The Opportunity for Next-Gen ERPs (February 2025 Enterprise Newsletter)

Lessons From NetSuite, and the Opportunity for Next-Gen ERPs

No company loves their enterprise resource planning system (ERP). ERPs may be business critical because they allow companies to better integrate sales, finance, human resources, and other functions into one system, but they’re also costly and time consuming to implement, difficult to use, and often full of stale data and features. 

Excitingly, we’ve recently seen a number of companies trying to solve these problems around usability and relevance, either by building a next-gen ERP from scratch (i.e., Doss, Rillet, Campfire, Toolkit) or an AI-native “system of engagement” that layers on top of existing ERPs to make them easier to use (i.e., Endeavor). So why are ERPs the way they are, and why is now the right time for them to change? To break this down, we first want to share the story of one of the most influential ERPs in history: NetSuite. Then, we’ll explain what we think the next generation of ERPs will need in order to break through the market. 

The Birth of the Modern ERP and the Coming of NetSuite

In the 1960s, companies began building and using what were known as material requirements planning (MRP) systems to optimize their manufacturing processes. These systems initially supported inventory control, production scheduling, as well as the actual manufacturing process, before later expanding to accounting, sales, engineering, finance, and other business functions — all in a single database. These early ERPs enabled cross-departmental data sharing and offered extensive tailoring to fit unique workflows. They were also typically on-prem, which meant more upfront investment in terms of servers and staff, but less flexibility in maintenance and usage.

In 1998, NetSuite entered the scene as one of the first cloud-based ERPs. Founder Evan Goldberg saw an opportunity to build an agile system that could handle complex business accounting better than QuickBooks and faster than SAP and other companies with on-prem solutions. To start, he launched a cloud-based ledger — aptly named NetLedger — that specifically focused on accounting and could offer real-time data access to a company’s financials, i.e., QuickBooks in the cloud. Boosted by a small initial investment from Oracle’s Larry Ellison, the original product took a lean team — of only four people! — just a year to build. 

NetLedger was a hit. Compared with on-prem ERPs, it was significantly faster to deploy, required no expensive hardware investments or installations, and eliminated the need for complex IT infrastructure management. Companies could access their data from anywhere with an internet connection, and automatic updates meant they always had the latest features without disruptive manual upgrades. The company also offered a subscription-based pricing model, making them more attractive to mid-sized businesses unable to afford a traditional, on-prem system. At launch, for instance, NetLedger targeted SMBs with 1-50 employees and charged just $4.95 per month per user. 

After accounting, the company quickly launched additional modules to help companies run more parts of their business, including ecommerce functionality in 2000 and CRM and Salesforce automation support in 2001. By 2002, the system gave small businesses the ability to manage accounting, payroll, inventory, expense reporting, and more all from a single place. Marketing, customer support automation, and other modules quickly followed, and the company officially rebranded as “NetSuite” in recognition of its new collection offerings. The company continued to grow through acquisitions following its 2007 IPO and was itself acquired by Oracle in 2016 for $9.3 billion.

The Opportunity for Next-Gen ERPs

So what can the next generation of challengers learn from the rise of NetSuite, and how can they apply the lessons learned?

NetSuite succeeded by riding the cloud wave and capturing the mid-market early on. Today’s AI wave presents similar transformative opportunities for ERPs. Just as the cloud solved infrastructure and accessibility challenges by making it easier and cheaper for businesses of all sizes to procure an ERP, AI can address the fundamental data quality and usability issues that plague current ERPs, and make the ERP more usable and powerful. For example, gen AI can automate manual data entry through intelligent document processing, translate complex ERP workflows into natural language interactions, and help reconcile data inconsistencies across modules. This could dramatically reduce both implementation time and user training needs — two of the biggest barriers to ERP adoption today. 

To go a bit deeper on this point: While ERPs serve as vast data repositories, they often fall short of being true operational command centers for businesses; they function more as historical systems of record rather than drivers of intelligent decision-making. This limitation stems from two key issues: data ingestion and data reconciliation. The data ERPs need often relies on manually entered documents like invoices, or fails to reconcile between sources (e.g., contract values between Salesforce and billing). ERPs weren’t built for modern APIs, making the ingestion and integration of new data sources difficult. Complicated implementation processes compound these challenges. Mid-market ERPs, for example, take at least 4 to 6 (and often up to 12) months to implement, while enterprise implementations can stretch 4 to 6 years and require consultant fees that exceed the software license costs.

