Imagem gerada por IA AI-generated summary: The transformation of marketing is already a reality: data, machine learning, and generative AI are allowing managers to delegate operational tasks to intelligent agents and focus their energy on strategic decisions. Predictive dashboards, automatic redistribution of media budgets, generation of personalized content, and personalization at scale are just a few examples of how technology increases efficiency and relevance. Case studies from companies like Zalando, IBM, Headway, and Google show concrete gains in ROI, speed, and engagement, while the BlueMetrics case with an e-commerce platform for corporate gifts demonstrates how the use of GenAI can improve the customer journey and, at the same time, enrich the content that fuels marketing campaigns. The result is more dynamic, precise marketing capable of generating real impact at scale. The new role of marketing Marketing is no longer just about attracting customers or creating creative campaigns. It has become a central pillar for generating sustainable revenue, brand trust, and customer experience. With increasingly competitive markets and more demanding consumers, creativity without data or quick reflexes is no longer enough. Today, marketing managers need to anticipate trends, automate repetitive tasks, and personalize communication on a large scale. Technologies such as machine learning, generative AI, and AI agents are no longer a differentiator, but essential tools for those who want to lead. From operational to strategic The routine of a marketing manager has changed radically. Where before it was necessary to review weekly reports, wait for analyses to adjust budgets, and manually monitor campaigns, today the reality is different. Intelligent dashboards offer a consolidated view and suggest automatic adjustments, transforming decisions that used to take days into almost instantaneous actions. These gains are possible because AI agents are already directly involved in execution, performing functions that were previously entirely human, such as: Identify campaigns with below-average ROI and automatically pause them. Redirecting funds to ads that are performing better. Suggest creative variations based on audience behavior. Indicate changes in email marketing flows based on the engagement of each recipient. With this support, there is more time for strategic decisions, such as exploring new channels, developing more consistent brand messages, and aligning marketing with growth goals. Data integration as a starting point One of the biggest historical challenges in marketing is the fragmentation of information. Data from analytics, CRM, social media, email campaigns, and purchase history are often isolated in different systems. This leads to incomplete views and decisions based on only part of the reality. Data integration allows each lead or customer's journey to be tracked on a single timeline, from first contact to conversion or eventual abandonment. From there, advanced analytics become possible: Identify patterns such as shopping cart abandonment or decreased engagement. Predicting customers with a higher probability of purchase or churn. Calibrate media investments in real time to increase ROI. Not surprisingly, a SurveyMonkey survey shows that 51% of marketing teams already use AI to optimize content and 43% apply the technology to automate repetitive tasks, demonstrating that data integration is the basis for more sophisticated analyses. Efficiency and productivity for the team. Another clear benefit of AI is increased efficiency and productivity. The technology acts as a creative and analytical co-pilot, providing support from content production to media management. This support translates into practical applications that are already part of the daily routine of many teams: Creating blog drafts, product descriptions, and posts adapted for different channels. Suggestions for more engaging email subject lines and message customization by segment. Automatic adjustment of email sending frequency based on each recipient's history. Continuous monitoring of media campaigns, pausing low-performing ads and redistributing budget in real time. According to McKinsey, companies that use AI in marketing and sales achieve 10 to 20% improvements in ROI, reinforcing that the productivity gain is not just theoretical, but has already been proven in different sectors. A more seamless customer experience From the customer's point of view, the transformation is equally significant. Communication ceases to be generic and becomes contextualized, offering messages that make sense at each stage of the buying journey. This impact is felt at various points of contact, such as: Virtual assistants that answer questions, recommend products, and help with purchasing decisions. Product descriptions automatically enriched with detailed information, comparisons, and benefits aligned with the visitor's profile. Personalized messages that recognize where the customer is in their journey, avoiding redundancies and increasing engagement. Recent reports confirm the relevance of these advances: email marketing remains one of the most efficient channels, with an average return of $36 for every dollar invested, and AI further enhances this performance by ensuring personalization and consistency. Customization at scale Historically, personalizing campaigns meant creating exclusive materials only for large clients or priority segments. Today, with structured data and generative models, personalization is possible at scale, without proportionally increasing the team's effort. The possibilities multiply across different fronts, such as: Dynamic segmentations automatically powered by variables such as browsing behavior or location. Real-time tailored advertising campaigns for different audience profiles. Product recommendations in e-commerce based on purchase history and abandoned cart alerts combined with relevant offers. According to Deloitte, companies that apply personalization at scale can significantly multiply their ROI and increase email open and click-through rates by up to 40% compared to generic campaigns. Imagem gerada por IA Real-world examples of AI applications in marketing. The adoption of AI in marketing has gone from being an isolated experiment to becoming a consolidated practice in companies across various sectors. Several case studies have already demonstrated that the technology can directly impact efficiency, cost, and return on investment metrics. Zalando: reducing time and cost in visual content production. Zalando, one of Europe's largest online fashion retailers, faced pressure to create visual campaigns that kept pace with rapidly evolving fashion trends. By adopting generative AI to create digital mockups and product images, the company reduced its average production time from six to eight weeks to just three to four days. In addition to speed, production costs fell by approximately 90%. The impact wasn't just internal: with shorter cycles, Zalando was able to react more quickly to trends on social media, which increased customer engagement. This case demonstrates how AI can balance cost reduction and market relevance. IBM: Personalization at scale with generative creativity IBM decided to test Adobe Firefly to expand its capacity for producing marketing materials. The goal was to create pieces with a large number of variations without compromising brand consistency. In a short time, the team generated about 200 images with over a thousand variations. Campaigns that used this material had 26 times more engagement than benchmarks from traditional campaigns. The result demonstrates that AI is not only efficient, but can also expand creative reach, allowing for much faster testing of different approaches. Headway: Impact on AI-powered video ads In the field of digital education, the startup Headway sought to increase the impact of its ads without raising production costs. Using AI tools like Midjourney and HeyGen, the company began producing video ads at scale and quickly. The return was significant: AI-powered ads delivered 40% higher ROI and reached 3.3 billion impressions in just six months. In addition to the performance gains, Headway managed to save production resources, which were redirected to strategic marketing initiatives. Google and Nielsen: more effective AI-based advertising A study conducted by Nielsen in partnership with Google analyzed AI-optimized digital campaigns compared to manually optimized campaigns. The results were consistent: solutions like Performance Max and Demand Gen delivered up to 17% more return on ad spend. These numbers confirm that AI has already become a competitive differentiator in digital media management, transforming optimization processes into automated and highly effective decisions. Case Study: BlueMetrics: Leading e-commerce company in the corporate gifts market. Context Our client is a well-established company in the corporate gifts segment, operating three online platforms that connect suppliers and buyers. The sector is highly competitive and demands personalized and agile service, as well as scalability to handle seasonal demand peaks, such as the end of the year. Problem The operation had limitations that directly affected the customer experience. Customer service was restricted to business hours, leading to delays in initial contact and user dissatisfaction. Category descriptions lacked semantic depth and did not provide sufficient context