AI customer service uses artificial intelligence technologies — including machine learning, natural language processing, and automation — to handle customer interactions, optimize support operations, and enhance the customer experience. Modern AI goes beyond basic chatbots to predict customer behavior, resolve complex issues, and scale support without proportional increases in headcount.
That scalability matters. According to our 2025 survey, 40% of support professionals say scaling support was their main focus. Yet 73% flagged customer resistance to AI interactions as a major obstacle — concerns that feel justified when Gartner research reveals that 64% of customers would prefer companies didn’t use AI for customer service.
The gap between promise and perception comes down to implementation. Support teams and customers often think of AI as limited chatbots and rigid automation, though 51% of customers would actually be willing to use a GenAI assistant for customer service interactions on their behalf. But the companies scaling successfully are using AI to handle everything from intelligent routing and workforce forecasting to full case resolution across voice, chat, and email. Let's look at 10 companies proving what AI customer service actually looks like when it's done right.
The evolution of AI in customer service
AI in customer service refers to the application of machine learning, natural language processing, and intelligent automation to manage customer interactions, optimize staffing decisions, and resolve support issues. The technology encompasses everything from conversational AI agents that handle full case resolution to predictive analytics that forecast demand and route cases intelligently.
The contact center has been at the forefront of AI adoption. According to McKinsey, contact centers were early adopters of generative AI — but the use cases have evolved dramatically. We've moved from basic chatbot deflection to full-scale operational optimization that touches every part of support delivery.
Modern AI support tools now handle four critical functions:
Automate decision-making across workflows
Workflow automation transforms how support teams handle routing, escalations, and approvals. AI tools can optimize complex processes that previously required constant human intervention — reducing bottlenecks, minimizing errors, and accelerating resolution times. The result is faster case handling and better customer outcomes.
Provide powerful, omnichannel support
Omnichannel consistency is no longer optional. Consumers expect conversations to move seamlessly between channels — from email and chat to phone and social media. AI-powered solutions maintain context across every touchpoint, eliminating the frustration of repeated explanations and disconnected experiences.
Empower agents with information
AI copilots deliver real-time insights, suggested responses, and historical customer data exactly when agents need it. Rather than searching through systems or escalating for help, agents get AI-generated recommendations at their fingertips. This cuts resolution times, improves accuracy, and creates more confident, effective support interactions.
Scale support while managing costs
Intelligent scaling means handling growth without proportional headcount increases. AI doesn't just automate routine tasks — it forecasts demand, optimizes schedules, and distributes workload intelligently. Support teams can expand capacity during peak periods and maintain service levels without the traditional cost curve of adding more agents.
The benefits of AI in customer service
When leaders think about AI, the focus is often on cost savings. But the real impact of modern AI goes far beyond the budget. It’s about building a more resilient, effective, and human support operation. When AI doesn’t just deflect tickets but actually resolves issues and assists agents, the benefits compound:
Deliver faster, more accurate resolutions. AI agents can provide immediate answers to common questions and execute tasks like refunds or order lookups, resolving issues in seconds. This frees up human agents to focus on complex problems that require empathy and judgment.
Reduce agent burnout and improve productivity. By handling repetitive, high-volume inquiries, AI acts as a partner to your team. It reduces the cognitive load on agents, allowing them to handle fewer, more meaningful interactions and avoid the burnout that comes from answering the same question for the tenth time in an hour.
Scale support without scaling headcount. AI provides a way to manage fluctuating volumes and seasonal peaks without the constant cycle of hiring and training. It allows you to grow your customer base while maintaining service levels and controlling operational costs.
Create more consistent customer experiences. AI ensures that every customer gets an answer that is aligned with your brand voice and business policies. It eliminates the variability that can come from a large, distributed team and ensures a baseline of quality for every interaction.
10 companies using AI for customer service
Real-world AI implementations span workforce optimization, intelligent routing, agent assistance, and full case automation. The following 10 companies show what's possible when AI is built to solve specific operational challenges — not just deflect volume.
