AI Integration in Communication

Most voice AI systems ignore 90% of the world’s languages. Why? Because data is scarce. Meta’s new Omnilingual Speech Recognition suite breaks that cycle. Existing models are trained on internet-rich languages and that dominates the research loop. Omnilingual can transcribe speech in over 1,600 languages, including 500 that no speech AI has ever supported. This is a glimpse into the next wave of AI: models that don’t assume the internet is the world. Highlights: – Transcription accuracy under 10% error for 78% of supported languages – In-context learning: adapt to new languages with just a few audio clips – Fully open-source: models, data, and the 7B Omnilingual w2v 2.0 foundation This isn’t about just recognizing speech. It’s about who gets included. If we can build models that work across dialects, cultures, and scarce data, the future of voice AI in enterprise, customer service, and global markets changes fast. - Announcement blog: https://go.meta.me/ff13fa - Download Omnilingual ASR: https://lnkd.in/g3w4FqY3 - Try the Language Exploration Demo: https://lnkd.in/gVzrcdbd - Try the Transcription Tool: https://lnkd.in/gRdZuZqP - Read the Paper: https://lnkd.in/giKrvniC

"Agentic" AI is invalidating the core premise of our digital infrastructure: Accountable interaction. With the introduction of OpenAI's new ChatGPT Agents mode and the proliferation of MCP servers connecting LLMs to everything, the core assumption we've built our entire digital infrastructure on is no longer valid: That an accountable human performed the critical interaction. This is beyond disruption; it's a world-altering shift, and we're not even aware it's happening. If we don't get this right, AI agents may propel us into a post-digital future where the only trusted interactions are done on physical media and person-to-person. We can get this right if we work together. The first step is to realize can does not imply ought, and some conveniences are just too expensive.

Neglecting AI advancements could prove detrimental: Is your organisation prepared to risk obsolescence? 🤖 Here’s a step-by-step guide to help you navigate this journey with confidence. Identify Key Areas for AI Integration → Start by assessing your current processes. → Look for repetitive tasks that can benefit from automation. → Pinpoint areas where data-driven decisions could enhance outcomes. Set Clear Objectives → Define what you aim to achieve with AI. → Are you looking to improve efficiency, reduce costs, or enhance customer experience? Gather Data → AI thrives on data, so ensure you have quality data to work with. → Map out where your data comes from and how it’s stored. 📊 Choose the Right AI Tools → Not all AI tools are created equal. → Research and select the ones that fit your specific needs and objectives. → Consider consulting with experts if needed. Pilot and Test → Before full-scale implementation, run a pilot. → This allows you to test AI tools in a controlled environment. → Gather feedback and make necessary adjustments. Train Your Team → AI is only as effective as the people using it. → Conduct training sessions to ensure your team is comfortable with the new tools. 🏋️♂️ Monitor and Optimise → Once AI is integrated, continuous monitoring is key. → Collect data on its performance and make iterative improvements. Scale Up → After successful testing and optimisation, scale up AI integration across other processes. 🚀 Remember, AI is a tool to enhance human capability, not replace it. With careful planning and execution, it can transform your business processes for the better. What’s your next step in your AI journey? 🌟

AI Breakthroughs in Animal Communication: A Game-Changer for Conservation and Ethics Artificial intelligence is unlocking the potential for two-way communication with animals, with profound impacts on conservation, welfare, and understanding non-human intelligence. Key Advances: 1. AI Algorithms: Detecting patterns in animal vocalizations and body language, pushing us toward automatic translation between species. 2. Data Collection: Portable devices enable continuous recording of animal sounds, with projects like the Earth Species Project building vast vocalization databases. 3. Research Highlights: Decoding sperm whale communication, analyzing honey bee signals, and using DeepSqueak to interpret rodent sounds. 4. Applications: Revolutionizing conservation strategies, improving animal welfare, and deepening our grasp of animal cognition. 5. Ethical Questions: Balancing animal privacy and responsible use of AI knowledge. 6. Challenges: Ensuring accuracy in AI interpretations and overcoming species-specific language structures. AI’s role in animal communication may reshape our relationship with nature. What are your thoughts on this emerging field? How might AI in animal communication impact your industry? #AI #AnimalCommunication #Conservation #Ethics #TechForGood