for effective recommendations. Furthermore, there was a high reliance on the individual knowledge of the service providers, which resulted in inconsistencies, unintentional favoritism towards certain suppliers, and difficulty in scaling service without proportionally increasing costs. Solution BlueMetrics has developed a GenAI-based solution structured around three main pillars. Data enrichment : the data extracted from the platforms were processed by language models, enriching the category descriptions with appropriate semantic context, purposes, and events. Intelligent knowledge base : the information has been organized into a vector repository for semantic search, constantly updated to maintain relevance and accuracy. Contextual virtual assistant : a conversational agent trained to understand specific requests and recommend product categories accurately, impartially, and in a personalized way, operating 24 hours a day. Results The implementation brought significant gains: Service available 24/7, eliminating the limitations of business hours. Significant reduction in initial waiting time and greater speed in recommendations. Standardization in the category suggestion process, with less reliance on individual team knowledge. Ability to scale service without increasing operational costs. A more satisfying shopping experience for the customer, with quick responses and contextualized recommendations. A strategic point was the reuse of the generated content. The enriched descriptions not only improved the e-commerce journey but also began to feed into marketing actions, such as personalized email marketing campaigns, social media posts, and ad creatives. This transformed the project into a valuable asset for the company's entire digital marketing strategy. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Conclusion: AI-powered marketing The examples presented make it clear that the adoption of AI in marketing is already yielding measurable results in diverse sectors, whether in fashion, technology, education, or e-commerce. The gains range from reduced costs and production time to significant increases in ROI and engagement. For marketing managers, the message is unequivocal. Building a reliable database is the first step to ensuring that analytics and automation deliver real value. Next, well-defined pilot programs allow for verifying the return on investment and justifying the expansion of initiatives. And, above all, it's important to see AI as a strategic partner: a tool that automates operations so that human talent can focus on what matters most, such as creativity, brand positioning, and customer relationships. The marketing of the future will not only be more digital. It will be dynamic, personalized, and driven by real-time decisions. Companies that begin structuring their AI-based strategies now will be in a privileged position to lead this new phase, in which data and intelligence do not replace humans, but enhance them. BlueMetrics has already delivered over 200 AI and data projects to more than 90 clients in the US, Brazil, and the rest of Latin America. We can develop customized solutions for your company's context, with agile implementation and concrete results in the short term. Let's talk about it? Learn about some Use Cases .

Imagem gerada por IA AI-generated summary: This article explores how data, machine learning, and GenAI are transforming Customer Success work, freeing professionals from manual tasks to focus on strategic actions such as journey personalization, revenue growth, and strengthening customer relationships. The first part addresses the main impacts of these technologies on the daily work of Customer Success professionals—efficiency, data integration, productivity, a more fluid experience, and collaborative governance. The second part presents real-world market cases, such as Verizon, CallHippo, and Agentic AI initiatives, as well as two cases from BlueMetrics in the education and e-commerce sectors, showing concrete results in operational efficiency, personalization at scale, customer satisfaction, and the generation of new business opportunities. The new role of Customer Success Customer Success has evolved from simply being the department that handles renewals into a strategic discipline that connects experience, revenue, and product. The growth of the SaaS model and the pressure for more seamless digital journeys have made CS teams central to customer loyalty and expansion. The good news is that advances in data, machine learning, and generative AI now offer concrete conditions for this role to be performed in a smarter, more productive, and scalable way. From operational to strategic Consider the daily routine of a Customer Success manager. Until recently, she spent hours gathering scattered information from spreadsheets, CRMs, and support records to prepare a QBR (Quality Reporting Breakdown). Today, she can start her day with a predictive dashboard that shows the health of her portfolio, receive automatic summaries of recent interactions, and have suggestions for next steps based on product usage data. This frees up time for what really matters: discussing strategic objectives, business value, and expansion. Data integration as a starting point Fragmentation has always been one of the biggest enemies of Customer Success. CRM, tickets, product logs, feedback, and meeting notes rarely "talk" to each other. With pipelines that unify this data into a single timeline per customer, the team gains a 360º view and can act based on context. This foundation is essential for more sophisticated analyses: from identifying risk patterns to mapping upsell opportunities or suggesting corrective actions before the problem manifests itself. Efficiency and productivity for the team. Generative models today act as co-pilots in communication-intensive tasks. Meetings become structured summaries with decisions, commitments, and risks highlighted. QBRs (Quality, Breakdown, and Report) are drafted with charts and impact analyses ready for review. Follow-up emails or meeting scripts can be generated in seconds. Nothing replaces the human eye, but the productivity leap is immediate: less time spent on manual tasks and more energy dedicated to high-value conversations with clients. A more seamless customer experience From the customer's perspective, the impact is equally visible. Contextual assistants offer immediate and accurate answers at any time, reducing friction in common queries. Onboarding becomes faster because predictive alerts warn when a critical step is delayed, and knowledge assistants help users find information without relying on support tickets. This shortens the time to the first perceived value, one of the most decisive moments for customer loyalty. Customization at scale Historically, personalization meant giving special attention only to the largest accounts. With well-managed data and GenAI, personalization becomes scalable: each customer can receive recommendations, content, and guidance tailored to their usage profile, maturity, and goals. This approach not only increases satisfaction but also creates more natural opportunities for expansion, because communication becomes connected to what truly matters to that client. Revenue growth and predictability By monitoring usage and engagement signals, models can identify customers who are more likely to adopt new modules or services. This strengthens the role of Customer Success as a growth partner, not just a support partner. Similarly, renewal forecasts become more accurate, reducing surprises at the end of the quarter and bringing more reliability to the company's financial planning. Governance and collaboration between areas Another key benefit is the integration between Customer Success and the rest of the organization. The voice of the customer, captured and summarized by AI, feeds into product, marketing, and support forums. These areas begin to make evidence-based decisions, and Customer Success gains more strength by presenting customer priorities in a structured way. This governance strengthens the relationship and generates shorter cycles between feedback and product evolution. Waves of responsible adoption For startups, the recommendation is to adopt the system in waves. The first focuses on unifying data and instrumenting events that truly reflect delivered value. The second introduces simple, transparent predictive models. The third brings generative layers to accelerate reporting, communication, and workflow automation. At each stage, it is crucial to address privacy, bias, and human alignment, ensuring that technology supports, and does not replace, relationships. More time for what matters. Ultimately, the most significant transformation is in the time mix of the Customer Success professional. Instead of getting lost in manual tasks, they can act as a strategic consultant, helping the client achieve business objectives and demonstrating value continuously. Data, ML, and GenAI do not replace human interaction; they create the conditions for it to be richer, more personalized, and more effective. In the second half of the article, we will detail real-world cases and show how these capabilities translate into concrete results in different CS contexts. Imagem gerada por IA Real-world examples of AI applications in Customer Success Verizon: Agent productivity and increased revenue Verizon implemented an AI assistant based on Google's Gemini model to support agents in real time. The