AI is working overtime and is performing better than human call centers in some cases. Before you start picturing how some robot named Eve replaces your entire call center instantly, let’s break this down.
This article is going to show you how AI can perform in call centers and whether humans are at risk of losing their jobs. Spoiler: AI is not replacing but rather transforming call centers!
The Current State of AI in Call Centers
Let’s go back a few decades. Call centers were initially built on landline phone systems, with humans making hundreds of identical calls every day, taking notes by hand, and saying “please hold” a hundred times a day as well.
Now, fast forward to the present-day digital transformation of call centers, where AI is not just a buzzword but a real tool for building successful and scalable businesses. AI can now free humanity from repetitive calls by making human-like conversations, generating notes automatically, and even logging all of those notes directly into your CRM!
And let’s not forget about the massive powerhouses for call centers – countries like the Philippines and India, which are known as BPO hubs. They handle millions of customer service calls every day. But AI still hasn’t replaced them – it has rather made the workforce more efficient and scalable.
You might not realize it, but AI is already present in most call centers. It comes in the form of different tools that aren’t necessarily just making conversation. Here’s a list:
- IVR (Interactive Voice Response): Press 1 for English, press 2 for Spanish… you know how it goes. Now, however, the IVR is slightly smarter due to advancements in NLP (natural language processing), and therefore can react to responses beyond pressed numbers or yes/no questions.
- Conversational AI: enables bots to hold human-like conversations with a person by understanding the language and context, and generating an intelligent response. This is a technology that includes NLP (natural language processing), TTS (text-to-speech), and STT (speech-to-text).
- Intelligent Virtual Agents (IVAs), chatbots: agents that use conversational AI technology to hold conversations with humans autonomously. The intelligent virtual agents (AI agents) are more than just a bot talking to you. For instance, AI agents at EVE.calls can autonomously initiate calls when triggered by a CRM update, and even perform actions during the call, like sending a follow-up email, booking an appointment, and more.
- Predictive Behavioral Routing: platforms like CallMiner can route the customers to the best-fit agent based on some variables, like voice tone, sentiment, or maybe even some previous conversations the person had with an agent.
- Real-time analytics, call monitoring, transcription: this ability is usually present in the AI Agent. Upon making the call campaign, real-time statistics are generated (e.g., the average duration of the call), along with transcription or other important details. The type of analytics depends on the platform and its offerings, but it can include cool insights like the most frequently asked question during the call, percentage of connected calls, percentage of voicemails, etc. Super helpful for call analysis for any business.
- Integration with CRMs and omnichannel support: creating a whole AI ecosystem is a popular practice nowadays in business. You can integrate your AI Agent (that will be having conversations with your customer) with your CRM, so the AI Agent logs some call details and results during the call. You can set up basically anything – integrate the AI Agent with your Calendly, so that it books appointments automatically.
How AI Augments, Not Replaces, Human Agents
AI removes repetitive work responsibilities from humans. Let’s be honest – nobody dreams of saying “hold on a moment” a hundred times a day or copy-pasting numbers into spreadsheets all day long. AI can handle that work, and let human agents focus on more interesting stuff like building relationships with clients. Dare we say, AI even lets humans enjoy their work a little more.
An AI agent takes all the mundane work. Here is an extensive table of all the superpowers an AI Agent has.
AI Agent Superpower | Examples |
Holds full conversations for routine queries/issues | Reminds people of appointments; answers repetitive FAQs like “Where is my order?”, “How do I reset my password?”; qualifies leads. |
Identifies the customer | Instantly recognizes the caller by their phone number or CRM ID. |
Retrieves customer details during the call | Pulls up customer details during the call if needed – for example, their name, past purchase history, or order number. |
Routes to the right department | Routes the customer to the necessary department – sales, billing, support, etc. – based on the issue type. |
Transcribes the full call | Creates a full, detailed transcript of the call, word by word. |
Summarizes the call | Generates a short or detailed (depending on your preferences) summary with key points. |
Logs call results in CRM | Logs the summary of the call or any important variables (e.g., whether the client was interested, asked a question, or needs follow-up) into the CRM or another internal system. |
Performs follow-up actions | Sends follow-up emails, SMS, books an appointment, or performs pretty much any other action. |
Trigger other workflows | Similar to sending a follow-up email, the AI Agent can also assign a task post-call for managers, mark that the user needs to be followed up later, etc. |
Handles a BUNCH of calls at once – ANY time | Manages thousands or more calls simultaneously without a drop in quality, and is available to answer calls anytime – 24/7, no schedule, no rest needed. |
Now that you’ve read all the benefits, you’re probably wondering – is there an AI Agent that can do all of this? Well, meet EVE – the AI Agent at EVE.calls. A well-rounded agent to support your call center, whether you’re in debt collection, healthcare, finance, or literally any other industry that relies on calls.
