Wintage Club Insights

Scaling sales teams: Lessons from pre-revenue to $1BN ARR

Manish talks about his experience scaling sales teams at different stages of an enterprise’s journey and shares his thoughts on the impact of AI.

Shrivarshini Somasekhar

July 31, 2024

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AI Summary

Each revenue stage demands different sales team structures: founder-led selling (pre-$1M), first sales hires ($1M–$5M), team building ($5M–$20M), scaling infrastructure ($20M–$100M+), and enterprise maturity ($100M+).
The most common scaling mistake is hiring ahead of process — adding reps before the playbook, content, and tools are ready to support them.
Tribal knowledge is the single biggest scaling risk: what lives in founders' or top reps' heads must be codified into systems before the team grows.
AI-native platforms like SiftHub accelerate scaling by democratizing knowledge, automating repetitive work, and ensuring consistent quality regardless of team size.
The lesson from $1BN ARR companies: invest in infrastructure (tools, process, enablement) one stage before you need it, not one stage after.

Each revenue stage demands different sales team structures: founder-led selling (pre-$1M), first sales hires ($1M–$5M), team building ($5M–$20M), scaling infrastructure ($20M–$100M+), and enterprise maturity ($100M+).
The most common scaling mistake is hiring ahead of process — adding reps before the playbook, content, and tools are ready to support them.
Tribal knowledge is the single biggest scaling risk: what lives in founders' or top reps' heads must be codified into systems before the team grows.
AI-native platforms like SiftHub accelerate scaling by democratizing knowledge, automating repetitive work, and ensuring consistent quality regardless of team size.
The lesson from $1BN ARR companies: invest in infrastructure (tools, process, enablement) one stage before you need it, not one stage after.

Manish Jindal has worn several hats over the years. He spent a decade building and scaling the revenue function at Cloudflare. Currently, he heads the go-to-market teams at DevRev. Manish has the unique experience of working in startups of all shapes and sizes, from pre-revenue to $1 billion ARR.

Now the Head of Revenue at DevRev, Manish was able to share his story with us on our flagship interview series, “Wintage Club Conversations”.

The shock of transitioning from a public company to a young startup

Having joined Cloudflare pre-revenue, Manish witnessed its growth right from securing the first customer to achieving $1 billion in ARR before going public. In his own words, “In my journey from Cloudflare, I saw the company…go from zero to getting our first customer, first $10 million, first $100 million, plus getting to a billion and being a public company.”

He described his experience of transitioning from the structured machine that is a public company to an early-stage startup: “For me, I'm a builder at heart, so I wanted to go back to a younger company and start building again. I felt that I was very prepared as I was joining DevRev. Of course, to my surprise and shock, as humans, we forget what happened 10 years ago, so there was a shock to the system because you get so used to having this whole ecosystem around you to do things for you. You have a person for everything, processes for everything, data available, systems, and tools. When you come to a younger company like DevRev, which was in a very early stage, you have none of that. You must exercise your muscle memory again and take one day at a time.”

PMF plays a key role in scaling sales

Manish was quick to point out the importance of not forcing product-market fit (PMF). "You cannot force PMF.” Companies that fail push their product onto the market without fully understanding if there is a genuine need. Instead, a mindset of iteration is needed: "You have to have the mindset where you have certain pieces and take them to the market, then iterate on them."

As a 'first principles thinker', Manish is not satisfied with templated go-to-market strategies. Instead, he believes in getting the product into the hands of customers to truly understand what works for GTM. He put this thinking to the test at Cloudflare.

Cloudflare adopted a product-led growth (PLG) strategy and sold infrastructure products for as low as $20 or $200 instead of targeting the top thousand companies. He recalled board members questioning this approach, "They’d ask ‘How are you going to build a billion-dollar business by selling something for $20 or $200?’ And my response was, ‘We’ll only win if we have lots of happy customers and the biggest network in the world’.”

It’s important to share value with customers early on

A part of Manish’s strategy involves 'value sharing', a concept not many fully understand. In Manish’s words, here’s how it works:

“When you have a product, you’re providing a certain value, and as a company, you try to capture as much value as possible. But initially, you have to share the value ... give something away and capture it back over time. Balancing being a revenue-generating company with not trying to become a business too fast is crucial.”

What he means is, companies trying to establish themselves in a saturated market, should be willing to pass on a substantial portion of the value to customers, at least initially. "If you provide X value, you might pass 80% of that value to the customer and only capture 20%. Over time, you can capture more value back." Capturing all the value immediately leads to hasty, short-term decisions, especially if the company hasn't achieved PMF.

Unlocking the real power of AI with a comprehensive knowledge map

Sales teams globally are welcoming AI capabilities, particularly for automation and augmentation. Automation can handle many tasks currently performed by humans, like RFPs, order creation, lead assignment, and initial discovery calls, reducing human involvement in routine tasks. On the augmentation side, AI enhances the efficiency of existing teams by providing information and insights for better decision-making.

The real power of AI comes from combining both automation and augmentation. However, as Manish pointed out, augmentation and automation both have different needs and challenges: “Augmentation is about making people more efficient by providing information to make decision-making faster and more efficient. If you have both automation and augmentation, you see the full power of AI as a company. However, for augmentation, you need a knowledge map of all your data, customers, products, and processes in one place. Currently, there are many disparate systems with different data structures, making it complex to stitch all the data together and enable natural language processing across them.”

Despite the complexities of integrating disparate data systems, Manish remained optimistic about the efficiency gains once companies achieve a comprehensive knowledge map.

