How to Build a Service Team That Thrives Alongside AI

AI agents are no longer a future investment. They now actively manage routine requests, amplify service representative capabilities, and fundamentally reshape operations across the contact center. For ambitious service teams, this presents a genuine, decisive opportunity.

Our latest State of Service research – containing insights from more than 3,000 service leaders globally – reveals that 66% of organizations already deploy agentic AI. These teams aren’t just resolving issues faster and cutting manual labor; they’re finally gaining the bandwidth to focus on what matters most: solving complex problems and cultivating meaningful customer relationships.

The most impactful organizations don’t just deploy tools – they invest strategically in their people. As AI assumes routine tasks, service professionals must expand their roles by shifting from task execution to exercising advanced judgment, guiding AI-driven experiences, and establishing the kind of customer trust no AI agent can ever replicate. This evolution is critical for both frontline reps and leaders. As a leader in customer service, I’ve redefined my own personal approach and built new skills to navigate this agentic world. It’s made me better at my job.

Here’s your strategic roadmap to build a service team that leads through change.

Three non-negotiables for service team leadership in the age of AI

1. Establish mandatory AI fluency as a core organizational competency

As AI becomes part of everyday service operations, knowing how to work effectively alongside agents is quickly becoming a foundational skill — not just for technical teams, but for everyone.

Service professionals increasingly need to master the art of determining when to trust AI outputs, when human intervention is necessary, and how to guide AI-enabled customer experiences. Our State of Service research confirms this shift, highlighting adaptability, learning agility, AI oversight, and complex problem-solving as top skill priorities. Only 3% of service reps report no engagement with upskilling, signaling a workforce eager to embrace growth. 

Prompt engineering, AI oversight, and data literacy now carry the same strategic weight as traditional service competencies.

Raphi Katz, Technology Director at Blink Payments, validates this imperative:

“The main skill is prompt engineering. Managing an AI agent is very different to a human agent where you need to be much more explicit.”

Peer-driven learning delivers one of the most effective and scalable paths to practical AI skill development. Many service professionals seek out communities like the Salesforce Trailblazer Community and certification programs on Trailhead to master these new challenges alongside their peers.

Some organizations amplify this effort by building their own internal learning communities. Magon Mair, Director of Presales Solution Engineering at Wilco Source, explains:

“We have created Agentforce and Data 360 learner groups to upskill staff and knowledge share. Having skilled employees ready to implement use cases for customers is critical.”

Other teams proactively drive AI fluency through direct experimentation. Piyusha Pilania, Solutions Architect at Implementology, notes:

“A big part of upskilling has come from hands-on implementation, meeting Trailblazers at conferences, webinars, and newsletters.”

Companies that achieve the greatest success with AI adoption treat workforce development as a continuous, collaborative team effort. Naveen Gabrani at Astrea IT Services emphasizes their approach:

“Our upskilling has been driven by client feedback. Most importantly, we’ve found that collaboration between technical teams and business domain experts is essential.”

This collaborative model enables the company to continuously refine human-AI collaboration and improve AI agent performance over time.

2. Refocus talent: Elevate service professionals from transactional closers to strategic relationship builders

As AI agents actively manage more repetitive and transactional work, uniquely human capabilities like judgment, empathy, and complex problem-solving magnify in value.

Magon Mair at Wilco Source describes how AI fundamentally reshapes the work:

“This agent automates low-value tasks and leaves the high-value work to the human agents.”

This pattern now defines the new industry standard. Organizations deploy AI to manage after-hours engagement, repetitive follow-ups, knowledge retrieval, and case intake, freeing employees to focus on strategic, relationship-driven customer interactions.

At Implementology, AI voice agents now manage customer outreach outside traditional business hours for home improvement companies, allowing sales teams to concentrate on higher-value opportunities. As Piyusha Pilania explains:

“We’re not using AI to replace humans, but to augment productivity.”

That distinction matters. The organizations seeing the strongest outcomes aren’t treating AI as a replacement for people. They’re using it to remove friction, automate repetitive work, and create more space for human expertise to shine.

The future of service will not be defined by a conflict between humans and AI. Organizations will win by defining how effectively they fuse the speed and efficiency of AI with the empathy, expertise, and judgment that only humans can provide.

3. Establish continuous learning as the engine of AI optimization

The organizations seeing the strongest results with AI aren’t treating deployment as the finish line. They’re building cultures of continuous learning — and it’s making a measurable difference.

Successful AI implementation demands continuous refinement, rigorous testing, and iterative learning. Davis Henry, Senior Director of Strategy at Red Argyle, emphasizes this reality:

“One of the most impressive things we saw was how quickly results improved once we began iterating on the implementation.”

By refining more complex scenarios, Red Argyle rapidly drove case deflection rates from over 50% to approximately 71% within hours. Achieving this speed of progress requires that organizations aggressively cultivate environments where proactive experimentation is encouraged and learning is ongoing.

The most effective teams treat their knowledge bases as a critical asset, ensuring information remains structured, current, and instantly accessible. When teams proactively govern their data, they govern their AI outcomes.

As Raphi Katz argues:

“Making sure that everything is in an accessible knowledge base for dual use by humans and AI.”

That work reinforces something important: AI is not a set-it-and-forget-it technology. Human teams remain essential for maintaining data quality, refining outputs, monitoring performance, and ensuring AI experiences continue to earn customer trust. That’s not a burden — it’s where human expertise becomes most visible.

The organizations that succeed with AI will do so by continuously evolving how their teams work — and investing in the people doing that work.

Get more insights in the State of Service report

AI agents are already reshaping how service organizations operate, from the skills teams need to how leaders are rethinking customer experiences. The organizations seeing the greatest success are investing not only in technology, but in the people working alongside it.

The latest State of Service report explores how more than 3,000 service professionals are navigating this shift — including how organizations are deploying AI today, the skills teams are prioritizing, and where leaders are seeing the biggest impact.

The research shows the path. The organizations moving fastest are the ones already walking it.