
Meltwater Unveils GenAI Lens
Meltwater has announced the launch of GenAI Lens, a tool that gives companies better visibility into how their brand, competitors and industry are discussed across all major AI Assistants and LLMs (Large Language Models), including ChatGPT, Claude, Gemini, Perplexity, Grok, and Deepseek.
Marketing, Comms, and PR professionals face a growing challenge with more channels to manage and even more to monitor. As generative AI becomes a driving force behind content creation and news cycles, teams need complete visibility into not just what is said, but how it's being shaped by LLMs.
As AI increasingly shapes public perception, it's critical for brands to understand how they appear in this LLM-generated content. Until now, brands have lacked visibility into how they're represented across GenAI, opening the door to misinformation, outdated content, and missed opportunities to strengthen brand presence.
Meltwater's GenAI Lens solution monitors and analyses responses from GenAI tools, offering transparency into what's being said and where the underlying LLMs are sourcing their information.
Users can track brand, product, or competitor mentions across more than 90% of LLMs, offering a 360-degree view of how their brand is represented alongside news and social data.
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Benefits of Meltwater's GenAI Lens: Understand how the brand is represented, with aggregated results across major LLMs, filling a critical blind spot as AI becomes a primary source of information for consumers and media.
Identify early signs of misinformation, negative sentiment, or misleading narratives, giving the team time to respond before issues escalate.
Use trend and emotion analysis from AI search outputs to shape smarter PR, brand, and content strategies based on how audiences are engaging with generative AI.
Monitor how competitors are portrayed and uncover competitive opportunities, industry trends or emerging topics, helping you stay ahead of the narrative and refine positioning.
Access visualisations that show brand sentiment, emotion, key phrases, people, products and things mentioned, alongside citations.
Customise built-in prompt templates to launch targeted monitoring in minutes.
'The way people look for and understand information has shifted fundamentally. Visits to AI chatbots grew nearly 81% in the last year alone, signalling these tools are becoming a primary source of discovery,' said Chris Hackney, CPO at Meltwater.
'Meltwater's latest solution empowers PR, marketing, and communications professionals to proactively monitor, analyse, and respond to narratives emerging from powerful AI engines like Perplexity, ChatGPT, and Gemini.'
'By surfacing real-time insights on trending topics, sentiment shifts, and new opportunities, we're giving our clients a strategic advantage, enabling them to protect their brand reputation, craft smarter communication strategies, and move at the speed of AI-driven conversations.'
Meltwater is committed to growing source models, deepening analytics and maintaining comprehensive coverage as new LLMs emerge in this dynamic field.
ALSO READ: Meltwater Launches AI Teammate Mira
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Magnolia is a composable DXP that comes with no-code connector packs and low-code micro-frameworks for third-party integrations. It also offers an open-source version alongside its commercial plans enabling enterprises to build tailored use cases.
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Liferay DXP helps marketers deliver personalised and connected digital experiences across a broad range of channels, including customer portals, websites, intranets, mobile apps, and connected devices. It offers intuitive CMS, user analytics, and site management tools that businesses need to launch, test and optimise digital experiences for faster go-to-market.
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Powered by IBM Consulting, IBM iX offers a composable DXP, providing a comprehensive solution to make enterprises' systems future-ready. Utilising its data-driven insights and intelligent workflows, marketers can design and deliver human-centred experiences across the customer lifecycle.
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HCL Digital Experience (DX), forms part of a wider HCL Customer Experience (CX) product portfolio, offering core capabilities such as content management, DAM, CDP called Signals, and low-code application development. It provides services in government, life sciences, insurance, financial services, and other verticals.
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Contentstack is a headless CMS and Composable Digital Experience Platform (DXP) solution provider that helps marketers gain a competitive edge. It recently launched into Google Cloud Marketplace, and is also available on Microsoft Azure and AWS. It seamlessly enables mid-market brands to adopt its omnichannel campaign engine to drive higher conversions and sales.
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Bloomreach Commerce Experience Cloud provides businesses an edge with its modular capabilities: Content Management System (CMS), Discovery features for search and merchandising optimisation, and Engagement tools such as Customer Data Platforms (CDP) for personalisation and analytics.
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Adobe Experience Cloud offers a comprehensive set of services specifically designed to address the day-to-day requirements for personalised customer experience at scale. Its platform helps manage different digital content and assets to improve customer satisfaction. Some of its products include Adobe Gen Studio, Experience Manager Sites, Real-time CDP, and Marketo Engage.
