But this advantage comes with a challenge: How do you design a marketing organization when yesterday’s playbook no longer applies? 

That tension inspired the presentation we’re sharing today: The Founder’s Guide to Building an AI-Native Marketing Team. Many of the questions I receive from early-stage founders circle this very topic. Some ask which AI tools to prioritize. Others want to discuss how AI fits into outbound prospecting or other tactical functions. These are all valuable conversations, but to truly unlock AI’s potential, founders need to approach org design from first principles.

This guide walks founders step-by-step through the process. It outlines which roles to hire, in what order, and which categories of AI tools to incorporate. One decision I made—perhaps controversially—was to represent AI tools in the org chart as if they were team members. Each reports to a human director, because these tools increasingly replicate the work once done by junior or mid-level marketers. This placement isn’t just cosmetic; it reinforces a mindset shift. If AI tools are doing the work of people, they should be evaluated, and even managed, like people too.

Of course, the hiring sequence I propose isn’t one-size-fits-all. In fact, some leaders who previewed the deck disagreed with my suggestion to delay hiring a product marketing lead. They argued that AI-accelerated feature rollouts will create earlier demand for that role. I’ve included caveats to account for those situations.

While this presentation answers many foundational questions, it also surfaces a more existential one: If AI replaces many of the roles traditionally held by early-career marketers, how will we develop the next generation of marketing leaders? That question—far more than which tools to choose or when to fill a specific role—is what keeps me up at night. I don’t yet have a clear answer. (And, for what it’s worth, ChatGPT’s answer —”replace the ladder with a lattice” — wasn’t all that practical either.)

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The post Designing an AI-Native Marketing Team: A Guide for Founders  appeared first on Battery Ventures.

By 2030, every Baby Boomer will be over 65, making older adults comprise one in five Americans. Age is directly tied to increased healthcare spending, with annual per-person health expenditures averaging $22,356 for those 65 and older – nearly 2.5 times higher than for working-age adults. Additionally, approximately 90% of the annual $4.5 trillion US healthcare spend is dedicated to managing chronic diseases, which now affect 129 million Americans and continue to rise steadily. 

At the same time, demand for healthcare professionals far exceeds supply. As of March 2025, the industry faced 1.37 million job openings  but only 740,000 hires, resulting in 1.8 vacancies for every filled position. Hospitals report nearly 10% RN vacancy rates, and within the next decade, the U.S. anticipates a shortage of up to 86,000 physicians, exacerbated by an aging workforce nearing retirement. 

Compounding this issue is clinician burnout: Over 50% of physicians and 56% of nurses report burnout, primarily driven by overwhelming administrative duties that detract from patient care. 

These challenges culminate in a severe financial squeeze for healthcare organizations, with 40% of hospitals operating at negative margins in 2023 and an additional 20% seeing margins below 5%. Financial stress is forcing health systems to consolidate: 28% of hospital mergers in 2023 involved distressed institutions. This underscores a fundamental truth: You need to deliver clear, bottom-line impact to be prioritized in healthcare procurement. 

With all of this in mind, we’ve been focusing our healthcare investing efforts recently in two main areas: 

1. Preventative Care: Solutions aimed at keeping patients healthier to reduce long-term healthcare strain. For instance, our recent investment in Cleerly* aids early detection of heart disease, addressing a condition that drives $254 billion annually in U.S. healthcare expenditures. Preventative solutions like Cleerly help mitigate the downstream impact of chronic illnesses.  
2. Administrative Efficiency: Solutions reducing bureaucratic burdens via automation, AI, and computer vision to alleviate workforce shortages, clinician burnout, and margin pressures.

Announcing our investment in AssistIQ 

Today, we’re thrilled to announce our investment in a company in that second “administrative efficiency” bucket: AssistIQ*, an AI company revolutionizing the management of supplies and implants in operating rooms and procedural areas through computer vision. 

The Broken Process of Charge Capture 

Expensive surgical supplies typically come from surgeon-specific “preference cards.” These physician preference items alone account for 40-60% of hospital supply costs. Stale preference cards are a notorious problem across healthcare: They lead to unnecessary supplies entering operating rooms (OR), creating substantial financial and physical waste.  

Currently, nurses manually enter supply and implant details into Electronic Health Records (EHR) to track them, an error-prone method with accuracy rates as shockingly low as 50-60%.

Some hospitals have tried barcode and RFID solutions to manage supplies and keep costs down, but many have seen minimal adoption when it comes to accurately tracking usage at the point of care due to technical limitations (for example, many implants are not in a hospital item master), forcing nurses to turn back to manual methods like paper sheets and manual EHR entry, leading to lost revenue and inaccurate supply tracking.  

