We examine where the call analytics market is headed and how companies are already gaining a competitive advantage today
Over the past decade, sales tools have evolved around CRM systems, scripts, and funnels. But despite all the automation, calls remained an area of “manual control.” At best, they were listened to selectively; at worst, they were not listened to at all.
This meant that decisions were made based on subjective judgments rather than data. “CRM marketing cements the connection between the business and the buyer”: what is a CRM model and why is it needed? With the growth of incoming communications, the acceleration of processes, and the demands for quality interaction, it became clear that outdated analysis methods were no longer sufficient.
Artificial intelligence technologies that can process, classify, and interpret each call in real time and with high accuracy have come to the fore.
What is happening in the market now? Call analytics using artificial intelligence is no longer a niche solution and is becoming one of the standard tools for companies seeking transparency and efficiency in communications.
According to Forbes, the global voice AI market was estimated at approximately $5.4 billion in 2024, showing a 25% increase over the previous year. According to various reports, the market will continue to grow exponentially in the coming years — up to $38–50 billion by 2029–2030 — at an average rate of more than 30% per year.
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This rapid development is not just for show. Companies are implementing AI call analysis to improve the quality of customer communication, automate feedback, and quickly find bottlenecks in the funnel. Informally, such solutions are already becoming the standard for mature sales departments focused on data and growth.
How to implement AI in your sales department: a step-by-step guide To ensure that artificial intelligence technologies do not become just a nice add-on, but actually boost sales, it is important to build a consistent implementation process.
AI in sales is not magic, but a tool. And like any tool, it works when it is integrated into a clear system. Below is a step-by-step plan that we use to start every implementation project. Risks of AI implementation: when it’s better to refuse
Step 1. Analyze what really works in your sales and what can be improved. The first thing we do with our clients is look at their current processes. Where are the bottlenecks? What is slowing things down? What actions are repeated? Which customers are “lost”? AI does not replace the salesperson. But it is capable of handling dozens of microtasks that hinder efficiency: analyzing calls, recording leads, monitoring scripts, and ensuring compliance with communication standards. The main thing is to answer honestly: where exactly are opportunities being lost? Without this step, implementation will be “blind.”
Step 2. Choose the right solution — off-the-shelf or custom. There are dozens of solutions on the market today, from boxed AI platforms to custom models trained for specific businesses. The choice depends on the tasks, call volume, team structure, and budget. A ready-made solution is faster and easier. A custom model is more flexible and deeper. It is important to assess the scale of implementation not only from an IT perspective, but also from the perspective of real benefits: how accurately AI will fill the very “gap” that you identified in the first step.
Step 3. Set up your CRM and contact points—only then connect AI. Before integrating AI, you need to put your infrastructure in order: CRM, funnel, lead processing. Surprisingly, even large companies often fail to identify key points of interaction with customers. Without this, AI will not be able to learn and deliver results.
