AI Integration in Communication

AI agents are 5% model, 95% engineering glue. You’re engineering workflows, control layers, routing logic, and observability around something that happens to “think.” In real-world use, the hardest part often isn’t the AI - it’s building the infrastructure that lets the AI actually do something useful. The real value shows up when agents can 𝐩𝐥𝐮𝐠 𝐢𝐧𝐭𝐨 𝐲𝐨𝐮𝐫 𝐬𝐭𝐚𝐜𝐤 𝐚𝐧𝐝 𝐚𝐜𝐭. Answer questions, trigger workflows, update records, follow up  - without needing a team of engineers behind the scenes. 𝐄𝐥𝐞𝐯𝐞𝐧𝐋𝐚𝐛𝐬 𝐀𝐠𝐞𝐧𝐭𝐬 is a great example to this direction: A production-ready platform for voice + text agents that actually run end-to-end workflows. It gives you: ▪️Real-time speech with fluid interruptions (“mm-hmm”, “go on”, etc.) ▪️Tool and API integration - out of the box or with custom logic ▪️Built-in evals, guardrails, and observability ▪️Multi-channel deployment (chat, phone, web) And most importantly, business-level control Support tickets, order changes, lead qualification, post-purchase follow-up - you don’t need to build it from scratch anymore. 📍Explore here https://lnkd.in/gpvnQAtC #AIEngineering #VoiceAI #AgenticAI #GenerativeAI #ad #ElevenLabsPartner

🚨 BREAKING: An extremely important lawsuit in the intersection of PRIVACY and AI was filed against Otter over its AI meeting assistant's lack of CONSENT from meeting participants. If you use meeting assistants, read this: Otter, the AI company being sued, offers an AI-powered service that, like many in this business niche, can transcribe and record the content of private conversations between its users and meeting participants (who are often NOT users and do not know that they are being recorded). Various privacy laws in the U.S. and beyond require that, in such cases, consent from meeting participants is obtained. The lawsuit specifically mentions: - The Electronic Communications Privacy Act; - The Computer Fraud and Abuse Act; - The California Invasion of Privacy Act; - California’s Comprehensive Computer Data and Fraud Access Act; - The California common law torts of intrusion upon seclusion and conversion; - The California Unfair Competition Law; As more and more people use AI agents, AI meeting assistants, and all sorts of AI-powered tools to "improve productivity," privacy aspects are often forgotten (in yet another manifestation of AI exceptionalism). In this case, according to the lawsuit, the company has explicitly stated that it trains its AI models on recordings and transcriptions made using its meeting assistant. The main allegation is that Otter obtains consent only from its account holders but not from other meeting participants. It asks users to make sure other participants consent, shifting the privacy responsibility. As many of you know, this practice is common, and various AI companies shift the privacy responsibility to users, who often ignore (or don't know) what national and state laws actually require. So if you use meeting assistants, you should know that it's UNETHICAL and in many places also ILLEGAL to record or transcribe meeting participants without obtaining their consent. Additionally, it's important to have in mind that AI companies might use this data (which often contains personal information) to train AI, and there could be leaks and other privacy risks involved. - 👉 Link to the lawsuit below. 👉 Never miss my curations and analyses on AI's legal and ethical challenges: join my newsletter's 74,000+ subscribers. 👉 To learn more about the intersection of privacy and AI (and many other topics), join the 24th cohort of my AI Governance Training in October.

AI is no longer just about automation—it’s evolving into Agentic AI, where systems operate independently, make decisions, learn from feedback, and interact intelligently with users and external environments. But what does that mean?  This Agentic AI Layers framework breaks it down into key components:  🔹 Governance & Auditability – Ensuring Transparency & Compliance  Transparent Decision Logs – AI maintains a history of its decisions for accountability and audits.   Regulatory Compliance – AI follows legal & ethical standards for responsible deployment.   Explainability – Provides clear reasoning behind AI decisions for trust and reliability.  🔹 Operational Independence – AI that Learns & Adapts   Self-Learning – Continuously improves performance based on feedback.   Autonomous Decisions – AI acts independently within defined rules.   Automated Workflows – Streamlines repetitive tasks for efficiency.   Scalability & Real-Time Decision Making – AI optimizes resources and makes instant decisions based on data.  🔹 External Interactions & Multi-Modal Interfaces   API Integrations – AI connects with external systems to fetch & process data.   Multi-Modal Support – AI interacts via text, voice, and images for a richer user experience.   User Input Processing – Uses NLP to understand and respond to human queries.  🔹 Ethics & Safety – Responsible AI Development   Privacy Protection – Ensures secure data handling & regulatory compliance.   Bias Detection – Identifies & mitigates biases in AI-generated content.   Harm Prevention – Avoids generating misleading or harmful content.  🔹 Knowledge Base & RAG (Retrieval-Augmented Generation)   Contextualization & Retrieval – AI fetches relevant information for better context-aware responses.   Fact-Checking – Ensures AI outputs are based on verified information.   Domain-Specific Enrichment – Enhances AI capabilities in specialized fields like healthcare, finance, and law.  🔹 LLM & Generative Capabilities – Advanced AI Thinking   Reasoning & Adaptability – AI processes complex queries and adapts to user intent.   Real-Time Data Retrieval – Pulls external data to generate accurate responses.   Contextual Augmentation – Expands AI’s knowledge by integrating external sources.   Training & Fine-Tuning – AI continuously improves through training updates.  Why is Agentic AI Important?   As AI systems become more autonomous and decision-driven, ensuring transparency, compliance, and ethical AI governance is crucial for industries like finance, healthcare, cybersecurity, and enterprise automation.  Do you think AI should operate with full autonomy, or should human oversight always be required? Drop your thoughts in the comments!  

