From Cost Center to Value Driver: How to Optimize Customer Service Through Digitalization in Just 6 Months

The traditional view of customer service as a cost center is outdated. Today, customer interactions hold immense value. Recent market studies reveal that when organizations invest wisely in digital tools and customer-centric strategies, service can both reduce operational costs and become a growth engine. Companies that shift repetitive inquiries to self-service channels often see dramatic cost savings. At the same time, these digital touchpoints become fertile ground for upselling and cross-selling, building customer loyalty and improving overall satisfaction scores. With a well-implemented digital strategy, service stops being a burden and starts driving meaningful business outcomes. 

But to fully capture this potential, organizations must rethink not only their technology stack but also their operating model, performance indicators, and team culture. It’s about turning service from a reactive department into a proactive business function. 

"The Best Service Is No Service"

In one case, a B2B company with a large customer base faced an unsustainable number of calls to its contact center. Long waiting times led to frustration, and maintaining high staffing levels was costly. Sales through the call center channel were stagnant, and team morale was low. 

What the data revealed was eye-opening: nearly half of the calls were related to invoices. However, these were not system errors — customers were confused by the format, naming, or availability of documents. When asked, many of them said, "I just didn’t know what the invoice referred to." 

The solution didn’t require expensive new systems. Instead, the team conducted a detailed root cause analysis and redesigned how invoices were displayed and explained. They simplified language, added contextual explanations in the portal, and improved the document layout. 

The results? Invoice-related calls dropped by 80%, freeing up agent time that was redirected toward value-added activities like upselling. Waiting times decreased, and customer satisfaction in this channel rose significantly. Most importantly, customers stopped needing help for things that could be solved through better design. The client said:

“The best support is the one you don’t need. It’s when the process works so well that the customer doesn’t even think of calling.” 

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Empowering Sales Through Self-Service

Another enterprise encountered a different but equally common issue. Sales reps, especially in the field, were inundated with routine requests from clients: help with ordering, checking stock, or reviewing previous invoices. While these reps were supposed to be building new business, they were acting more like service agents. 

Customers didn’t use the e-commerce portal. When asked why, their feedback was consistent: “It’s hard to find what I need. I don’t know if my order was submitted. I always have to call to be sure.” 

The company conducted customer interviews and usability tests. The root cause? A lack of education, no proactive onboarding, and poor user experience in the portal. Many buyers didn’t even know certain features existed. 

A dedicated self-service environment was launched with improved navigation, a guided product selection flow, purchase history access, and contextual recommendations. More importantly, KPIs for sales reps were changed — success was no longer about handling requests but about enabling autonomy. 

The transformation was dramatic. Remote sales increased by 600%, and the number of users actively engaging with the platform rose sevenfold. Collections improved, and the sales team could focus on complex deals. The client said:

“Hunters shouldn’t be shepherds. We gave them time back to hunt.” 

Despite all its promise, digital self-service doesn’t always deliver. Gartner reports that only 14% of customer journeys are fully supported through self-service. Often, the issue isn’t with the concept, but the execution. 

Many initiatives are treated as one-off projects rather than ongoing programs. Interfaces are built from an internal perspective rather than designed around real customer needs. Users struggle with confusing navigation, insufficient content, or generic experiences. Many companies simply deploy technology and expect magic. But if it wasn’t designed for the customer, it won’t bring results. 

To succeed, companies need to view self-service as an evolving product. This means establishing feedback loops, continuously refining content and UX, integrating with human support when needed, and personalizing the experience based on behavior and context. True success comes when customers prefer self-service because it simply works better. 

A useful model for designing and evaluating self-service capabilities is the "11 capabilities of digital self-service" identified by Gartner: 

1. Search (Context & SEO) 
   A solid self-service foundation starts with intelligent search. Whether through internal platforms or public engines, users must easily locate relevant, contextual information — even when using natural language or vague queries. Structuring content with proper metadata and optimizing for SEO (including AI indexing) ensures accessibility in the moment it’s needed. 

2. Usability, Clarity, and Navigation 
   Self-service tools must be visually and functionally easy to use. Design systems should ensure consistency across devices, and accessibility guidelines must support users with disabilities. With AI-driven interfaces emerging, usability now includes voice, chat, and predictive interaction patterns. 

3. Personalization 
   Self-service should adapt to individual needs. That means prioritizing relevant content and functionality, but also letting users configure preferences, such as communication channels or notification settings, to shape a more tailored experience. 

4. Globalization 
   Enterprises operating across countries need self-service that supports multiple languages and formats. While some elements can be standardized globally, solutions should also be flexible enough to comply with local cultural and legal constraints. 

5. Content, Knowledge, and Education 
   Effective self-service depends on the quality, clarity, and structure of content. This includes written articles, videos, graphics, and even interactive formats — all centrally managed and aligned with a strategic content governance framework. High-quality input is critical, especially in AI-driven environments. 

