How an AI Customer Service Agent Can Save Your Business 20-30% of Annual Costs

TL;DR

• A virtual AI customer service agent can cut annual support costs by up to 30% and speed up ticket resolution by 52%.
• Our Case Study: An AI-powered support agent reduced overall support costs by 35% in just one quarter. It resolved 95% of customer queries without any human involvement.
• Unity, the game engine company, saved $1.3 million by deflecting 8,000 tickets using AI-driven tools.
• $80 billion in agent labor costs could be saved by 2026 with conversational AI.
• 80% of all customer requests are expected to be AI-handled by 2030.
• 54% of global companies already have conversational AI in customer support stack.

How AI Helps Save Time and Money

• Deflecting: Dynamic FAQs, conversational search, and virtual agents answer routine questions instantly. 81% of customers say they’d rather solve an issue through self-service before contacting a human.
• Automating: AI helps customer service teams solve more tickets per hour, on average, a 14% bump in productivity.
• Accelerating Resolutions: Companies see up to 52% faster resolution times when AI supports agents during active cases.
• Scaling Smarter: AI can handle customer questions on its own and automatically scale during busy periods.

Benefits of AI Agents in Customer Support

• Customer Satisfaction: 64% of agents say AI helps make conversations more personal.
• Knowledge Sharing: 37% of employees say AI has helped them collaborate better.
• Lower Burnout: With fewer repetitive tasks, support work becomes more meaningful.

What Happened When an E-Commerce Brand Let a Customer Service AI Agent Handle Support: Success Story

When you work in retail, you have enough things to worry about: inventory management, financial fraud, high marketing costs, etc. And, of course, customer support issues can go unnoticed until you can’t ignore them anymore. That was the case with our client, a growing e-commerce business, and we’re here to tell you about our solution. 

Client’s Challenges

• Every time the team launched a sale, support tickets for order status, return questions, shipping delays, etc., started to accumulate—issues that could have easily been answered by a machine.
• The company already had a huge team and spent lots of resources to sustain it, but it still wasn’t covering the demand during peak season.
• Customers were waiting too long, and the company started getting poor reviews for slow client support.

Behind the Scenes

Our first guess was simple: a case of needing more hands, which we could solve with a chatbot. Yet, the issue turned out to be trickier. As we mentioned, the business was growing and expanding into other countries, but the hiring process for support teams was moving much slower. Priority queries were often lost among the hundreds of repetitive ones, which hurt customer trust and the company’s profits.

Investigating further, we found another, even bigger issue. Our client’s support channels weren’t connected to the CRM, so staff had to transfer data manually, which led to client details being missing. That all meant a simple chatbot wasn’t going to solve the problem, so we decided to go bigger.

Final Solution

After a short brainstorming session with the client, we decided to build artificial intelligence for customer service and train it on their unique workflows. We also integrated our solution with the company’s core systems, including their CRM. Here’s how the system works in brief:

• Respond instantly to FAQs, learning to handle more complex questions
• Automate order tracking and update clients on their status
• Manage returns without any human intervention

That change was received well, as agents could finally concentrate on high-priority tasks and apply their soft skills instead of answering “Where’s my order?” 300 times a day. Suddenly, the support inbox became more manageable—and customers noticed the difference, too.

Results Achieved

A few months later, we came back to see the difference our AI agent customer service had made in numbers. And the results didn’t disappoint:

• 95% of simple queries answered by AI
• 35% reduction in support costs
• CSAT score grew by 20%
• Faster response times, even during the busiest sales

Want your support team to have the same kind of help? Let’s talk.

Why Support Operations Are a Cost Sink, and Where AI Agents Fit In

Customer support looks like a fixed function: you hire some managers, reply to tickets, and maintain a certain level of service quality. But the longer your company operates, the less viable this setup becomes. 

The turnover rate in customer care is very high, so you constantly have to search, hire, onboard, train, and then do it all over again. On top of that, there are different time zones and seasonal traffic spikes, so you have to figure out how to cover those, too. Another problem is the backlog of tickets that just wait there, forgotten and unresolved for months.

Even if you’ve got it all under control, each customer interaction still costs you somewhere between $15 and $25, depending on how complex it is and whether it comes through chat, email, or a phone call. So, if you have a global client base that expects urgent, high-quality service, not automating can be the biggest mistake for your business.

Why Traditional Models Break as You Grow

By design, the old-school approach to customer service is labor-intensive and reliant on people. That means you can only grow by hiring more staff, and that’s not ideal in times of talent shortage. Plus, over time, it gets harder to maintain response times, consistency, and quality without spending more and more.

“It’s tough to balance between cost and quality, especially during peak hours. You either have to overstaff or play catch-up—never somewhere in the middle. And when you operate like this, your team gets exhausted quickly, which leads to high turnover and rising cost-to-serve.”

— says our COO, Nazar Kvartalnyi.

