Think Like a Product Manager: Build an AI-Ready RevOps Engine

Revenue operations is evolving faster than ever. What started as tactical sales support has transformed into strategic revenue orchestration, and now we're entering a new era entirely. Welcome to RevOps 3.0—where operators don't just maintain systems, they build them.

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In this digital event, Cass Ernst-Faletto, Senior Director of Revenue Operations at Pendo, shared how RevOps teams can adopt a product management mindset to create AI-ready revenue engines that deliver measurable business impact. Her insights reveal why the most successful RevOps professionals are thinking less like administrators and more like builders.

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The Evolution of Revenue Operations: From 1.0 to 3.0

To understand where RevOps is heading, it's essential to recognize how far we've come. Ernst-Faletto outlined three distinct eras that define the evolution of revenue operations:

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RevOps 1.0: The Tactical Era

‍This was the beginning—when "RevOps" was really just sales operations in disguise. Teams were reactive, working in spreadsheets, and functioning as order-takers for sales leadership. The focus was purely tactical: change something in Salesforce, update a report, move on to the next request.

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RevOps 2.0: The Strategic Era

‍Around six to seven years ago, the function matured into true revenue operations. Teams began thinking about the entire customer funnel, bridging gaps between marketing, sales, and customer success. They became data-driven thought partners, involved in strategic conversations early and often. This era brought systematic approaches to revenue operations and cross-functional alignment.

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RevOps 3.0: The Builder Era

‍Today, we're entering RevOps 3.0—the era of strategic builders. It's not enough to be strategic; RevOps teams must also be capable of shipping solutions.

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"We're not just buying software anymore, we can actually build things in-house." - Cass Ernst-Faletto

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This evolution mirrors broader trends in RevOps technology adoption and the increasing sophistication of revenue operations as a discipline.

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Applying the Software Development Life Cycle to RevOps

The key to succeeding in RevOps 3.0 lies in adopting proven frameworks from product management. Ernst-Faletto demonstrated how her team at Pendo applies the Software Development Life Cycle (SDLC) to RevOps projects, creating a systematic approach to building and shipping solutions.

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The SDLC framework consists of six phases:

1. Plan: Define the problem and gather requirements
2. Discover: Conduct research and user interviews
3. Design: Create the solution architecture
4. Build: Develop the actual solution
5. Test: Validate with a pilot group
6. Ship: Deploy to the full organization

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This approach ensures that RevOps initiatives are grounded in real user needs rather than assumptions, leading to higher adoption rates and better business outcomes.

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Case Study: Reducing Support Cases by 60% with AI

To illustrate the power of this approach, Ernst-Faletto shared a compelling case study about implementing Salesforce Agentforce at Pendo. The challenge was significant: her team was handling 4,300 support cases per quarter from sales reps—a volume that was overwhelming for a lean RevOps team.

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The Planning Phase

Using ChatGPT, the team analyzed historical case data to identify patterns. They discovered that most requests were highly repetitive: account merges, hierarchy changes, website updates, and commission record modifications. These tasks were time-consuming but not complex, making them perfect candidates for automation.

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The Discovery Phase

Rather than assuming they understood user needs, the team conducted extensive interviews with sales reps. This research revealed frustrations and provided crucial insights that shaped the solution design.  

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"It's very easy to be behind spreadsheets and just think that reps think a certain way, but actually talking and taking the time you kind of get a lot more nuggets of information." - Cass Ernst-Faletto

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The Design and Build Phase

The team focused on four specific use cases: account merges, account hierarchy changes, website changes, and commission record updates. They built logic to prevent errors (like merging customer accounts accidentally) while maintaining a simple interface for reps.

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The Test Phase

Crucially, they piloted the solution with just 15 users out of 250 total reps—about 6-7% of the user base. They selected power users who generated the most cases, ensuring they were testing with the people who would benefit most from the solution.

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The testing phase revealed unexpected insights, such as reps not knowing where to find account IDs in Salesforce. Using Pendo's own analytics platform, they identified exactly where users got stuck and created guides to help them navigate the process.

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The Results

After shipping the solution to all 250+ reps, Pendo reduced their quarterly case volume from 4,300 to about 2,000—a reduction of more than 60%. This freed up significant time for the RevOps team to focus on higher-value strategic initiatives.

