{"id":3250,"date":"2025-05-27T14:29:00","date_gmt":"2025-05-27T11:29:00","guid":{"rendered":"https:\/\/novatalks.ai\/?p=3250"},"modified":"2025-09-12T16:47:49","modified_gmt":"2025-09-12T13:47:49","slug":"ai-in-contact-centers-debunking-myths","status":"publish","type":"post","link":"https:\/\/novatalks.ai\/en\/blog\/ai-in-contact-centers-debunking-myths\/","title":{"rendered":"Artificial Intelligence in Contact Centers: Debunking the Myths"},"content":{"rendered":"\n

Is AI really the “magic pill” everyone\u2019s talking about? The joint webinar by NovaIT and UCCAI provided honest answers to the most pressing questions.<\/em><\/p>\n\n\n\n

In the era of digital transformation, artificial intelligence has become the most talked-about technology in the field of contact centers. Some see it as a panacea for all customer service issues, while others view it as an overhyped technology fraught with hidden pitfalls. To set the record straight, NovaIT, in collaboration with the Ukrainian Contact Center Association International (UCCAI), conducted an in-depth analysis of what AI can truly do in contact centers.<\/p>\n\n\n\n

Expertise Proven Over Time<\/h4>\n\n\n\n

The speaker was Khrystyna Podoliuh, Head of Business Analytics at NovaIT \u2013 a company with solid credentials in the contact center space. Over 10 years, the team has created solutions for the biggest players in the market: nine out of ten top banks in Ukraine, Vodafone, Nova Poshta, Ukrposhta, and Ukrtelecom. NovaIT was also the first in Ukraine to launch a voice bot that not only understood the Ukrainian language but also its dialects and surzhyk \u2013 something many AI solutions still struggle with.<\/p>\n\n\n\n

The company has developed its own omnichannel platform, NovaTalks, which integrates telephony, messengers, and email into a single workspace. \u201cWe decided to build what the market lacked \u2013 true unity of all communication channels,\u201d explains Khrystyna.<\/p>\n\n\n\n

Technology Battle: Traditional Bots vs. AI<\/h4>\n\n\n\n

The first myth debunked by experts is the supposed rivalry between traditional bots and LLM-based solutions. In reality, each technology has its strengths and weaknesses.<\/p>\n\n\n\n

LLM bots are revolutionary in two key ways. First, they talk like humans: they can adapt to the user’s age, switch from formal to friendly tone, and give synonymous responses to the same question. This creates the illusion of human interaction, greatly enhancing the user experience.<\/p>\n\n\n\n

Second, the deployment speed is impressive: upload a knowledge base, write a page of instructions \u2013 and the bot is ready. No developers, no lengthy setup. Even non-tech employees can make updates themselves.<\/p>\n\n\n\n

But no technology is perfect. The biggest challenge with LLM bots is so-called “hallucinations.” Imagine you\u2019ve created 100 categories for request classification and clearly instructed the bot to use only them. Still, in 2\u20133% of cases, it invents its own category. \u201cNo matter how much we emphasize it in the instructions \u2013 it hallucinates,\u201d Khrystyna admits.<\/p>\n\n\n\n

The second issue is confidentiality. It\u2019s not as critical as it might seem, but additional security measures are absolutely necessary.<\/p>\n\n\n\n

According to recent research, LLM bot success rates have reached 80%. \u201cTwo years ago, the story was completely different. The progress is astonishing,\u201d the expert notes.<\/p>\n\n\n\n

Hybrid Approach: When 1 + 1 = 3<\/h4>\n\n\n\n

The most compelling part of the webinar addressed the idea that the most effective solution isn\u2019t choosing between technologies, but smartly combining them. Imagine this situation: a customer writes, \u201cSend me the same order I had second to last.\u201d<\/p>\n\n\n\n

A traditional bot would be confused by such phrasing \u2013 it expects clear commands. An LLM bot would understand, but might mishandle the database query. A hybrid solution handles it gracefully: AI interprets the natural language and hands off the execution to a traditional bot for precise operations. The result is natural conversation with reliable execution.<\/p>\n\n\n\n

This approach leverages AI where it excels \u2013 categorizing requests and working with knowledge bases \u2013 and uses traditional algorithms for complex integrations and precise operations.<\/p>\n\n\n\n

Omilia: When a Bot Outperforms a Human<\/h4>\n\n\n\n

A separate section of the webinar was dedicated to Omilia \u2013 a platform that combines LLM, speech recognition, voice synthesis, and biometrics. You\u2019ve likely interacted with it when calling your bank.<\/p>\n\n\n\n

