Artificial Intelligence in Business

Within organisations, Large Language Models (LLMs) are gaining increasing significance. This is not just a fleeting fad but part of a transformative shift that all forward-thinking organisations must come to terms with. I believe, for an organisation to succeed in this transition, effectively leveraging ontologies is a crucial factor. LLMs possess remarkable AI capabilities, allowing them to comprehend and generate human-like text by learning intricate patterns from vast volumes of training data. These powerful models are capable of crafting eloquent letters, analysing data, generating code, orchestrating workflows, and performing a myriad of other complex tasks. Their potential seems increasingly disruptive, with Microsoft even 'betting the house' on them. However, when deploying LLMs within an enterprise context, reliability, trustworthiness, and understandability are vital concerns for those running and governing these systems. Hallucination is simply not an option. Ontologies offer structured and formal representations of knowledge, defining relationships between concepts within specific domains. These structures enable computers to comprehend and reason in a logical, consistent, and comprehensible manner. Yet, designing and maintaining ontologies requires substantial effort. Before LLMs came along, they were the ‘top dog in town’ when it came to a semantic understanding, but now they seem relatively inflexible, incomplete and slow to change. Enter the intriguing and powerful synergy created by the convergence of LLMs AND Ontologies. The ability of LLMs to generate and extend ontologies is a game-changer. Although you still need a 'human-in-the-loop,' the top LLMs demonstrate surprising effectiveness. Simultaneously, ontologies provide vital context to the prompts given to LLMs, enriching the accuracy and relevance of the LLM's responses. Ontologies can also be used to validate the consistency of those responses. 🔍 The LLMs help discover new knowledge, and the ontologies compile that knowledge down for future use🔍 This collaborative partnership between LLMs and ontologies establishes a reinforcing feedback loop of continuous improvement. As LLMs help generate better ontologies faster and more dynamically, the ontologies, in turn, elevate the performance of LLMs by offering a more comprehensive context of the data and text they analyse. I believe this positive feedback loop has the potential to catalyse an exponential leap in the capabilities of AI applications within organisations, streamlining processes, adding intelligence, and enhancing customer experiences like never before. ⭕WMG: https://lnkd.in/eQF4PE27 ⭕Demo: https://lnkd.in/ecdEdnKc ⭕Governance: https://lnkd.in/esqu4ucz ⭕Semantic Ontology: https://lnkd.in/eKhADJGd

PR is becoming the new marketing. I know, I'm biased. But we're witnessing a fundamental shift in how companies get discovered, and I'm confident most businesses aren't prepared. LLMs are rewriting the rules of business visibility. Here's what's happening: When someone asks ChatGPT, Claude or [insert AI assistant] about solutions in your industry, these models aren't crawling your keyword-stuffed landing pages. They're drawing from trade publications, press releases, industry reports, and authoritative media coverage to determine which companies to recommend. Think about it - when I ask Siri to look something up, she automatically asks if I want to use ChatGPT for the search. And most of the time, I do. This isn't a "5 years in the future" situation - it's happening NOW. This means the companies that will thrive are those building genuine thought leadership, earning media coverage, and establishing credibility through PR rather than trying to game search algorithms. Your press mentions, industry awards, expert commentary, and feature stories are becoming your new SEO. The old playbook of keyword optimization and paid ads is giving way to something more authentic. What matters now? Building real relationships with journalists, contributing meaningful insights to industry conversations, and earning recognition through substance rather than strategy. The question isn't whether this shift will happen. It's already here. Is your team ready?

Your business strategy and data strategy are no longer two strategies. They have become one. For years, companies treated them as separate tracks. Business leaders made decisions. Data teams produced reports. IT kept the systems alive. And the two worlds occasionally touched but rarely moved as a single unit. That era is over. As digital systems expanded, as operations became more connected, and as the pace of decision making accelerated, data stopped being a supporting function and became the structural backbone of how a company competes. The lines blurred. Then they merged completely. Today, a business strategy is a data strategy because every growth lever depends on it. Customer experience. Supply chain. Sales. Operations. R&D. Finance. Everything. 𝐁𝐮𝐭 𝐡𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐬𝐡𝐢𝐟𝐭 𝐦𝐨𝐬𝐭 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐰𝐚𝐤𝐢𝐧𝐠 𝐮𝐩 𝐭𝐨: Being data-driven is no longer a differentiator. It is the minimum requirement for being AI-ready. AI will not thrive on partial visibility, inconsistent definitions, disconnected systems, or gut-based decision making. It needs high-quality, contextualized, governed data that flows across the enterprise. And as AI becomes central to competitiveness, the companies that win will be the ones whose business strategy was designed from the start around the data needed to power it. This is why we are entering the era of the data-driven business strategy, where data is not an enabler but the language of the business itself. AI is simply accelerating the trend that was already unfolding. 𝐋𝐢𝐤𝐞 𝐭𝐡𝐢𝐬 𝐩𝐨𝐬𝐭 𝐚𝐧𝐝 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐫𝐞𝐚𝐝 𝐦𝐨𝐫𝐞, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐚𝐝𝐯𝐢𝐜𝐞? https://lnkd.in/euSANUJN

