IBM Integrates Voice AI with Deepgram and ElevenLabs

IBM is expanding watsonx Orchestrate with voice AI capabilities through its partnership with ElevenLabs. By combining text-to-speech and speech-to-text functionality with built-in PCI compliance, Zero Retention Mode, and data residency controls, the platform enables multilingual AI agents across more than 70 languages and a broad range of voice options for regulated environments. With these voice capabilities in place, IBM extends agentic AI beyond text-based interfaces and supports more natural interaction across customer and employee workflows. 💡 Learn how IBM is integrating voice into enterprise AI workflows: https://buff.ly/LpmKuBQ #AI #EnterpriseAI #VoiceAI #IBM #Futurum

How should enterprise leaders approach API observability for the AI era? AI agents don’t operate in isolation; they call enterprise APIs, databases, and third-party services. Yet, observability often stops at the LLM. To truly secure and monitor AI usage, leaders need to prioritize four capabilities: 1️⃣ Unified observability across AI and traditional APIs 2️⃣ AI-specific cost attribution 3️⃣ Compliance-ready audit trails 4️⃣ Autonomous agent behavior monitoring Swipe through to see why each capability matters and how to prevent infinite retry loops from draining your budget. 👉 Learn more at: https://lnkd.in/gibqn8_Y #APIwiz #APIObservability #AIAgents #EnterpriseAI #Compliance #LLM #AIGovernance

Ever wondered how AI is evolving beyond simple chatbots? 🤔 I've tried to illustrate the path to intelligent, automated systems. It’s not just about one large model anymore; it's about a coordinated entity. Here’s the breakdown: It starts with the LLM, the core intelligence, which we enhance with RAG (Retrieval-Augmented Generation) to give it real-world knowledge. Then, the AI Agent steps in, acting like a skilled hand to execute complex tasks. The real magic happens with the MCP (Multi-Channel Platform), which connects everything and manages data flow like a central nervous system. ✅ We're quickly moving towards smarter, more action-oriented AI. ✅ It can reduce your repetitive tasks dramatically so you can focus on the more important creative human orientated tasks. 🔔 Follow me https://lnkd.in/evdH4Hru Are you ready to stop guessing? Our AI Readiness Assessment identifies your current maturity level so you can build a path to actual success. Microsoft Copilot & AI Readiness Assessment: https://lnkd.in/eY4BBiYM #AIAgent #RAG #AIArchitecture #FutureOfTech #Innovation

Pattern I’m seeing in enterprise AI buying decisions: The technical evaluation is actually the easy part. CTOs can assess platforms relatively quickly. The hard part is the organizational question: who owns AI operations? Is it IT? Is it operations? Is it a new type of hybrid ‘AI team’? In our most successful deployments, the answer is: the business users who own the workflow, supported by IT for governance and infrastructure. Not a separate team building in isolation. When the people who understand the work are part of the process, the agents solve real problems; when they’re not, you get technically impressive demos that nobody uses. #Nebelus #AI #Governance

If you want AI to create real value, stop asking for “use cases.” Ask for an operating model. Here is the stack I use with executives. Layer 1: Outcome What number moves? Baseline, target, deadline, approver. Layer 2: Constraints Security, compliance, integration, “never do” rules. Layer 3: Orchestration Roles, modules, single source of truth, handoffs. Layer 4: Verification Acceptance tests, failure modes, logging, rollback. Layer 5: Adoption Workflow integration, training, governance, measurement. Most AI efforts fail at Layer 5. They generate artifacts. They do not change behavior. Full write-up on Substack: https://lnkd.in/eDAZJwn2 Which layer is weakest in your organization right now? #MedTech #HealthcareSales #CommercialEngineering #RevenueOperations #AI

Q1 2026 is officially over tomorrow. Here's the enterprise AI report card: WINS: → AI agent adoption up 340% YoY → Average enterprise now runs 7 AI agents → Customer service automation hit 60% → Code generation saves 4.2 hours/developer/week LOSSES: → 78% of AI projects still in pilot → AI governance adoption below 15% → Shadow AI usage up 200% → Average time to production: 14 months VERDICT: The technology is ready. The organizations aren't. Q2 prediction: The companies that solve the PEOPLE problem will win the AI race. Not the ones with the best models. The ones with the best change management. #AI #Q1Review #Enterprise #Strategy #Sovrana

Enterprise AI success is not about models—it’s about scaling them effectively. Many organizations struggle due to data silos, integration debt, and lack of governance. Explore how CTOs can implement scalable, risk-aware AI strategies. https://lnkd.in/d8GjeMsW #EnterpriseAI #AIstrategy #TechLeadership #DigitalTransformation #Innovation #cisin

AI progress can stall without a clear plan. 🚨 The report, "Microsoft 365 Copilot Chat Implementation framework," focuses on 5️⃣ drivers of a successful AI framework, helping you think through how to move from experimentation to sustainable results. Download the report to understand what supports long-term AI success and where to focus first. @microsoft-copilot @Microsoft-365

AI progress can stall without a clear plan. 🚨 The report, "Microsoft 365 Copilot Chat Implementation framework," focuses on 5️⃣ drivers of a successful AI framework, helping you think through how to move from experimentation to sustainable results. Download the report to understand what supports long-term AI success and where to focus first. @microsoft-copilot @Microsoft-365

AI progress can stall without a clear plan. 🚨 The report, "Microsoft 365 Copilot Chat Implementation framework," focuses on 5️⃣ drivers of a successful AI framework, helping you think through how to move from experimentation to sustainable results. Download the report to understand what supports long-term AI success and where to focus first. @microsoft-copilot @Microsoft-365