Key Trends in AI for Contact Centers

AI’s full takeover of the Call Center is now more of a rollout and change management problem than anything else. Speech quality, agentic orchestration, and compliance are continuously improving and are on track for the takeover scenario. If procurement cycles and change-management hurdles don’t slow things down, my guess is that Tier-1 voice support should be 90% automated by 2030, with humans fully shifting to AI training, coaching and exception handling roles by 2032 (also AI assisted). For highly regulated industries like banks, insurers, and telecoms, they’ll likely choose a two-layer strategy: hyperscaler CCaaS for the spine, plus a specialized voice-bot vendor for high-stakes domains (fraud, collections) until confidence, cost, and regulation catch up. Here’s what needs to be true for this all to happen: First, you need an ultra-reliable voice stack that’s <300 ms bidirectional latency so conversations feel human. WER <3% across dialects and background noise (the latest speech models set the bar here). Agentic orchestration, not just intent detection, has to be available as models must engage backend systems (think CRM, payments, logistics) safely and independently. Also, multi-agent planners (like those announced in Microsoft Copilot Studio in 2025) can deliver on the architectural path. Third, the right guardrails to deliver retrieval-augmented generation tied to authoritative knowledge bases; proofs of source logged for compliance. Real-time redaction and PII masking baked into the pipeline to satisfy HIPAA, PCI-DSS, and any emerging AI policy requirements. Cost parity will be another key ingredient. Inference plus carrier fees needs to stay below what the fully-loaded labor cost of an offshore agent is in 2025. Onshore comes later. Finally, you need supervisor copilots, quality-assurance bots like Genesys’ “AI for Supervisors” or NICE’s Enlighten Copilot, and continuous training loops to replace the traditional floor manager model. This is already happening, so it’s more a matter of time to see the capabilities improve. Gartner still expects only ~10% of all agent interactions to be automated by 2026, up from 1.8% in 2022. So the gap between achievable tech and enterprise rollout is the real drag on the timeline. The tech is largely “here.” The reason you aren’t seeing +80% voice automation in the average contact centre yet is mostly enterprise readiness. Think legacy systems, undocumented know-how, risk governance, and slow org redesign. Yes, the tech still needs polish in multi-step reasoning and compliance, but the heavier lift right now is inside the enterprise walls, not the model weights. If you’re in a call center role today, how are you approaching this? #ccaas #contactcenter #ai

The AI Revolution in Call Centers: From Chatbots to Voice Synthesis In 2024, artificial intelligence is dramatically reshaping customer service, particularly in call centers, where 90% now utilize AI technology. This transformation is redefining how businesses engage with customers, offering enhanced efficiency and personalization. 🌍 Key Features and Benefits - Enhanced Efficiency: AI automates routine tasks, allowing human agents to focus on complex issues. - Improved Customer Experience: Faster, personalized service through data analysis and predictive capabilities. - Boosted Agent Productivity: Real-time assistance and automated post-call tasks streamline operations. - Cost Reduction: Automation and smart routing lead to significant savings. 🌍 Cutting-Edge Voice AI Technologies Recent advancements in voice tokenization and AI voice synthesis are pushing the boundaries of customer interactions: 1. dMel: A novel speech tokenization method that outperforms existing techniques in recognition and synthesis. 2. SpeechTokenizer: Combines semantic and acoustic tokens for a comprehensive speech representation. 3. Vec-Tok Speech Framework: A system for speech vectorization showing strong performance across various speech tasks. 🌍 Applications of Voice AI - Voice Cloning: Companies like ElevenLabs are creating high-fidelity voice cloning for customized AI agents. - Multilingual Support: AI-generated speech enables seamless multilingual service. - Emotional Intelligence: AI can modulate tone and emotion for empathetic interactions. - Personalization: Unique voice identities tailored to different customer segments. 🌍 Implementation Strategies 1. Assess Needs: Identify areas for AI implementation. 2. Start Small: Begin with select AI applications like chatbots. 3. Invest in Training: Prepare your team to work with AI technologies. 4. Choose Compatible Tech: Ensure seamless integration with existing systems. 5. Monitor and Iterate: Continuously evaluate and adjust AI performance. 🌍 Ethical Considerations Address ethical concerns regarding disclosure and potential misuse, prioritizing transparency in AI voice technologies. 🌍 Future Outlook The integration of advanced voice AI with existing solutions will redefine call center operations. With predictions of a 50% productivity increase and enhanced customer experiences, AI is set to deliver unprecedented efficiency and personalization in customer service. By leveraging these cutting-edge technologies, businesses can create more responsive and efficient customer service experiences, positioning themselves for success in an increasingly digital world. 1. Wang, L., et al. (2023). Voice‐based AI in call center customer service: A natural field experiment. Production and Operations Management. 2. Cornell University. (n.d.). AI in Contact Centers: Artificial Intelligence and Algorithmic Management in Frontline Service Workplaces. 4Enlight, AI Innovation Lab, AI Research Lab