Heard of MCP? Well, meet ACP! 😅 Our talented IBM Research team is tackling AI agent-to-agent communication challenges, and we've just published our initial draft of ACP (Agent Communication Protocol). 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔𝗖𝗣? ACP standardizes agent interactions, enabling automation, collaboration, UI integration, and developer tools. Instead of enforcing strict rules upfront, ACP prioritizes practical features, ensuring adoption before formal standardization. 𝗠𝗼𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 Agent systems often face integration issues due to varied communication standards. ACP addresses this by streamlining interactions for agents that process natural language and rely on external models, fostering easier integration and collaboration. 𝗪𝗵𝘆 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Standardization ensures reliable and widely adopted features. ACP evolves based on real-world use, offering: 1/ Interoperability: Smooth interaction between agents and systems. 2/ Simplified Development: Less complexity, more innovation. 3/ Efficiency: Reduced redundancy through reusable solutions. ACP also enhances agent-specific capabilities, such as: - Natural Language Flexibility: Optimized for handling ambiguous language. - Model Dependency Management: Simplifies external model integration and resource management. I invite you to join the discussion here or on GitHub about the ideas we're implementing: https://lnkd.in/gYkbbK5Y. Link to the initial draft: https://lnkd.in/gMeWegfb

🔮 Design Patterns For AI Interfaces (https://lnkd.in/dyyMKuU9), a practical overview with emerging AI UI patterns, layout considerations and real-life examples — along with interaction patterns and limitations. Neatly put together by Sharang Sharma. One of the major shifts is the move away from traditional “chat-alike” AI interfaces. As Luke Wroblewski wrote, when agents can use multiple tools, call other agents and run in the background, users orchestrate AI work — there’s a lot less chatting back and forth. In fact, chatbot widgets are rarely an experience paradigm that people truly enjoy and can fall in love with. Mostly because the burden of articulating intent efficiently lies on the user. It can be done (and we’ve learned to do that), but it takes an incredible amount of time and articulation to give AI enough meaningful context for it to produce meaningful insights. As it turned out, AI is much better at generating prompt based on user’s context to then feed it into itself. So we see more task-oriented UIs, semantic spreadsheets and infinite canvases — with AI proactively asking questions with predefined options, or where AI suggests presets and templates to get started. Or where AI agents collect context autonomously, and emphasize the work, the plan, the tasks — the outcome, instead of the chat input. All of it are examples of great User-First, AI-Second experiences. Not experiences circling around AI features, but experiences that truly amplify value for users by sprinkling a bit of AI in places where it delivers real value to real users. And that’s what makes truly great products — with AI or without. ✤ Useful Design Patterns Catalogs: Shape of AI: Design Patterns, by Emily Campbell 👍 https://shapeof.ai/ AI UX Patterns, by Luke Bennis 👍 https://lnkd.in/dF9AZeKZ Design Patterns For Trust With AI, via Sarah Gold 👍 https://lnkd.in/etZ7mm2Y AI Guidebook Design Patterns, by Google https://lnkd.in/dTAHuZxh ✤ Useful resources: Usable Chat Interfaces to AI Models, by Luke Wroblewski https://lnkd.in/d-Ssb5G7 The Receding Role of AI Chat, by Luke Wroblewski https://lnkd.in/d8xcujMC Agent Management Interface Patterns, by Luke Wroblewski https://lnkd.in/dp2H9-HQ Designing for AI Engineers, by Eve Weinberg https://lnkd.in/dWHstucP #ux #ai #design