tool was trained with 15,000 internal documents and can suggest accurate answers during calls, preventing agents from wasting time navigating multiple systems. The impact was direct on productivity and revenue: the accuracy rate reached 95%, and sales assisted by agents grew by approximately 40%. In addition to reducing customer effort, the solution transformed the call center into a strategic value-generating channel, with faster and more consistent service. CallHippo: customer satisfaction and operational efficiency CallHippo, a SaaS telephony platform, faced challenges in customer retention and experience. To improve its customer service operation, it adopted Enthu.AI 's conversational intelligence solution , which analyzes calls, identifies signs of dissatisfaction, and generates actionable insights for teams. The results were impressive: a 20% reduction in revenue churn, a 13% increase in new revenue generation, and a 21% growth in customer satisfaction (CSAT). The case study demonstrates how AI applied to conversation analytics can increase efficiency, improve the quality of interactions, and directly impact customer perception. Agentic AI: Proactivity and Expansion in SaaS SaaS companies have been exploring agentic AI models to monitor engagement signals in real time and trigger automated intervention flows. These systems analyze dozens of variables, from usage frequency to support tickets, and respond with personalized actions, such as targeted content, invitations to training, or offers of specialized support. In some cases, the adoption of these solutions has led to churn reductions of nearly 40% and increased adoption of new features, demonstrating that AI can not only preserve revenue but also stimulate expansion and continuous engagement. A case study from BlueMetrics in the area of Customer Success. Educational Institution: Generative AI for recruitment and guidance Context One of the largest educational institutions in Brazil, with over 400,000 students distributed across various units and campuses, faced challenges in communication, guidance, and lead management. Especially during periods of high demand, the limited human support and lack of automation led to delays, unresolved recurring questions, manual information gathering processes, and difficulty in personalizing initial contact with potential students. Solution BlueMetrics has developed a GenAI-based virtual assistant solution that: Engage in natural conversation with leads, provide guidance on courses/modalities, and gather important data during the dialogue. Automatically summarizes conversations for the fundraising team; It integrates all these interactions into the CRM (Salesforce) for daily recording and monitoring; It maintains a constantly updated knowledge base, with content scraping and automation for courses. Results 24/7 support for prospective students, ensuring that questions are answered even outside of in-person hours; Reducing the operational workload of the customer service team; Significant improvement in guidance, clarity for leads, and streamlining of the decision-making process; More qualified leads are reaching the funnel, with a better-defined context. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Final reflections: practical impacts for those who live CS When viewing these cases side-by-side, some practical impacts stand out for someone working in Customer Success: Less operational rework: many repetitive tasks (summaries, follow-ups, call logging) can be automated or partially automated, freeing up time for strategic focus. More proactive and less reactive: instead of waiting for the customer to express dissatisfaction, the systems anticipate signs of risk and allow intervention before churn or serious problems occur. Personalization at scale: it's no longer necessary to treat every large client as an exception; technology exists to adapt communication, recommendations, and interventions according to profile and usage, while maintaining consistency. Improved visibility of metrics that matter: churn, satisfaction, product usage, engagement, expansion. With well-calibrated predictive models, reports and forecasts become more reliable, providing support for decisions that impact the business. Synergy between CS and other areas: product, marketing, support, and finance benefit from the CS database and models: usage insights feed into the product, which improves the experience; churn alerts help finance in planning; marketing can create content and campaigns geared towards the detected patterns. For those who experience the day-to-day realities of Customer Success, the message is clear: with well-structured data, machine learning, and GenAI, there's more time for impactful conversations with the customer and less for manual tasks. Ultimately, technology doesn't replace human relationships, but it creates the conditions for them to be more consultative, strategic, and long-lasting. If your company is looking to build an intelligent Customer Success ecosystem capable of uniting data, machine learning, and GenAI to deliver personalized experiences, increase efficiency, and boost results, let's talk. At BlueMetrics, we accelerate this journey with practical, results-oriented solutions. Learn about some Use Cases .

Imagem gerada por IA AI-generated summary: The article shows how supermarkets and wholesalers face complex challenges—such as managing perishable inventory, tight margins, fragmented customer experience, losses, and omnichannel integration—and how data, machine learning, and GenAI are already being applied to transform these pain points into operational and commercial gains. Using international examples from chains like UVESCO, Migros, Walmart, and Sainsbury's, as well as a real-world case study from BlueMetrics in digital retail, the text highlights that AI-based solutions bring personalization, efficiency, and scalability, becoming not only a competitive differentiator but a strategic requirement for the future of the supermarket and wholesale sector. When a consumer enters a supermarket, they carry not only a shopping list, but also unspoken expectations: finding the right product, without queues, in a well-stocked store; offers that make sense; a seamless shopping experience, whether in a physical or digital channel. For wholesale and supermarket chains, meeting these expectations is becoming increasingly complex. Rapid changes in consumer habits, rising operating costs, labor shortages, regulatory and sustainability pressures—all of this imposes an unprecedented combination of challenges. The good news is that technologies and solutions already exist that can address all of these problems. How data, GenAI, and machine learning improve management and customer experience. Imagine a supermarket chain with 200 stores spread across different cities. Today, it suffers from stockouts in some locations: milk, eggs, and fresh fruit frequently run out before more deliveries arrive; in others, there are losses due to products that become non-conforming or expire. Traditional analysis via spreadsheets and monthly reports only identifies this after the damage has been done. Using machine learning and real-time data, this network can detect patterns: a heat wave is predicted for one of the cities; this indicates that customers will buy more yogurt, cold drinks, and ice cream. The system suggests increasing the order in advance to the distribution center in that region. It identifies that in stores near the center, the turnover of these items increases by 30% during a hot week. It also detects that if a supplier has a longer lead time, the safety stock needs to increase slightly to compensate. Simultaneously, GenAI steps in to automatically adjust communications: generating personalized offers for customers in that city for refreshing drinks, with coupons in the app or messages on WhatsApp. The suggested store layout can adjust the display of these products to make them more visible. The self-checkout or scan-&-go system can free up employees for customer service or internal logistics, reducing queues. At the same time, the network closely monitors which promotions work best in each store, which products are sensitive to discounts, which ones have promotions to clear stock before expiration, and which ones simply have stable sales and don't justify expensive promotions. All this with intelligent dashboards, preventive alerts, and the ability to simulate scenarios before executing actions. Imagem gerada por IA Main pain points in the supermarket/wholesale sector. Inventory management: stockouts versus surpluses Perishable products have a short shelf life. If they are not sold in time, they are losses. On the other hand, excess inventory generates costs related to storage, deterioration, and immobilization of capital. Many retail chains still operate with forecasts based on simple historical data (moving averages, rudimentary seasonality) without incorporating external variables such as weather, holidays, local events, promotional campaigns, delayed logistics, or unstable suppliers. This leads either to empty shelves (which irritate customers) or overflowing shelves and waste. Narrow margins and strong competition Gas, energy, transportation, ingredients, packaging: all cost more. More demanding buyers, in terms of price and quality, also raise expectations. The challenge of setting prices that cover costs and desired profit without losing competitiveness is enormous. Poorly calibrated