For years, everyone predicted the same thing: Chatbots would kill the phone call. Digital channels would make voice obsolete. The entire industry believed it, until new data revealed the exact opposite: AI isn't replacing voice but fueling its resurgence. 82% of organizations now expect AI to increase voice traffic, not reduce it. The paradox is striking: The same technology expected to eliminate phone calls is making them cheaper, smarter, and more valuable than ever. Voice isn’t dying, it’s evolving into something far more powerful. Here’s what’s really happening: 1. AI fixes the economics that made voice expensive For decades, more than 80% of call costs came from one thing: agent time. That single constraint made voice the most expensive service channel. AI is rewriting that equation: Conversational agents handle routine calls instantly Real-time assist tools help junior agents perform at senior levels Training cycles shrink dramatically Hold queues vanish 81% of organizations already have AI-enabled voice environments. 69% expect customer ratings to rise because of AI. But this is where most enterprises make a critical mistake: 2. AI can’t thrive on legacy voice infrastructure It’s not the AI that fails, it’s the plumbing underneath it. The data is clear: - 84% of enterprises use cloud for part of their contact center - 65% still rely on on-prem systems - 97% are now consolidating vendors to modernize voice Hybrid environments create latency, inconsistent quality, and data silos that block AI from doing what it’s capable of doing. Modern cloud voice infrastructure changes everything: Dynamic routing. Virtual agents. Proactive ML-driven monitoring. CRM-aware conversations. Global scalability without complexity. 3. Real-world proof: Flutterwave Flutterwave, Africa’s leading payments company, needed to operate across 30+ countries as it shifted from enterprise B2B to high-volume omnichannel serving both businesses and consumers. The key wasn’t chatbot adoption or more agents. It was a cloud voice: - Centralized control. - Consistent experience across markets. - Local presence numbers. - Direct CRM and workflow integration. As Oluwaseun Olatunde put it: “Africans are big on ‘I want to talk to somebody’ because of trust deficits.” Voice builds trust that digital channels simply can’t match, especially in fast-growing markets. The real trend: AI is making voice indispensable When voice becomes intelligent, contextual, and instantly available, it transforms from a cost center into a competitive advantage. At Voice.ai, we’re democratizing synthetic voice so any founder, team, or enterprise can tap into this surge, not by replacing voice, but by transforming what voice can do. If you're building in voice AI, working on customer automation, or thinking about where AI-powered communication is heading, I’d love to connect.

Why Traditional Call Centers Are Transitioning to AI-First Support Customer expectations have evolved. They now demand instant responses, round-the-clock availability, and consistent experiences across every channel. Traditional call-center models cannot meet these requirements at scale - AI can. Key Drivers Behind the Shift Rising Customer Expectations Customers prefer real-time support over waiting on hold. AI enables instant, accurate responses across chat, voice, and digital channels. Increasing Operational Costs Recruitment, training, and agent attrition create ongoing cost pressures. AI manages repetitive queries at near-zero marginal cost, allowing organizations to scale efficiently. High Volume of Repetitive Queries Up to 70% of support requests are routine (order updates, resets, FAQs). AI resolves these immediately, allowing human agents to focus on complex, high-value interactions. 24×7 Availability Is Now Essential While human agents work in shifts, customers expect continuous support. AI ensures uninterrupted service - even during nights, weekends, and peak times. Faster Resolution, Better CX AI can instantly search knowledge bases, suggest responses, and predict next issues, reducing handling time and minimizing customer frustration. Seamless Omnichannel Experience AI connects conversations across chat, email, voice, WhatsApp, and in-app channels, ensuring context moves with the customer. AI Enhances Human Capability AI is not replacing human agents - it is augmenting them. AI handles scale and speed. Humans handle empathy and complex decision-making. The result: higher customer satisfaction and more empowered support teams.

