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

Conversational AI platforms provide today's benchmark for self-service and AI-driven customer engagement. The core capabilities of these platforms span 4 areas: • Integrations with back-end systems, communication channels, and knowledge sources. • AI technologies for speech and natural language processing, understanding, and generation (NLP/NLU/NLG). • No-code conversation design environment. • Toolsets for defining, testing, and refining intents and entities. In just 18 months, GenAI has reshaped the conversational AI market. Platforms have undergone two rounds of evolution—sometimes requiring a complete rebuild of functions—and must keep pace with relentless innovation. A new generation of platforms is emerging, driven by key trends and evolving needs: 1) Proprietary NLP/U is no longer the differentiator—platforms must orchestrate best-of-breed AI models and enable the combination of multiple specialized models. 2) GenAI simplifies intent management, but a new toolset is needed to customize and optimize models beyond basic prompting and RAG. 3) Voice AI requires best-in-class speech-to-text, text-to-speech, and speech-to-speech to meet performance and experience demands. 4) Platforms need to support both transactional and informational interactions. 5) Deterministic workflows will dominate CX and sales in the short term, but autonomous agents will redefine application development. 6) Integration capabilities will evolve into orchestrated, agent-driven ecosystems with robust governance. 7) Platforms must manage context over longer conversations. 8) Orchestration must extend beyond interactions and AI to enable sophisticated AI-human collaboration. 9) Platforms need to enable faster iterations and continuous expansion of use cases The tension between disaggregating functions for independent evolution and assembling an expanding set of technologies makes it difficult to predict what platforms will look like in a few years. Not all providers will successfully transition—some, burdened by technical debt, will be forced to pivot toward specialized solutions. When evaluating platforms, the key is to define the flexibility you truly need and make tradeoffs accordingly. A purpose-built solution may be a better fit than a broad platform, allowing you to leverage the vendor’s deep domain expertise. But that doesn’t eliminate the need for rigorous validation of their technology stack and architecture. Given that 'platform' is a catch-all term in vendor messaging, it’s essential to cut through the noise and classify offerings accurately. As conversational AI evolves toward the orchestration of conversations, technologies, and human-AI collaboration, use these trends as strategic lenses to guide your decisions. Above all, prioritize openness to navigate this evolving landscape. I trimmed the article to fit this post; the full version is linked in the first comment. #conversationalai #ai #cx #salestech

AI Agents in Contact Centers is the hottest topic in the Voice AI industry 🔥 People are both excited as well as scared of their potential impact. In this interview, Malte Kosub, Co-Founder & CEO at Parloa and I do a deep dive into how to build AI Agents for Contact Centers. Super interesting! Here’s what stood out to me most 👇 1) Parloa got started in 2018 with a clear focus on voice, seeing the most relevant channel for companies was still the phone. 2) The shift from chatbots to AI voice agents is happening faster than expected, driven by customer preference for voice. 3) As AI adoption grows, Parloa is leading the shift toward smarter, more personalized voice interactions that feel less robotic and more human. 4) Scaling AI in CX requires deep industry-specific customization, not just a one-size-fits-all model. 5) Businesses now see AI as a way to improve customer experience, not just cut costs. 6) Legacy call center systems weren’t designed for AI, making integration one of the biggest barriers to adoption. 7) Training AI for customer service takes a lot of data—more than most companies expect. 8) Comparing AI to older software is the wrong approach—AI should be measured against human performance. 9) Parloa uses real-world simulation and evaluation test cases to train AI models to make sure it understands and responds correctly before going live. 10) Shifting to AI-driven customer service requires a major change in mindset, not just better tools. 11) Many believe speech-to-text is a solved problem, but it still struggles with accuracy in real conversations at scale. 12) The goal isn’t to replace human processes but to bring them into the AI era for better efficiency. 13) AI success depends on how well companies design and use it, not just the technology itself. 14) The industry is moving beyond “AI for the sake of AI” and focusing on real outcomes like CSAT and impact on revenue. 15) AI-powered customer support is just the beginning—there’s a bigger transformation happening beyond support. Malte, thanks for your time and insights 🙏 Full interview here 👉 https://lnkd.in/dHz-s3Dc

It’s going to be another big year for #AI in the contact center. Here’s my paradoxical prediction: 💥The adoption of AI agents will be both faster AND slower than most people expect. But what do I mean by that? 🤝 For end customers: The move to AI-powered interactions may take longer than people want. While #GenAI is becoming a new norm in our daily routines, there’s an assumption that contact centers will quickly follow suit—flipping a switch to become fully AI-driven with virtual agents. It’s a similar idea to how tollbooths transitioned from human operators to technology. However, it’s not that simple. These enterprise organizations need time to ensure AI meets the high standards for delivering seamless, reliable, secure, and human-like experiences. ☑️For contact center leaders: AI agent adoption is set to accelerate. The complexity of operations—spanning processes, tools, and systems—might seem like a huge hurdle, but the results after implementation will be substantial. Leveraging AI in the contact center is quickly proving its ability to enhance efficiency and boost effectiveness across teams. With this clear value on the table, leaders will face growing pressure to adopt AI agents, moving faster than they may feel ready for. This will likely be the year for the majority to step into agent copilot functionality, and test virtual agents for a portion of their volume. The result? A compelling push-and-pull dynamic: 💡 Customer-facing transformation will feel gradual. 💡 Internal adoption will be a race, with transformative outcomes. I'm excited to see how it all plays out.