Technology & Services Industry Association (TSIA)

Customer success is at a turning point. For years, most customer success organizations have operated with a straightforward formula: assign customer success managers (CSMs) to accounts, monitor customer health, and step in when risk appears. This model, which TSIA has dubbed Customer Success 1.0, helped software companies improve retention and expansion during the early years of SaaS growth. But that model is becoming harder to sustain.

If your professional services (PS) organization is still managing staffing decisions with spreadsheets, static skills lists, and last-minute fire drills, you’re not alone. For years, professional services resource management has been treated as an operational function focused on filling open roles and tracking utilization. But in the PS 2.0 era, that approach is no longer enough. Today, your resource pool is far more complex.

If you’re still measuring success by how many deals you close, you’re already behind. That was one of the clearest signals from the TSIA Board Summit 2026—an invite-only experience for executive leaders focused on the future of technology services. Across keynotes, panels, and peer discussions, one theme kept surfacing: the way you engage customers today is no longer built for how value is created in the AI era.

If your field service organization is still operating in a reactive, break-fix model, you’re already behind. Today, field service customer experience is no longer defined by how quickly you fix something. It’s defined by how well you anticipate issues, communicate with customers, and turn every service interaction into a moment of value. Your customers expect more. They want proactive support, knowledgeable technicians, and a seamless experience from start to finish.

If you’re relying on a single customer health score to tell you which accounts are thriving and which are at risk, you’re not alone. For years, customer health scoring has been the backbone of customer success. A simple red-yellow-green indicator promised clarity, alignment, and early warning signals. But today, that model is breaking down. You’re being asked to scale customer success in a world driven by consumption, digital engagement, and AI.

If you're still thinking about AI as something you add to your managed services portfolio, you're already behind. The shift happening right now isn’t incremental—it’s foundational. AI is no longer just a tool inside your operations. It’s becoming the engine that powers how you design offers, deliver services, and grow revenue. That’s why the conversation around AI managed services is quickly evolving. It’s no longer about layering AI onto existing models.

If you’re leading or supporting a professional services organization right now, you’re likely feeling the pressure from all sides. Customers expect faster implementations. Costs are rising. And AI is no longer a future conversation; it’s already reshaping how work gets done. The challenge isn’t whether AI will impact your services business. It’s whether you have a clear plan for where and how to apply it. That’s exactly why this case study matters.

Customer success is no longer a reactive function. It’s a core driver of retention, expansion, and long-term revenue growth. But as your customer base grows and expectations rise, managing customer success manually or with disconnected tools quickly becomes unsustainable. That’s where customer success platforms come in. If you’re evaluating your options, you’re likely facing a familiar challenge: Should you extend your CRM? Invest in a dedicated customer success platform?

If you’re running a managed services business, pricing isn’t just a financial decision—it’s a strategic one. The challenge? There’s no single “standard” way to price managed services. Your offerings can vary widely in scope, complexity, and outcomes, which makes pricing comparisons difficult for both you and your customers. At the same time, the pressure is building.

If you’re building, selling, or scaling AI-powered solutions, your pricing strategy is no longer a packaging decision—it’s a business model decision. Traditional pricing models, especially per-user or seat-based approaches, are quickly breaking down in an AI-driven world. Why? Because AI changes how value is created. The more efficient your product becomes, the fewer users there may be to charge.