Can GenAI improve customer experiences and reduce costs? Can AI agents in customer support functions improve customer self-service capabilities, create upsell opportunities, and increase intelligence on customer needs?   

I use chatbots and regularly reach out to customer support. Some SaaS companies deliver exceptional customer experiences (CX) through their genAI-enabled chatbots, self-service tools, and comprehensive knowledge bases. Bravo!

Others take days to respond, rarely recognize my persona as a tech expert, and send me down the 101 rabbit hole. Restart your computer, clear your cache, update drivers – anything to avoid having someone look into the issue. I wrote an article last year venting my frustrations about why SaaS and low-code solutions need AI for effective technical support.

Agentic AI in customer support is a game-changer

The recent AI in CX Benchmark Report suggests companies can have their cake and eat it too. GenAI can be a force multiplier by reducing costs and improving customer experience. Specifically, the report states that companies using agentic AI on a dedicated platform

• Had, on average, 33% higher deflection (self-service) rates
• Cut average resolution costs by more than 20%
• 64% had higher CSAT scores in 2025, compared to 55% of those using RAG-based AI and 49% of those using no AI.

Deon Nicholas, founder and president of Forethought, shared with me these three pillars to creating an amazing customer experience:

• You need to resolve the issues, whether informational or action-oriented
• You need to do so with a great user experience
• And you need to do so with a simple deployment

“For too long, we’ve had unreliable customer experiences driven by decision tree chatbots that have aimed to deploy and reduce costs without a focus on CX,” says  Nicholas. “With agentic AI, we have the power to facilitate customer support that is both LLM-native, seamless, and personable, as well as capable of resolving complex issues quickly and with thorough follow-ups and reasoning.”

How GenAI can improve customer experiences

Rule-based chatbots are limited in their support for various use cases and actions. They may send customers down the wrong paths, add to their frustrations, and force them to hunt for the link to open a customer support ticket.

Agentic AI chatbots accept natural language inputs, connect to CRMs for customer information, and integrate with a wider range of automated responses. The result is that more issues are resolved faster, often without human intervention. At other times, the customer support agent resolves issues effectively and empathetically, utilizing the most relevant information that the AI agent prioritized.

Shlomi Dagan, VP of global support at Sisense, says that at his company, AI chatbots handle common inquiries and automatically create support tickets, while AI tools enable human agents to access insights from past cases and internal sources quickly. He says, “This dual approach has achieved 30% case deflection and boosted our CSAT to 4.7/5 — allowing the team to focus on complex customer needs with greater speed and accuracy.”

Amol Ajgaonkar, Insight’s CTO of product innovation, says genAI is transforming UX by moving beyond rigid chatbots. “We’re developing agentic AI that understands context, accesses real-time data, and provides personalized responses. By configuring agents to emulate personas, we enable them to show perceived empathy, understanding not just what customers are asking, but how they feel.”

Ajgaonkar shared this example with me. “Imagine systems that understand sentiment, pull info from various sources, and offer real-time assistance via voice or text. This emotional intelligence creates meaningful interactions rather than frustrating, scripted exchanges. This means faster, more accurate, and more empathetic support. Plus, automating complex queries and backend data processing significantly reduces costs. While still early, these developments promise to revolutionize customer service, creating a seamless, efficient, and emotionally resonant experience.”

How Gen AI reduces customer support costs

As self-service increases, case deflection improves, and customers have their inquiries and issues resolved to greater benefit, a secondary opportunity arises. Organizations can reduce costs, increase scale, or do both.

“Generative AI is not adding another UX option, it’s adding a layer of intelligence to pre-existing tools that will enable them to deliver on unrealized promises from the past two decades,” says Chris Arnold, VP, customer experience strategy at ASAPP. “AI can manage over 90% of the customer interactions to enable a complete transformation of the CX labor equation.”

For large companies with significant customer support functions, the aggregate of small efficiency improvements can yield significant cost benefits.

“Enterprises spend hundreds of millions of dollars, sometimes billions, supporting customers with salary and benefits, often representing 80-85% of this spend,” says Arnold. “Deploying well-planned and well-executed AI strategies will provide enterprises with the choice to either substantially reduce operating expenses or avoid escalating expenses in growing businesses.”

Cost savings and customer experience improvements can be implemented in several call center and customer support functions. StarCIO Digital Trailblazers should define their vision statements around AI opportunities, then determine where to prioritize implementations.

“Integrating Gen AI-powered chatbots and virtual agents, ACDs (Automated Call Distribution), IVRs (Interactive Voice Response), and PBXs has delivered significant benefits, including cost savings, improved operational efficiency, multilingual support, and personalization at scale,” says Satish Shenoy, global VP of technology alliances and genAI GTM at SS&C Blue Prism. “The AI-enabled contact center can also provide 24/7 support, effectively resolving client and employee issues while streamlining interactions.”

Can genAI improve customer experiences and reduce costs?

• Yes, especially for organizations with traditional approaches to customer support.
• Yes, to organizations running scalable call centers.
• Yes to SaaS, B2C, and B2B customer support functions.
• Yes, to companies targeting customer and employee experiences.

Who is doing this well? Leave me a comment.