promotions, untimely discounts, or overly generic offers can further erode margins. Fragmented customer experience and the expectation of personalization. Today's consumer is familiar with digital trends: buying online, in-store pickup, apps with personalized offers, knowing if a product is available via smartphone, contactless payment, among others. In physical supermarkets, many of these conveniences are still in their infancy. If this journey is full of friction, such as queues, outdated information, and irrelevant offers, the customer loses satisfaction. Costly physical operation and labor shortage. Shelf restocking, fruit weighing, batch checking, cashier duties, cleaning, expiration date control: many tasks require continuous and specialized human labor. In environments where wages are high, workers are scarce, or they lack the necessary qualifications, productivity drops. And high turnover generates additional inefficiencies. Sustainability, losses and waste There is increasing pressure from consumers, regulators, and investors to reduce food, energy, and packaging waste, and to improve the environmental footprint. Expired products, poorly planned transportation, and failures in cold chains all weigh not only on direct costs but also on brand reputation. Integration between channels (omnichannel) Today, many supermarkets have physical stores, e-commerce platforms, apps, click & collect pickup, and delivery services. Making each channel work in isolation is easier; integrating them so that in-store inventory feeds into the digital world, digital offers are consistent with in-store offers, and logistics feeds back into purchasing decisions requires robust data collection, real-time visibility, and coordination between systems. Strategic decisions based on deficient data. Some retailers still work with outdated reports, unreliable forecasts, or even overly intuitive decisions. Without a solid data foundation, it becomes difficult to plan store expansion, product assortment, dynamic pricing, and loyalty programs that truly work. How can GenAI, Machine Learning, and data mitigate these pain points? This is where emerging technologies and data techniques that go beyond pretty dashboards come in: they allow you to anticipate, simulate, adapt, and customize. More sophisticated demand forecasting Machine learning models powered by internal data (historical sales, turnover, seasonality) and external data (weather, holidays, local events, demographics). These models learn complex patterns: for example, recognizing that unusually hot weather generates higher demand for drinks or ice cream; that a local sporting event increases demand for snacks; that holidays have a greater influence than traditional reports capture. This reduces stockouts and excess inventory. Dynamic pricing and price sensitivity Use models that assess the elasticity of demand for different products, in different stores, at different times. This makes it possible to dynamically adjust prices/promotions: for example, increasing the discount on items with weak demand before they expire; reducing offers on items that already sell well without promotion; testing personalized offers for customer segments. Personalization and customer engagement Data from purchase history, preferences, digital behavior, and GenAI techniques can enable customized offers and personalized suggestions (in apps, SMS, or via voice assistant/bot). The customer begins to see the supermarket not as a mere dispenser of products, but as a service tailored to their tastes and routines. This fosters loyalty, reduces wasted inventory from unproductive offers, and improves digital conversion. Operational improvement through automation and operational intelligence. AI can support or automate repetitive tasks: automatic replenishment based on empty shelf alerts, real-time inventory visibility systems, robotics or vision systems to monitor shelves, standardize checkouts (self-checkout, scan & go). Machine learning can also optimize delivery routes, employee allocation, and deploy the right team at the right time. Seamless shopping experiences and omnichannel Data-driven applications allow for mirroring inventory in both physical and digital time, managing deliveries with predictability, and offering quick pickup. GenAI can help with chatbots, shopping assistants that suggest lists or recipes based on what you have at home. The entire journey (app, store, website) can cease to seem like separate compartments and become a coherent flow. Reducing losses, extending shelf life, and reducing waste. Models that assess the risk of expired products, perishable goods, and transportation. Predictions that indicate when there will be low turnover of certain items. Systems that simulate the impact of changes (e.g., promotions, store layout) to see if they help to improve inventory turnover. Using generative AI (GenAI) for exploratory insights and decision support. Beyond traditional ML models, GenAI can synthesize reports, generate scenarios ("What if I run a beverage promotion next week, given the predicted warm weather?"), create catalog drafts, automate some of the offer copywriting, and personalize communication. GenAI can also serve strategic planning: helping managers envision new strategies based on historical data without requiring entire teams to simulate everything manually. Real-World Case Studies of AI and Data Application in Supermarket and Wholesale Retail Let's look at some concrete initiatives that show how AI and data science have moved beyond experiments and become strategic cornerstones in food retail. UVESCO (Spain): shelves always full The UVESCO supermarket chain in northern Spain collaborated with Neurolabs to apply synthetic computer vision to the task of automatically monitoring shelves. The idea was to detect, in real time, when certain products were out of stock or overdue for restocking, without relying exclusively on manual inspections, which are costly, less frequent, and prone to error. In this project, the UVESCO chain used digital twins of the SKUs to train machine vision models even before many of these products physically appeared in stores. This simplified visual identification, even when there are variations in packaging or shelf placement. Following a pilot implementation in some stores, it resulted in continuous visibility of stock levels on the shelves, allowing for faster reactions from store staff and distributors. In just a few months, UVESCO managed to reduce visible stockouts for consumers, improve shelf occupancy rates, and detect early which SKUs were most affected by lack of display or delivery delays. Migros (Switzerland, Sweden, Türkiye): Demand management at the next level. The Migros network operates thousands of stores with a huge variety of SKUs and distribution centers. They faced the classic dilemma: maintaining high product availability, especially of perishable and fresh goods, while simultaneously reducing capital tied up in inventory. By adopting an AI platform that combines demand forecasting, product turnover tracking, consumption patterns by location, and channel integration (physical stores + online), the Migros network implemented automatic adjustments to its replenishment processes. Results: An approximate 11% reduction in inventory days, freeing up working capital and reducing storage costs. Product availability on shelves increased by 1.7%, meaning fewer stockouts perceived by customers. A 1.3% reduction in lost sales, with fewer product shortages at the moment the customer wants to buy. Walmart (United States) and Sainsbury's (United Kingdom): data in service of efficiency. Walmart has been using AI/ML to forecast demand, optimize its supply chain, manage fresh produce, and monitor temperature during transport to reduce losses. The company also uses algorithms to decide on product substitutes when a SKU is out of stock and to balance inventory across stores based on local demand. The Sainsbury's chain has formed a strategic partnership with Microsoft to use AI to generate better data insights, personalize the online shopping experience, improve the e-commerce search system, and provide employees in physical stores with real-time data for shelf restocking. Other initiatives The use of smart carts equipped with sensors, cameras, scales, and automatic identification can offer customers a smoother experience, reduce checkout lines, and improve traceability. An example of this is the Instacart/Caper AI, used by global retailers such as Wegmans, ALDI, and Coles. Finally, building online order fulfillment centers that use automation, robotics, integrated inventory control systems, and AI forecasting can minimize product substitutions, ensure more accurate delivery times, and better preserve perishable goods. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? BlueMetrics Case Study: GenAI and data for large-scale personalization in digital retail. Context BlueMetrics' client is a well-established company in the corporate gifts market, operating three online platforms that connect suppliers and buyers. In this highly competitive sector, the variety of products and the specificities of each demand make the decision-making process complex. To differentiate itself, the company needed to offer faster and more personalized service, while also seeking to scale its operations without increasing costs proportionally. As Gabriel Casara, CGO of BlueMetrics, points out: “We specialize in developing real solutions for real problems. This type of challenge aligns perfectly with both our work methodology and our solutions offering.” Problem The operation had clear bottlenecks: limited service during business hours, dependence on the individual knowledge of service representatives, and a manual process for interpreting requests that resulted in delays, errors, and dissatisfaction. Furthermore, the available data on product categories was poorly structured and lacked semantic content, hindering the adoption of intelligent recommendations. During peak demand periods, such as Christmas, staff overload became even more evident, leading to inaccurate directives and missed opportunities. Therefore, there was a need for a scalable, impartial solution available 24/7 to reduce response time and democratize access to supplier options. Solution BlueMetrics has developed a GenAI-based solution supported by three main pillars: Data enrichment: using LLM models in Amazon Bedrock to provide more context to product category descriptions, including appropriate events and purposes. This resulted in a semantically rich knowledge base. Intelligent knowledge base: enriched information organized in a vector database, optimized for semantic search and continuously updated. Contextual virtual assistant: a bot that understands the context of requests and suggests product categories accurately and impartially, using Information Retrieval (IRR) techniques. According to Diórgenes Eugênio, Head of GenAI at BlueMetrics, “The project's key differentiator was creating a robust knowledge base from poorly structured data. We were able to semantically enrich the information and offer the virtual assistant much more context, making the recommendations more useful and relevant.” Results The implementation brought operational, technical, and customer experience gains: Operational advantages: 24/7 service, reduced initial waiting time, standardized recommendations, scalability in service volume, and less manual workload. Technical advantages: semantically enriched knowledge base, flexible and scalable architecture, easy maintenance, and the possibility of incorporating new LLM models. Customer experience: instant answers, more precise and contextualized recommendations, unbiased suggestions, and greater accuracy in product selection. The result was a more efficient digital operation, prepared to handle increased demand without sacrificing service quality. As Luciano Rocha, commercial director of BlueMetrics, summarizes: “When you offer speed, accuracy, and impartiality in the first contact, the customer perceives value immediately. And this is a competitive advantage that can be transferred to any digital retail segment, including supermarkets and wholesalers.” Conclusion Increasingly, the adoption of AI and structured data in supermarkets and wholesalers is ceasing to be an experimental differentiator and becoming a highly strategic competitive requirement. The key is to integrate data and AI technologies with each other and into daily operations, the customer journey, the supply chain, marketing, and internal processes. Or, as we like to say here at BlueMetrics: AI and data solutions for the real world, to solve real problems and generate measurable results in the short term. For supermarket or wholesale chains that haven't yet fully embarked on this journey, some key lessons stand out: Start small, with well-defined pilot projects (a group of stores, a product category, or a channel). - Ensuring data quality and integration: without reliable, well-structured data integrated across systems (ERP, e-commerce, physical stores, logistics), AI models lose effectiveness. - Involve multidisciplinary teams: operations, technology, finance, marketing, logistics, data science, so that insights are translated into practical actions. - Monitor metrics beyond immediate marginal gains: customer satisfaction, delivery time, waste, sustainability footprint. - To develop a vision of continuous improvement on the platform, as well as to be attentive to its applicability in other areas or verticals with similar problems. The future is shaping up with a more agile supermarket and wholesale retail sector, capable of anticipating customer desires, adapting to supply chain disruptions, offering coherent experiences between physical and digital stores, and operating with less waste and lower costs. With over 200 AI and data projects delivered to more than 90 clients in Brazil, the USA, and other Latin American countries, BlueMetrics is an ideal partner to accelerate your digital journey. Let's talk about it? Learn about some Use Cases .

Automated revenue forecasting with Machine Learning Accuracy and impartiality with generative AI for data access F aster and more informed decisions can be made with a margin of error of less than 5%. Imagem gerada por IA AI-generated summary: To make its financial planning more agile and accurate, a US real estate fund manager adopted a solution developed by BlueMetrics that combines generative AI and machine learning. Previously dependent on manual and subjective analyses, the company began forecasting revenues with a margin of error of less than 5%, directly through a conversational interface. This eliminated operational bottlenecks, accelerated strategic decisions, and incorporated artificial intelligence as an ally in asset management. Overview In a dynamic market like the American real estate market, the ability to accurately forecast revenue is essential for making strategic decisions quickly. It was precisely this objective that led a US real estate fund manager, part of one of the country's largest business groups, to adopt artificial intelligence in its financial planning process, with surprising results. BlueMetrics had already developed several data engineering and analytics projects for this client, building and ensuring a solid foundation for governance and information quality. This history of partnership was fundamental in enabling the agile development of the new solution, allowing the predictive models to be trained with structured, reliable data aligned with the business context. Market context: High volatility and competitiveness in the US real estate market. The need for more accurate and reliable financial forecasts. Reliance on manual and subjective analyses in revenue planning. Demand for solutions that combine data, automation, and agile decision-making. Problem: manual and poorly standardized forecasts Despite having business intelligence tools and a large volume of historical data, the company still relied on the individual experience of its managers to estimate the future revenue of each asset. The process was manual, time-consuming, and vulnerable to subjectivity. With dozens of properties spread across different regions and multiple variables at play, predicting results in a practical and reliable way was a constant challenge, as well as a clear bottleneck for operational efficiency. Main challenges: Operational limitations: Financial forecasts are made manually and without standardization. Reliance on individual managers' experience for revenue estimates. The simulation process is lengthy and requires support from technical teams. Business limitations: Subjectivity in the analyses compromises the accuracy of the planning. Difficulty in quickly anticipating results in a dynamic market. Limited autonomy for managers to simulate scenarios without technical support. Technological limitations: Lack of integration between BI tools and predictive models. Lack of a simple interface for querying predictions in natural language. The solution: Generative AI and Machine Learning for automated predictions. Imagem gerada por IA BlueMetrics developed a complete solution that combines generative AI for interaction and machine learning for prediction. Through an intuitive conversational interface, managers were able to interact directly with the company's financial data using natural language, without relying on complex dashboards or specialized technical support. Now, questions like “What will fund X’s revenue be in the next 6 months?” , “ What was the average revenue for the last few quarters by region?” or “What is the projected impact of a 10% vacancy rate on properties in portfolio Y? ” can be asked directly via chat. The AI agent understands the request, interprets the context, and automatically triggers predictive models based on time series to generate clear, accurate, and actionable answers. This approach democratized access to data and advanced analytics, allowing professionals from different areas, even without technical knowledge in data science, to make faster and more informed decisions. As a result, the team gained more autonomy, financial planning became more agile and reliable, and the organization reduced its dependence on manual processes and spreadsheets. The solution's architecture was built using scalable AWS technologies, such as Amazon Bedrock and Amazon SageMaker, ensuring performance, security, and native integration with the company's existing systems and data. This enabled rapid adoption and continuous use of the tool as part of the manager's strategic daily routine. Main components: Conversational interface based on natural language for data querying. Predictive model trained with historical revenue time series. AI agent that integrates the predictive model with user interaction. Integration with the company's existing database. Technological differentiators: Combining Generative AI with Machine Learning for automated predictions. Using scalable AWS technologies (Amazon Bedrock and SageMaker) Integrated architecture with high availability and native scalability. Simplified user experience, no technical knowledge required. Immediate benefits: Revenue forecasts with a margin of error of less than 5%. Greater autonomy for managers in decision-making. Reducing reliance on manual and subjective analyses. Faster, more reliable, and more accessible financial planning. Increasing the organization's analytical maturity. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Results: With the new solution, the asset manager began obtaining forecasts with a margin of error of less than 5%, eliminating the subjectivity of the analyses and allowing for faster and more informed decisions. Revenue estimates, previously based primarily on the intuition and experience of managers, are now grounded in statistical models trained with the company's own historical data, which has brought more confidence to the planning processes. The ability to simulate scenarios directly via chat, without spreadsheets, manual cross-referencing, or the involvement of technical teams, significantly increased managers' autonomy and accelerated financial planning. Through the conversational interface, objective questions about the real estate asset portfolio began to be answered instantly and in context, democratizing access to analytical intelligence. The impact was directly on the organization's analytical maturity. AI ceased to be merely a support tool and began to occupy a strategic role in day-to-day management, guiding everything from short-term tactical decisions to scenario analyses for setting goals and allocating resources. With this solution, the company gained greater predictability, agility, and precision to operate in a highly competitive market, such as real estate assets. Operational efficiency: Automated revenue forecasting with a margin of error of less than 5%. Reducing reliance on manual analysis and complex spreadsheets. More agile financial planning, with immediate answers via a conversational interface. Increased team autonomy in strategic decision-making. Technological advancement and integration: Combining generative AI and machine learning in a single integrated solution. Use of predictive models based on time series, with on-demand execution. Native integration with the manager's existing database. Scalable architecture with AWS technologies Technologies used The solution was designed using several AWS technologies, including: AWS Services Sagemaker Bedrock S3 DynamoDB MemoryDB Languages, Libraries, and Frameworks Python Javascript Node React Conclusion: This case demonstrates how the combination of generative AI and machine learning can transform data into decisions with precision and speed. By automating revenue forecasting, the company not only increased its operational efficiency but also took an important step towards intelligent asset management in the real estate market. A key factor in the solution's success was the asset manager's well-structured database, built with the support of BlueMetrics. This data maturity enabled seamless integration between predictive models and the generative AI interface, ensuring an agile and reliable experience from the very first tests. As Gabriel Casara, CGO of BlueMetrics, points out: “Projects like this only gain scale and generate real value when there is a well-balanced data structure behind them. Our expertise in data engineering is a differentiator that guarantees not only speed in delivery, but also technical responsibility in building the foundations of any GenAI or Machine Learning application. Or, as in this case, precisely in the combination of both technologies.” More than a technological solution, the project symbolizes a strategic evolution: by placing artificial intelligence at the center of the decision-making process, the company strengthened its competitiveness in one of the world's most dynamic markets, with faster, safer, and data-driven decisions. How about creating a case study like this for your company? Let's schedule a call? Learn about some Use Cases . About BlueMetrics BlueMetrics was founded in 2016 and has already delivered over 200 successful projects in the areas of Data & Analytics, GenAI, and Machine Learning for more than 90 companies in the United States, Brazil, Argentina, Colombia, and Mexico. It has its own methodology and a multidisciplinary team focused on delivering solutions to real-world business challenges.

Imagem gerada por IA AI-generated summary: This article explores how data, machine learning, and generative AI are transforming journalism by automating story ideas, summaries, and multimedia production. It highlights cases such as IDEIA, from SJCC, which suggests headlines in real time; content licensing agreements between outlets like The Guardian and OpenAI; automation of financial reports by the Associated Press; and real-time news detection by Reuters. It also addresses ethical challenges, such as transparency in the use of AI, and presents a case study from BlueMetrics, which implemented automated transcription for a major Brazilian TV network, reducing time and costs while increasing the quality and scalability of the operation. Imagine entering a TV studio where, as soon as the recording ends, a platform automatically combines voice, image, and text to generate subtitles, summaries, and even web-ready clips in just a few minutes. Or consider a news portal that, instead of waiting for a reporter to write, uses market data to publish financial bulletins in near real-time. These aren't futuristic scenarios: they're already part of the present reality in newsrooms, driven by the strategic use of data, machine learning, and generative artificial intelligence. Journalism, historically seen as an essentially human and creative activity, is increasingly incorporating intelligent systems into its routines. From story selection to editing, from licensing to monetization, AI is redesigning processes and opening new debates about efficiency, ethics, and credibility. Editorial automation and ideation with GenAI in Brazil In Brazil, a relevant example is IDEIA (Intelligent Engine for Editorial Ideation and Assistance), developed in partnership with the Sistema Jornais do Commercio de Comunicação (SJCC), the largest media conglomerate in the North and Northeast regions of Brazil. The solution integrates Google Trends and Gemini to suggest topics in real time, automate headline creation, and generate summaries instantly. The impact is clear: in some workflows, there was a reduction of up to 70% in the time spent on editorial ideation. This demonstrates how the combination of data and GenAI not only accelerates decisions but also increases the relevance of published content. Imagem gerada por IA Monetization and content licensing with GenAI Another significant development is the relationship between major media outlets and AI providers. The Guardian, The Washington Post, and Agence France-Presse have signed licensing agreements for their content to be used in services like ChatGPT, always with proper attribution and financial compensation. In practice, this means that when information from these sources is retrieved by an AI model, the credits are preserved and the publication is monetized. The Associated Press has also partnered with Google to provide up-to-date news to Gemini, establishing a new monetization model for the sector. Mass automation: Associated Press and Reuters Automation isn't limited to summaries and licensing. Since 2014, the Associated Press has used Automated Insights' Wordsmith platform to transform financial data into comprehensive news reports. The result has been an exponential leap in earnings report production, multiplied more than tenfold. Reuters followed a similar path with its Tracer system, capable of monitoring millions of tweets daily, identifying emerging topics, and generating news in near real-time. In addition to detecting trends, the system assesses the credibility of information and provides editorial context. The agency also adopted AI tools for transcriptions, automatic translations, and shotlist generation in journalistic videos. This significantly shortened multimedia production time while maintaining the quality standards required by a global agency. The delicate balance between efficiency, ethics, and trust. While the efficiency gains are clear, so are the ethical challenges. A report by the Thomson Reuters Foundation showed that over 80% of journalists in the Global South already use AI tools in their work. However, only 13% work for organizations that have internal policies on the responsible use of these technologies. Specific cases reinforce this concern. The Guardian reported flaws in AI-generated summaries that downplayed references to sensitive topics, such as the Ku Klux Klan, revealing the risks of biased or superficial interpretations. From the public's point of view, there is also a growing expectation for transparency. A study by eMarketer indicated that 80% of American consumers believe that publications should clearly indicate when AI was used in creating content. Trust, therefore, becomes a central element for the sustainable adoption of these technologies in newsrooms. Our case study: BlueMetrics and automated transcription via GenAI Context One of Brazil's largest TV networks faced a recurring challenge: the enormous amount of