For instance, many debt collection companies have their agents making thousands of repetitive calls daily – “When will you pay?”, “Why haven’t you paid?”, etc. What EVE does instead is this: it checks the CRM to see if the person has an overdue payment, calls them with the proper disclosure and purpose, then pulls up details in real time – like their name, days overdue, and total amount – and uses these variables in the conversation for confirmation.
If the person promises to pay in 3 days, EVE nicely logs that timeframe into the CRM and can even send an SMS reminder. And if the case is more complex, for example, the person wants to negotiate a partial payment, EVE understands that and transfers the call to a human. So, EVE handles the repetitive workload at scale and only involves a human when a complex issue arises.
Why Human Agents Remain Essential
Human agents are still needed. Let’s make it clear so you’re not scared by headlines like “AI is taking away your job.” Humans and AI work together in call centers – both boosting the customer centricity of the business. We’ve just explored how AI performs different tasks; now let’s explore what humans do best (and better than AI Agents).
Human Agent Superpower | Examples |
Empathy & Emotional Intelligence | Calming a frustrated customer about a lost item; showing empathy when the person mentions the reason they can’t make a debt payment due to loss of a significant other, etc. |
Cultural Understanding | Understanding that a customer from Europe doesn’t like small talk – in contrast to a customer in the USA. Or even catching someone’s sarcasm in a joke – AI might flag it as rude, while it was just some cultural joke. |
Complex Problem-Solving | Resolving a client’s request for setting up a partial payment with different deadlines, or advising the person on some legal risk. |
High-Stakes Negotiation | Handling a call from a client making a big dispute, or negotiating the price for a property in real estate. |
Ethical Decision Making | Deciding whether to extend the payment deadline for a person with a medical emergency or loss of a significant other, even if the extension is against the usual terms. |
Relationship Building | Remembering some client’s details from a previous booking and making a joke about it, or mentioning that you have something in common. |
Now, with all these superpowers of human agents, it’s clear they are still needed in the call center – at least for now. AI, of course, might simulate a polite response with some empathetic tone, but it can’t truly express compassion when someone talks about something tragic happening to them. Similarly, resolving high-stakes situations requires improvisation, trust, and emotional intelligence – things humans bring naturally and something AI still lacks.
The human touch – the ability to understand nuances, connect with a person, and make morally weighted decisions – is irreplaceable with AI, for now. Let’s look more deeply into the limitations of AI agents.
Key Limitations, Challenges, and Risks of AI in Call Centers
AI Agents can pose some risks and challenges to call centers. Well, you don’t need to get scared now – implementing AI Agents is still proven to be the key to business success. However, just keep in mind that there are some limitations.
Data Privacy and Security
Call centers usually must comply with strict regulatory standards – such as GDPR (for data protection), HIPAA (for healthcare), PCI DSS (for payment information), and others. Failure to comply might result in legal penalties, significant financial losses, and a loss of customer trust.
Let’s say a debt collection AI Agent accidentally emails the wrong person with another customer’s debt details. This is a direct violation of GDPR and PCI DSS. To prevent this, it's crucial to monitor and audit AI behavior regularly to avoid risks.
Also, AI, just like humans, can have biases. AI Agents are usually trained on large datasets. If the datasets are imbalanced or non-diverse, the bot can make unfair decisions. For instance, imagine an AI Agent calling a person to make the payment. The person says they are a veteran and eligible for a discount, but the bot wasn’t trained on such cases and simply insists on the full payment. This creates a poor and disrespectful user experience and can damage the brand's reputation. Obviously, this example is very preventable – it’s just highly important to train AI on diverse datasets and consider all possible edge cases.
Industry-Specific Limitations
The healthcare, finance, and insurance industries have strict limitations. The calls need to abide by precise requirements – HIPAA in healthcare, PCI DSS in finance, and various regional insurance laws. This leaves quite little room for errors.
Also, the nuances of languages, regional dialects, or some local regulations might be hard for AI to handle. Certain local regulations may require call disclosures or specific phrasing, and missing those details can lead to non-compliance. That’s why using AI in these industries often requires extra customization and close collaboration with legal and cultural experts.