6. Bots, Virtual Assistants, and AI Agents 
   Modern self-service is powered by intelligent agents that interpret user intent and execute actions through system integrations. These go far beyond simple chatbots — they act as AI intermediaries between the user and complex internal systems. 

7. Case Management 
   Users must be able to initiate and manage service cases end-to-end, such as contract changes or problem reports. These processes must be designed from the customer’s perspective, not just backend efficiency, ensuring visibility and coordination across internal teams and systems. 

8. Assisted Support Integration 
   Not every issue can be resolved through automation. There must be seamless escalation paths to human agents, with full context passed along. Support levels can also be tiered based on customer segment or case complexity. 

9. Community 
   A community space allows users to help each other and share insights, often more efficiently than formal support channels. When moderated and supported by company experts, such communities become self-sustaining knowledge hubs and a rich source of customer intelligence. 

10. Tools and Functionalities 
    True self-service means enabling customers to take action — from downloading documents and checking order status to scheduling visits or accessing advanced tools like digital twins. These functions extend the portal’s value far beyond static information. 

11. Reporting, Analytics, and KPIs 
    Every element of the self-service capability should be measurable. Monitoring usage, success rates, and outcomes is critical to understanding performance and guiding continuous improvement. 

12. Voice of the Customer 
    Although sometimes grouped under analytics, in Hycom we believe, this element deserves its own focus. Capturing and acting on direct feedback — via surveys, interviews, and behavior data — helps organizations tune their self-service capabilities based on real user needs. 

Creating a customer service experience that’s both effective and scalable requires a deliberate, cross-functional approach. It starts with aligning on business goals, KPIs, and shared ownership across departments. Next, teams need to analyze real customer journeys, not just imagined ones, to uncover friction points and opportunities. 

From there, a flexible technology foundation is key. Composable architectures, AI-powered components, and seamless integrations enable agility and responsiveness. Just as important is how the solution is rolled out: iteratively, with continuous support for adoption and improvement. 

When executed well, such a transformation can be achieved in six months — not because everything is built instantly, but because each step is focused on delivering real value, fast. In our experience, success also hinges on the ability to bridge business goals with technological feasibility — and to maintain momentum with structured roadmaps and measurable KPIs. 

The presenter highlighted the importance of "designing for adoption, not just delivery". Without business readiness and user enablement, even the best systems remain unused. 

Gartner offers a valuable framework for thinking about the use of AI in customer service - through four distinct models of implementation. These models are derived from a simple matrix defined by two intersecting dimensions: 

Vertically, we consider the primary goal for using AI. Are we aiming to reduce costs, or is the focus on enhancing customer experience? 

Horizontally, we assess the role of AI. Should it act as a support tool for human agents, or should it take the lead and operate autonomously? 

Click to zoomimg > From Cost Center to Value Driver_3

From this matrix, four strategic models of AI usage emerge: 

High Efficiency (AI supports agents | Focus: Cost reduction)

In this model, human agents remain at the forefront of customer interactions, but they are supported by AI-powered tools that significantly boost productivity and reduce operational costs. For instance, AI can guide the customer through the initial stages of the inquiry, collect relevant information, and route the case to the appropriate team. The agent receives a pre-briefed summary, saving time and effort. Speech-to-text tools eliminate the need for manual note-taking, while AI can draft responses for emails or chats—contextually enriched with knowledge base entries. 

Hyper-Personalization (AI supports agents | Focus: Enhancing customer experience)

When the priority is to improve customer satisfaction, AI still plays a supporting role—but with a different emphasis. Here, AI anticipates individual customer needs, understands sentiment, and recommends personalized offers or next steps. The agent’s job is to ensure the best possible outcome for the customer, and AI becomes a smart assistant—providing real-time suggestions based on a deep understanding of the customer’s profile and history. 

Scalable Supervision (AI leads the process | Focus: Cost reduction)

If the goal is maximum efficiency, AI can take the lead in handling customer service tasks. Human agents then shift into supervisory roles, overseeing performance based on aggregated data rather than individual cases. Their responsibilities include identifying service quality trends, spotting anomalies, and fine-tuning AI behavior—e.g., adjusting prompts or refining automated workflows—to optimize the system over time. 

Seamless Escalation (AI leads the process | Focus: Enhancing customer experience)

Handing over control to AI introduces the risk that not all customer issues can be resolved via self-service. However, when supported by high-quality knowledge bases and well-structured APIs, AI can significantly improve resolution rates. Still, there will always be edge cases where AI falls short. In this model, it’s crucial to ensure a smooth transition from AI to human support. The agent must receive complete context from the AI-driven interaction and continue to be supported with predictive insights and real-time recommendations—especially since this handover typically occurs when the customer is already frustrated or facing a more complex issue. 

Companies adopting AI as a self-service engine in customer support don’t need to stick to just one model. In fact, the most effective strategies often involve a tailored mix of these approaches—adjusted based on customer segments, use cases, or business priorities.