By 2025, your call center might sound nothing like it does today—here’s why. As CEO of Tomato.ai, I’ve been closely following the rapid advancement of AI technologies in the call center space, and I’ve distilled my thoughts into three key predictions that I believe will define 2025. 1. 𝐕𝐨𝐢𝐜𝐞 𝐀𝐈 𝐓𝐡𝐚𝐭 𝐅𝐞𝐞𝐥𝐬 𝐑𝐞𝐚𝐥 First, we’ll see major strides in voice AI—both for virtual agents and as support for human agents. On the virtual front, speech-to-speech technology (like what OpenAI is pioneering) will eliminate the need for transcription before response. The result? Ultra-low latency, natural-sounding conversations that flow more like human-to-human interactions. On the human agent side, AI will help refine accents and improve intelligibility, building more trust and clearer communication with customers. 2. 𝐋𝐋𝐌𝐬 𝐏𝐨𝐰𝐞𝐫𝐢𝐧𝐠 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 Next, advanced Language Learning Models (LLMs) will become cheaper, faster, and more accurate. They’ll transform everything from call summaries and analytics to powering next-gen virtual agents. Simply put, these models will be the engine behind more efficient, insightful, and responsive call center operations. 3. 𝐀𝐠𝐞𝐧𝐭 𝐀𝐬𝐬𝐢𝐬𝐭 𝐅𝐢𝐧𝐚𝐥𝐥𝐲 𝐂𝐨𝐦𝐢𝐧𝐠 𝐨𝐟 𝐀𝐠𝐞 Lastly, after years of proofs-of-concept and incremental improvements, 2025 will be the year agent assist tools truly hit their stride. Seamless integrations, refined user experiences, and tangible ROI will become the norm—driving down costs and enhancing the overall customer journey. Now, I’d love to hear from you. Which of these trends do you see making the biggest impact, and why? Let’s start a conversation—leave your thoughts in the comments. #AIinBusiness #VoiceAI #CallCenterInnovation #CustomerExperience #FutureOfWork #TechTrends #LLM #DigitalTransformation

IVRs are dead and scripted AI agents are next. In my chat with Rebecca Greene, CTO of Regal.ai, we explored what’s actually holding AI agents back, from overengineered guardrails to simulation failures, and why the future of contact centers is 90% AI-led voice. The biggest barrier isn’t hallucination—it’s forcing AI to follow scripts your best human agents already ignore. Here’s what stood out 👇 1. Voice AI works best in industries where conversations drive value, not just cost savings. 2. Contact center use cases that involve emotion or complexity are more compelling for AI agents than low-stakes retail. 3. AI agents can outperform humans in uncomfortable tasks because they lack shame or fatigue. 4. Inbound support is being redefined by voice agents that actually understand intent and take action. 5. The real threat to IVRs isn’t better UX, it’s full obsolescence via intelligent voice agents. 6. Long conversations, emotional complaints, and group calls are still weak spots for AI agents. 7. Customers over-engineer guardrails out of fear of hallucination, then wonder why performance lags human agents. 8. Top-performing humans agents routinely go off-script, yet companies expect AI agents to stick to it. This creates false performance comparisons. 9. Real innovation starts by training agents based on top human reps, not static call scripts. 10. Every stage of the AI agent lifecycle—especially post-deployment—still has unsolved product gaps holding back performance and adoption. 11. Simulated testing is unreliable because it assumes “perfect users” but real customers are messy, emotional, and long-winded. 12. Agent testing and improvement today is more product design than engineering—continuous iteration is the norm. 13. Most enterprise customers aren’t equipped to build or manage AI agents without hands-on help, even if they are tech savvy. 14. Prompts for production agents are wildly different from ChatGPT-style usage and most contact center leaders don’t realize the complexity. 15. Regal’s approach includes embedding engineers to co-build with customers, showing that hands-on support is still a differentiator in Voice AI. 16. Rebecca predicts 90% of contact center voice interactions will be AI-led within five years, unless regulators step in. 17. AI agents won’t just match human performance, they’ll eventually surpass it with personalization, memory, and scalability. 18. Future agents won’t just optimize for the “average” customer—they’ll dynamically shift tone and pacing based on individual behavior. 19. Voice interfaces will move from the call center to the product itself and embed real-time conversation into apps as a core feature. Full ep: https://lnkd.in/eAghNuTC Awesome discussion. Thanks Rebecca!

