As businesses grow, so does the pressure on customer support teams. More customers mean more queries, more complexities, and higher expectations. Scaling customer support teams is an operational challenge that growing businesses must overcome. According to Zendesk, scaling customer support involves a delicate balance of maintaining quality service while managing costs. Companies that get this balance right not only grow, but they also turn their support function into a competitive advantage.
Scaling a customer support team is no longer a simple staffing exercise; it is a strategic alignment of technology, talent, and process design. The urgency has never been greater: The Salesforce 7th State of Service report, based on a global survey of 6,500 service professionals, found that in one year, artificial intelligence (AI) has jumped to second place from tenth in terms of priority for service leaders. At the same time, Gartner reports that 91% of customer service leaders are now under immense pressure to implement AI, not just for efficiency, but to improve customer satisfaction. Against this backdrop, organisations that deploy scalable service desk services are best positioned to grow without compromising quality.
Here are seven of the most effective strategies to scale a team without sacrificing quality.
Unify data before scaling technology
Effective scaling begins with a sound data strategy. Salesforce found that companies unifying their customer service channel data are 40% more likely to implement AI successfully. Without this foundation, AI tools underperform regardless of how sophisticated they are. Gartner's survey of customer service leaders reinforces this fact. About 55% of organisations are now handling higher customer volumes without increasing their headcount; this is a direct result of data-driven gains in efficiency. A unified service desk platform that consolidates all customer touchpoints is the enabling foundation for every other strategy.
Adopt an automation-first mindset
AI-powered automation has moved from pilot projects to core operational strategy. According to Salesforce, service teams estimate that 30% of cases are currently resolved by AI agents, with that figure projected to reach 50% by 2027. Teams using AI agents expect service costs and case resolution times to decrease by about 20%. Gartner's forecast takes a longer view and states that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, delivering a 30% reduction in operational costs. Automation should ideally first target Tier 1 queries, ticket triage, and routing, thereby freeing human agents for complex, high-empathy interactions where human judgment is crucial.
Invest in AI-optimised knowledge management
Self-service, as visualised by automation, only works when the knowledge base behind it is accurate and current. Gartner's survey of service leaders found that knowledge management gaps are the primary barrier to successful conversational AI deployment. Gartner also projects that by 2028, 40% of large enterprises will adopt AI-powered knowledge automation, a sharp rise from less than 5% in 2025. The implication for scaling teams is clear: the knowledge base must be treated like a living product that is continuously refined by real data, and not as a one-time documentation project.
Build a true omnichannel infrastructure
Customers today navigate multiple channels to solve a single service problem. McKinsey research shows that companies implementing omnichannel transformations report revenue growth of 5-15% and service cost reductions of 3-7%. Organisations with strong omnichannel engagement retain 89% of customers, compared to just 33% for those without strong channel integration. Salesforce further reports that 73% of customers expect to start on one channel, such as a chat, and finish on another, such as email, without having to repeat themselves — an experience very few companies can deliver. Scalable service desk services must provide agents with complete cross-channel information to make this happen. Teams that achieve this reduce handling time and improve satisfaction scores.
Evolve the human agent’s role
A critical misconception in scaling discussions is that AI replaces human agents. Gartner predicts that by 2027, 50% of organisations that planned to significantly reduce their customer service workforce will change those plans. A poll shows that 95% of service leaders confirmed they plan to retain human agents specifically to define and govern AI's role. Gartner further found that organisations are now hiring for specialised roles — AI strategists, conversational AI designers, and automation analysts — to manage their AI investments. Scaling successfully means retraining existing agents for higher-value work, not eliminating them. Adaptability, communication, empathy, a customer-first attitude, and problem-solving abilities are human skills that complement AI rather than compete with it.
Shift leadership from people management to AI governance
As AI reshapes frontline operations, the support leader's role is also transforming. An analysis of future customer experience (CX) trends identifies a decisive shift. Successful leaders must move from reactive demand management to proactive experience management. This requires fluency in AI literacy, data analysis, and workflow design. A Gartner survey of service leaders found that 77% feel pressure to deploy AI and 75% report already-increased AI budgets.
Measure outcomes, not just activity
Scaling without measurement is guesswork. Salesforce reports that 86% of service reps working with AI have developed new skills, and 81% say their role has become more specialised. Such metrics crop up only when organisations track workforce transformation, not just ticket volume. Meanwhile, Gartner notes that 51% of customers are now willing to use a generative AI (GenAI) assistant to handle service interactions on their behalf, a development that will alter the nature and volume of inbound contacts entirely. Teams that track first contact resolution, AI deflection rates, agent specialisation, and customer effort scores will be better positioned to adapt as these dynamics evolve.
Conclusion
Scaling customer support successfully clearly involves more than adding seats. It demands a systems-level approach. Recent research fromindustry leaders all point to the same conclusion: the companies winning on customer service are not those that spend the most — they are those that invest most strategically in the service desk services infrastructure and operating model, which allows both their human and AI teams to perform at their best.
How can Infosys BPM help?
Infosys BPM’s Customer Service Outsourcing Services has in-depth experience across every kind of customer support requirements. Our approach involves operationalising digital capabilities that redefine customer experience with enhanced self-service, analytics, streamlined technologies, and transparency. We offer end-to-end customer support solutions that deliver a single omnichannel experience, enabling businesses to outsource customer service effectively.