These examples cover:
Workforce management that balances human agents, BPOs, and AI across time zones
Intelligent routing that matches customers to the right agent based on skill, capacity, and context
Agent copilots that reduce escalations and accelerate case resolution
Full automation that resolves common issues end-to-end while maintaining brand voice
1. Intercom
Customer service solution, Intercom, has teams spread across Chicago, Sydney, and Dublin. It wasn’t just the team that was dispersed — the operations were too. Managed with a patchwork of disparate tools, the team struggled with inefficiencies in scheduling, resource allocation, and real-time visibility.
While Intercom relied on Fin AI, the company’s own AI agent, to manage simpler customer queries, the team recognized they needed some internal help too. Human agents were left to handle the more complex customer issues, which made effective forecasting and scheduling trickier.
Intercom selected Assembled for smarter workforce management. Integrating directly with Intercom’s existing tech stack, Assembled creates streamlined schedules, ensures automatic updates during vacation or sick time, and makes real-time scheduling adjustments.
As a result, over 90% of Intercom's workforce management processes now run within Assembled, reducing scheduling time by 5 hours per week.
2. monday.com
Any call center recognizes the importance of efficient and effective call routing — and it's another area where AI can be a huge help. Take workOS monday.com as an example.
Using Assembled's API, the company built a proprietary routing tool called Zendesk Round Robin (ZDRR), which connects to the team's Zendesk instance and monday.com boards to automatically prioritize and route customer inquiries to the best-fit agent. While that might sound basic, the real-world application is remarkably nuanced.
The ZDRR tool evaluates multiple factors in real time:
Productive events: Routes around lunch breaks, meetings, and training time
End-of-shift protection: Prevents ticket assignments 15–30 minutes before agents log off
Queue management: Automatically redistributes tickets when agents go offline
Workload balancing: Ensures no single queue gets overloaded
This resulted in lower wait times, more contextually appropriate routing, and better experiences for both customers and agents.
3. GoFundMe
As crowdfunding and fundraising platform, GoFundMe, grew, so did the support team. There were more time zones, more specializations, more channels, and more offices. But the team knew that a bigger operation couldn’t translate to longer response times — issues with campaigns needed to be handled quickly.
The company needed team members available for customer needs, but also wanted to prioritize team development. Recognizing that it was too complex to manage with jumbled spreadsheets and basic scheduling software, GoFundMe turned to Assembled to balance prompt service with professional development.
Now, with Assembled’s Zendesk integration, GoFundMe uses Assembled’s sophisticated scheduling algorithms to generate schedules that balance their email and chat needs alongside project time and meetings for the team.
As a result, GoFundMe generates sophisticated forecasts that regularly achieve within 10% weekly accuracy and saves an estimated 2 hours per day on scheduling.
4. Thrasio
Thrasio is an e-commerce giant, with about 190 unique brands and more than 70,000 customer interactions per month. While things were going well with an 87% customer satisfaction (CSAT) score, the team knew they were capable of more — provided they had the right technology partner.
Thrasio adopted Assembled and immediately pushed over 420,000 customer tickets to Assembled AI's machine learning engine to process six months' worth of interactions. The team configured Assembled AI to handle the intricacies of all 190 brands, each with unique rules, processes, voices, and product details.
With that groundwork, Assembled AI now provides agents with brand-specific replies and related knowledge base articles instantly.
The results included:
97% CSAT (up from 87%)
1-minute response times (down from several minutes)
12-minute full resolutions (significantly faster than previous average)
$1.8 million in cost savings annually
5. Poshmark
Fashion resale marketplace Poshmark started with an entirely in-house and co-located support team. But as the company grew, shifted to remote work, and brought in outsourced agents, the team knew they needed to improve their processes.