Are contact center jobs heading to zero? AI-related layoffs continue to dominate headlines, with contact center employment often cited as most exposed to AI disruption. Agentic AI is reaching a level of capability where it can absorb a large share of first-level and transactional interactions. Anthropic finds customer service roles among the most exposed, with up to 70% of tasks potentially automated. The US Bureau of Labor Statistics recently published its annual report, offering a lens on the contact center workforce. For the 2nd year in a row, employment of Customer Service Representatives has declined by 5%. The label is misleading, as it captures only a fraction of roles. Computer Support Specialists moved differently, growing 2% after prior declines. Combined, the two categories show a 7.5% contraction over 3 years. While Gartner’s 2023 forecast of a 20–30% reduction by 2026 has not materialized, the direction signals structural decline. But these categories capture only part of the picture — roughly 75% and declining — of the workforce using customer communication technologies across the customer lifecycle, missing the expansion of engagement work across roles and the enterprise. We often assume a stable interaction workload. In reality, volume and complexity continue to rise, offsetting efficiency gains. Digitally handled activity keeps expanding. Processes are becoming more complex, driven by compliance and regulatory requirements. Digitization generates exceptions, increasing workload; ContactBabel finds 80% of complaints stem from back-office failures. The contact center infrastructure now extends into sales and service delivery, including financial advisory and healthcare interactions. The rise of agents acting on behalf of consumers will further increase volumes. Zendesk found that automation was actually driving a significant increase in interaction volumes, suggesting that Jevons Paradox is at play. Occupational definitions are increasingly misaligned with how work is structured. The contact center reflects a traditional model of decomposing service work across the enterprise, mirroring Taylorism. It is a carved-out front-end layer optimized for rapid responses via large agent pools, offloading tasks to specialized functions. AI is both the “front door” to service and a tool for employees, enabling reaggregation of work. Instead of routing interactions through the contact center, a broader workforce will increasingly engage directly with customers. Taken together, these dynamics suggest the contact center is likely to dissolve into a broader operating model. Rather than framing this shift as AI-driven job elimination, the focus should be on how customer engagement work is redistributed between AI and humans across the enterprise, and how it reshapes customer-facing roles. Way too much ground to cover in one post; check the full analysis: https://lnkd.in/g8kdpTmh

Data from #CCWLasVegas paints a clear picture: we are at a critical turning point in Customer Experience. While organizations rush to deploy Agentic AI, a fundamental truth has emerged—AI is only as good as the knowledge architecture supporting it. Here are 5 major trends redefining the contact center, and why Knowledge Management (KM) sits at the epicenter: 1. The Self-Service Evolution Preference for self-service has grown to 30%. But if limited to self-service, Voicebot and IVR dominate at 37%, beating traditional portals and standard chatbots. ☆ The KM Takeaway: Conversational voice AI needs lightning-fast, accurate contextual knowledge. Without a single source of truth, you deliver faster friction. 2. The Composable Stack Creates Chaos 51% of companies added 2–3 new tools this year. However, 71% of customers frequently have to repeat information—a direct result of systems failing to share context. ☆ The KM Takeaway: Composable architecture is useless without a unified knowledge layer. Siloed tools create siloed context, destroying the customer experience. 3. The "Non-Interaction" Tax Why pivot to Agentic AI? 52% of CX leaders reveal employees spend too much time on non-interaction work, specifically tedious knowledge lookups. ☆ The KM Takeaway: Centralizing knowledge and making it retrievable is the exact bridge needed to safely launch automated AI agents. 4. A Massive Competency Gap 37% of leaders expect frontline teams to pivot toward managing high-complexity cases. Yet, only 17% believe agents are adequately equipped. ☆ The KM Takeaway: We ask agents to act as expert problem-solvers while starving them of dynamic knowledge. Closing this requires real-time context delivery. 5. KM is a Boardroom Priority Improving Agentic AI and Knowledge Management sit squarely together in the highest-importance, highest-difficulty quadrant for executives. ☆ The KM Takeaway: Modern KM is no longer optional—it is foundational infrastructure required to scale enterprise AI safely and effectively. Conclusion: The future isn't defined by how many AI tools you buy, but how intelligently systems share information. Fix the customer experience by fixing how your enterprise manages knowledge. How is your team tackling context sharing? Let's discuss in the comments. #CustomerExperience #KnowledgeManagement #GenerativeAI #AgenticAI #ContactCenter #CXStrategy #TechLeadership #UplandSoftware

