, and experiences, so the community around this substack helps as many people as possible. Should they be founders, business leaders or aspiring professionals.

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What should you expect? 🧐

It’s no secret that growth is a startup’s most important metric. It is the cornerstone of startup success, validates the business model, attracts investment, secures competitive positioning, and sets the foundation for long-term sustainability.

But not all growth is created equal. Sometimes, aggressive growth makes sense; sometimes, a more cautionary approach is required, and in other instances, profitability is much more advisable than growth.

There is no right or wrong; it’s all contextual. We will explore lessons learned through use cases (past and current), analyses, and personal experiences.

But I’m also convinced that leading modern organizations through growth in a rapidly changing business world requires adopting new technologies like Artificial Intelligence (AI), the proper operating culture/mindset, and financial discipline.

Without it, it would be difficult for any startup to achieve profitable growth.

So, expect this newsletter to cover all of the above in a way that is nicely and logically organized, creating options for you to consider as you drive business growth.

This will be a safe space for anyone to share their opinions, advice, and experiences with this community, as no one has all the answers!

When it comes to publishing:

• I will start posting weekly.

• Each post should take you ~5 min. to read — no more.

That’s about it for now. Thank you again for stopping by! 👋 📈

Best,

JP