In the world of Generative AI, 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) is a game-changer. By combining the capabilities of LLMs with domain-specific knowledge retrieval, RAG enables smarter, more relevant AI-driven solutions. But to truly leverage its potential, we must follow some essential 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗖𝗹𝗲𝗮𝗿 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 Define your problem statement. Whether it’s building intelligent chatbots, document summarization, or customer support systems, clarity on the goal ensures efficient implementation. 2️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 - Ensure your knowledge base is 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗮𝗻𝗱 𝘂𝗽-𝘁𝗼-𝗱𝗮𝘁𝗲. - Use vector embeddings (e.g., pgvector in PostgreSQL) to represent your data for efficient similarity search. 3️⃣ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 - Use hybrid search techniques (semantic + keyword search) for better precision. - Tools like 𝗽𝗴𝗔𝗜, 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲, or 𝗣𝗶𝗻𝗲𝗰𝗼𝗻𝗲 can enhance retrieval speed and accuracy. 4️⃣ 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗲 𝗬𝗼𝘂𝗿 𝗟𝗟𝗠 (𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹) - If your use case demands it, fine-tune the LLM on your domain-specific data for improved contextual understanding. 5️⃣ 𝗘𝗻𝘀𝘂𝗿𝗲 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 - Architect your solution to scale. Use caching, indexing, and distributed architectures to handle growing data and user demands. 6️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 - Continuously monitor performance using metrics like retrieval accuracy, response time, and user satisfaction. - Incorporate feedback loops to refine your knowledge base and model performance. 7️⃣ 𝗦𝘁𝗮𝘆 𝗦𝗲𝗰𝘂𝗿𝗲 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝘁 - Handle sensitive data responsibly with encryption and access controls. - Ensure compliance with industry standards (e.g., GDPR, HIPAA). With the right practices, you can unlock its full potential to build powerful, domain-specific AI applications. What are your top tips or challenges?

Boom is a two-year-old AI-powered hospitality management platform whose latest funding round is a shot across the bow for every CXM platform with a foot in hospitality. The Bay Area-based company just raised $12.7 million to weave AI into the operational fabric of hotels. If you recall, Medallia started with Hilton as its first customer, so this is a particularly interesting story to follow. Boom isn't offering a chatbot in the lobby. On the contrary, they're promising conversational AI, hyper‑personalization, and predictive analytics that can learn, adapt, and autonomously manage complex tasks. Why does this matter for Qualtrics, Medallia, Sprinklr, and every other CXM vendor with hospitality clients? Because the data plumbing and decision‑making layers are moving deeper into the hotel. They're not going to live on a dashboard or inside a GenAI capability that a hotel manager uses to automatically generate a response to a low-NPS guest. This stuff will go by the way of the dodo bird. Imagine what this could look like: At Hilton, their Watson‑powered concierge “Connie” (now nearly 10 years old) answers questions about amenities and local restaurants. With Boom's AI capability, Connie could remember your running route from your last stay, pre-book your gym slot, and push a personalized offer through your loyalty app before you even unpack. Marriott Hotels has tested in‑room voice assistants that let guests control lighting and temperature. Layer predictive analytics on top, and the system could anticipate when you typically request room service, ask if you’d like your favorite snack delivered, and feed that behavior back into Qualtrics or Medallia for real‑time NPS tracking if you're into that sort of thing. Here’s how hospitality brands can turn this technology into magic: Connect your feedback loop. Integrate AI‑driven interactions with your CXM platform so every guest preference and sentiment automatically informs product and service tweaks. Train employees to be AI translators. Your staff should know how to interpret AI signals and add the human touch, whether it’s a concierge upselling a spa package or a manager smoothing out a glitch. Pilot, then scale. Start with a single property or service (e.g., check‑in) and use tiger teams to refine the experience before rolling it out chain‑wide. Frankly, I think Boom is ripe for a CXM provider looking for a nice tuck-in acquisition to boost their action-focused future and valuation. Because the future is not about delivering thermometers. The future is about enabling action at scale. Boom’s vision hints at a future where hotel stays feel bespoke at scale. If you were running Hilton or Marriott’s CX program, what’s one AI‑driven experience you’d implement tomorrow? #customerexperience #hospitality #ai #futureofwork #cxm #saas

