Latest Customer Service Statistics To Move Your Business Forward

Here’s a new customer service statistic that really explains the modern service landscape: 79% percent of service leaders say investment in AI agents is essential to meet business demands. In field service, 85% leaders believe their AI investments will increase over the next year.

Why the sudden boost in AI investment? It may have something to do with the struggles service teams face these days. Eighty-two percent of service professionals agree that customer expectations are higher than they used to be. And though 81% of service representatives say building relationships with customers is an important part of their job, they spend less than half of their time (46%) with customers due in part to administrative tasks and internal responsibilities.

With customers looking for top-notch experiences, it makes you wonder: Are you doing enough to meet their expectations? Are you using the right customer service platforms like Agentforce Service and AI technologies like Agentforce to deliver what customers want? And is your customer service the best possible representation of your brand?

Whether you’re fine-tuning your customer service strategy, looking for insights about customer service rep burnout, or curious about the latest technological advancements in AI in customer service, these 42 customer service statistics offer valuable benchmarks so you can stay ahead in the future of customer service.

Service organizations are facing rising customer demands — along with a series of internal challenges. Our research found that 12% of service employees left their company over the past year, and these highly-trained individuals are often hard to replace.

According to the latest State of Service, the top service challenges are:

1. Keeping up with changing customer expectations
2. High operational costs
3. Difficulty hiring and/or retaining employees

To address service capacity demands, leaders are turning to AI to implement self-service for customers. By boosting self-service offerings with AI, organizations can also save valuable resources like their service representatives’ time, which can be used for higher-value cases.

If higher budgets aren’t enough to keep pace with rising case volume, then technologies like AI agents built with Agentforce and automation could help bridge the gap. The Serviceblazer Community on Slack can help you gain valuable insights on how to tackle these challenges and set up for success with AI.

Companies that use AI agents anticipate better results across their KPIs, from customer satisfaction scores to case deflection. Service teams that use AI agents expect their service costs and case resolution times to decrease by an average of 20%.

Against this backdrop of growing AI case resolution, self-service emerges as the leading use case for AI agents — a natural fit given their role in helping customers to resolve issues independently.

Respondents said the top AI agent use cases in customer service are:

1. Customer FAQ
2. Order inquiries
3. Conversation summaries
4. Knowledge retrieval for representatives/technicians
5. Personalized product recommendations

Thirty percent of service cases were resolved by AI in 2025. In 2027, that number is expected to rise to a whopping 50%.

Multimodal, conversational AI is bridging the service gap across customer service channels by handling different types of input‌ — ‌ voice, text, chat, and visual‌ — ‌ all in one system. AI agents are turning these touchpoints into conversations using natural language.

Here are a few key insights service professionals:

• 88% say conversational AI accelerates resolution times
• 85% say transitions from voice AI to human reps are seamless for customers
• 87% say it frees representatives to handle more complex issues

This partnership between humans and AI is reshaping how service teams operate. Sixty-five percent of teams with AI report more opportunities to focus on developing relationships with customers.

Mobile workers estimate that 18% of their working hours — more than 7 hours per standard working week — are “wasted” on admin duties, such as filling out forms and hunting for information instead of fixing problems for customers.

When 37% of technicians say admin tasks keep them from doing their actual jobs, it’s more than a drain on morale. These numbers represent a serious productivity loss — and a threat to the bottom line.

It’s a hopeful sign that these technicians show real optimism about AI agents. Here’s what our research found:

• 81% of techs think AI agents could help them do their jobs more efficiently
• 80% of techs think AI agents could allow them to focus on more fulfilling parts of the job
• 77% of techs think AI agents could improve appointment window accuracy

Technicians also think AI agents could do 35% of admin tasks, saving around 14 hours per week.

You might be thinking: OK, research is fine. But what do I do about it? Here are five practical ways that high-performing service leaders are tackling today’s most pressing challenges:

Our guide on field service trends shows that 90% of decision makers at organizations with mobile workers are embracing the future of field service. They’re investing in the right field service management software, field service AI, and field service automation to enhance mobile worker productivity and experience and improve field service metrics. Our Salesforce Snapshot Survey shows that 81% of this overworked field service workforce believe that AI agents will help them work more efficiently. These workers estimate they waste more than 7 hours per week on inefficient, unproductive, or low-value tasks. Implementing agent-first field service with AI could significantly benefit field service technicians by reducing administrative tasks by 35%. This could help to alleviate burnout and free them up to focus more on service delivery, ultimately boosting customer satisfaction.

If you’re like most service professionals, you might be curious how AI like agents built with Agentforce will impact your work. The preliminary results are in — and the results are already impressive: over 90% of organizations with AI report time and cost savings, and 87% of service decision makers say this technology helps them better serve customers.

Our research shows that 61% of customers would prefer to use self-service to resolve simple issues. When self-service is done right, your customers can resolve problems faster and your reps can focus their time and attention on higher-value tasks like revenue growth through upselling and cross-selling. Just as important, data from our research shows that self-service is a major differentiator between high-performing and underperforming companies: 80% of high-performing service organizations provide a self-service solution, compared to just 56% of low performers.

We found that 26% of reps say they often lack context about a customer’s situation, while 80% believe that better access to other departments’ data would improve their work.

Perhaps that’s why the highest-performing service organizations are unifying their data to achieve more cohesive and compelling customer service experiences. For example, many teams are connecting customer relationship management (CRM) systems and sharing accountability for metrics like customer satisfaction scores (CSAT), customer effort scores, and Net Promoter Scores (NPS).

Inefficient processes and manual taskwork monopolize service and field service professionals’ time and jeopardize the customer experience. That might explain why so many service organizations are turning to customer automation software to enable greater efficiency at scale, with 83% of decision makers planning to increase investments in automation over the next year.

When you consider these 42 customer service statistics, the conclusion is clear: by understanding customer needs, embracing innovation, and committing to continuous improvement, high-performing service organizations are finding new ways to navigate a highly competitive market.