Service and Support teams are at a turning point. AI and automation are rapidly changing how work gets done on the front lines. Learn to treat change as an opportunity to evolve your team’s skills and talents, not a threat to headcount. Modernization isn’t about replacing people with AI; it's about redesigning existing roles to take on new, higher-value work.
How do you do that? Let’s look at how this shift impacts traditional service and support and how a team can evolve to embrace a new approach.
Routine Work
Old way: The support team gets a high volume of basic, repetitive work. Things like password resets, ticket status checks and simple inquiries. While it’s important to address these issues, the work can be mind-numbingly dull, and workers without an occasional challenge burn out quickly. Often, the team is so busy with routine work that they simply don’t have time to address more complex issues.
New way: Routine interactions can be handled very capably by AI, allowing these simple issues to be resolved quickly without human intervention. Organizations that have adopted AI to do this have seen a 20-40% reduction in the volume going to the support team. This frees up the team to focus on bigger, more complex issues where the human touch is important — when there needs to be a judgment call, empathy towards a customer or the various non-routine edge cases that would flummox an AI.
Knowledge Management
Old way: How often do we spend time updating and organizing a knowledge base, only to have it out of date and obsolete in a matter of months? A poorly designed or rarely updated knowledge base means the support team spends time searching for answers or re-creating solutions. Worse, it can lead to inconsistent support, as each analyst develops their own solutions.
New way: The support team owns and stewards the knowledge. Rather than an occasional review, the focus is on regular evaluation, refinement and expansion of the knowledge base content. This is becoming increasingly important because AI systems rely on accurate, up-to-date knowledge to provide effective solutions.
Agent Roles and Skills
Old way: A traditional support team that’s organized as a Tier 1 team, designed to handle high contact volumes. Roles are basic and entry-level, resulting in limited opportunities for growth.
New way: As AI is trained to handle routine work and contact volume decreases, support staff can be shifted from entry-level roles to mid-level or more skilled roles. Train them to become problem solvers, technical specialists and subject matter experts in critical systems, processes or applications, and to take on roles focused on collaborating with AI. Despite concerns about AI driving job loss, Gartner research shows only about 20% of service and support teams have reduced headcount due to AI. Nearly 80% plan to transition staff into new positions, and 84% are adding new skills or roles for frontline staff.
How Success is Measured
Old way: A simple numbers game. The focus is on call volumes, speed of answer, abandonment rates and handle times.
New way: As the support focus shifts from routine, basic issues to more complex ones, metrics will necessarily shift as well. Metrics will shift toward successful AI handoffs, resolution quality on complex issues, and customer experience measures.
Customer Experience
Old way: Purely reactive — answer the call, resolve it or escalate, then on to the next.
New way: A more proactive and guided approach, with the team focusing on issues that require judgment and empathy — the interactions that build lasting relationships.
How do you get from the old to the new? If you have budget and tools, build a clear plan to transition people into more complex roles based on their strengths and aptitudes.
If budgets are tight, start with what you have: improve self-service for the highest-volume simple issues, and have experienced analysts own knowledge creation and become subject-matter experts on critical systems.
Modernization done well removes routine work and gives the team the chance to apply their existing expertise to more complex problems and to build the knowledge that supports AI.