Recently, Hospitality Net asked their Digital Marketing in Hospitality panel how hotels could use AI to shift bookings from OTAs to their direct channels. You might find this answer interesting: In the near term, the biggest opportunities AI provides for driving direct revenue revolve around creating richer, more personalized experiences at each stage of the guest journey. Hotel marketers can use AI to better segment potential guests based on behaviors and deliver content and offers — at scale — that match those segments’ intent. Increasingly, you can let the AI select and orchestrate campaign messages, images, and offers that align with the needs of potential guests, and drive conversion. Similarly, these tools can provide intelligent rate displays and offer attractive upsell opportunities to guests to improve the revenue you achieve during each stay. Real-time guest service during the booking process, including chat, can help improve that experience and increase conversion rate.  Of course, the guest journey doesn’t end at time of booking. Again, savvy hotel commercial teams are beginning to put AI to work to upsell and cross-sell on-property experiences during the pre-arrival and on-property stages of the guest journey to drive greater share of wallet. And, of course, intelligent, automated post-stay campaigns are beginning to produce results in driving repeat bookings from past guests.  In the longer term, we’ve not yet seen how universal access to AI assistants will shape guest behavior. These tools are likely to shift the way guests interact with information and experiences every bit as much as the internet, mobile, and social media have. We should expect to see new marketing and distribution channels that make it easy for us to reach guests directly — and new gatekeepers who seek to insert themselves into that process. Every silver lining comes wrapped in its own cloud.  Regardless, these benefits come with a cost. Hoteliers must take a serious look at their existing tech stack and team skills to ensure they’re ready to put these tools to work. Take a look at the partners you work with. Do they make it easy to connect with new sales and marketing partners? Do they have a well-articulated vision for how they’ll incorporate AI into their products? Have they begun to deliver on that vision? If so, you’re in great shape. If not, it may be time to start looking at alternatives.  And, finally, don’t ignore your people. Does your team have the skills, the resources, and the vision needed to adapt to a changing customer and technology landscape? You will want to give them the support they need to quickly adjust as guest behaviors — and those of your competitors — evolve. The hoteliers who are able to learn the fastest, and put those learnings to use, are the ones most likely to succeed at driving more direct business as AI becomes more common. And there’s nothing artificial about that. #AI #hospitalitymarketing

Got a chance to write for InfoWorld on why RAG - enabled AI agents are forming the foundation of successful enterprise genAI deployments to automate end to end knowledge workflows. “...the real magic of RAG is in the retrieval model and its upstream components. RAG deployments live and die by the quality of the source content and the retrieval model’s ability to filter the large data source down to useful data points before feeding it to an LLM…” “...a well-designed AI agents approach to the automation of complex knowledge workflows can help mitigate risks with RAG deployments by breaking down large use cases into discrete “jobs to be done…” Auquan is helping teams across private markets, investment banks, insurance firms and other financial services extract meaningful insights from large complex, noisy data quickly and cost effectively, by leveraging RAG and AI agents. If you are similarly looking to buy or build infra to transform troves of valuable unstructured data into timely, actionable intelligence for your firm, give us a shout. Article: https://lnkd.in/gfsis8QB #EnterpriseAI #RAG #AIagents #GenAI #finance

SMBs are facing a critical challenge: how to maximize efficiency, connectivity, and communication without massive resources. The answer? Strategic AI implementation. Many small business owners tell me they're intimidated by AI. But the truth is you don't need to overhaul your entire operation overnight. The most successful AI adoptions I've seen follow these six straightforward steps: 1️⃣ Identify Immediate Needs: Look for quick wins where AI can make an immediate impact. Customer response automation is often the perfect starting point because it delivers instant value while freeing your team for higher-value work. 2️⃣ Choose User-Friendly Tools: The best AI solutions integrate seamlessly with your existing technology stack. Don't force your team to learn entirely new systems. Find tools that enhance what you're already using. 3️⃣ Start Small, Scale Gradually: Begin with focused implementations in 1-2 key areas. This builds confidence, demonstrates value, and creates organizational momentum before expanding. 4️⃣ Measure and Adjust Continuously: Set clear KPIs from the start. Monitor performance religiously and be ready to refine your AI configurations to optimize results. 5️⃣ Invest in Team Education: The most overlooked success factor? Proper training. When your team understands both the "how" and "why" behind AI tools, adoption rates soar. 6️⃣ Look Beyond Automation: While efficiency gains are valuable, the real competitive advantage comes from AI-driven insights. Let the technology reveal patterns in your business processes and customer behaviors that inform better strategic decisions. The bottom line: AI adoption doesn't require disruption. The most effective approaches complement your existing workflows, enabling incremental improvements that compound over time. What's been your experience implementing AI in your business? I'd love to hear what's working (or not) for you in the comments below. #SmallBusiness #AI #BusinessStrategy #DigitalTransformation

