What sets great companies apart? It’s not just their products or profits, but their relentless focus on customers.
Customer-obsessed organizations don’t just react to customer needs—they anticipate, prioritize, and embed them into every decision, process, and interaction. According to Forrester, customer-obsessed companies grow revenue 28% faster, achieve 33% higher profitability, and boast 43% better customer retention than their less-focused counterparts.
But achieving true customer obsession is easier said than done. The modern customer demands hyper-personalized, seamless experiences across every touchpoint. They expect instant resolutions, proactive service, and consistency.
Companies attempt to meet these sky-high customer expectations by throwing resources at the problem—hiring more people, increasing operational layers, and scaling teams indiscriminately. It might work in the short term, but it’s not sustainable. Costs spiral, inefficiencies multiply, and the customer experience suffers as complexity takes over.
This is where AI steps in as a game-changer for customer success and support teams. AI offers a path forward for businesses to scale and manage complexity without breaking the bank, to deliver consistent, high-quality customer experiences.
To explore how AI enables sustainable, customer-first growth, I sat down with Mollie Holland, CVP of Customer Experience at DevRev. We discussed the transformative role of AI, the pitfalls that trip companies up, and how businesses can position themselves to succeed in an AI-driven future. The insights she shared offer a blueprint for navigating this new landscape and thriving in a world where customer obsession and AI intersect.
Q: How do you define sustainable growth in today’s customer-first world?
To me, sustainable growth in a customer-first world is about achieving more with less—less cost, less complexity, and less friction. It’s about delivering exceptional customer experiences at scale without sacrificing quality or overwhelming your teams. For me, AI plays a pivotal role in making this possible by being a catalyst for efficiency and innovation, that gives your people resources more time to do deeper work.
I think of AI much like the cloud a decade ago. Just as the cloud revolutionized how we manage data and infrastructure, AI is now reshaping how we engage with customers and drive team productivity. The companies that thrive in this new landscape are the ones that use AI strategically, aligning it with their goals and the needs of their customers.
One of the biggest drivers of this shift is what I call “consumer-grade expectations” in B2B. Today’s business customers expect the same level of seamless, personalized, and proactive interactions they get in their personal lives. They want omnichannel options, self-service capabilities, and predictive insights. Embracing AI allows companies to scale exponentially, delivering value in ways that traditional, linear growth models simply can’t.
I’ve seen this firsthand with Bolt, a leading checkout technology platform that leveraged DevRev’s intelligent automation capabilities to streamline agent workflows and reduce manual tasks, translating into increased productivity and revenue growth.
For me, AI’s ability to scale personalized experiences economically is one of its most exciting aspects. Legacy systems struggle to handle complex resource-intensive tasks like data labeling and clustering, but modern AI makes these processes effortless. It’s a game-changer for companies looking to do more with less.
Q: Why are people getting AI wrong, and why is it such a hard problem?
AI is one of the most transformative technologies of our time, but it’s also one of the most misunderstood. While the promise of AI is immense, the execution often falls short—sometimes disastrously so. As much as 80% of AI projects fail, according to Harvard Business Review, and the reasons why are as complex as the technology itself.
To me, there seem to be 3 major reasons why companies end up getting AI wrong. The first is the prevalence of what I like to call “AI snake-oil salesmen.” The market is saturated with vendors who promise revolutionary outcomes but deliver little more than expensive disappointments. These solutions might look compelling on the surface, but without proper scrutiny, they end up wasting your time, resources, and competitive edge.
Another issue lies in the misconception that AI implementation needs to be long, arduous, and resource-heavy. The truth is, effective AI doesn’t have to be complicated. Solutions designed with scalability and simplicity in mind can provide value quickly, without draining your team’s energy or budget.
The third, and perhaps the most critical issue, is related to data and context. Even the most sophisticated AI can falter if it lacks context and ends up generating hallucinated responses riddled with inaccuracies or glaring gaps. This happens when the AI doesn’t have access to a robust knowledge graph or the ability to understand the nuances of your business data. Without these, you’re left with unreliable outputs that undermine confidence and productivity.
