Sep 15, 2025

As AI tools become more capable of handling basic inquiries, human agents will need to shift their focus to complex or specialized work that AI still can’t manage. "I believe agents need to become SMEs in specific areas," Jorge Damico, founder of Auxyly , a fractional customer support provider, told CMSWire. "With deeper, specialized knowledge, they gain the advantage of solving complex problems AI still can’t handle." He explained that industries dependent on human interaction—such as field services—are less vulnerable to AI displacement, but digital-first sectors will need to upskill support teams quickly. As AI continues to improve, customer support roles will shift toward more strategic, judgment-based work. Agents may evolve into escalation specialists, journey analysts, or AI trainers—roles that didn’t exist a decade ago. The future of AI in customer service isn’t about removing people from the equation, but empowering them to do more of what makes them uniquely human. Choosing the Right AI Customer Service Tools How to Choose AI Customer Support Tools With so many platforms promising smarter, faster service, selecting the right AI-powered customer support tools can feel overwhelming. The key is to focus on how well a solution aligns with your team’s workflows, data ecosystem, and customer experience goals—not just the flashiest features. First, look for platforms that offer true AI-driven customer support, meaning they go beyond basic automation and provide capabilities like intent recognition , contextual understanding , and real-time learning. Equally important is integration: AI tools should easily connect with a business’s existing CRM, helpdesk and communication channels to avoid data silos and duplicated effort. Scalability and customization are also critical. Can the solution grow with the brand’s support volume and business complexity? Can it be tailored to its tone, branding and escalation policies? These factors often make the difference between a one-size-fits-all chatbot and a fully integrated customer support assistant. Measure What Matters: Accuracy, CSAT and Containment While flashy features get attention,  urged businesses to focus on metrics that reflect real value—starting with response accuracy. "Higher response accuracy improves the customer experience," Balaceanu said. "It also dictates downstream value, whether it’s revenue generation or cost savings through contact containment." He also stressed the importance of tracking these KPIs over time to fine-tune the platform and maximize long-term performance. Finally, vendor reputation matters. Look for proven track records, transparent AI practices and strong support offerings. A good partner doesn’t just sell you software—they help you evolve your support strategy. What Are Some AI Customer Support Platforms to Consider? While dozens of tools offer AI-powered customer support capabilities, here's a look at a few platforms that feature enterprise readiness, flexibilit and innovation: Zendesk AI - A customer experience staple, Zendesk now offers AI-powered bots, intelligent triage, and agent assist tools natively within its platform. It’s easy to implement for existing Zendesk users but can be costly at scale and less customizable than some newer entrants. Intercom Fin AI - Intercom’s Fin uses GPT technology to answer questions based on help center content and automatically escalate to human agents when needed. It’s ideal for fast-growing SaaS businesses, though it relies on structured content for high accuracy. Salesforce Service GPT - Part of the Einstein 1 Platform, Service GPT offers generative AI for case summaries, response suggestions and proactive support insights. Best suited for large enterprises already on Salesforce, but implementation can be complex. Ada - Ada specializes in AI-powered customer support automation with strong NLP capabilities and customizable workflows. It’s known for fast deployment and high containment rates but may require hands-on tuning for optimal performance. Rep AI - As highlighted in the SNOW Cosmetics example, Rep AI turns customer support into a revenue driver through personalized shopping experiences, proactive engagement and sales attribution tracking. It’s particularly strong for ecommerce use cases. Each of these platforms reflects a different approach to AI-powered customer support—from embedded automation in traditional helpdesk systems to newer tools that blend service with sales. The right solution will depend on your business model, support complexity and integration needs. How to Improve Customer Support With AI For brands ready to embrace smarter service, the key to success lies in thoughtful implementation—not just adopting AI, but using it for customer service in a way that aligns with business goals and enhances the human experience . While many companies rush to adopt AI to stay competitive, few step back to assess their foundational readiness—and it often leads to poor outcomes. Damico told CMSWire, "Sometimes the real problem isn’t lack of AI, it’s the lack of solid processes, clean data, or a decent knowledge base. If your house isn’t in order, AI won’t fix it. It’ll just make the mess faster. Prioritize clarity over trend chasing." Damico stressed that AI works best when paired with strong operational hygiene and a clearly defined success framework. Before implementation, businesses should honestly evaluate whether they’re solving the right problem—and whether AI is the best tool for the job. Practical Steps: Diagnose Pain Points, Involve Agents, Track KPIs Start by identifying the pain points the brand is seeking to resolve. Do they need faster response times? Better self-service options? More efficient escalation handling? Setting clear goals helps businesses design AI workflows that deliver meaningful results, whether they’re launching a chatbot, an AI-driven knowledge base, or real-time agent assist tools. Next, involve your support agents from the start. They understand customer needs better than anyone and can help train, test, and improve AI models. When agents see AI as a partner—rather than a threat—they’re more likely to adopt it and use it effectively. Finally, track your progress. The best way to improve customer support with AI is to measure its real-world impact with metrics such as resolution speed, CSAT scores, agent productivity and cost savings. These metrics will help you refine your approach and build the case for continued investment. Conclusion: The Real Role of AI in Customer Service AI customer support isn’t about replacing the human touch—it’s about strengthening it. By automating routine inquiries and delivering real-time insights, AI frees agents to focus on complex issues, empathy and relationship-building. The businesses that win will be those that blend speed and scale with human judgment, creating experiences that drive loyalty and growth. The future of customer service is clear: success depends on strategically using AI customer support to combine efficiency, personalization and trust. FAQ on AI Customer Support Here are some questions around AI customer support: About the Author Scott Clark is a seasoned journalist based in Columbus, Ohio, who has made a name for himself covering the ever-evolving landscape of customer experience, marketing and technology. He has over 20 years of experience covering Information Technology and 27 years as a web developer. His coverage ranges across customer experience, AI, social media marketing, voice of customer, diversity & inclusion and more. Scott is a strong advocate for customer experience and corporate responsibility, bringing together statistics, facts, and insights from leading thought leaders to provide informative and thought-provoking articles. Connect with Scott Clark: Tags