𝗔𝗜 𝗶𝗻 𝗥𝗲𝗮𝗹 𝗘𝘀𝘁𝗮𝘁𝗲: 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 The real estate industry is undergoing a significant transformation with the integration of Artificial Intelligence (AI). The market potential of AI in real estate is substantial, with an estimated market size of $15.3 billion by 2028, growing at a CAGR of 38.3% from 2020 to 2028. Key segments driving this growth include property search and matching, predictive analytics and forecasting, virtual assistants and chatbots, property valuation and appraisal, and smart buildings and facilities management. 𝗕𝘆 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗔𝗜, 𝗿𝗲𝗮𝗹 𝗲𝘀𝘁𝗮𝘁𝗲 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 𝗰𝗮𝗻: 📍 Automate routine tasks using Natural Language Processing (NLP) and Robotic Process Automation (RPA) 📍 Analyze vast amounts of data using Machine Learning (ML) algorithms and Deep Learning (DL) techniques to gain valuable insights and identify trends 📍 Enhance customer experiences through personalized recommendations using Collaborative Filtering and Content-Based Filtering 📍 Improve property valuations and predictions using Regression Analysis and Time Series Forecasting 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀 𝗮𝗻𝗱 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 𝗮𝗿𝗲 𝗮𝗹𝘀𝗼 𝗯𝗲𝗶𝗻𝗴 𝘂𝘀𝗲𝗱 𝘁𝗼: 📍 Provide 24/7 customer support using Intent Recognition and Sentiment Analysis 📍 Help with property searches and match clients with suitable options using Knowledge Graph Embeddings and Recommendation Systems 📍 Assist with paperwork and documentation using Optical Character Recognition (OCR) and Natural Language Generation (NLG) 𝗠𝗼𝗿𝗲𝗼𝘃𝗲𝗿, 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝗿𝗲𝗮𝗹 𝗲𝘀𝘁𝗮𝘁𝗲 𝗶𝗻𝘃𝗲𝘀𝘁𝗼𝗿𝘀 𝗮𝗻𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: 📍 Identify potential risks and opportunities using Risk Analysis and Predictive Modeling 📍 Make data-driven decisions about investments and development projects using Decision Trees and Random Forests 📍 Optimise property management and maintenance operations using IoT sensors and Anomaly Detection As AI continues to evolve, its applications in real estate will only grow. By leveraging AI, real estate professionals can stay ahead of the curve by enhancing operational efficiency, and delivering exceptional customer experiences. #ArtificialIntelligence #AIinRealEstate #PropTech #RealEstateInnovation #MachineLearning #DataScience #NLP #DeepLearning #SmartBuildings #PredictiveAnalytics #VirtualAssistants #RPA #RealEstateTech #Innovation #AIApplications

₹0 fuel cost, 100% efficiency – How drones are cutting delivery expenses while going green? A few years ago, if someone told me drones would deliver groceries, medicines, and even fresh apples in minutes, I would’ve laughed. But after this podcast things changed entirely for me. I recently spoke with Ankit Kumar, Founder of Skye Air Mobility, in my latest podcast, and what they’re building is mind-blowing. They’ve turned science fiction into reality, using AI-powered drones to solve one of India's biggest problems—slow and inefficient logistics. Here’s how I Leveled Up and you can too : ➡Speed wins always. Skye Air’s drones have slashed delivery times from hours to minutes. Whether it’s transporting farm produce, electronics, or urgent medical supplies, their drones get the job done faster and more efficiently. A great example? Farmers in Himachal Pradesh now transport fresh apples in just 6 minutes—a process that used to take 6 hours. ➡AI & Automation are changing logistics Their drones aren’t just flying—they’re thinking. AI enables them to autonomously navigate obstacles, optimize routes in real-time, and operate beyond visual line-of-sight (BVLOS). This ensures deliveries are not just fast but also safe and highly efficient. ➡Owning infrastructure = Competitive edge Unlike companies that depend on third-party logistics, Skye Air built its own Unmanned Traffic Management (UTM) system. This gives them: ✅ Faster scaling with complete operational control ✅ Lower costs by reducing dependence on external networks ✅ Full compliance with aviation regulations for seamless operations ➡Government support is fueling growth With India actively promoting drone-friendly policies, subsidies for agriculture, and BVLOS approvals, startups like Skye Air are scaling at an incredible pace. The future of drone logistics in India looks stronger than ever. We also spoke about :  ✅ AI-driven logistics will dominate the future ✅ Owning your tech = better control & higher margins ✅ Drones aren’t coming—they’re already here. Skye Air isn’t just improving deliveries—they’re redefining the future of logistics in India. Watch the full episode: https://lnkd.in/dyANcEd9 Would you trust a drone to deliver your next order? Drop your thoughts below! #DroneDelivery #LogisticsInnovation #AI #StartupIndia #Hyperlocal #LevelUpPodcast