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This success story demonstrates principles that align with broader RevOps automation strategies and shows how AI can be practically implemented to solve real operational challenges.

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Rethinking Adoption: Why 100% Isn't the Goal

One of the most counterintuitive insights from Ernst-Faletto's presentation was about adoption rates. Many RevOps professionals delay launching new processes or tools because they're worried about achieving 100% adoption. This perfectionist mindset is not only unrealistic—it's counterproductive.

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"Best in class products for all companies of all sizes have about 15.6% average feature adoption." - Cass Ernst-Faletto

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This statistic should be liberating for RevOps teams who have been holding themselves to impossible standards.

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The key is understanding the product adoption curve:

• Innovators (2.5%): Early adopters who want to try everything
• Early Adopters (13.5%): Users who see clear value and adopt quickly
• Early Majority (34%): Users who adopt after seeing others succeed
• Late Majority (34%): Users who adopt when it becomes standard
• Laggards (16%): Users who resist change

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Rather than trying to convert everyone immediately, focus on the innovators and early adopters. Their success will naturally influence the broader organization over time. This approach aligns with effective change management in RevOps and helps teams avoid the perfectionism trap.

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Strategic Tool Evaluation: The 40+ Demo Approach

Another fascinating aspect of Pendo's RevOps 3.0 strategy was their approach to competitive intelligence. Rather than evaluating tools reactively when problems arose, Ernst-Faletto's team proactively demoed 40+ tools in a single quarter.

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This wasn't about buying everything they saw. Instead, it was about understanding "the art of the possible"—what solutions existed in the market and what capabilities they could potentially build in-house.

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"We came in and we didn't have a single issue that we were trying to solve. We wanted to just understand like, what does this tool do for us in the go-to-market space?" - Cass Ernst-Faletto

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This approach provided several benefits:

• Market Intelligence: Understanding current capabilities and trends
• Build vs. Buy Decisions: Informing whether to develop solutions internally or purchase them
• Vendor Relationships: Building connections for future needs
• Team Education: Expanding the team's knowledge of available solutions

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The result? They didn't purchase a single tool from those 40+ demos, but they gained invaluable insights that informed their internal development priorities. This strategic approach to RevOps technology evaluation helps teams make more informed decisions about their tech stack.

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Building the AI-Ready RevOps Team

As RevOps enters the builder era, hiring strategies must evolve. Ernst-Faletto outlined four key characteristics to look for when building an AI-ready RevOps team:

1. Experiment Fast - Look for people who aren't afraid to try new things and iterate quickly. The "if you're not embarrassed by your V1, you launched too late" mentality is crucial for RevOps 3.0 success.
2. Systems Thinking - RevOps professionals must understand how different systems and processes interconnect. They need to think beyond individual tools to consider the entire revenue ecosystem.
3. AI Collaborative - This doesn't mean being a technical expert, but rather being comfortable working with AI tools and understanding their capabilities and limitations.
4. Builder Mindset - The most important characteristic is the willingness to build solutions rather than just implement them. This includes both technical building (using no-code/low-code tools) and process building (designing workflows and frameworks).

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Pendo has even created a new role: "Go-to-Market Engineers." These team members bridge the gap between traditional RevOps and technical development, helping to build custom solutions for complex go-to-market challenges.

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When interviewing candidates, Ernst-Faletto asks about their awareness of current market trends: "What are the latest tools that you're really excited about? What have you tried?" This reveals whether candidates are staying current with the rapidly evolving RevOps landscape.

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These hiring insights complement broader trends in RevOps career development and the evolving skill requirements for revenue operations professionals.

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The Build vs. Buy Framework

One of the most practical takeaways from Ernst-Faletto's presentation was her framework for making build vs. buy decisions. This is increasingly important as RevOps teams gain more technical capabilities and face pressure to optimize their tech stacks.

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When to Build:

• The process is highly specific to your organization
• Existing solutions would require extensive customization
• You have the internal capability and capacity
• The solution provides competitive advantage

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When to Buy:

• The problem is common across many organizations
• Best-in-class solutions already exist
• The cost of building exceeds the cost of buying
• It's not a core competency for your team

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Ernst-Faletto provided a concrete example: "Why would I build a tool to round-robin leads in Salesforce? It's very simple. I'm gonna go buy a tool that helps me do that." But for complex, company-specific processes like Pendo's ROE (Rules of Engagement), building internally makes more sense.