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Acquia offers DXP solutions, comprising of two main elements: Acquia Drupal Cloud and Acquia Marketing Cloud. It can be accessed in both platform-as-a-service (PaaS) and software-as-a-service (SaaS) with additional components such as Site Factory for multisite management, digital asset management (DAM), CDP, personalisation, and Campaign Studio.
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Hi Dubai
2 days ago
- Hi Dubai
How to Build Your AI Strategy in Dubai's Evolving Tech Ecosystem
Dubai's tech ecosystem is rapidly evolving into a global benchmark for innovation, moving with a speed and strategic vision that sets it apart. In this dynamic landscape, the conversation around artificial intelligence has shifted dramatically. AI is no longer a futuristic concept or a standalone, single-purpose tool to be dabbled with; it has become a fundamental pillar of sustainable business growth. Entrepreneurs and business leaders in the emirate are now faced with a crucial challenge: how to move beyond the simple adoption of a tool like ChatGPT and, instead, build a cohesive, long-term AI strategy that is deeply integrated into their business model. This article provides a strategic roadmap for creating an AI strategy that not only aligns with Dubai's visionary outlook but also ensures a business is resilient, competitive, and prepared to lead in the future. It's about building a framework, not just using a technology. The Foundation: Understanding Dubai's AI Ecosystem and Its Strategic Importance To build an effective AI strategy, one must first understand the environment in which it will operate. Dubai's tech ecosystem is unique because the government is a proactive partner in innovation, not just a regulator. This collaborative framework provides a significant advantage for businesses. The UAE Strategy for Artificial Intelligence 2031 is a national roadmap that aims to make the UAE a world leader in AI. It is not a passive document; it actively supports startups and established businesses through various programs and initiatives. Likewise, the Dubai Future Foundation and its ventures, such as the Area 2071 hub, offer a nurturing environment for AI companies by providing mentorship, networking, and access to funding. These government-backed initiatives create a fertile ground for AI innovation and demonstrate a commitment to embedding AI into the very fabric of the economy. An entrepreneur's strategy, therefore, should not be developed in isolation but should actively seek to align with and leverage these public-sector efforts. A key element of Dubai's AI-friendly environment is its wealth of data and robust infrastructure. The emirate's "Smart City" initiatives, from smart traffic management to digital government services, generate vast amounts of data. For a business, this data is the new oil. A successful AI strategy must prioritize a data-first approach, focusing on how to ethically and effectively access, process, and leverage both public and private datasets. For example, a logistics company in Dubai could potentially use public traffic flow data to optimize its delivery routes in real-time, while a FinTech startup could leverage anonymized customer data to build more accurate credit scoring models. The emirate is also home to state-of-the-art cloud computing infrastructure from global providers like Amazon Web Services (AWS) and Microsoft Azure, ensuring that businesses have access to the computational power required to run sophisticated AI models without significant capital expenditure. Finally, the availability of specialized talent is a critical component. Dubai has positioned itself as a magnet for skilled professionals from around the world. Tech hubs like the Dubai International Financial Centre (DIFC) and Dubai Internet City are more than just office parks; they are concentrated ecosystems where AI and data science professionals converge. These hubs offer specialized business licenses, co-working spaces, and a continuous stream of networking events and conferences. A robust AI strategy must include a plan for engaging with this talent pool, whether through direct recruitment, partnering with local universities for research, or collaborating with specialist consulting firms. Beyond the Tool: Adopting an AI-First Mindset and Business Model The most common pitfall for businesses is to view AI as a feature or a single tool to be deployed for a specific task. A truly transformative AI strategy requires a fundamental shift in perspective: an "AI-first" mindset where AI is a core driver of the business model itself. To illustrate this difference, consider the analogy of a builder. A builder who sees a hammer as a tool simply uses it to hit nails. A master builder, however, understands how the hammer fits into the larger, strategic plan of constructing a durable and beautiful house. The same applies to AI. An entrepreneur with a "tool" mindset might use ChatGPT to quickly draft a marketing email. An entrepreneur with an "AI-first" mindset, on the other hand, would build an AI system that analyzes customer behavior, identifies their specific needs, and then automatically generates a series of personalized emails and social media posts, all tailored to maximize conversion. The former is a one-off task; the latter is a scalable, self-improving system that creates a competitive