AI in Customer Service: How to Minimize Technical Debt

Tech debt isn’t just an IT challenge. With annual costs of over $2.41 trillion in the US alone, tech debt is a significant problem that warrants attention from top management.

Companies with the most success dealing with tech have a few things in common.

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As reported in an MIT Sloan Management Review article, Accenture studied 1,500 companies across 10 countries to understand how business leaders are reinventing their organization, including the role of tech debt. Accenture found three key components in companies well-positioned to deal with change and tech debt: AI that can be easily updated, data, and cloud infrastructure.

In this guide, we’ll examine the role of AI in reducing tech debt when automating customer support. Let’s dive in.

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What is technical debt?

Technical debt refers to the long-term costs of shortcuts, quick fixes, and suboptimal solutions used when writing code to achieve a goal. You save time upfront but pay for it later with inefficiencies, inconsistencies, and growing maintenance nightmares.

In customer service, tech debt results from using legacy systems and implementing systems without adequate planning, training, or refinement. Think scripted chatbots with canned responses or clunky integrations that create more frustration than they solve.

Your goal should be to minimize tech debt by implementing modern, AI-powered solutions built by a team that consistently works to pay down that debt by refining models, improving natural language understanding, and keeping a human in the loop to catch missteps.

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Technical debt is bad news

Tech debt in customer service can seem like a deceptively minor inconvenience… until it snowballs into an operational crisis. Let’s look at why tech debt in your customer service tech stack is bad news for your business.

Cost implications

Poorly trained models, outdated algorithms, and inefficient integrations eventually demand costly overhauls, emergency patches, and manual interventions. The longer you let technical debt pile up, the more expensive it becomes to fix, eating into your profits.

According to McKinsey, CIOs reported that 10 to 20 percent of the technology budget dedicated to new products is diverted to resolve issues related to tech debt.

Scalability challenges

The bottlenecks of a legacy system might stay under the radar when handling a small customer base, but the moment demand spikes, the cracks start showing. Things get worse when you keep applying constant patches and fixes to your traditional platform—which it requires to scale effectively—as it leads to mounting technical debt.

Customer experience degradation

Old-school support systems are more likely to cause frustration because long wait times, inaccurate responses, and siloed data make it difficult to resolve issues quickly. Your agents end up wasting valuable time searching for information, leading to inconsistent service quality and dissatisfied customers.

Operational inefficiencies

Tech debt caused by legacy support platforms forces your team to rely on manual workarounds. They spend too much time unnecessarily categorizing tickets, escalating cases, and jumping between disconnected tools. The result? Slow response times and a higher cost per ticket.

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How AI helps reduce technical debt in customer service

Bringing an AI-powered system into your tech stack reduces tech debt by modernizing outdated systems, automating labor-intensive processes, and more. Let’s look at six ways AI reduces tech debt in customer service.

Reducing legacy system dependencies

Clinging to outdated infrastructure burdens your business with technical debt that only becomes more expensive to maintain over time.

One of the biggest challenges with legacy systems? Their lack of flexibility.

Traditional on-premise customer support platforms often operate in silos, making it challenging to integrate new technologies like AI, omnichannel support, and real-time analytics.

If you’re still using a traditional customer support platform, consider phasing it out in favor of an AI-powered cloud-based platform. Gradually offload high-volume, repetitive tasks to an AI agent while maintaining core functions on the legacy platform during this transition.

You can deploy an AI assistant to tackle routine inquiries such as password resets, order status updates, and FAQs before a human agent steps in. Human agents continue to use your legacy system while you continue scaling your new AI agent by integrating it into your tech stack and workflow.

This phased approach minimizes disruption and ensures that legacy systems remain operational while AI incrementally assumes greater responsibility.

Automating repetitive tasks to reduce workarounds

Tech debt sneakily forces employees into a cycle of inefficient, manual workarounds. Manual ticket tagging, routing inquiries through convoluted processes, and relying on agents to answer routine queries daily are all symptoms of a legacy system stealing time from your support team.

AI systems substitute the need for patchwork. They can handle high-frequency, repetitive queries, deflecting a large portion of tickets before they reach a human agent.

Think about your current customer support workflow.

Now, compare it with how an AI agent like Yuma handles it.