The contact center industry has been talking about agentic AI for two years. What it's been short on is vendors actually delivering it in a way that's integrated, operationally coherent, and built for how businesses run in the real world. RingCentral's expansion of AIR Pro — announced this week at Customer Contact Week Las Vegas — is worth paying attention to, and here's why. The headline is native AI agents embedded directly into RingCX workflows, handling multi-step inbound and outbound interactions across voice and digital channels without requiring a human in the loop. Not layered on. Not bolted on. Native. That distinction matters more than it might sound. The capability I'm most excited about is Autonomous Outreach, the ability to trigger AI-initiated conversations based on real-world events like a missed payment or an appointment reminder. That's the contact center moving from reactive cost center to proactive engagement engine and something I think will deliver real value. Pair that with Intelligent Handoffs that carry full customer context, you know, all the stuff you so often have to repeat over an over again (think: CRM data, prior interactions, recordings, etc.) to a live agent when escalation is needed, and you're eliminating one of the most persistent frustrations in customer experience and one that drives me crazy pretty much weekly. The numbers backing this up aren't soft: RingCX reports the company has has surpassed 1,700 enterprise customers (up 70%+ YoY), with more than half already using AI capabilities. Some customer examples: Sun River Health hit a 95% first-call resolution rate — 25 points above industry standard. The Escape Game cut costs by 50% while growing bookings 7%. The San Diego Symphony reduced hold times by 95%. These are real world customer experiences and the kind of bottom line impact organizations are looking for. What's becoming increasingly clear is that organizations need a unified framework for managing performance, quality, analytics, and governance across both AI agents and human workers, and having that infrastructure native to the platform, rather than assembled from point solutions, is the right approach. RingCentral is building toward that. New capabilities are in beta now, GA targeted for H2 2026. I've written up a full analysis of what's here, what it means for enterprise and mid-market buyers, and where the real differentiation lies. Read my full assessment below Tim Dreyer  #ringcentral #AIagent #agenticAI #CX #contactcenter #customerexperience

AI is transforming contact centers from cost centers into competitive advantages when done right. At Amazon Web Services (AWS) re:Invent, Amazon Connect showcased how unified platforms with embedded AI are changing customer experience: → 12 billion minutes of AI-assisted interactions annually (doubled from last year) → Real-time quality analysis of 100% of interactions vs. the industry standard of 1-5% → Seamless context preservation across voice, chat, and automated channels The Centrica case study demonstrates measurable impact: • Average handle time: down from 140 to 87 seconds • Net Promoter Score: up 89% on key journeys • Chat fulfillment rate: doubled overnight switching to generative AI • Complaint volume: reduced 28% by identifying root causes through AI analysis But here's what matters most: The shift from standalone AI tools to integrated platforms is accelerating. Many organizations start with point solutions for simple cases, then return to unified systems when complexity increases. Key takeaways for contact center leaders: 1. Start with clear pain points and baseline metrics—not features 2. Ensure data quality and governance before scaling 3. Adopt a hybrid approach: AI for repetitive tasks, humans for empathy and complexity 4. Look for platforms that analyze every interaction, not just a sample 5. Choose pricing models that encourage innovation, not feature gatekeeping The technology exists today to deliver personalized, contextual service at scale. The question is whether organizations have the discipline to implement it systematically. What's your experience with AI in customer service? Are you seeing similar results? Read my take here: https://lnkd.in/e8kTk6Sw #CustomerExperience #AI #ContactCenter #DigitalTransformation #ArtificialIntelligence #AWSreInvent cc Tim Crawford Zeus Kerravala Liz Miller Julie Ask Tanya (Blackburn) Shuckhart Melissa Grant Bola Rotibi Max Ball Katharine Kemp