An AI Agent for Customer Support: Why It Makes a Real Difference

You may think that an AI agent is just a cost-cutting patch, but it’s really not. Let us explain how agents work in more depth:

• Automation: Resolve low-complexity service inquiries like order tracking, return policies, and password resets.
• Deflection: Provide people with self-service portals like smart FAQs or conversational search before the user even creates a ticket.
• Augmentation: For the stuff that does need a person, AI helps agents reply faster, find info quicker, and route issues to the right place.

So, instead of hiring more people to manage more volume, you build a system that absorbs a large portion of the workload. That allows your employees to do what only they can — empathize and solve problems that need human judgment. Look at how it works in practice:

• Unity, the game engine, saved $1.3 million by deflecting 8,000 tickets.
• Gartner projects that AI will reduce contact center labor costs by $80 billion by 2026.

Breaking Down the Cost Levers: How AI for Customer Care Delivers Real Savings

Don’t think AI agents just save you a few minutes here and there—they work on multiple levels and optimize different cost levers at the same time. Let’s go into the details with the expert comments from a senior business analyst at Inoxoft.

Deflecting Routine Inquiries with Smart Self-Service

One of the immediate wins of using an agent is that it deflects tickets that never needed human attention. It means that every time a customer wants to check their order status or ask for a refund, these queries don’t turn into support tickets but get answered instantly by an AI. 

AI agents give your customers self-service solutions like FAQs, conversational search, or virtual assistants if they ask for a chat. And you may be surprised, but consumers like this even more than talking to a real person: 81% say they prefer solving issues without speaking to support.

“Smart assistants and other AI tools can help you with almost 40% of all tickets. But don’t think that giving answers to your customers is enough. Your final goal is to leave them with no questions at all – because the real scale happens quietly, in the background.”

Automating First-Line Triage and Routing

Sorting tickets may be the least valuable work to assign to a person, wasting their skills and time on something simple algorithms can do. AI systems take over this first layer of triage—classifying issues, prioritizing them based on urgency, and routing them to the right person.

And this setup is more accurate, too. AI trained on past cases and natural language processing models does a better job of understanding intent than if-this-then-that rules. Some operational gains you can achieve here:

• On average, people resolve 14% more issues per hour with AI.
• It’s even more useful for less experienced staff – AI helps them go through cases 34% faster, providing quick context.

Reducing Resolution Time with AI-Augmented Agents

Even when a person is the one working, AI can still be their right hand. For example, it can search the necessary customer data, generate answers that are relevant to the context, or simply take notes and fill in fields. Using this “combination”, you get better results and reduce your employees’ stress levels.

• Companies see 52% faster resolution times when AI supports agents during active cases.
• AI helps people work 13.8% faster by promoting smarter ways of doing things.

Optimizing Staffing and Load Balancing

You can’t win when staffing for support—some weeks, it’s dead quiet; other times, it’s chaos. So you either hire more than you need or work your team into the ground with overtime. AI helps level that out, taking on routine tasks and flexing up when ticket volume jumps.

“You can have a great team, but there’s no way to plan perfectly for every peak… without AI. Smart agents deal with overflow during busy times and make you less dependent on staffing. So, you stop throwing people at problems and build something that breaks this ‘unhealthy’ pattern for good.”

— adds an AI engineer at Inoxoft. 

Automating Low-Stakes Transactions End-to-End

And now for the best news: AI can completely take over the boring stuff—like order updates, password resets, and appointment bookings. All you have to do is connect it to your backend systems (CRMs, ERPs, ticketing tools) so it can work independently. Faster, more consistent, and way less boring—it’s a win for everyone.

• 84% of support teams say AI makes their job easier. When it takes care of the routine tasks, they can focus on high-value conversations and people, not process flows.

Work smarter – leave the busywork to AI! Contact us to learn more.

Beyond Cost: Strategic Benefits of AI Agents in Customer Support

When talking about artificial intelligence customer support, we usually start with how it saves money. But that’s just one part of the picture. What often gets ignored are the internal improvements it brings, like better teamwork, higher customer retention, and just a lighter atmosphere at the office. Let’s discuss.

Better Customer Experience and Higher Retention

With AI, customers get faster, more consistent, and context-aware responses, be they from a bot or a human. That flawless experience, where clients don’t have to repeat their query five times, makes people come back, even when competitors are around. 

Personalized service also plays its part in building brand loyalty. Right now, about 64% of support agents use AI to adjust tone, catch intent, and discover past context to increase customer satisfaction. You can’t achieve that with the basic pre-written scripts everyone uses, so expect clients to repay you with the same amount of trust and love for your company.

Improved Internal Collaboration and Knowledge Sharing

If you manage a distributed team, you may know the struggle of synchronizing their work. In such cases, an AI can become a common denominator, keeping workflows consistent and filling in knowledge gaps between staff, not relying on tribal knowledge.

About 37% of employees say AI tools have helped them collaborate better, mostly because everyone is looking at the same, relevant information. With more structured ticket classification, better insights into issues, and faster knowledge retrieval, teams operate with greater clarity.

Higher Morale, Lower Burnout, Better Retention

As you may expect, when agents don’t have to switch between five systems to solve one ticket or spend hours on routine tasks, team motivation grows a lot. 