AI doesn’t speak just one language. It never should. It should speak to, and for, all of us! From the steppes of Mongolia to the villages of India and the ministries of Chile, local AI experts are proving that sovereign, locally useful AI models can flourish even with limited resources. These efforts show that the barriers to multilingual AI can be overcome with creativity, determination, and modest funding. The question now is: how can we support and scale these efforts globally? #Mongolia – Egune AI Very happy to see Bloomberg News highlight Egune AI today, a small startup that built the first Mongolian-language foundation model from scratch. This team made the country 1 of just 8 to develop its own national model. With only $3.5M in local seed funding, they now power over 70% of the nation’s AI market. Their work protects Mongolian language and culture through homegrown AI - a powerful example of what’s possible when communities build for themselves. #India – Bhashini India’s BHASHINI - (Digital India BHASHINI Division) is a government-backed, public–private mission to make AI inclusive for all Indian languages. Launched under the National Language Translation Mission, Bhashini supports over 35 languages through an open-source model which provides real-time translation tools in text -to-text, speech-to-text, and video translation services. Through the “Bhasha Daan” crowdsourcing initiative, thousands of people are contributing text, voice and video data and translations to help the AI learn. Bhashini bridges digital gaps across the country and creates datasets for underrepresented languages. It has  already hit 1 billion+ inferences.     #Chile (Latin America) – #LatamGPT Chile is leading a regional push for AI sovereignty through a Spanish-language foundation model called Latam GPT. Under the leadership of my dear friend Minister Aisen Etcheverry, the Ministry of Science, Technology, Knowledge and Innovation is building a model that reflects Latin America’s own histories, dialects, and values. With support from CENIA and a university-backed supercomputer, the project is advancing on just a few million dollars in funding. The model is designed to be open, adaptable, and shared across countries — “AI by Latin America, for Latin America.”    The call to action: Multilingual AI capacity is often described as a roadblock to universal access. But these efforts prove it doesn’t have to be. 🔹 How do we support and scale grassroots AI infrastructure? 🔹 Can we pool funding, talent, and knowledge to help more countries build their own models? 🔹 What does a global ecosystem look like when every language has a voice in shaping it? #AIforAll #LocalAI #MultilingualAI #Innovation #aipolicy Nick Martin Hugging Face Satwik Mishra Bloomberg News Nick Cain Mary Rodriguez, MBA Mathilde Barge Nagi Otgonshar  Ashwini Vaishnaw S Krishnan Abhishek Singh Tara Chklovski Room to Read Vivian Schiller Aspen Digital

Everyone's using AI to write faster. The best leaders are using it to sound more human. As a communications strategist and LinkedIn Learning Instructor, I've watched AI transform workplace communication, but not in the way most people think. The real power isn't in letting AI write for you. It's in using AI to amplify what makes you irreplaceable: your clarity, your empathy, your voice. My most-used prompt? 👉🏽 "Help me simplify this message so it's clear, confident, and human." That one sentence saves me hours while keeping my communication authentic. It's a reminder that the best AI doesn't replace you, it reveals you more clearly. Here's the skill gap no one's talking about: humanizing AI outputs. Anyone can generate text. But leaders who can take that output and infuse it with empathy, nuance, and authenticity? They're the ones building trust in an AI-powered world. Your humanity isn't just your competitive edge, it's what AI can never replicate. What's one way you're using AI without losing your authentic voice? Drop it in the comments. 👇🏽 📸 Teaching an AI + Content Creation workshop in partnership with Richland Library #AIinWork #LinkedInLearning #Communication #AI #Leadership

How to Built #EnterpriseAI? Building enterprise AI requires a structured approach that blends clear business alignment, robust data systems, the right technology, security, compliance, and ongoing human oversight. 1. Start by identifying the most valuable problems that AI can solve for the business and ensuring these initiatives fit the organization’s strategic goals. Next, establish a strong data foundation — this means gathering, processing, and securing data for both quality and compliance. 2. Carefully evaluate and select appropriate AI technologies, whether off-the-shelf models or custom solutions, and design the AI system to scale and integrate with existing processes. 3. Finally, prioritize trust, responsible deployment, and continuous improvement as the AI system is launched and scaled across the enterprise. Actionable Steps to #BuildEnterpriseAI : - Align AI With Business Strategy - Identify key business problems and objectives where AI can provide measurable impact. - Ensure each AI project is connected to core organizational KPIs and existing business goals moveworks. - Build a Data Foundation. - Collect and assess the quality, relevancy, and structure of enterprise data, ensuring all data is clean, comprehensive, and accessible. - Implement robust data pipelines and governance frameworks for security and compliance.. - Choose the Right Technology: Select suitable AI tools, platforms, and algorithms based on business needs and data complexity. - Design a scalable infrastructure — decide between cloud, on-premises, or hybrid models — ready for growth and integration. - Design and Train AI Models. - Develop models guided by clear evaluation criteria and business outcomes; iterate continuously for accuracy. - Integrate #explainableAI features to build user trust and transparent decision-making. - Integrate With Enterprise Systems. - Connect AI models to existing IT systems (CRM, ERP, etc.) and workflows for seamless user adoption. - Ensure secure, observable integrations with backend systems and build intuitive, user-focused interfaces. - Implement Governance and Security: Enforce data privacy, access controls, and regulatory compliance throughout development and deployment. - Include a “kill switch” or fail-safe processes for critical deployments to manage risk. - Pilot, Monitor, and Scale: Start with controlled pilots; monitor performance and iterate rapidly based on user and system feedback. - Set up dashboards for metrics, observability, and ongoing monitoring. - Continuous Improvement: Embed AI management and change processes into normal business operations for ongoing refinement and value delivery. Using these actionables ensures enterprise AI initiatives are not only technically sound, but also strategically valuable, ethically governed, and positioned for continual business impact.