Your voice AI platform isn't a product. It's a markup. Most companies pay $0.13/min for managed voice agents. Same models. Same quality. 10x the cost. The difference? They bought a black box. → We built the infrastructure. Here's the 8-layer production stack ↳ that drops voice AI to $0.01/min: Reality 1: Transport + Detection → Daily.io for WebRTC rooms, global edge → Pipecat for open-source orchestration → Silero VAD for voice detection in <50ms ↳ No GPU required. Reality 2: Speech Processing → Gladia for STT — 100+ languages, custom vocabulary → Claude Sonnet for reasoning + tool calling → Claude Haiku for fast routing ↳ Hot-swappable. Zero lock-in. Reality 3: Memory + Personalization → Mem0 for semantic memory across sessions → Redis for <1ms state retrieval → Pinecone for vector RAG ↳ Your agent remembers everything. Every time. Reality 4: Voice + Security → Cartesia for streaming TTS, <300ms first byte → PII filtering, audit streams, kill switches → SOC2, GDPR, HIPAA architecture-ready ↳ Enterprise-grade. Not startup-grade. The results at scale: → <800ms voice-to-voice latency → $0.01/min vs $0.13/min on managed platforms → Swap any provider in minutes ⇒ 80% cost reduction. Same output. Managed platforms sell convenience. Open-source infrastructure sells control. The companies building on black-box platforms today ↳ will be migrating to this stack tomorrow. Which layer matters most to your stack? Drop it below. P.S. The infographic above maps the entire decision tree. Save it.

Buyers are showing up to sales calls more informed than ever. That means sellers need to show up more prepared than ever. In the past, buyers would reach out to sales when they still knew very little about a product. They wanted to learn more about the features, pricing, and how it compares to competitors – and they expected the salesperson to provide that information. Today, buyers are gathering all that information (and more) long before they talk to sales. They’re reading review sites like G2. They’re scrolling communities like Reddit. They’re watching product walkthroughs on YouTube. Most significantly, they’re doing deep research with LLMs like ChatGPT, asking questions like “Is Product A or Product B better for my business?” Now, when a buyer gets on a call with sales, they expect more than basic information. Instead, they're looking for: Detailed examples of how other companies in their industry are using the product. Custom demos that show how the product works in their specific use case. Clear plans for how the product will be implemented and adopted. Here’s the good news. Just as buyers use AI to learn more about products, salespeople can use it to learn more about prospects. If I were in sales again, I would: 1. Use an AI assistant to do advanced research about your prospects before every call. 2. Use AI to find the best examples of similar companies seeing success with your product. 3. Build bespoke demos that highlight the most relevant features. Buyers today are more informed than ever. The best sellers I know are more prepared than ever. The result? More productive conversations, deeper connections and higher trust.

If 2025 is the year of agents, 2026 will be the year of abundant software. I want to give you the no-BS summary of OpenAI Dev Day yesterday and why I hope every business professional who follows me is paying attention. Top takeaways: ⏩ Apps in ChatGPT— Apps from Canva, Coursera, Expedia, Booking.com, Figma, Spotify, Zillow, and more can now run inside ChatGPT. Devs can also get the Apps SDK and bring their own interactive tools into ChatGPT, complete with UI, context, and instant checkout. Commerce is getting prioritized. Crowd didn’t go nuts for this but brands might. If you have not yet realized the power of context engineering and connecting your AI systems with apps, I’m dropping a link to a guide I built below. ⏩ AgentKit — The closest we’ve seen to a real agent production workflow from OpenAI: agent builder, ChatKit, evals, connectors, guardrails checklist, all inside OpenAI’s dev platform. AgentKit takes on Make, Zapier, and n8n. In my opinion, enterprises need a bullpen (yes, like investment banking) of 30 Swiss Army knife employees that act as AI operators to build these out. I’d be looking for sharp operations or analyst minds and make them take an intro to python intensive, then have two energetic-for-AI engineers learn agent builder and upskill the 30. ⏩ Codex — Prepare for self-fixing software. Kind of. Upgraded with a Slack integration, SDK, and tools for code review and refactoring. Cisco cut code review time by 50% using Codex. The demos weren’t my favorite (they used it to control the lights on stage), but the much bigger hint was integrating Codex inside of apps with the SDK. Send this sentence to your engineers “Allie told me to tell you to review the Codex SDK announcement and consider embedding it into some test apps - it would allow our software to build more software and turn it into a self-evolving app.” And if you use slack “maybe we also add it into slack as a coworker so we can ping it requests like adding dark mode or voice input?” ⏩ New models — GPT-5 Pro is now in the API (big for legal, finance, and healthcare). GPT-realtime-mini is 70% cheaper (expect more voice-first interfaces). Sora-2 API is in preview for semi-controllable video + audio (big for design and product development). ⏩ Overall signal from the room — agents took the cake. Hints at a universal OpenAI login. Software will become more ephemeral. Strong signals the models will keep getting better. More interactive, adaptive, personalized AI. More interface changes to come. More user-to-user collaboration opportunities. More everything, probably. What questions do you have about the releases? 🎥 I also had 1-on-1 chats with Greg Brockman and Sam Altman - sharing more soon. Get the free guide here: https://lnkd.in/e2h_9gsr