This is why it’s so important to differentiate between true AI innovation and marketing hype. I always recommend asking hard, probing questions before investing in any AI solution, like:
- How are their models built?
- How does the system prioritize and filter information?
- How much context does it have (is this narrow or broad, generalized or specific)?
- Is the architecture built to scale economically?
- What has to be done (by us) before we can effectively utilize the AI capabilities?
The final question is perhaps the most important one to ask, as many companies built their platforms a decade or more ago, before the modern demands of AI were even conceivable. AI that is bolted on legacy platforms with outdated architectures often crumbles under these demands, leading to higher costs and diminishing returns.
AI isn’t easy, but isn’t insurmountable either. With the right strategy, the right technology, and the right partnerships, it can be a catalyst for growth and innovation. Join our webinars to cut through the hype and learn how to unlock the full potential of AI.
Q: How does AI help drive better outcomes in customer success?
In my role, I’ve seen firsthand how fragmented tools and siloed teams can slow down or completely derail customer experience. When your sales, support, customer success, and engineering teams aren’t aligned, you lose valuable time and context. And when every team is speaking a different language or working from a different set of data, it’s your customers who feel the friction.
AI changes all of that. Imagine having all your tools, data, and workflows unified in one place. With AI, businesses get the context needed to break down silos and align everyone around a single goal: delivering exceptional customer outcomes. This alignment enables faster onboarding, quicker resolutions, and a better overall customer experience.
Take the example of two of our customers, Uniphore and Phenom. Both companies used DevRev’s AI capabilities to tighten the feedback loop between their support and engineering teams. And both Uniphore and Phenom saw reduced resolution times—Phenom, in particular, reduced their ticket resolution time by 30%—and were able to improve cross-team collaboration, which, ultimately, enhanced customer journey.
AI can also bring a new level of precision to decision-making. If it has a full 360-degree view of the customer spanning every interaction, ticket, and engagement, and can make the right connections (customer-centric) teams can act with confidence. This unified view eliminates guesswork and ensures everyone is on the same page.
With AI breaking down barriers and enabling seamless collaboration, teams can now deliver results that were previously out of reach. And when customers feel that seamless, unified experience, they stick around. That’s how AI turns customer success into a driver of growth.
Q: What does a successful human-AI collaboration look like in creating transformative customer experiences?
When people think of AI in customer service, they often picture chatbots—usually the bad kind that frustrate more than they help. But the real power of AI lies in its ability to work alongside humans, enhancing every stage of the customer journey.
The book Working Backwards: Insights, Stories, and Secrets from Inside Amazon by Colin Bryar and Bill Carr popularized the “Amazon flywheel” concept. This means improving customer experience would lead to more traffic, which attracts more sellers, and more sellers lead to a wider selection, which, in turn, enhances customer experience to complete the cycle.
Similarly, I like to think of an AI flywheel: More users of the product generate more data, more data helps in training the AI model better, and this improved AI results in a better product, which gets more users and creates a virtuous cycle.
Take automated knowledge base creation, for example. Based on what customers are asking you through messages, meetings, tickets, reading habits and feedback, AI can identify gaps in the existing articles, and even revise and update existing articles. This turns your knowledge base into a living, breathing resource that empowers both your customers and your teams.
But none of this works without the human touch. AI is incredible at processing data, identifying patterns, and predicting outcomes, but it’s humans who bring empathy, creativity, and critical thinking to the table. The best results come when AI and people work together, each playing to their strengths. That’s the future I see for customer success—and it’s already happening.
Q: What’s your support philosophy, and which metrics matter the most to you?
Unfortunately, many companies struggle with distinguishing the roles of customer support and customer success. Support and customer success have distinct but complementary roles.
Customer success is about driving outcomes, ensuring customers achieve their goals with your product. Support, on the other hand, focuses on solving problems, resolving issues, and being a reliable, trusted resource when customers need help. Both are essential, but they require different approaches and, critically, different metrics.
The key to solving this was enabling the support team to operate at peak productivity without sacrificing quality. To me, this is the hallmark of a great support organization: a team that’s as effective and trusted as your customer success function. Achieving this level of performance isn’t just about hiring more people or increasing headcount—it requires leveraging AI.