However, he pointed out challenges in implementing AI in sales and support: “When it comes to using AI to help customers at different parts of the sales or support cycle…the human touch cannot be fully substituted. When you automate with chatbots, customer satisfaction can go down initially because the human touch is missing, even if the answers are faster and more accurate.” AI needs human monitoring, especially in the beginning.

The challenges of selling AI technology

The sales process for AI-native businesses differs from other tech companies that have deployed AI marginally. For starters, AI-native companies need to educate their prospects on the tech and its benefits so the sales process starts with creating awareness and understanding about their product and the problem they are solving. Manish asserted that "Buyers are embracing AI, and selling AI technology is becoming easier. However, there are some challenges when using AI to assist customers in various parts of the sales and support cycles."

Manish agreed that for such companies, the focus shifts from selling features to selling value and outcomes: "Conversations are increasingly focused on the value AI brings. For example, in support, AI can reduce the need for agents and make existing agents more efficient. The discussion then revolves around how much value to pass on to customers and how much to capture."

AI is also expensive due to the infrastructure required. AI-native companies like DevRev have addressed this by building their vector database to reduce costs. This allows them to offer AI solutions at a lower price and capture more value while still being competitive.

Ultimately, the solution's tangible benefits—such as increased efficiency, cost savings, and improved decision-making—need to be showcased.

Standing out in a crowded market

When transformative technology like AI emerges, many companies innovate, but only a few will survive. Manish lent his insights on how companies solving a niche using AI compete with the likes of OpenAI: “Companies that focus solely on making existing AI slightly better are at risk of being outpaced by advancements from major players. To succeed, companies need to build applications that leverage AI to solve specific problems in new ways, rather than just improving existing AI capabilities.”

The primary goal for Manish and his team is to quadruple DevRev’s ARR this year. Beyond this, they’ll focus on “finding a repeatable and predictable sales motion” for effective scaling. Manish maintains a long-term view and avoids decisions that could be an obstacle to future growth.

Check out the full video of our conversation with Manish Jindal .

What are the most important process investments to make before scaling a sales team?

Before scaling headcount, teams must establish: a documented, consistently executed sales process with clear stage criteria; a knowledge management system that captures institutional expertise; a forecast methodology that produces reliable visibility; and a coaching structure that can operate at scale without requiring founder or VP direct involvement in every deal. Teams that scale without these foundations discover that adding reps multiplies the existing chaos rather than replicating the early success that made growth seem possible.

How does the AE-to-SE ratio change as a company scales from startup to enterprise?

At early stage, founders and first SE often support multiple AEs informally with high deal knowledge and direct communication. This breaks down quickly as the team grows past 10–15 people: informal knowledge sharing stops working, SE demand from AEs exceeds capacity, and deals receive uneven support based on which AE has the best SE relationship rather than which deal has the highest potential. Scaling through this wall requires: formal SE capacity management, AI assistance to multiply SE effective capacity, and a tiered SE model for different deal complexities.

What knowledge management practices prevent cultural knowledge loss during hypergrowth?

Preventing knowledge loss during hypergrowth requires: systematic capture of winning talk tracks, objection responses, and deal narratives before they're lost as experienced reps become managers or depart; a knowledge platform that makes institutional knowledge accessible to new hires without requiring mentorship from specific individuals; and documentation practices that are embedded in daily workflows rather than treated as separate administrative work. Knowledge loss during growth is almost always gradual—invisible until suddenly the team is full of experienced reps who don't know things they should.

How do forecasting requirements change from early stage to $100M ARR?

Early stage: founders track every deal personally with informal visibility. $10M ARR: weekly forecast calls with spreadsheet tracking, first signs of forecast inaccuracy. $50M ARR: CRM discipline becomes essential; forecast variance becomes a board-level topic; pipeline health analytics needed. $100M ARR: multiple product lines, multiple segments, multiple geos all require unified forecast visibility with automated health signals. Each stage requires progressively more sophisticated forecasting infrastructure—teams that don't invest ahead of the next stage consistently face credibility problems with investors and leadership.

What go-to-market model changes are required as a company crosses key ARR milestones?

Crossing $10M ARR typically requires: moving from founder-led sales to a repeatable process that generalizes. Crossing $50M ARR typically requires: dedicated presales function, formalized RFP process, and specialization by segment or vertical. Crossing $100M ARR typically requires: regional expansion with localized sales leadership, more sophisticated customer success and expansion motion, and investment in partner channels. Each transition requires organizational, process, and technology changes that need to be anticipated before the milestone arrives rather than addressed reactively after growth has already exposed the gap.

How does AI enable companies to scale revenue more efficiently than through headcount alone?

AI fundamentally changes the headcount-to-revenue ratio by enabling each person to handle more deals without sacrificing quality. A 10-person sales team with SiftHub can pursue and close at the volume that previously required 15–20 people because AI handles the information work that consumed the surplus capacity. This efficiency has compounding effects: lower cost of sale improves unit economics, faster response times improve win rates, and higher per-rep productivity delays the hiring that itself creates organizational coordination overhead.

What is the most common mistake companies make when scaling their sales team?

The most common mistake is prioritizing headcount over systems. Leaders who add reps because last quarter's team hit quota often discover that the additional reps don't perform because the processes, knowledge infrastructure, and management capacity don't scale. The rep-to-manager ratio stretches, coaching quality drops, and new reps ramp slower because the institutional knowledge transfer capacity is diluted. The companies that scale most efficiently invest in knowledge systems, process documentation, and AI assistance before each headcount expansion—multiplying the output of their existing team before adding to its size.

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