The Paradox of Growth: The Bigger You Get, the Less You Know I came across something that stuck with me: When companies scale, they gain users — but lose understanding. Not because they stop caring, but because their customer feedback starts living everywhere — support tickets, sales calls, forums, surveys, social media, and app store reviews. That thought really made me pause. I’ve seen this firsthand. When a company is small, every piece of feedback feels personal — every bug report or review has a face behind it. But as you grow, those voices scatter across platforms and departments. Support sees the frustration, sales hears the hesitation, leadership sees the numbers — and somehow, everyone’s looking at the same customers, but no one’s hearing them anymore. That, in my opinion, is the quiet cost of growth. This is the problem Enterpret is solving — by helping teams stay in tune with their customers even as they scale. Here’s how it works: → It collects real-time customer feedback from 55+ channels — support tickets, sales calls, social media (X, Reddit, Instagram, Facebook), app store reviews, community forums, surveys, Slack, and more. → It analyzes all that feedback using AI and tells you exactly what to fix or build next. → It maps everything through a customer knowledge graph that connects feedback, complaints, and requests by channel, user, and payment data. → It even provides a chat interface where you can directly ask questions, and AI agents that flag bugs or issues automatically. That’s why teams like Notion, Perplexity, Canva, Chipotle, and The Farmer’s Dog use it — to make sure customer voices never get lost in the noise. In my view, the real lesson here isn’t about using more tools — it’s about staying close to the people you build for. Here’s how I’d approach it: ✅ Centralize every piece of feedback — even if it’s messy. ✅ Look for patterns instead of isolated complaints. ✅ Use AI systems like Enterpret to uncover the “why” behind what customers say. Because in the end, growth shouldn’t make you deaf. It should make you listen better — just faster. How does your team make sure you’re hearing what customers really mean, not just what they say? #CustomerFeedback #AIProducts #ProductStrategy #VoiceOfCustomer #Enterpret #Leadership

There’s a secret trap MANY people fall into when using AI to create their presentations. After years of studying what makes presentations succeed or fail, I'm noticing a concerning pattern as leaders rush to adopt AI for their high-stakes communications. In 1964, media theorist Marshall McLuhan said, "The medium is the message." His framework helps us understand what happens when a new technology enters our lives. When I applied his Tetrad of Media Effects to AI in presentations, the pattern became clear . Here's what AI is doing to your presentation process: AI gives you a 24/7 thinking partner. Need headline variations for your product launch? Want to test different story angles for your board presentation? AI accelerates all of that exploration. You're no longer building in isolation. Like the ancient oral traditions, you can shape ideas through dialogue before they're polished. It's collaborative, iterative, and fast. This transforms your role from slide creator to story architect. Your job isn't to fill slides, but to shape the logical and emotional journey your audience experiences. But there's a dangerous trade-off emerging. I've watched brilliant leaders deliver AI-generated presentations that looked perfect on paper, yet completely failed to move their audiences to action. Their messages were efficient... but empty. Here's the trap: When your presentation arrives instantly through AI, you skip the mental friction that creates genuine breakthrough thinking. The quiet walk. The reflective pause. The deep consideration of your audience's specific needs. Without realizing it, you become reactive rather than purposeful. Your thinking is outsourced rather than enhanced. The most devastating consequence? Your audience feels it immediately. They detect the generic thinking. They sense the lack of true empathy for their situation. And they don't take action. The very tool that makes you faster can undermine what makes you persuasive. The solution isn't avoiding AI. It's using it while preserving four essential human capabilities: 1. Empathy: Deeply understanding your audience's context 2. Message: Testing for clarity and resonance 3. Visuals: Creating memorable images that guide understanding 4. Delivery: Bringing it to life through authentic presence Because every presentation that moves people to action still starts with human empathy, not algorithmic efficiency.