We've automated writing interview scripts. We've automated synthesizing the results. But conducting the actual interviews? That still takes massive time. Until now. AI-moderated interviews are transforming what's possible in customer discovery. Instead of a human interviewing another human, AI conducts the conversation—asking questions, following up based on responses, and adapting in real-time. This isn't theoretical. Anthropic ran 81,000 AI-moderated interviews. Read that again: 81,000. They collected insights from 159 countries in 70 languages. For the first time, AI enabled them to gather rich, open-ended interviews at extraordinary scale. Here's what's now unlocked: 1. Scale: You're no longer bottlenecked by your own calendar. Run hundreds or thousands of interviews simultaneously. 2. Global reach: Interview customers across the world, regardless of what language they speak. AI handles translation and transcription. 3. Speed: Launch research today, have results tomorrow. No scheduling delays, no coordination overhead. Tools like ListenLabs, Outset, Maze, and Reforge now offer these capabilities. The workflow is straightforward: you work with AI to draft your interview plan, AI conducts the interviews asynchronously, then AI helps synthesize the findings. The bottleneck in product development is shifting. It's no longer how fast we can build—it's how fast we can learn. AI-moderated interviews might be the answer to closing that gap.

🚀 Vernacular LLMs – Why “voice-first” AI could be India’s big leap forward When most people think of AI, they picture English text on a screen. But in India, that leaves out the majority of our population. A vernacular LLM (Large Language Model) is essentially an AI brain trained to understand and respond in our Indian languages , not just in text, but through voice. Imagine talking to AI in Hindi, Tamil, Bengali, Kannada, Marathi, or a mix like “Hinglish,” and getting an instant response in the same language, with the right accent, tone, and words. This is what “voice-first” vernacular AI aims to do, making technology natural and intuitive for everyone, not just the English-speaking segment. This shift could transform how India uses technology: 👉 Farmers could get crop advice and weather updates in their dialect. 👉 Students could learn science in their mother tongue. 👉 Small business owners could create ads or check GST rules without typing in English. 👉 Citizens could get healthcare or banking guidance without needing to translate forms. The result is a massive wave of inclusion, where technology becomes a bridge instead of a barrier. But building such a system isn’t simple. Indian languages are full of quirks that are second nature to us but incredibly complex for AI. 1️⃣ The same word can mean two very different things in different languages, for example, “Banda” means man/person in Hindi, but in Telugu, “Banda” means rock/stone. Without context, an AI could make hilarious or harmful mistakes. 2️⃣ Then there’s tone. In Hindi, the word “Chal re” can mean “Let’s go” in a friendly way or “Get lost” in an irritated tone. The same word, same spelling, but the meaning flips entirely based on how it’s said. I had this problem recently in an enterprise usecase where we were unable to roll out an AI Agent which was in Japanese language. Same issue, it gave incorrect responses when the tone and context became important. Training an AI to handle this means teaching it not just vocabulary, but cultural nuance, tonal variation, and the ability to infer intent from surrounding words and context. That’s hard even for humans when they step outside their own language zone, for AI, it’s an enormous challenge. As you can see from the table below, we have a long way to go in most of the sectors - especially considering that we are still a country that is driven by Tier 3+ and Rural population. If we get this right, vernacular LLMs could unlock access for hundreds of millions of people, create new markets, and fuel entrepreneurship in every corner of the country. This is not just about technology, it’s about enabling India’s growth by making digital tools truly accessible to all. I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

Mastering the Art of Communicating with AI Two weeks ago, I met with senior communications officials at the Ministry of Transportation. These are the professionals responsible for ensuring that the minister’s messages are delivered clearly, strategically, and effectively to the public. They are masters of public communication. Yet during our workshop, they posed a powerful question: How do we master communicating with AI, so it becomes a true partner in crafting innovative strategies and impactful messages? I began with the foundation: to unlock AI’s full potential in public communication, clarity and purpose are essential. AI performs best when it knows exactly what you want. That means being specific about your goals, your audience, and the format. Instead of saying, “Make an announcement,” try, “Write a short, friendly Instagram post encouraging young people in Jakarta to use public transport.” Clear prompts lead to sharper, more relevant, and engaging results. AI should be seen as a creative collaborator. It can help brainstorm ideas, draft messages, create video, simplify complex language, and tailor tone for different audiences. But the final voice must remain human, rooted in empathy, cultural understanding, and public values. AI also opens the door to more inclusive and responsive outreach. It can translate messages into local languages, adapt content for different literacy levels, and analyze public sentiment in real time. With the right approach, governments can reach more people, in more meaningful ways, across every corner of society. I also encouraged them to explore Sahabat-AI, a locally developed model by PT. Indosat Tbk, built for Bahasa Indonesia and regional languages. Designed to support Indonesia’s digital sovereignty, it understands local culture and communication styles. Making it a powerful tool for public service, education, and inclusive engagement. This is more than just about technology. It’s about building trust and serving every citizen with clarity, care, and connection. #digitaltransfirmation #aiforgood