Everyone with whom we speak – from nurses and perioperative leaders to professionals in supply chain and finance – are aware of revenue leakage and savings opportunities in charge capture, but they’ve lacked effective solutions. 

Enter AssistIQ 

Imagine having a dedicated team member in every OR and procedural area whose sole responsibility is ensuring the accurate capture of supplies used. 

Imagine that team member can instantly recognize millions of supplies with ~98% accuracy (even for implants and ‘trunk stock’ not listed in a standard hospital item master).They automatically document what is used in real-time, provide cost discrepancy analyses by surgeon, identifies opportunities to reduce waste and calls out cost-effective alternatives. With reliable capture, patient records are accurate and chargeable items flow through to the EHR for instant billing. 

That employee is AssistIQ. 

AssistIQ’s product leverages computer vision and machine learning, identifying supplies and implants in real-time without barcodes, RFID, or specialized hardware. Nurses simply place supply items under a tablet camera, and AssistIQ references the AIQ Supply Cloud database containing over four million products. It seamlessly records case-costing data in the EHR and updates inventory in the ERP. 

This innovative approach raises capture rates from 50%-60% to over 98%, dramatically increasing revenue capture and reducing costs. 

Gone are the days of manual entry in the operating room and tracking down documentation inaccuracies following a procedure. AssistIQ allows nurses to focus on what they’re trained to do: provide patient care. 

AssistIQ aligns perfectly with macro trends we are focused on: addressing workforce shortages, reducing administrative burden, and – crucially – providing tangible, measurable ROI improvements to hospital margins. Operating rooms generate 60-70% of hospital revenues but represent approximately 40% of total expenses, making them prime areas for efficiency and cost-reduction interventions. 

We are incredibly excited to partner with the AssistIQ team as they reshape operational efficiency in healthcare. If you’re innovating in this space, we’d love to connect. 

The post Using AI to Make Healthcare More Efficient: Our Investment in AssistIQ appeared first on Battery Ventures.

That’s a missed opportunity. As CFOs take on broader mandates around data, capital efficiency, and long-range planning, I believe engineering productivity belongs squarely within their remit. But measurement requires alignment. To get there, CFOs and CTOs must learn to speak each other’s language and build a shared framework for tracking what matters.

Here are four steps to get you started:

1. Start with a common lexicon—and a shared commitment
   It’s easy to analyze pipeline velocity or CAC payback. But what’s the engineering equivalent of these metrics? The answer varies depending on a company’s product maturity, development model and team structure. That’s why the goal isn’t a universal dashboard; it’s an evolving framework, co-developed by finance and engineering and grounded in trend-based tracking and continuous iteration. For example, in a company shipping weekly releases, it might make sense to measure deployment frequency and mean time to recovery (MTTR). In a company building a complex infrastructure product with longer development cycles, planning accuracy and cycle time might be more relevant metrics. A CFO doesn’t need to know what every pull request does. But they do need to understand how engineering is allocating resources, delivering value and responding to change. Moreover, CFOs can bring structure to the conversation: aligning metrics to investment theses, time horizons and business outcomes.
2. Build a consistent communication rhythm
   We often ask CFOs when they last had a one-on-one with their CTO. The answer, more often than not: “It’s been a while. ”For CFOs, understanding engineering productivity isn’t about micro-management—it’s about visibility. Regular touchpoints, monthly or even quarterly, can help bridge the gap. These conversations shouldn’t just be about budgets or headcount. They should include discussions of development goals, challenges and how engineering output ties to strategic priorities. CFOs can help clarify what trade-offs the business is willing to make (e.g., time-to-market vs. technical debt), while CTOs can shed light on where additional investment would accelerate velocity or improve quality. When these conversations are grounded in data, they move from abstract to actionable. Of course, CFOs must also be thoughtful about how they approach these conversations. A common misstep is leading with budget scrutiny or requests for cost cuts, which can put technical leaders on the defensive. Instead, CFOs can frame their involvement as an opportunity to unlock more impact from engineering, not limit it. One powerful way to earn trust is by asking how finance can help remove friction, asking questions like “Where do we lose momentum?” or “Is there anywhere we’re underinvesting that’s slowing you down?” By showing genuine curiosity and offering operational leverage—whether that’s better forecasting, smarter capital allocation or tools to surface hidden bottlenecks—CFOs can become valued partners, not gatekeepers.
3. Choose metrics that evolve over time
   Engineering productivity is multidimensional: There’s no single metric that captures it all. Instead of searching for the perfect KPIs, choose a handful that provide directional insight into throughput, speed, quality and team health. Start by asking: What are we trying to improve? Then align metrics accordingly. You don’t need to measure everything all at once. In fact, the most effective metrics programs start small, focusing on a few data points that help explain what’s working, what’s slowing things down and what’s improving over time. At the end of this post, I’ve included a sample table of commonly used engineering productivity metrics, complete with definitions and benchmarks, to help jumpstart those conversations.
4. Start with a spreadsheet—and scale from there
   Don’t over-engineer the process. In the early days of this new collaboration, a shared spreadsheet or lightweight dashboard can go a long way towards building trust and visibility. The goal isn’t to launch a full-blown reporting suite overnight. It’s to establish a habit of reviewing and discussing metrics together on a regular cadence. As teams mature and the need for scale increases, purpose-built tools can dramatically improve the fidelity and timeliness of your metrics. For example, LinearB* provides real-time visibility into engineering performance with minimal setup. It integrates directly with your existing developer tools (e.g., Git, Jira) and automates reporting on metrics like cycle time, deploy frequency and code churn—helping teams identify inefficiencies, reduce bottlenecks and accelerate delivery. Tools like this help transform engineering conversations from anecdotal to analytical—and make it easier for both finance and engineering leaders to work from a shared source of truth.