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This framework helps RevOps teams make strategic decisions about where to invest their building efforts, ensuring they focus on high-impact, differentiated solutions rather than recreating commodity functionality.

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Practical Implementation: The Book Building App

To demonstrate the builder mindset in action, Ernst-Faletto shared another case study: creating a custom book building application. Like many RevOps teams, Pendo spent countless hours in Google Sheets managing territory assignments and account distributions—a process that was both time-consuming and error-prone.

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Using the SDLC framework, they:

1. Planned by analyzing the data and identifying pain points
2. Discovered user needs through interviews and observation
3. Designed a solution using no-code tools like Lovable and Supabase
4. Built an application that automates book balancing with AI
5. Tested the solution during fiscal year planning
6. Shipped it to leadership for broader use

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The result was a custom application that provides real-time visibility into account distributions, automatically balances territories using AI, and eliminates the manual spreadsheet work that previously consumed weeks of time.

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This example shows how RevOps teams can leverage modern no-code/low-code tools to build sophisticated solutions without requiring extensive technical expertise. It also demonstrates the importance of systematic process management in scaling RevOps operations.

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Key Takeaways for RevOps Leaders

Ernst-Faletto's insights provide a roadmap for RevOps teams ready to embrace the builder era:

1. Adopt Product Management Frameworks - Use structured approaches like the SDLC to ensure your RevOps initiatives are user-centered and systematically executed. This reduces the risk of building solutions that nobody uses.
2. Embrace Imperfect Launches - Don't wait for 100% adoption before launching new processes or tools. Focus on early adopters and let success spread organically through your organization.
3. Invest in Market Intelligence - Regularly evaluate new tools and technologies, not just when you have immediate needs. This builds your understanding of what's possible and informs future build vs. buy decisions.
4. Hire for the Builder Mindset - Look for team members who are comfortable with experimentation, systems thinking, and AI collaboration. Technical skills can be taught, but mindset is harder to change.
5. Focus on High-Impact Automation - Use AI and automation to eliminate repetitive, low-value tasks so your team can focus on strategic initiatives that drive business growth.
6. Build Strategic Partnerships - Work closely with other teams (like IT and product) to ensure your solutions integrate well with existing systems and processes.

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These principles align with broader trends in RevOps thought leadership and provide a foundation for building more effective, scalable revenue operations.

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The Future of RevOps: Jobs to Be Done

Looking ahead, Ernst-Faletto sees RevOps 3.0 evolving beyond traditional functional boundaries. Instead of thinking about marketing operations, sales operations, and customer success operations as separate disciplines, the focus is shifting to "jobs to be done."

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"What is our pipeline workflow? What is our go-to-market workflow?" she asked. This represents a fundamental shift from organizing around teams to organizing around outcomes and processes.

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This evolution reflects the maturation of RevOps as a discipline and its increasing importance in driving business results. As AI continues to reshape go-to-market operations, RevOps teams that embrace the builder mindset will be best positioned to create competitive advantages for their organizations.

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Conclusion: Building the Future of Revenue Operations

The transition to RevOps 3.0 isn't just about adopting new technologies—it's about fundamentally changing how revenue operations teams think about their role. By embracing product management principles, focusing on user needs, and developing a builder mindset, RevOps professionals can create AI-ready revenue engines that drive sustainable business growth.

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As Ernst-Faletto demonstrated through her real-world examples at Pendo, the most successful RevOps teams are those that ship solutions, not just maintain systems. They understand that perfect is the enemy of good, that adoption takes time, and that the right framework can turn complex challenges into manageable projects.

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The future belongs to RevOps teams that think like product managers: user-focused, data-driven, and relentlessly focused on delivering value. Whether you're implementing AI automation, building custom applications, or redesigning core processes, the principles remain the same: plan systematically, discover user needs, design thoughtfully, build iteratively, test thoroughly, and ship confidently.

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For RevOps professionals ready to embrace this evolution, the opportunity is enormous. As Ernst-Faletto noted, "Things are moving really fast, and so always making sure to be evolving and thinking through that." The question isn't whether RevOps will continue to evolve—it's whether your team will lead that evolution or follow it.

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