Ethical & Societal Concerns
When people talk about AI, they most often bring up job displacement. For workers in call centers, it’s a very sensitive topic, as many fear losing their jobs. This fear is exaggerated, but still valid. The reality is slightly more nuanced: AI will take over the mundane and repetitive tasks, while human agents will be responsible for emotionally complex, strategic, and high-stakes conversations. So, for workers who are doing exclusively repetitive calls – yes, AI might feel like a threat.
Also, AI hallucinations are one of the most common concerns. If an AI Agent is trained on a non-diverse dataset, it might handle some edge-case situations inappropriately. It can unknowingly favor certain groups of people while disadvantaging others.
AI-Human Collaboration: The Hybrid Model and the Future of Call Center Work
According to Amir Liberman, CEO of Emotion Logic, about 70–80% of customer service interactions could be handled by AI within the next 2–3 years, while the remaining 20–30% will still require skilled human agents, supported by AI to boost efficiency (Forbes, Dec 2024).
To put it shortly, the future of call centers is a hybrid model, which is essentially the synergy between humans and AI. In this setup, AI handles repetitive, routine, and high-volume tasks. Humans, meanwhile, get involved in the conversation when it becomes emotionally complex, requires negotiation, ethically weighted decisions, or some legal sensitivity.
Here is a nice example of step-by-step call process in a hybrid model in the healthcare industry:
- A patient calls the clinic. An AI Agent answers the call instantly.
- The AI Agent verifies the patient's identity using their phone number and date of birth.
- The AI Agent asks what they can help with. The person says they want to reschedule an appointment.
- The AI Agent checks availability in real time, offers open slots, and confirms the new appointment.
- Then the patient asks to verify insurance coverage. The AI Agent pulls up the basic status. Suppose the insurance requires some special documentation.
- The AI then connects the person to a human agent.
- The trained human agent joins the conversation, already having the full transcript of the call from the AI Agent, and smoothly continues solving the patient’s problem.
- Post-call, the AI summarizes the conversation and logs all the call details in the hospital CRM.
- The AI Agent then triggers follow-up tasks, like sending an email or SMS with appointment details to the patient.
From this AI-human collaboration example, we see that humans perform only important tasks with prior AI assistance. This makes their work more strategic and important, as well as the expectations higher. Now, people wanting to work in call centers need to be skilled in the following:
✅ Tech adoption – knowing how to use CRM-integrated AI tools, dashboards, and automated workflows.
✅ Data literacy – being able to read, understand, and make decisions based on the AI outputs (transcripts, summarizations, other AI triggers).
✅ Empathy and communication – handling high-emotion, high-stakes conversations.
Now the job of human agents will not be boring but valuable and strategic, and potentially higher paid. Companies will begin to value call center agents not just as operators, but as customer success managers, relationship managers, crisis solvers, and brand representatives. Plus, the cost of AI Agents is cheaper in the long term and scalable, meaning more can be invested in humans in their training, wellbeing, and pay.
Lastly, ROI – the hybrid model is the best at delivering tangible results. First – cost reduction: Businesses using AI Agents observe a 30–70% reduction in cost per call, according to industry data. Thanks to faster resolutions, there is a significant improvement in NPS (Net Promoter Score). And the best part – the volume of call handling increases by 2 or 3 times with the same team size!
So, now, it should be clear: hybrid models that connect human agents with AI are the key to business success.
FAQ
Will AI fully replace call center agents, and when?
Not fully, and not yet. AI Agents still lack empathy, ethical judgment, and the ability to solve complex, strategic problems – qualities that remain uniquely human… at least for now. AI Agents assist humans by handling the boring and repetitive tasks, leaving the strategic and complex ones to humans.
What can AI do today that humans can’t?
Work 24/7 and work faster. AI Agents can accept calls instantly, at any time of day, and perform actions in real time during the call, like summarizing the conversation, booking an appointment, logging call details into the CRM, and more.
Are jobs at risk, or just changing?
Changing. Jobs at call centers now require higher-level skills: data literacy, professional and empathetic communication, relationship and trust building. Instead of being operators who repeat the same sentence over and over, humans at call centers are becoming more like relationship managers, customer success leads, and brand representatives.
How do I future-proof my call center/team?
Invest in training your call center team to acquire new skills, such as data literacy and understanding how AI tools work. Also, focus on training them to be more empathetic, build relationships, and stay customer-centric.
What are signs of responsible AI adoption?
Keeping humans in the loop to handle sensitive or high-stakes situations. Being aware of industry regulations like GDPR, HIPAA, and PCI DSS to ensure customer data is handled properly. Making sure AI Agents are trained on diverse datasets to avoid being biased toward certain groups of customers. And lastly, training human agents to work alongside AI Agents effectively.
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