What Leading a Business Unit P&L Has Taught Me About Customer Success

After nearly seven years as a customer success leader, I recently stepped into a role overseeing a business unit with P&L responsibility—managing cross-functional teams across sales, product, customer success, solution engineering, and research. Being responsible for delivering a product to market has shifted my perspective on the optimal role of customer success.

The Traditional CS Dichotomy

While at its core, customer success is primarily focused on retaining customers and revenue, in my experience, customer success teams typically lean toward one of two orientations:

Sales-Driven CS: These teams focus on identifying new use cases, building executive relationships, and partnering with sales to execute on new opportunities. They often support renewals planning, negotiations, and drive expansion revenue.

Product-Driven CS: These teams serve as the voice of the customer within the company while acting as product specialists externally. They drive adoption, gather feedback, and ensure the product delivers value.

While both of these focuses should continue to have a retention target (both in terms of revenue and customer logos), retention is ultimately a lagging indicator. In both a sales and product focused role CS role, client value must be the first consideration in order to help drive that revenue retention.

Both orientations come with inherent challenges. Sales-driven CSMs risk being perceived as "too salesly" and focused on growth at the expense of customer value. Meanwhile, product-driven CSMs can easily fall into reactive support roles, troubleshooting issues without moving customers forward strategically.

The Product Maturity Connection

Ultimately, CSMs (as with any other role within the company) should be aligned to the company objectives and value. However, as I have taken on overseeing across multiple functions, including driving new product development to market. I realized when shaping the function and priorities of the CSM team, a fundamental consideration is:

"How mature is your product?"

This question has significant implications for how customer success teams should be structured and operate.

The Product Maturity Continuum

The right balance between sales and product focus should shift based on where your product sits on the maturity spectrum:

Low Maturity (Early-Stage Products)

• You have an initial MVP or immature product

• CS Role: Product Focused 

• CSMs should heavily emphasize gathering customer feedback, identifying product gaps, and ensuring early customers achieve baseline value

• They become critical conduits to product teams shaping the roadmap

Medium Maturity (Growing Products)

• Your product is in the market but still refining its product-market fit

• CS Role: Product/Sales Mix

• CSMs balance championing product improvements while beginning to identify expansion opportunities

• They help validate new use cases while ensuring existing capabilities deliver value

High Maturity (Established Products)

• You have an established product and are focused on market expansion

• CS Role: Sales Focused 

• CSMs can lean more heavily into identifying new use cases, driving account growth, and supporting renewals

• Product feedback remains important but becomes more incremental than transformational

Beyond Maturity: Additional Considerations

While product maturity provides a strong foundation for CS role definition, several other factors influence the optimal balance:

• Team Resourcing: Larger organizations may support specialized CS roles while startups often need versatile generalists

• Product Complexity: More sophisticated products may require greater product-focused guidance regardless of maturity

• Customer Segment: Enterprise customers typically demand more strategic engagement than SMB customers

• Competitive Landscape: Highly competitive markets may necessitate more sales-oriented CS to prevent churn

For more considerations about setting up your CS team, check out my Customer Success Organizational Assessment Form (coming soon). 

Bridging the Gap: How AI-Powered Prototyping is Revolutionizing Customer Success and Product Collaboration

Customer Success teams sit at the epicenter of customer feedback—fielding feature requests, processing complaints, and translating user pain points into actionable insights. Yet one of our greatest challenges remains: effectively communicating customer needs to product teams in a way that drives meaningful development.

Too often, customer feedback arrives as vague requests: "I don't like this feature" or "I want something different." While these statements signal dissatisfaction, they rarely illuminate the underlying problem or desired outcome. The knee-jerk reaction? Build exactly what the customer asks for. But this approach overlooks critical considerations like development effort, broader market impact, roadmap alignment, and resource allocation.

The Translation Challenge

Product managers are trained to probe deeper, asking the right questions to uncover true customer needs. However, they don't always have direct customer access, and when they do, their technical focus can sometimes miss the emotional and operational context that CSMs naturally understand.

On the flip side, Customer Success Managers excel at building relationships and understanding customer pain points, but may struggle to extract the specific technical requirements that product teams need to prioritize and scope development work effectively.

The traditional solution—getting product teams in front of customers more frequently—helps but creates its own challenges, pulling developers away from building and extending feedback cycles.

Enter AI-Powered Prototyping

Artificial Intelligence presents a game-changing opportunity to bridge this communication gap. CSMs can now leverage AI as a rapid prototyping tool, transforming abstract customer feedback into tangible concepts that spark productive conversations.

Here's how it works in practice:

Imagine you're a CSM at an e-commerce platform, and a key customer complains that their storefront feels "too busy and overwhelming." They want a "simplified experience" that makes it easier to repurchase frequently ordered items. Rather than passing along this vague feedback, you can prompt an AI tool like Claude to generate visual mockups in under a minute.

Sample prompt: "Create a simplified e-commerce storefront mockup focused on repeat purchases, with clean design and easy reorder functionality for frequently bought items."

Within moments, you have concrete prototypes to share with your customer.

Turning Prototypes into Insights

These AI-generated mockups become conversation starters that help you ask the right questions:

• What specifically appeals to you about this design approach?

• How would this improved functionality change your daily workflow?

• What business outcomes would this enable for your team?

• What time, cost, or resource savings do you anticipate?

• How might this impact user adoption across your organization?

Through this structured dialogue, you're not just gathering feature requests—you're uncovering the business case and ROI that product teams need to make informed prioritization decisions.

From Conversation to User Story

The process doesn't stop at customer validation. By recording and transcribing these prototype feedback sessions, you can leverage AI again to generate comprehensive user stories for your product team.

Follow-up prompt: "Based on this customer feedback conversation about storefront simplification, create a detailed user story including the use case, desired outcome, and business impact for our product development team."

The result? Rich, contextual requirements that go far beyond "make it simpler" to explain the who, what, why, and expected impact of the requested functionality.

Real-Time Collaboration

Perhaps most exciting is the potential for real-time collaboration. AI prototyping is fast enough to happen live during customer calls. With screen sharing, you can iterate on designs in real-time, getting immediate feedback and refinement until you reach alignment on the desired solution.

This dynamic approach transforms customer feedback sessions from complaint resolution into collaborative design sessions.

Expanding the CS-Product Partnership

While AI already supports traditional Customer Success functions—account research, Executive Business Review preparation, risk analysis—its application to product development represents a new frontier. As AI continues to democratize design and development capabilities, it creates unprecedented opportunities for CSMs to become more strategic partners in the product development process.

The traditional handoff from "customer wants X" to "product builds X" evolves into a collaborative process where CSMs arrive with validated concepts, clear business cases, and detailed user stories that make product teams more effective and customer-focused.

Implementation Considerations

Before diving in, ensure your AI prototyping approach aligns with both your organization's and your customers' privacy policies. When incorporating screenshots or product information, use private enterprise AI tools or limit content to publicly available information.

The Future of Customer Success

AI-powered prototyping represents more than just a new tool—it's a fundamental shift in how Customer Success teams can contribute to product strategy. By transforming vague feedback into visual concepts and business cases, CSMs become strategic translators who bridge the gap between customer needs and product execution.

The organizations that embrace this approach will find their Customer Success teams driving not just retention and expansion, but actual product innovation that delivers measurable business value for both their customers and their company.