Plus, if you can promise work that’s rewarding, the ramp-up time for new hires shortens, and human employees can spare their efforts on building meaningful relationships with important clients. You might not be able to put these outcomes – less burnout, fewer people leaving, and a team that feels more capable – in numbers or an ROI model, but they’re deeply felt over time.

How to Architect Customer Service Artificial Intelligence That Works

Lots of businesses get quickly disillusioned with their machine learning systems, not because AI doesn’t work, but because the setup behind it is too thin. When you hurry to deploy a ready-made product to “catch the hype train,” you end up with solutions that look promising in demos but fail in reality. We’re explaining why this happens.

Why Shallow Chatbots Fail

Most chatbots are what we call surface-level solutions, meaning they can only go as deep as decision trees and keyword matching allow them. As soon as a customer asks something different or makes a typo, the bot panics, leaving humans to deal with it. Some of the usual reasons this happens:

• Weak intent recognition – the system can’t correctly understand what users are asking.
• Lack of backend integration –  it can’t check order details, account info, or anything in your CRM.
• One-track logic – it follows a fixed flow and can’t adjust or learn from past interactions.

What Actually Works

Making your AI investment work at full capacity takes some wise decisions on its architecture, especially when integrating new tech with the existing one. Most successful setups have these things in common:

• Intent-based triage models that are trained on actual support tickets and real agent-customer conversations. That helps AI learn how people actually describe their problems, not just how we wish they would.
• Vector search engines. When someone asks a question, the system can find the answer in a massive knowledge base, even if the wording is different.
• Domain-specific LLMs that can analyze customer sentiment, understand what they mean, their intonations, and subtext.

“Chatbots are only part of the equation that makes up an AI agent. Bots can answer a question, sure, but we start talking innovation when LLMs predict customer needs or, through a single word choice, can tell whether you’re upset or not. Then, they sync with automation frameworks like RPA to take the next steps. Connecting these systems, we’re basically creating a super-human that can process an order or a return, update account info, or send you an update—all on its own and at once. In the end, your customers, your team, and your business all get what they want: a great experience, less boring work, and easy scaling opportunities.”

— comments a senior AI engineer at Inoxoft. 

Artificial Intelligence and Customer Service: Cost-Benefit Simulation

Now, let’s take a practical look at some gains you can achieve with an AI customer support agent. Below is a simulation that shows a typical mid-sized operation volume for both B2C and B2B—about 100,000 support tickets a year.

We want to stress that these aren’t abstract benchmarks but real results we’ve seen from implementing similar solutions. A drop from $18 to $11 may not look impressive at first, but at scale, it makes a huge difference. Plus, 40% automation means 1.2 hours less repetitive work for every team member – and fewer backlogs. As for the most exciting result: on average, companies get $3.50 back for every $1 invested in AI, with some getting closer to $8.

Want to achieve these results for your team? Let’s connect and make it happen together.

Will AI Customer Support and Assistance Be the New Norm?

We always talk about growing customer expectations, and AI in support services has already been promoted from an extra helper to something your customers use. Modern consumers appreciate immediate replies, one-click payments, and targeted promotions—so if you don’t have support software to provide all of that, it’s time for a change. Look at the stats:

• PwC predicts that by 2030, 80% of all customer interactions will be AI-handled, and we’re talking not some basic FAQs but meaningful conversations powered by large language models.
• Salesforce reports that 50% of global companies are already using some form of conversational AI, and another 44% plan to implement it within the next two years.

For every business owner, this means AI is your differentiator today—and your baseline in the future. If you don’t want to risk your market position or your loyal customers, it’s time to scale up and offer smarter, faster service.

Strengthen Support with Your Custom AI Agent at a Lower Cost

Many companies hesitate to build their own AI agents because it seems too expensive or too complex, but we’ve learned how to dispel these doubts. Here’s why you should consider us as your development partner:

• Deployment in 1–4 weeks: The market average for AI development is 2–6 months, but we can deliver in as little as 1–4 weeks. Having a solid groundwork in place, we use ready-made AI architectures and adjust models through automated hyperparameter tuning to speed things up.

• 40% faster time-to-market: With a library of pre-configured NLP models, chatbot flows, analytics engines, and process automation frameworks, we release our products faster so your team can start seeing real value sooner.
• Up to 3X lower costs: We work with clients in industries like finance, healthcare, and logistics, where speed is everything. Our faster, leaner development cycles turn into a huge market advantage for you.

We have exceptional technical expertise, 10 years of experience, 170+ professionals on our team, a 5/5 rating on Clutch, 230 completed projects, and 100% dedication to your success. 

Schedule a free consultation with our experts to discuss the details.

Key Throughs

Despite all the capabilities of AI agents, they can’t fully replace humans in customer support. But what they can do is save money through automated workflows, keep your staff more satisfied with their work, and deliver a better, more enjoyable customer service experience, resolving even complex issues in under 10 minutes at a lower cost.

Don’t wait for AI to become a necessity for your business—make it your prime tool now. And we’ll gladly help you do that. With 15 AI agent cases in our portfolio, we know how to make a difference.

Contact us for a project estimate and further details.