Final thoughts: Align on the why

Ultimately, engineering is one of the largest, most complex and least understood cost centers in modern software businesses. CFOs and CTOs who align early—on goals, language and metrics—are far better positioned to optimize both investment and impact.

Remember, this isn’t just about improving margins. It’s about staying competitive, delivering value to customers faster and positioning R&D as a strategic advantage. When finance and engineering collaborate, the result isn’t just better measurement, it’s better business.

Appendix: Engineering Productivity Metrics – Definitions & Benchmarks

CFOs can use the table below to familiarize themselves with common engineering metrics. Don’t think of these as rigid KPIs–they’re more directional benchmarks to help CFOs and CTOs start speaking the same language, ask better questions, spot patterns and make smarter trade-offs together.

The information contained here is based solely on the opinion of Alex Auchter, and nothing should be construed as investment advice. This material is provided for informational purposes, and it is not, and may not be relied on in any manner as legal, tax or investment advice or as an offer to sell or a solicitation of an offer to buy an interest in any fund or investment vehicle managed by Battery Ventures or any other Battery entity. The views expressed here are solely those of the author.

*Denotes a Battery portfolio company. For a full list of all Battery investments and exits, please click here.

The information above may contain projections or other forward-looking statements regarding future events or expectations. Predictions, opinions and other information discussed in this publication are subject to change continually and without notice of any kind and may no longer be true after the date indicated. Battery Ventures assumes no duty to and does not undertake to update forward-looking statements.

The post 4 Ways Software CFOs Can Partner with CTOs to Unlock Engineering Productivity appeared first on Battery Ventures.

At Battery, we’ve been closely tracking this shift. A recent, weeklong trip across Bangalore, Chennai, and Mumbai gave us a front-row seat to the energy and ambition pulsing through India’s tech corridors. What we saw reaffirmed a powerful insight: India’s decades-long experience as the “back office” for global enterprises is morphing into a critical asset for building the next generation of AI-native SaaS companies.

Historically, India has been known as the world’s back office. The country in many ways built the IT services and BPO/KPO empires that power global businesses, largely through Indian companies like TCS, Infosys, and Wipro that serviced Western companies. And many of those large corporations—like Accenture, Deloitte, KPMG, Gartner, and Nielsen—now have their largest global workforce based in India. Over time, this has created a massive base of highly skilled knowledge workers based in India who are deeply familiar with U.S. workflows, software systems, and business culture.

About a decade ago, Indian tech took another turn, as homegrown Indian entrepreneurs—some of them supported by organizations like NASSCOM and SaaSBoomi–set out to transform the country from a services hub into a product powerhouse. That vision gave rise to India-based software companies like Zoho, Freshdesk, Icertis, and HighRadius, which served a global market. Since then, over $20 billion in venture capital has flowed into Indian SaaS startups, giving birth to more than 20 unicorns, including Postman*, Druva, and Chargebee. The “Product Nation” movement continues to gain momentum. But now, a new wave is cresting: AI.

In the AI era, software isn’t just a tool—it’s the worker. AI-powered SaaS is now automating, augmenting, and in many cases replacing traditional SaaS digital workflows. For example, Cursor is building an AI code editor that understands your codebase and generates code based on simple English prompts, which frees up time for developers or negates the need to rely on junior software engineers. Similarly, Writer’s AI agents can generate copy and images that are in-line with your brand guidelines. To do that well, deep domain expertise is no longer optional—it’s essential. That’s where we feel India has a structural edge.