The numbers speak for themselves: Omilia processed a balance inquiry in just 26 seconds. In comparison, using DTMF (the \u201cpress 1 to\u2026\u201d systems) or a human agent would take much longer.<\/p>\n\n\n\n

Most impressive were the results in outbound calls. In one project, the database contained 150,000 contacts per month. The connection rate was 90%, interested clients \u2013 6%, and closed deals \u2013 11%. \u201cThat\u2019s a fantastic outcome for an AI assistant,\u201d says Khrystyna.<\/p>\n\n\n\n

A particularly interesting use case was in cross-selling: when a client refused a credit card, Omilia automatically offered a deposit \u2013 and the client accepted. This level of sales flexibility used to be possible only for experienced agents.<\/p>\n\n\n\n

The secret? Details. In one project, voice recordings were made directly in the contact center, using background noise and the voice of a regular female operator, not a professional voice actor. As a result, most customers didn\u2019t even realize they were speaking with a bot.<\/p>\n\n\n\n

Revolution for Agents: From Routine to Creativity<\/h4>\n\n\n\n

While bots handle typical requests, agents are left with complex cases \u2013 and here too, AI can be an invaluable assistant.<\/p>\n\n\n\n

The simplest tool is an AI chat assistant. It corrects errors, adjusts tone, translates into any language, expands or shortens text. \u201cWhen I write to a company, I pay close attention to the literacy of the response. Mistakes are off-putting, while emojis and a friendly tone build trust,\u201d Khrystyna shares.<\/p>\n\n\n\n

But the real game-changer is AI Copilot. The agent answers a call, and the system transcribes the conversation in real-time and provides suggestions for each client phrase. When the client names four card digits and a transaction amount, Copilot automatically adds this to the notes. The agent just needs to say \u201cyes\u201d or \u201cno\u201d and voice the confirmation. \u201cIf this had been available earlier, working in a contact center would have been a dream job,\u201d the expert admits.<\/p>\n\n\n\n

Analytics: From 1% to the Full Picture<\/h4>\n\n\n\n

Traditional quality control is a major pain point for contact centers. Supervisors can physically review only 1% of dialogues, and statistically, these are always average calls. The worst and best cases \u2013 long calls, repeated contacts, emotional interactions \u2013 are often missed.<\/p>\n\n\n\n

AI-powered quality control analyzes 100% of conversations based on your criteria. Supervisors see only those that require attention and can reach out to dissatisfied customers in time.<\/p>\n\n\n\n

But there\u2019s a catch: focusing on agents doesn\u2019t solve the root problems. Research shows that only 20\u201325% of issues are related to agent performance. The rest stem from product flaws, complex processes, and system errors.<\/p>\n\n\n\n

This is where AI Speech Analytics comes in \u2013 a tool that provides a \u201cbird\u2019s-eye view\u201d of the contact center. The system analyzes all interactions and identifies not only agent issues, but also systemic failures, product shortcomings, and inefficient processes.<\/p>\n\n\n\n

A real case: a B2B payments company used Speech Analytics to identify at-risk clients. During the call, the system flagged such a client, segmented them by revenue, and initiated a personalized retention offer. Result: 600 retained key clients and $1.7 million in saved revenue within the first three months.<\/p>\n\n\n\n

Security: Not Paranoia, But a Necessity<\/h4>\n\n\n\n

Security concerns are among the most common questions NovaIT receives \u2013 and rightly so. Horror stories about AI violating ethics and damaging reputations are alarming.<\/p>\n\n\n\n

There are two solutions: either implement AI in-house, or meticulously configure cloud security settings.<\/p>\n\n\n\n

To protect personal data, experts recommend masking sensitive information before feeding it to AI \u2013 emails, phone numbers, and names are replaced with placeholders. AI receives the conversation\u2019s content without the ability to identify a specific person.<\/p>\n\n\n\n

Monitoring includes a module for detecting sensitive topics, ongoing response review, and mandatory human review of ethical cases. \u201cNo matter how advanced AI is, it still can\u2019t show human empathy,\u201d Khrystyna emphasizes.<\/p>\n\n\n\n

Cost Efficiency: Cheaper Than You Think<\/h4>\n\n\n\n

Cost is often the deciding factor in AI adoption. The numbers are pleasantly surprising: AI quality scoring costs $1\u201315 per 100 dialogues, and transcription \u2013 $6 per 1,000 minutes. That\u2019s significantly less than traditional supervisor costs.<\/p>\n\n\n\n

The Future Is Already Here<\/h4>\n\n\n\n

Summing up the webinar, Khrystyna stated: \u201cAI is not a magic pill, but it is a powerful tool that can radically improve a contact center\u2019s efficiency when used correctly.\u201d<\/p>\n\n\n\n

Key takeaways:<\/p>\n\n\n\n