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Customer service is undergoing the biggest transformation in decades. Artificial intelligence in customer care is no longer just a thing of the future—or something that might help you, but isn’t a priority right now. AI chatbots, voicebots, and emailbots are becoming a major competitive advantage today, separating efficient companies from those stuck in an endless cycle of overwhelmed agents and rising costs.
When people say “AI in support,” most imagine a chat window. But the reality is far more complex. Modern systems built on large language models (LLMs) can now answer questions not only in chat, but also in emails and through advanced phone voicebots.
A specific challenge is email communication, where requests are often long and unstructured. That’s exactly where the emailbot from coworkers.ai excels. Unlike standard automated replies, it truly “reads” the message and understands its context. Within seconds, it identifies the customer’s intent, matches it with data in your CRM, and either resolves the request on its own or prepares a precise draft response for an agent to approve. Emailbots eliminate the biggest weakness of email support—long waiting times. With their speed, they turn email into a channel that can confidently compete with live chat.
A similar revolution is happening in phone support thanks to AI voicebots that handle inbound and outbound calls with a natural-sounding voice. For inbound calls, a voicebot works like an intelligent receptionist: it resolves common questions instantly without queueing, or identifies the caller and their issue before transferring them to a specialist. For outbound calls, it can automatically confirm appointments, send payment reminders, or run quick satisfaction surveys.
Successful AI implementation in customer service requires a clear-eyed view of what the technology can do. Automation makes the most sense where there is a high volume of routine, transactional queries. If your team spends hours every day answering questions about order status, delivery detail changes, or explaining complaint and returns policies, that’s an ideal area for automation.
When evaluating success, you shouldn’t focus only on cost savings. Key metrics include First Response Time (FRT) and Customer Satisfaction Score (CSAT). AI can respond in milliseconds, 24/7, which directly impacts overall customer satisfaction. At the same time, it’s essential to track the Deflection Rate (how many requests AI resolves without human involvement) and the moments when AI needs to hand the conversation over to a person. The smoothness of that handoff ultimately determines whether the customer leaves delighted or frustrated.
With AI, the role of a customer support leader changes dramatically. You’re no longer just managing people—you become the guardian of knowledge and processes. AI is only as good as the quality of the data you provide. Your new job is to ensure your internal knowledge base is always up to date and that communication rules reflect your brand’s tone precisely. If AI lacks information or relies on outdated procedures, it will make mistakes.
It’s important to treat AI as a new, extremely fast team member that needs continuous coaching. That requires new processes such as regular conversation audits and setting guardrails. These ensure the technology doesn’t make things up (“hallucinate”) and hands the conversation over to a human early enough in sensitive situations.
The managerial role also shifts toward developing your team’s skills. While you used to train people in fast typing and routine procedures, the focus now moves to analytical thinking, empathy, and the ability to work with AI tools. As a support leader, you need to reassure your team that AI isn’t their competition—it’s a tool that frees them from digital busywork.
Thanks to modern LLM technologies—used by our emailbot, chatbot, and voicebot—deployment timelines have shortened from months to weeks. An initial version, trained on your knowledge base and ticket history, can often be launched within a few weeks. This is followed by a tuning phase during live operation, where the bot gradually learns to handle more specific requests.
This phenomenon is called “hallucination.” In our approach, the AI doesn’t use the entire internet—it works only with your approved data. The key is that you have full control over how confident the AI must be before it responds. If the bot isn’t confident enough based on a percentage you define, it won’t answer on its own. Depending on your setup, it can ask the customer for more information, attempt to rephrase the query, or immediately and smoothly hand the conversation off to a live colleague—along with the full context.
No. Our AI solutions are designed to integrate with the tools you already use—whether that’s Salesforce, Zendesk, Daktela, or your own internal system. AI works as an intelligent layer on top of your existing data.