India’s triple advantage in AI SaaS

We see three core advantages positioning India to lead the next wave of AI software companies:

1. Fastest-growing developer talent pool: India is home to the fastest-growing developer pool globally, with 15 million software developers today. This number is projected to surpass 20 million by 2027. Already, one in four AI projects on GitHub originate from India.

2. Deep professional services expertise: With decades of experience across IT services, customer support, healthcare operations, compliance, and financial back-office workflows, teams in India have the right kind of domain expertise needed to build AI agents that can effectively automate and augment human work.

3. Capital efficiency: Indian startups benefit from a 3–5x cost advantage across key functions like software R&D, inside sales, technical support, and customer success. We believe this structural edge enables capital-efficient scaling—especially in product-led, go-to-market models where lean, high-velocity teams are a competitive advantage.

From Services to “Service-as-a-Software”

Let’s take Revenue Cycle Management (RCM) as an example—a critical function in healthcare that includes processes like insurance eligibility verification, medical coding, and claims submission and follow-up. Many U.S. health systems have long outsourced this labor-intensive work to Indian services firms due to chronic shortages of medical coders and high turnover in billing roles. This created an opening for American companies like Omega Healthcare, R1 RCM, and GeBBS to build billion-dollar revenue businesses, collectively employing tens of thousands of people in India to manually process healthcare claims.

But now, the traditional RCM services model faces fundamental disruption with the rise of foundation models. For example, a recent study by researchers at Mt. Sinai demonstrated that the GPT-4 model achieved a peak accuracy of around 50% in coding various medical classifications (ICD and CPT codes). While this represents a significant starting point, with substantial room for improvement through fine-tuning on millions of medical claims records, even achieving 90% accuracy may not suffice in this critical domain. The reality is that even a seemingly small error rate can lead to substantial financial losses for healthcare providers. Therefore, the most promising path forward lies in an AI + human-in-the-loop architecture—strategically marrying the automation capabilities of AI with essential expert human oversight to enhance billing accuracy and reduce administrative costs. This presents a unique opportunity for Indian founders to build the next generation of AI-powered RCM services companies that effectively blend the strengths of LLMs with critical human expertise to maximize efficiency and minimize costly errors.

A similar disruption is unfolding in pharma and life-sciences compliance, a sector traditionally reliant on manual processes for regulatory documentation, audit preparation, and supply-chain oversight. Tasks such as regulatory filing, serialization reporting, and compliance tracking have often been outsourced to global service providers like IQVIA (formerly IMS Health), which offers comprehensive regulatory affairs services to pharmaceutical companies worldwide.

However, the advent of Large Language Models (LLMs) is transforming this landscape. LLMs excel at document ingestion, analysis, and generation, enabling the automation of complex compliance tasks. When combined with technologies like computer vision, these models can handle intricate processes such as interpreting regulatory guidelines and preparing submission documents. Nevertheless, due to the high stakes involved in regulatory compliance, we feel a human-in-the-loop approach remains essential to ensure accuracy and address nuanced scenarios. Innovative startups like AltiusHub are leading this transformation by offering AI-powered solutions that enhance supply-chain traceability and ensure compliance with global regulations.

The next-generation Indian SaaS playbook is emerging, focused on replacing legacy services with intelligent, AI-driven software solutions that combine automation with human expertise to meet the rigorous demands of pharma and life sciences compliance.

Hopping the pond—with Battery

At Battery, we’ve long believed in the “hop the pond” model: partnering with exceptional European and Israeli founders to scale their companies by tapping the vast, U.S. enterprise-IT market. Now, we’re seeing the same momentum from India. We already backed Postman*, which has become the global standard for API development, and more recently, Atomicwork*, an agentic IT service-management platform.

If you’re an Indian founder building an AI SaaS company with global ambition, we want to hear from you. Let’s build the next generation of intelligent enterprise software—from India to the world!

The information contained in this market commentary is based solely on the opinions of Dharmesh Thakker and Sudhee Chilappagari, and nothing should be construed as investment advice. This material is provided for informational purposes, and it is not, and may not be relied on in any manner as legal, tax or investment advice or as an offer to sell or a solicitation of an offer to buy an interest in any fund or investment vehicle managed by Battery Ventures or any other Battery entity. The views expressed here are solely those of the authors.

The information above may contain projections or other forward-looking statements regarding future events or expectations. Predictions, opinions and other information discussed in this publication are subject to change continually and without notice of any kind and may no longer be true after the date indicated. Battery Ventures assumes no duty to and does not undertake to update forward-looking statements.

* Denotes a Battery portfolio investment. For a full list of all Battery investments, click here.

The post From IT Outsourcing to AI Innovation: Why We’re Excited About the New Startup Surge in India appeared first on Battery Ventures.