Agentic AI set to handle 68% of customer service interactions by 2028

Customers who contact call centers often seek empathy, understanding, and personalized communications, which can be difficult for AI to replicate. Treat AI systems as tools to augment human agents’ capabilities rather than replace them. Combine their advanced functionalities with the warmth of human interaction to maintain high service standards. By introducing artificial intelligence (AI) into call center workflows, businesses can deliver exceptional customer experiences while streamlining business processes and boosting agent performance. The trajectory of AI in support engineering points toward increasingly sophisticated systems capable of handling more complex tasks with minimal human oversight.

Datadog President Amit Agarwal on Trends in…

The toolbox for excellent field service must also include AI-powered applications that enable field services organizations to deliver value at the speed of need. Businesses that utilize AI powered field service delivery capabilities are most likely to win the battle — the battle for customer retention, customer love and advocacy, and the battle to earn future business. And the best customer care in today’s economy is powered by real-time intelligence and personalization at scale. Emotion AI, or affective computing, is transforming how call centers handle customer interactions. This technology enables systems to assess and respond to emotions in real time by analyzing subtle cues, like voice tone and speech patterns.

Executives overestimate consumer willingness, expecting a 76 per cent premium—23 points higher than reality. Additionally, leaders think 24/7 availability is what consumers want the most, while consumers rank speed of resolution as their top priority. What’s more, while 68% of C-suite leaders cite customer service as their AI and automation investment priority, only 44 per cent feel very equipped to manage the transition.

Top AI Call Center Software to Consider

These technologies will mold a future where call centers are more responsive, proactive, and customer-focused than ever. An AI chatbot can be used to understand the customer in their given language and craft responses specifically in that particular language. Hence, customer agents don’t need to worry about the translation or being unable to understand customers perfectly, leaving no space for errors during the communication process and saving them significant time. Cisco stated that the findings confirmed some preconceived notions while totally challenging others. Three months after Open AI debuted ChatGPT in November of 2022, Juniper Research analysts predicted that, by the end of 2023, chatbots would be involved in 70% of all customer service interactions. High performing service organizations use field service management to drive innovation and reduce costs.

Sentiment analysis analyzes a customer’s tone, word choice, and the context of their messages to gauge how they feel—whether frustrated or satisfied.
This transformation is evident across various industries, with businesses adopting AI-driven solutions to enhance customer interactions and streamline support operations.
This level of customization fosters a deeper connection with the customer and encourages repeat business.
For example, if a customer previously spoke to a specific agent about a problem, AI can redirect them to the same agent for a more personalized follow-up.

Integration with Existing Systems

They can escalate the conversation to human agents for more complex or sensitive matters. This reduces the workload on teams and ensures that customers can get quick, consistent, and accurate responses at any time of day. This demand was intensifying, with 56% of respondents expecting their customer experience interactions to be handled by agentic AI within 12 months – with that number jumping to 68% within three years. AI-powered knowledge graphs represent a transformative tool for businesses seeking to deliver personalized and seamless customer journeys.

For call centers, this means analyzing past data to forecast peak call times, identify common customer issues, and allocate agents effectively. For instance, predictive insights could prepare staff for high-demand periods or suggest proactive solutions for recurring concerns. In 2025, the integration of artificial intelligence into support engineering has revolutionized customer service, enabling companies to provide efficient, personalized and round-the-clock assistance. This transformation is evident across various industries, with businesses adopting AI-driven solutions to enhance customer interactions and streamline support operations. For instance, a telecom company might use a knowledge graph to help a customer troubleshoot an issue with their internet connection.

Use Case #1: Enhanced Data Understanding

Future advancements are set to refine its ability to interpret complicated emotional states, allowing AI to assist agents by suggesting empathetic responses or escalating sensitive matters to specialized staff. With the emotion AI market expected to grow to $13.8 billion by 2032, its influence in enriching customer interactions is becoming more clear. Intelligent routing is one of the most effective ways AI is enhancing call center services. AI determines which agent best suits a particular inquiry by analyzing past interactions, call history, and even customer preferences. For example, if a customer previously spoke to a specific agent about a problem, AI can redirect them to the same agent for a more personalized follow-up. This helps reduce customer frustration, as they don’t have to repeat their issues to different agents.

With AI technology now influencing almost every sector, the potential for this technology to streamline and enhance customer service processes and increase the prospects of a successful business can’t be overlooked. Additionally, the accuracy and reliability of a knowledge graph depends on the quality of the data it ingests. Inaccurate or incomplete data can lead to flawed insights and recommendations, undermining the customer experience. To address these issues, businesses must invest in robust data governance practices and ensure their knowledge graphs are regularly updated and refined. One of the most impactful applications of AI-powered knowledge graphs is in delivering personalized recommendations. By understanding the relationships between customers, products and content, knowledge graphs enable businesses to offer highly relevant suggestions that resonate with individual needs.

While HubSpot Service Hub is excellent call center software, its AI capabilities are not as advanced as those of its competitors.
For example, AI-powered chatbots can adjust their tone and responses based on a customer’s sentiment or previous experiences with your company.
The chatbot could analyze the customer’s account details, previous interactions and technical data to diagnose the problem and suggest a resolution in real time.
Successful integration requires an in-depth assessment of the current infrastructure and strategic planning.

At the heart of AI-powered knowledge graphs lies their ability to integrate and analyze data from various sources. In today’s data-driven world, businesses often struggle to make sense of fragmented customer information stored across multiple systems. Knowledge graphs solve this challenge by connecting these disparate data points, creating a unified view of customer preferences, purchase history and interactions. Dialpad Ai is an advanced customer intelligence platform with AI features specifically designed for call centers. The platform’s key features include Ai Recap for summarizing calls and meetings and Ai Playbooks for real-time and context-sensitive suggestions to agents. Dialpad also has robust transcription and sentiment analysis tools, giving instant insights from conversations and letting agents adjust as customer sentiments shift.