advantage. An AI-first strategy defines long-term value, not short-term gains. This value can be realized in three primary areas: Operational Excellence: Using AI to automate repetitive tasks and optimize complex processes can lead to unprecedented levels of efficiency and cost savings. For example, a large-scale manufacturing plant could deploy an AI system to analyze sensor data from its machinery and predict maintenance needs before a failure occurs. A retail chain could use AI to forecast demand, preventing overstocking and stockouts. Using AI to automate repetitive tasks and optimize complex processes can lead to unprecedented levels of efficiency and cost savings. For example, a large-scale manufacturing plant could deploy an AI system to analyze sensor data from its machinery and predict maintenance needs before a failure occurs. A retail chain could use AI to forecast demand, preventing overstocking and stockouts. Customer Experience: AI can enable hyper-personalization, creating unique and memorable experiences that build customer loyalty. An e-commerce platform could use AI to recommend products based on a customer's entire Browse history and purchasing patterns, while an AI-powered chatbot could provide instant, multilingual customer support, answering queries in both English and Arabic with equal fluency. AI can enable hyper-personalization, creating unique and memorable experiences that build customer loyalty. An e-commerce platform could use AI to recommend products based on a customer's entire Browse history and purchasing patterns, while an AI-powered chatbot could provide instant, multilingual customer support, answering queries in both English and Arabic with equal fluency. Innovative Products: AI can be the core technology behind entirely new products and services. A startup might develop an AI-powered personal finance manager that provides customized investment advice, or a healthcare company could create an AI diagnostic tool that assists doctors in identifying diseases more accurately. This strategic thinking leads to the concept of the AI Flywheel, a self-reinforcing cycle that drives exponential growth. The flywheel works like this: an AI-powered product or service improves with more data. More users are attracted to this superior product, and their engagement generates even more data. This new data is fed back into the AI models, making them even smarter, which in turn attracts an even larger user base. This virtuous cycle creates a sustainable and defensible competitive advantage that is incredibly difficult for competitors to replicate. Actionable Steps for Building and Implementing Your Strategy A long-term AI strategy is not an abstract concept; it is built on a series of deliberate, actionable steps. Step 1: The AI Audit Before you can build, you must assess. The first step is to conduct a comprehensive audit of your current business processes. This is an internal investigation to identify data-rich areas and operational bottlenecks. What are your key business metrics? Where is your data stored (e.g., CRM, ERP, website analytics)? What are the manual, repetitive tasks that consume the most time? By interviewing different teams—from sales and marketing to operations and HR—you can pinpoint the "low-hanging fruit" for quick, impactful AI projects. This audit provides a clear, evidence-based starting point for your entire strategy. Step 2: Develop a Phased Investment Plan You don't have to go all-in on AI from day one. A strategic approach involves a phased investment plan that minimizes risk and maximizes learning. Phase 1: Proof-of-Concept: Start small. Choose a single, well-defined problem identified in your audit and run a pilot project using an off-the-shelf AI service. For example, use a sentiment analysis tool to automatically categorize customer reviews. The goal of this phase is not a full-scale rollout, but a low-cost, high-visibility proof-of-concept that demonstrates the value of AI and builds internal buy-in. Start small. Choose a single, well-defined problem identified in your audit and run a pilot project using an off-the-shelf AI service. For example, use a sentiment analysis tool to automatically categorize customer reviews. The goal of this phase is not a full-scale rollout, but a low-cost, high-visibility proof-of-concept that demonstrates the value of AI and builds internal buy-in. Phase 2: Scaling Up: Once you have a successful proof-of-concept, you can scale. This involves investing in more dedicated resources, such as a small internal data science team or strategic consultants, and leveraging powerful, scalable cloud-based AI platforms like AWS SageMaker or Azure AI. At this stage, you focus on automating a larger workflow or developing a more sophisticated model. Once you have a successful proof-of-concept, you can scale. This involves investing in more dedicated resources, such as a small internal data science team or strategic consultants, and leveraging powerful, scalable cloud-based AI platforms like AWS SageMaker or Azure AI. At this stage, you focus on automating a larger workflow or developing a more sophisticated model. Phase 3: Proprietary AI: This is the phase where you build a unique competitive edge. You invest in building custom AI models and proprietary technology that are deeply embedded in your product or service. This could be a unique recommendation algorithm, a custom