AI is non-negotiable because it enables productivity at a scale that simply isn’t possible with human effort alone. AI can automate repetitive tasks like ticket categorization and sentiment analysis, freeing your agents to focus on high-value customer interactions. It ensures that your support team isn’t just reactive but also anticipatory, delivering the kind of experiences that foster loyalty and trust.
When it comes to metrics, I value those that connect support directly to customer satisfaction and growth. Sentiment analysis is a critical indicator—it tells you not just what your customers are saying but how they’re feeling. Productivity metrics like time-to-resolution are essential, but they must be balanced with quality-focused metrics like Customer Satisfaction Score (CSAT) and Net Promoter Score (NPS). Together, these metrics create a comprehensive picture of how well your support team is meeting customer needs and driving overall success.
Q: What does a good customer support team look like? What do you think is the future of customer success?
A truly exceptional support team doesn’t just resolve issues but solves problems well once so they never have to be solved again, drives loyalty, reinforces trust, and becomes an engine for growth by contributing directly to customer lifetime value (LTV). In my view, the companies that succeed in the future will be the ones that recognize and act on this potential.
Customer loyalty and trust aren’t just nice-to-haves anymore—they’re essential drivers of retention and expansion. When customers know they can rely on your support team for fast, empathetic, and effective resolutions, it strengthens their connection to your brand. Customers don’t just stick around, but actively advocate for your product, driving referrals and growth without significant effort from your sales or customer success teams.
The future of customer success and support is about measuring their impact on business growth. For example, support should be closely linked to metrics like customer lifetime value and overall revenue growth. This means going beyond Net Revenue Retention (NRR) and exploring how support impacts customer expansion, advocacy, and cross-functional alignment. It’s not just about retaining customers—it’s about creating a support experience that actively drives growth.
AI will play a pivotal role in transforming these teams, enabling them to scale while maintaining a deeply personalized approach. But at its core, the future of customer success and support will always come down to one thing: how well you understand and serve your customers.
Q: How has DevRev helped our customer experience team?
At DevRev, we’ve always believed that sustainable, customer-first growth isn’t just something we help our customers achieve—it’s a philosophy we live by ourselves. To deliver the kind of experiences our own customers expect, we built DevRev’s conversational business operating system that brings complete clarity to customers, products, and teams.
The first and perhaps most transformative impact has been creating barrierless interactions via democratized search, workflows and analytics between our engineering, product, and customer-facing teams. These functions often operate in silos in traditional organizations, creating delays, miscommunication, and inefficiencies that ultimately trickle down to the customer. With DevRev, those silos are gone. Everyone, regardless of team, is aligned around a unified view of the customer journey, with real-time access to the same data, insights, and priorities. This has fundamentally changed the way we work, enabling seamless collaboration across teams that were once fragmented.
Another key benefit is the visibility DevRev provides. The platform gives us a clear, unified view of what’s coming, whether it’s product updates, bug fixes, or feature launches. This visibility allows every team to anticipate what’s next and act proactively, saving countless hours while improving overall responsiveness to customer needs.
Finally, the flexibility and extensibility of DevRev’s Marketplace have allowed us to automate processes in ways that fit our unique needs. One-size-fits-all tools often force companies to adapt their workflows to rigid systems, but DevRev is different. Various snap-ins that add new functionality are available in the Marketplace, enabling us to scale sustainably without sacrificing agility or our customer-first ethos.
Reimagine growth with DevRev
Sustainable, customer-first growth is no longer optional—it’s a necessity. With the right AI strategy, you can break down silos, improve collaboration, and deliver the kind of experiences that turn customers into advocates. But success depends on choosing the right tools and the right partners.
At DevRev, we’re committed to helping businesses like yours navigate this journey. DevRev’s AgentOS is built on an AI-native architecture and features a smart knowledge graph that seamlessly integrates diverse data sources. This helps businesses to align teams, streamline processes, and create exceptional customer experiences—all powered by the transformative potential of AI.
Ready to see how DevRev can help your team scale sustainably and grow faster? Book a demo today and discover what’s possible. Your customers—and your bottom line—will thank you.