Poshmark opted for Assembled to manage BPO operations and stay on top of metrics like utilization rate, adherence rate, shrinkage, productivity, time to first response, and time to resolution. The company also uses staffing heat maps and timelines to get a big-picture understanding of how the team is meeting customer demand every week.
This resulted in a 10% year-over-year improvement in first response times and a 15% increase in team productivity thanks to insights from Assembled.
6. Brooks
As a creator of performance running gear and experiences, Brooks knows the importance of moving quickly. Yet disjointed systems and a lack of visibility kept the customer experience team from achieving peak efficiency.
Brooks implemented Assembled to help leaders make in-the-moment modifications for the support team, like tailoring focus hours for specific channels or keeping a close eye on agent adherence in real time. Assembled’s forecasting tools and arrival pattern insights have also helped the team understand business trends, make data-driven decisions, and keep service levels in check.
As a result, average phone wait times were reduced by 66%, giving agents more time to recharge while still maintaining adequate staffing levels.
7. Honeylove
It makes sense that a garment brand like Honeylove would be focused on top-notch support. However, the team was struggling with escalations, slowing things down. Waiting for help from a team lead was a major bottleneck for agents — one they knew AI could solve.
Honeylove partnered with Assembled to develop a tailored version of Assembled AI focused on helping agents answer questions they would have otherwise escalated to team leads. The result was a system fine-tuned to Honeylove that knew about garment sizing, upcoming sales, and the intricacies of Honeylove’s return policy that could provide accurate, auto-drafted replies for agents.
This led to a 20% reduction in ticket escalations, and power users increased their solves per hour by 54% after just 5 months of using Assembled AI.
8. Lulu and Georgia
Home decor company, Lulu and Georgia, had tried AI tools before — primarily using AI to instantly analyze content, context, and customer sentiment to route tickets to the right agent. However, the team was constantly disappointed in the results, with a lot of missed tickets and misinterpreted customer intent. Lulu and Georgia wanted a new solution that could improve case routing and also offer agent assistance and full AI automation.
The team’s top priority was streamlining the damaged item process. Without AI, this process involved numerous manual steps: requesting photos from the customer, filing claims with suppliers, arranging refunds or replacements, offering discounts, and more. Agents often had to do that all while simultaneously sympathizing with an upset customer.
With Assembled, the team automated several steps of the process, pushing tickets through the system faster and giving human customer service agents more time to focus on empathizing with and supporting customers.
As a result, the team successfully automated the complex damaged item process, and AI now plays a central role in all of Lulu and Georgia’s support operations.
9. Tithely
Tithely makes it easy for churches to accept donations online, with a mobile app, or through text. With a fully remote customer support team and no centralized system, Tithely was struggling to ensure alignment on tasks and schedules.
Tithley implemented Assembled to create a more structured and efficient support operation. Recognizing the opportunity to do even more, the team also adopted Assembled AI to empower agents with AI-driven insights and automated workflows.
As a result, the most productive agents saw an 11% improvement in average handle time for email and a 26% improvement for chat, while power users increased case solves by an impressive 205% for chat.
10. aXcelerate
Australian training management software provider, aXcelerate, is intensely focused on helping people learn and grow. So, when aXcelerate’s Board of Directors asked the support team to invest in AI, the team jumped at the chance to learn more about the technology.
The team first considered deflection tools but found they didn’t align with their human-first support philosophy, especially given the diverse range of customer inquiries. Instead, they prioritized metrics like reducing case resolution times and boosting customer engagement and satisfaction scores.
Having already achieved efficiency improvements with Assembled’s workforce management solutions, aXcelerate implemented Assembled AI.
This resulted in a 50% reduction in training time for new support agents, which enabled the company to expand its talent pool and onboard agents from more diverse backgrounds.