image recognition system, or an AI-powered tool that is unique to your business. Step 3: Data Governance and Ethics AI is only as good as the data it's trained on. Therefore, a robust data governance framework is non-negotiable. This plan must address data collection, storage, security, and quality. In Dubai, compliance with regulations like the Dubai Data Law and new privacy standards is paramount. An ethical framework for AI is equally crucial. As AI systems become more autonomous, they can exhibit unintended biases. Your strategy must include a plan to identify and mitigate these biases, ensuring your AI operates fairly and transparently. This builds trust with your customers and aligns with Dubai's global and multicultural identity. A robust AI strategy is not merely a technological decision; it is a business imperative that will define the leaders of tomorrow's economy. By moving beyond a one-off tool and embracing an AI-first mindset, entrepreneurs in Dubai can transform their businesses into resilient, innovative, and market-leading enterprises. By understanding and leveraging the emirate's supportive ecosystem, taking a deliberate and phased approach, and prioritizing data governance and ethics, businesses can build an AI strategy that is not only effective but also sustainable. In Dubai's dynamic environment, a well-structured AI strategy is the ultimate key to sustained success and a vital step in helping the emirate achieve its ambitious vision for the future. Also Read: How AI & Automation Are Transforming Businesses in Dubai Discover how AI and automation are reshaping businesses in Dubai, driving innovation, efficiency, and growth across industries in the UAE. Explore key trends now! AI & Robotics in Dubai: Future Innovations & Tech Trends 2025 Learn how AI and robotics are revolutionizing Dubai's technology landscape with futuristic innovations, smart automation, and AI-powered solutions. Empowering Startups: Dubai's New AI Playbook for Entrepreneurs Discover Dubai's new 'The Entrepreneur's AI Playbook,' a comprehensive guide from the Dubai Chamber of Digital Economy on how startups can leverage AI to launch, manage, and scale their businesses. Dubai Launches World's Largest AI Training Initiative 'One Million Prompters' Dubai Centre for Artificial Intelligence (DCAI) has announced the official opening of registrations for the groundbreaking 'One Million Prompters' initiative, aimed at training one million individuals in AI prompt engineering.


The National
2 days ago
- The National
OpenAI rolls out 'PhD-level' GPT-5 to all of its users
OpenAI has released the latest version of its ChatGPT artificial intelligence platform, GPT-5, promising to deliver 'PhD-level' intelligence as it continues to fend off ever-growing competition in technology's latest battleground. GPT-5 is its 'smartest, fastest and most useful model yet, with thinking built in', California-based OpenAI said on Thursday. It is 'smarter across the board, providing more useful responses across maths, science, finance, law and more,' it added, 'like having a team of experts on call for whatever you want to know'. In particular, OpenAI said, GPT-5 is adept at providing coding, writing and health answers, while being safer and more accurate. 'GPT-5 is the smartest model we've ever done, but the main thing we pushed for is for real-world utility and mass accessibility/affordability,' OpenAI chief executive Sam Altman said in a post on X. The service will be available to all ChatGPT users, including those using it for free. A fifth of paying users – who will also have access to GPT-5 Pro and higher usage limits – are now able to access the service, Mr Altman said on X. 'We can release much, much smarter models, and we will, but this is something a billion+ people will benefit from,' he added. The release of GPT-5 comes amid OpenAI's efforts to solidify its leadership in generative AI, whose popularity it kick-started two years ago with the release of the original ChatGPT. That sparked a new technology arms race as companies tapped into the potential of AI, which is now being used in several major industries and key aspects of society. OpenAI has had to deal with several rivals, including tech majors Microsoft, Google, Facebook parent Meta Platforms and Elon Musk's xAI, upstarts such as Anthropic and Perplexity, and disruptive players DeepSeek and Qwen from China. OpenAI, however, has sought to maintain its edge by boosting its offerings for free users, businesses and developers, banking on its substantial resources and expertise in generative AI. However, in the lead-up to GPT-5's release, concerns over generative AI's problems have persisted, including misinformation and bias. Mr Altman recently admitted that the thought of generative AI being used to help develop bioweapons in the future is causing him to lose sleep, and he is scared of voice ID fraudsters using AI to dupe people. However, OpenAI's confidence in GPT-5 is high and the company said it continues to raise its safety bars. There have been no substantial reports of the platform going rogue hours after its launch. 'Our understanding of how to advance safety has evolved a lot over time … we are not certain everything we believe is correct,' OpenAI said. 'We do know AI will transform most aspects of our world, and so we should think through the benefits, changes and risks of this technology early.'