When a customer reaches out via chat or email, Yuma reads the query and understands the intent. It uses training data or information in your knowledge base to answer the question or routes it to a human agent if needed or requested by the customer.

The result? A more strategically inclined support team that doesn’t waste time answering common questions or performing mundane tasks.

Intelligent knowledge management

Many enterprise customer support teams are bogged down by outdated, fragmented, poorly maintained knowledge repositories.

Agents waste time searching for relevant articles, customers struggle with irrelevant self-service content, and redundant information creates confusion rather than clarity.

The time wasted looking for information translates to longer wait times and erodes customer confidence.

AI agents can change the game. They use AI-powered search and natural language processing (NLP) instead of surface-level keyword-based search. This allows AI agents to dynamically interpret user queries and identify intent more effectively, equipping them with the information needed to pull relevant and contextually accurate information from your knowledge base.

At the same time, AI agents help identify gaps in existing resources, which helps update documentation and prepare it for future interactions.

Enhancing scalability without additional complexity

Scaling traditional customer support systems is like upgrading a typewriter to a laptop, one key at a time.

It’s painfully slow. It’s expensive. It’s impractical.

When support traffic spikes, legacy systems leave you with two bad options: overload existing infrastructure or hire more agents to compensate. Neither is sustainable.

With an AI system, you can empower customers to self-serve. Doing this absorbs a large chunk of routine inquiries.

An AI agent can handle thousands of queries simultaneously, guiding customers through troubleshooting processes. They also ensure complex cases go directly to the right agent, eliminating unnecessary escalations.

That’s how AI agents help you scale support operations without ballooning infrastructure costs or overwhelming your support team. No matter how much your support traffic grows, you don’t need to increase headcount proportionally, worry about increasing wait times, or constantly monitor response quality during peak season.

Make no mistake—AI agents don’t just focus on solving most queries. They do so while meeting your customers’ response quality expectations.

Optimizing API and system integrations

Legacy customer support ecosystems are often a patchwork of loosely connected tools, which increases tech debt. Each tool in the ecosystem solves a specific problem at a specific time.

The ad-hoc integrations create a tangled web of dependencies, making even minor system updates risky and time-consuming. It’s an anxiety-inducing system where every API call feels like it could trigger a domino effect of failures.

On the other hand, AI agents integrate seamlessly with commonly used tools in your tech stack. They cut through complexity by automatically mapping data, identifying inefficiencies, and suggesting optimized workflows.

AI makes integrations adaptive rather than reactive. Traditional APIs are often hardcoded, meaning any changes to upstream or downstream require manual configuration.

On the other hand, AI agent solutions can dynamically adjust to changes, ensuring agility as business needs evolve, reducing maintenance costs, and preventing slow accumulation of integration-related tech debt.

Identifying and resolving bottlenecks proactively

Traditional systems rely on historical data and agent feedback to identify problem areas. But by the time an issue is flagged, it has already impacted CX and operational costs.

AI agents help you identify bottlenecks proactively before they cause real damage. They continuously monitor patterns in ticket escalations, response times, and resolution efficiency and find inefficiencies that may otherwise go unnoticed for a longer period.

AI also ensures that software integrations and automated workflows remain efficient over time. As you introduce new tools or updates, AI analytics audits system performance. It flags API failures, data inconsistencies, and automation breakdowns before they become widespread problems.

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Be mindful of limitations

AI is a powerful tool, but it’s not a magic bullet. While AI can automate processes, optimize workflows, and reduce inefficiencies, it can’t replace the need for strategic refactoring.

Fundamentally outdated legacy systems still require architectural overhauls. AI can ease this transition but can’t eliminate the need for larger structural changes.

In fact, AI can introduce its own forms of tech debt if implemented poorly. Over-reliance on outdated ML models, improper training, or failure to monitor AI performance can create inefficiencies rather than solving existing ones.

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That’s why you must carefully assess an AI agent before implementing it. If you want to minimize tech debt and automate customer support, choose a reliable AI agent backed by a team committed to keeping models up to date and helping you deliver personalized support at scale.

As explained in our case studies, Yuma has repeatedly proven that it increases agent productivity, reduces operating costs, and improves customer satisfaction.

Book a demo today to learn more about how Yuma can help your business minimize tech debt and automate customer service.

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