Best practices for implementing AI in support operations
The above real-world examples show there's no shortage of ways to implement AI for customer service. As you explore how to use this technology on your own support team, here are five best practices to guide your approach:
Start with problems, not possibilities: The most effective AI solves specific operational challenges. Before evaluating tools, identify your team's biggest bottlenecks — long wait times, inconsistent responses, inefficient routing, or forecasting gaps. Map AI capabilities (natural language processing, predictive analytics, and intelligent automation) directly to those pain points.
Integrate with your existing stack: AI should enhance your tech ecosystem, not complicate it. Prioritize solutions that connect seamlessly with your CRM, ticketing system, help desk, and communication platforms. The goal is cohesive workflows and unified data — not another disconnected tool that creates more work.
Train your team for partnership: Successful AI adoption depends on people, not just technology. Invest in training that helps agents understand how AI works, when to trust it, and how to intervene when needed. The best implementations treat AI as a tool that scales and augments human judgment — not a replacement.
Monitor, measure, and iterate: AI isn't set-it-and-forget-it. Establish clear metrics for resolution rates, CSAT, handle time, and accuracy. Review performance regularly, update knowledge bases with new information, and refine automation rules based on real outcomes. Continuous improvement is what separates effective AI from expensive experiments.
Build for scalability: Choose solutions that grow with your business. As you expand support channels, add product lines, or shift pricing strategies, your AI should adapt without requiring a rebuild. Look for platforms that handle increasing complexity through configuration, not custom engineering.
These 10 companies prove that AI in customer service isn't theoretical — it's operational. The teams seeing results aren't chasing automation for its own sake. They're solving specific problems: reducing scheduling time, routing tickets intelligently, accelerating case resolution, and scaling support without sacrificing quality. The common thread? They chose AI solutions built to partner with humans, not replace them.
Experience the benefits of advanced AI for customer service
For many people, AI is synonymous with ChatGPT. But AI in customer service is no longer about just simple chat automation — it’s about transforming your support operations in ways that benefit your team and your customers.
As these real-world examples show, AI can help teams reduce wait times, boost agent productivity, and improve customer satisfaction without sacrificing your customer experience. These companies didn’t just adopt AI. They implemented the right AI solutions to optimize workflows, empower agents, and achieve the ultimate goal: scaling efficiently.
Your key to experiencing similar AI-driven success starts here. Request a demo to see how Assembled can transform your customer service operations.
Frequently asked questions about AI customer service
What is the best AI tool for customer service?
The “best” tool depends entirely on the problems you’re trying to solve. Many tools focus on simple deflection or basic chatbots, which can frustrate customers. A better approach is to look for a platform that orchestrates AI and human agents together. The most effective solutions are resolution-first, meaning they are built to solve the customer’s issue, not just route the ticket. They integrate with your existing systems to take action and provide a seamless handoff to a human agent when a judgment call is needed.
How much does AI customer service cost?
Pricing for AI customer service varies widely. Some tools charge per conversation or per minute, while others are priced per agent or as part of a larger platform license. It’s important to look beyond the sticker price and consider the total return on investment. A cheaper tool that only deflects 10% of inquiries may cost you more in the long run than a more advanced system that resolves 50% or more of your volume. The true cost should be weighed against savings in agent time, improvements in CSAT, and the ability to scale without adding headcount.
How long does it take to implement AI customer service?
Implementation time depends on the complexity of the tool. Simple, script-based chatbots can be set up in a few hours. However, a true AI agent that integrates with your backend systems and handles complex workflows can take longer. Modern platforms like Assembled are designed for rapid implementation, often going live in a matter of days or weeks. The key is a tool that can learn from your existing data and doesn’t require a heavy engineering lift to get started.
Will AI replace human customer service agents?
No. AI is not a replacement for human agents; instead, it acts as a partner. The goal of AI in customer service is to handle the repetitive, predictable tasks that cause agent burnout. This frees up your human team to focus on what they do best: solving complex problems, handling sensitive escalations, and building customer relationships. The future of support is a hybrid model where AI and humans work together, each playing to their strengths. AI handles the scale, and humans handle the nuance.