daily programming needed to be repurposed into multiple formats, such as subtitles, clips for social media, summaries for digital portals, and archive files. The transcription process was manual, slow, and costly, as well as subject to human error, which compromised the final quality. Problem The traditional workflow couldn't keep up with the speed demanded by multimedia content production. Furthermore, with the expansion of news consumption on mobile devices and digital platforms, the broadcaster needed to deliver content with subtitles and descriptions almost in real time. The lack of automation hindered both the speed and scalability of the operation. Solution BlueMetrics implemented an automated transcription architecture with GenAI, adapted to the context of Brazilian Portuguese and the specific vocabulary of the television industry. The solution involved: Advanced speech recognition models, trained to understand regional accents and technical terms. A secure and scalable data pipeline that integrates real-time transcription into the video production workflow. Post-processing layer with GenAI, capable of reviewing punctuation, normalizing proper names, and adjusting formatting for different media. AI governance and continuous monitoring to ensure accuracy, compliance, and information security. Results Drastic reduction in transcription time: from hours to minutes, even in large blocks of programming. Improved editorial quality: more accurate captions aligned with the broadcaster's tone. Scalability: the ability to simultaneously transcribe multiple programs, with immediate use of the content on digital platforms. Operational savings: significant reduction in reliance on manual transcription services. Perceived innovation: the broadcaster strengthened its leadership position in the sector, showing the market that it is possible to combine journalistic tradition and cutting-edge technology. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Conclusion: AI as a partner of modern journalism The examples presented show that data, machine learning, and GenAI are strategic tools that are already shaping the future of communication. These technologies: They revolutionize story generation and editorial ideation. They are expanding the automation of data-driven reporting. They are creating new models for conscious monetization and licensing. They demand ethical responsibility and transparency. They accelerate transcription and multimedia production at scale. Modern journalism is at a turning point: it has never been more necessary to balance speed and quality with credibility and ethics. At the same time, there have never been so many tools available to innovate. Through the responsible use of AI, newsrooms can transform the daily pressure for efficiency into an opportunity to further strengthen their mission of providing quality information. If you believe that examples like this apply to your company, whether it's in this sector or not, let's schedule a conversation . Our focus is on delivering real solutions to our clients that solve real problems. Learn about some Use Cases .

Imagem gerada por IA AI-generated summary: The article shows how data, machine learning, and GenAI are revolutionizing the insurance sector by bringing more precision, agility, and transparency to the entire chain. Examples include fair policy pricing with behavioral analysis, automated claims assessment via computer vision (Tractable), more empathetic communications with GenAI (Allstate), strategic AI integration at AIG, automated appeals against health insurance denials (Counterforce Health), and the use of explainable AI for auditable decisions (UnlikelyAI). The text also presents a case study from BlueMetrics on multimodal document validation automation, applicable to the insurance sector, with gains in efficiency, scalability, security, and a better customer experience. Imagine a scenario where the decision about the value of your policy is based on historical patterns and behavioral data, instead of generic estimates. Now, also imagine the moment when, after filing a claim, you receive a personalized and empathetic coverage analysis in seconds, with a conversational tone close to human, instead of sounding like an automated response. This future is already becoming a reality in the insurance sector. The combination of data, Machine Learning (ML), and Generative Artificial Intelligence (GenAI) not only streamlines processes but redefines how insurers interact with clients, regulators, and partners. According to Gabriel Casara, CGO of BlueMetrics: “We believe that artificial intelligence, when based on a well-balanced data structure, has the potential to revolutionize absolutely all sectors of the economy, and this certainly includes the insurance sector. To cite just a few practical applications, it is possible to optimize processes, reduce costs, and offer far superior customer experiences.” Next, we explore real-world cases and practical applications that are already transforming this market. Pricing, underwriting, and risk assessment with Machine Learning One of the most sensitive aspects of insurance operations is pricing. If done generically, it can drive away good clients and increase default rates. If it's too conservative, it can compromise the company's competitiveness. With the use of ML, insurance companies are reorganizing how they price risks and underwrite policies. By analyzing large volumes of historical and behavioral data, the models are able to calculate premiums more fairly and in line with the insured's actual profile. Platforms like Zest AI exemplify this trend, applying machine learning to improve risk assessment in auto and life insurance, making analyses more accurate and consistent. Imagem gerada por IA Claims processing and fraud detection using computer vision. Another area of innovation lies in claims processing. Tractable, for example, demonstrates how data and AI can shorten previously lengthy processes: through computer vision and deep learning, simply sending photos of the damage is enough for the tool to automatically generate an assessment. The impact is direct, both in reducing waiting time and in the accuracy of the analysis. This transformation not only streamlines processes but also improves the customer experience, eliminating lengthy bureaucratic hurdles and providing more transparent and agile support. More human communication with GenAI Insurance is, first and foremost, a service based on trust. Therefore, communication with clients needs to be clear, empathetic, and accessible. Allstate implemented GenAI precisely for this reason: language models began writing simpler communications that were closer to human language, replacing technical and distant texts. Today, the company's more than 50,000 daily communications with claims clients are initially written by AI and then reviewed by humans. The feedback is positive: clients report feeling more understood and respected. Automation, in this case, does not diminish the human element of the process, but rather enhances it. Strategic transformation with AI at AIG Some companies are going beyond automating specific tasks and adopting AI as a strategic pillar. Under Peter Zaffino's leadership, AIG has been integrating generative AI, LLMs, and data technologies into its underwriting operations. Through established partnerships with solution ecosystems, the insurer seeks to accelerate data ingestion, improve decision-making, and reduce the processing time of complex operations. This move signals a vision of AI not as an auxiliary tool, but as a transformative infrastructure for the entire company. Automated appeals for health insurance denials Bureaucracy in health insurance generates dissatisfaction and loss of trust. The startup Counterforce Health is directly tackling this problem. Its platform uses AI to analyze denial documents, internal policies, and medical records, automatically generating personalized appeal letters. The reversal rate of negative claims reaches approximately 70%, above the industry average. This represents not only savings for policyholders, but also a way to restore credibility to the sector, showing that technology can balance historically asymmetrical relationships between companies and clients. Clear and scalable: AI governance and reliability None of these transformations would be sustainable without governance. A recurring challenge is the explainability of AI: how to ensure that automated decisions are transparent and auditable? UnlikelyAI, founded by one of the creators of Alexa, proposes an answer. Its architecture combines LLMs with symbolic reasoning, generating auditable decisions. In a pilot with SBS Insurance Services, it was possible to automate 40% of claims cases with an impressive 99% accuracy. The initiative shows that efficiency does not need to be dissociated from trust and that explainable AI can become a competitive differentiator. The BlueMetrics experience: a case study of automation applicable to the insurance market. Context A prominent fintech company in the financial sector was looking to improve its document validation processes, such as driver's licenses and identity cards, which were manual, slow, and prone to errors. The difficulty in dealing with different document formats and orientations increased costs, generated rework, and negatively impacted the customer experience. The solution developed by BlueMetrics offers a model that is easily adaptable to the