AI, however, is uniquely positioned to transform ERPs from passive data stores into proactive business intelligence platforms. First, AI can dramatically improve data capture and integration — whether through next-gen, LLM-powered document processing and extraction or innovative collection methods like voice data. Second, AI can better consolidate and reconcile data across different sources, automatically flagging discrepancies and creating workflows to resolve them. This enhanced data foundation enables ERPs to move beyond simple record-keeping to provide actionable insights, proactive alerts, and intelligent suggestions for business operations.

This AI-enabled data transformation creates particularly compelling opportunities for vertical ERPs. By combining improved data integration capabilities with industry-specific modules, such as commission planning for insurance or inventory management for retail, vertical ERPs can become even more powerful operational platforms for their target industries. While vertical ERPs have existed for a while, the combination of easier, expanded data access and AI-powered analytics significantly expands their potential scope and value to more comprehensive operational command centers. 

Similarly, next-gen ERPs should craft their initial go-to-market strategy around companies of specific sizes or levels of complexity in order to stay focused and avoid building tons of ad hoc customizations. This strategy is already prevalent throughout the marketplace today — many AI-native software companies building in this space think about their ICP in terms of either vertical (as described above) or size, primarily by the incumbent that serves them: namely, Quickbooks, NetSuite, and Sage. Because ERPs are feature-rich and implementation-heavy, startups either try to catch prospects just as they’re ready to “graduate” from Quickbooks (i.e., first introducing multiple currencies or legal entities) or if they’ve been sold a more expensive NetSuite or Sage contract they are not fully utilizing. Finding customers at these moments of transition makes the “rip and replace” sale easier — when the CFO or Controller is not actively feeling pain from their current system of record, it’s very difficult to convince them to take on the burden of switching to a new one.

All this being said, replacing the ERP can be a big build and requires trust! When NetSuite launched, the existing ERPs in the market were not as full featured as they are today — it was easier to compete. The AI-enabled ERP likely starts with a wedge or set of features and expands from there. The key thing, of course, is making the module you land with valuable enough to plant a flag.

Seema Amble is a partner at Andreessen Horowitz, where she focuses on SaaS and fintech investments in B2B fintech, payments, CFO tools, and vertical software.

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Eric Zhou is a partner at Andreessen Horowitz, where he focuses on companies building at the application layer in Generative AI.

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Marc Andrusko is a partner at Andreessen Horowitz, where he focuses on B2B AI applications and fintech.

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When to Hire A CFO

After achieving product-market fit, early stage companies hire a finance leader who is tactically strong and capable of wearing multiple hats. Then, as the business scales and complexity mushrooms, startups often graduate to recruiting a head of finance. But at what stage of a company is a proper CFO needed? In our current era, there’s no predictable timeline for an IPO. Companies remain private longer for any number of reasons that make good business sense.

You should already have an effective leader who is capable of building your financial systems and controls. They should excel in areas such as controllership, FP&A, investor relations, risk, and treasury. Do they need to have the latest FASB Technical Bulletins memorized or be a specialist in international tax intricacies? Not necessarily. But they should have the technical depth to engage subject matter experts meaningfully and hold their own in any of these domains. 

As the former CFO of Splunk, Lyft, and OpenSea, I’m frequently asked by founders about the next step — when to hire a CFO. You and your board should start your decision-making process with a candid evaluation of your organization’s finance needs and continue this assessment on a semi-annual basis, at the very least. 

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Brian Roberts is a General Partner at Andreessen Horowitz, where he works with the American Dynamism and AI Apps funds.

Why DeepSeek Is a Gift to the American People

On October 4, 1957, the USSR launched Sputnik, a 184-pound satellite, into Earth’s orbit. The satellite didn’t do much — it just “beeped” over radio waves. But those beeps sounded a wake-up call for the United States.

Fearful of falling behind its Cold War strategic rival, the federal government launched an expensive crash program to spur technological development. The U.S. went from a laggard in the space race to unified, fast-moving behemoth. By 1969, we had landed men on the moon, a feat the Soviets never accomplished. And more than that: The Sputnik moment began decades of across-the-board American dominance in science and engineering.

Now comes what many are calling a new Sputnik moment: the release of DeepSeek, a low cost, high-performing Chinese-created artificial intelligence (AI) model. The analogy is a bit imprecise though — and probably understates the significance of this event.

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Alex Rampell is a General Partner at Andreessen Horowitz, where he leads the firm’s $1 billion Apps practice.

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