Gulf Business
2 days ago
- Gulf Business
GPT-5 is here: Here's why the entire AI industry is watching
Image credit: Getty Images OpenAI's GPT models are the AI technology that powers the popular ChatGPT chatbot, and GPT-5 will be available to all 700 million ChatGPT users, OpenAI said. Read- The big question is whether the company that kicked off the generative AI frenzy will be capable of continuing to drive significant technological advancements that attract enterprise-level users to justify the enormous sums of money it is investing to fuel these developments. The release comes at a critical time for the AI industry. The world's biggest AI developers – Alphabet, Meta, Amazon and Microsoft, which backs OpenAI – have dramatically increased capital expenditures to pay for AI data centers, nourishing investor hopes for great returns. These four companies expect to spend nearly $400bn this fiscal year in total. OpenAI is now in early discussions to allow employees to cash out at a $500bn valuation, a huge step-up from its current $300bn valuation. Top AI researchers now command $100m signing bonuses. 'So far, business spending on AI has been pretty weak, while consumer spending on AI has been fairly robust because people love to chat with ChatGPT,' said economics writer Noah Smith. 'But the consumer spending on AI just isn't going to be nearly enough to justify all the money that is being spent on AI data centers.' OpenAI is emphasising GPT-5's enterprise prowess. In addition to software development, the company said GPT-5 excels in writing, health-related queries, and finance. 'GPT-5 is really the first time that I think one of our mainline models has felt like you can ask a legitimate expert, a PhD-level expert, anything,' OpenAI CEO Sam Altman said at a press briefing. 'One of the coolest things it can do is write you good instantaneous software. This idea of software on demand is going to be one of the defining features of the GPT-5 era.' In demos on Thursday, OpenAI showed how GPT-5 could be used to create entire working pieces of software based on written text prompts, commonly known as 'vibe coding.' One key measure of success is whether the step up from GPT-4 to GPT-5 is on par with the research lab's previous improvements. Two early reviewers told Reuters that while the new model impressed them with its ability to code and solve science and math problems, they believe the leap from the GPT-4 to GPT-5 was not as large as OpenAI's prior improvements. Even if the improvements are large, GPT-5 is not advanced enough to wholesale replace humans. Altman said that GPT-5 still lacks the ability to learn on its own, a key component to enabling AI to match human abilities. On his popular AI podcast, Dwarkesh Patel compared current AI to teaching a child to play a saxophone by reading notes from the last student. 'A student takes one attempt,' he said. 'The moment they make a mistake, you send them away and write detailed instructions about what went wrong. The next student reads your notes and tries to play Charlie Parker cold. When they fail, you refine the instructions for the next student. This just wouldn't work.' More thinking Nearly three years ago, ChatGPT introduced the world to generative AI, dazzling users with its ability to write humanlike prose and poetry, quickly becoming one of the fastest growing apps ever. In March 2023, OpenAI followed up ChatGPT with the release of GPT-4, a large language model that made huge leaps forward in intelligence. While GPT-3.5, an earlier version, received a bar exam score in the bottom 10 per cent, GPT-4 passed the simulated bar exam in the top 10 per cent. GPT-4's leap was based on more compute power and data, and the company was hoping that 'scaling up' in a similar way would consistently lead to improved AI models. But OpenAI ran into issues scaling up. One problem was the data wall the company ran into, and OpenAI's former chief scientist Ilya Sutskever said last year that while processing power was growing, the amount of data was not. He was referring to the fact that large language models are trained on massive datasets that scrape the entire internet, and AI labs have no other options for large troves of human-generated textual data. Apart from the lack of data, another problem was that 'training runs' for large models are more likely to have hardware-induced failures given how complicated the system is, and researchers may not know the eventual performance of the models until the end of the run, which can take months. At the same time, OpenAI discovered another route to smarter AI, called 'test-time compute,' a way to have the AI model spend more time compute power 'thinking' about each question, allowing it to solve challenging tasks such as math or complex operations that demand advanced reasoning and decision-making. GPT-5 acts as a router, meaning if a user asks GPT-5 a particularly hard problem, it will use test-time compute to answer the question. This is the first time the general public will have access to OpenAI's test-time compute technology, something that Altman said is important to the company's mission to build AI that benefits all of humanity. Altman believes the current investment in AI is still inadequate. 'We need to build a lot more infrastructure globally to have AI locally available in all these markets,' Altman said.