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Generative AI for customer service: 5 essential takeaways for your contact center
June 13, 2025 • 4 minutes
Contact centers are experiencing a fundamental shift as generative AI for customer service (and beyond) moves from theoretical potential to practical implementation. At LivePerson, we recently gathered experts from Boston Consulting Group (BCG), Outsource Consultants, and our own team to explore how organizations are successfully implementing these AI-powered technologies to meet both customer expectations and ROI goals.
Our panel featuring Uche Monu (BCG), Tom Luther (Outsource Consultants), and Nirali Amin (LivePerson) shared insights that cut through the hype to focus on practical implementation strategies. Whether you’re just starting your generative AI journey or looking to optimize existing contact center deployments, these five takeaways offer a clear path forward.
1. Generative AI drives speed, personalization, and scalability at once
The days of time-consuming bot development are fading fast. According to Nirali Amin, today’s generative AI tools offer unprecedented agility, adapting quickly to shifting contexts and customer interactions while connecting to your backend systems to recognize individual customer preferences.
“What we’re seeing is a fundamental shift toward AI that can act as a personal digital concierge,” Amin explained. “These models craft personalized responses and assist customers across channels without the traditional development overhead we’ve all struggled with.”
This capability to deliver tailored support services at scale represents a cornerstone for modern customer engagement, allowing brands to be both more responsive to customer queries and more personal simultaneously.
2. Moving from reactive support to AI-powered prediction
Most organizations start their generative AI journey by focusing on support team response and agent productivity. Uche Monu from BCG shared that while reducing average handle time (AHT) is valuable on customer calls and interactions, the real transformative potential lies in shifting “left” on the customer service spectrum.
“Forward-thinking companies are using generative AI to move toward proactive, pre-emptive support and enhanced self-service solutions,” noted Monu. “This approach not only reduces contact volume but significantly improves customer satisfaction, first contact resolution, and operational efficiency.”
This evolution from putting out fires to preventing them altogether represents the next frontier in customer experience transformation.
3. The 10-20-70 rule: Why organizational transformation matters most
One of the most eye-opening insights came from BCG’s “10-20-70” rule for AI implementation:
- 10% of value comes from the generative AI models themselves
- 20% from customer data and tech stack integration
- 70% from transforming the way you do business (i.e., customer service operations)
“Many organizations focus exclusively on the tip of the AI iceberg — the models and technology — without addressing the organizational transformation required underneath,” Monu cautioned. “Without a clear North Star vision and cross-functional collaboration, even the most promising AI initiatives tend to stall before delivering their full potential.”
Senior leadership buy-in, intentional collaboration across departments, and thoughtful change management aren’t just nice-to-haves — they’re essential ingredients for success.
4. Operational KPIs that truly matter for measuring impact
Tom Luther brought a refreshingly practical perspective, focusing on the KPIs that actually demonstrate generative AI’s impact on customer service:
- First Contact Resolution (FCR): AI-powered triage and intelligent suggestions measurably improve a service representative’s accuracy and speed.
- Cost per Contact: Automating routine tasks reduces manual labor needs while providing powerful AI agent assist tools.
- CSAT and NPS: For positive customer feedback, AI agents need to correctly interpret customer intent and sentiment while maintaining conversational interactions.
- Agent Experience: By removing monotonous tasks and providing efficiency tools, generative AI can significantly enhance job satisfaction.
Luther also introduced a practical model for brands with limited budgets: “Consider optimizing labor through strategic outsourcing and reinvest those savings into AI technologies,” he suggested. “This creates a virtuous cycle of continuous improvement.”
5. Human-AI collaboration is the future of customer experience
Perhaps the most important takeaway? Generative AI for customer service isn’t about replacing humans — it’s about elevating them.
All panelists agreed that agent roles are evolving, with new positions like “AI Ops,” “prompt engineers,” and “CX data analysts” emerging. Nirali emphasized that successful deployments typically start with testing AI tools in agent assist mode before going consumer-facing.
“This approach not only ensures accuracy and compliance but builds crucial trust with your frontline teams,” she explained.
For regulated industries like financial services, staged rollouts and multi-LLM strategies (large language models) are becoming standard practice, with governance frameworks and hallucination detection serving as essential guardrails.
Start small, scale smart to serve customers better
Generative AI is already reshaping how brands deliver conversational customer service experiences. The winning strategy? Start with high-impact use cases, align them to your long-term improved customer experience vision, and ensure you have the data, governance, and cross-functional collaboration to sustain momentum.
Most importantly, position generative AI as a partner to your human agents, not a replacement. When implemented thoughtfully, it becomes a multiplier for speed, satisfaction, and scale—transforming your contact center into a strategic asset for the entire organization.
Ready to take the next step in your generative AI journey?