insurance sector, a segment that also deals with various types of documentation (identification, receipts, and medical reports, for example) and requires speed and accuracy in its operations. Problem: the challenge of validating efficiently. The provision of services required quick and efficient validation of financial documents, but the following obstacles existed: Time-consuming manual processes that are prone to human error; Difficulty in extracting accurate data from documents with different layouts and orientations; The high volume of documents made operational scalability difficult. These challenges are directly transferable to the insurance sector, where documents such as proof of residence, driver's licenses, medical reports, and claims reports demand the same efficiency and reliability to expedite the underwriting or settlement of policies. Solution: automation, data extraction, and intelligent categorization. BlueMetrics has developed a multimodal Generative AI solution with the following characteristics: Automation in the processing of identification documents (driver's license, national identity card) in multiple formats and orientations; Precise data extraction (name, date of birth, document number, etc.) using multimodal GenAI models; Intelligent categorization and parallel processing system , based on a cloud-native architecture, for high scalability. In the insurance context, these same technologies can be used to process different types of documents, speeding up proposal approval, automating claims authorization, or facilitating audits with integrated governance. Results The implementation resulted in concrete benefits: Operational efficiency : reduction of costs and average onboarding time, with the elimination of bottlenecks in document processing; Quality and precision : high accuracy in data extraction and categorization, with a significant reduction in errors and rework; Scalability : the ability to meet peak demand with the flexibility to process multiple document types; Compliance and security : complete traceability, in line with financial regulations, and improved fraud detection. For insurance companies, these results mean greater agility in risk analysis, improved customer experience, and a solid foundation for detecting inconsistencies or large-scale fraud attempts. Conclusion This real-world case demonstrates how a GenAI-centric technology solution can transform critical document validation processes with accuracy, security, and scalability—factors that are equally essential for the insurance industry. By adapting this architecture to your operation, your company gains efficiency, quality, and confidence in document automation. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Conclusion: Data, ML, and GenAI as pillars of trust and efficiency. The examples analyzed make it clear that data integration, Machine Learning, and GenAI are no longer optional in the insurance sector. These technologies are becoming central elements of competitiveness, bringing direct impacts on: Accuracy in pricing and risk assessment; Speed and transparency in claims processing; Humanizing communication with customers; Automation of appeals and resource reviews; Governance and trust through explainable AI. The insurance sector, historically associated with prudence and careful analysis, now finds in AI an ally to balance operational efficiency and customer confidence. If your company is looking to transform insurance operations with data-driven and AI-powered solutions, or if you're in another segment but believe that case studies like these can be adapted to your market context, now is the ideal time to take the next step. Shall we talk about it? Learn about some Use Cases .

Intelligent monitoring Pix transaction Fraud detection without relying on labeled data Real-time inference. with response in milliseconds Imagem gerada por IA AI-generated summary: In response to the rise in fraud in the Pix instant payment system, a technology company specializing in banking software, with support from BlueMetrics, implemented a solution based on unsupervised machine learning to detect anomalies in real time. Without relying on labeled data, the system uses clustering techniques to understand the standard behavior of each account and identify suspicious transactions with precision and speed, processing each operation in milliseconds without impacting Pix's response time. Overview The advancement of the instant payment system in Brazil, Pix, has brought unprecedented speed to users and businesses, quickly establishing itself as one of the main means of transfer in the country. However, this transformation has also opened space for the emergence of new forms of fraud, increasingly sophisticated and difficult to detect using traditional methods. Faced with this challenging scenario, a banking software provider decided to innovate and offer its clients, banks and fintechs, a new layer of security based on artificial intelligence. The goal was clear: to ensure that fraud could be identified accurately and in advance, without compromising transaction response times, a critical factor in the Pix ecosystem. The great challenge lay precisely in balancing performance and intelligence: how to detect fraud in real time, even without having a labeled history of previous cases (a common scenario in financial institutions)? The answer required an innovative approach, capable of learning from account behavior and reacting quickly to unusual patterns. Market context: Accelerated growth of Pix and digital banking. Increase in real-time fraud attempts High demands for performance and security in transactions. Problem: How can you achieve millisecond precision without labeled data? Pix imposes a maximum transaction completion time of 40 seconds. This means that any anti-fraud analysis needs to be extremely fast, efficient, and, above all, seamlessly integrated into the operation. To make the challenge even greater, the company lacked a dataset labeled with examples of fraud, a common scenario in the banking sector, where fraud is often not documented with the detail necessary for supervised model training. Furthermore, each bank account exhibits unique behavioral patterns, which vary according to the type of client (individual or legal entity), transaction profile, days and times of operation, among other factors. In this context, the use of fixed rules simply would not be able to capture all the nuances and exceptions, and could even generate false positives or miss suspicious transactions. It was necessary to adopt an intelligent approach, capable of learning from data and continuously adapting to different usage profiles. “This is exactly the type of challenge that motivates us here at BlueMetrics: it’s strategic for the client and has the potential to generate concrete and measurable results even in the short term,” says Gabriel Casara, CGO of BlueMetrics. “With a well-designed solution, it’s possible to combine intelligence and agility without sacrificing reliability.” Main challenges: Operational limitations: Impossibility of applying fixed rules to varying customer profiles. Difficulty in responding to the behavioral complexity of accounts. Lack of an intelligent system capable of operating in real time. Business limitations: Risk of financial losses due to lack of prompt prevention. Inability to offer protection as a competitive advantage. Lack of clear metrics to detect anomalies by customer or cluster. Technological limitations: Lack of adaptive models for new transaction profiles. Lack of fast inference without compromising Pix's processing time. Absence of an unsupervised solution trained based on real-world behavior. The solution: anomaly detection with unsupervised machine learning Imagem gerada por IA With the support of BlueMetrics, the company implemented an unsupervised machine learning model, specifically aimed at identifying behavioral anomalies in high-volume transaction environments. The lack of labeled data required a clustering- based approach, in which the system autonomously learns the typical movement patterns of each account, considering variables such as frequency, value, and time of transactions, and then compares each new operation with this history, measuring its statistical "distance" from the expected behavior. This behavior-driven architecture was essential for capturing subtle, yet potentially fraud-indicative, deviations without relying on fixed rules or pre-set lists of exceptions. The solution was built using native AWS technologies, ensuring scalability, security, and high availability, and incorporated real-time inference mechanisms that allow transactions to be classified in milliseconds. Each transaction is automatically analyzed and receives a percentage risk score, enabling immediate decisions. All this without compromising the response time required by the Central Bank for Pix settlement. This speed, combined with the statistical precision of the model, allowed the company to offer its bank and fintech clients a significant competitive advantage: an effective, discreet security layer that is fully integrated into the user journey. Main components: Unsupervised machine learning model Clustering techniques for behavioral analysis by account Real-time inference with Amazon SageMaker Technological differentiators: Solution based on a 100% cloud architecture (AWS) Anomaly detection without the need for labeled data. Response time less than 1 second Immediate benefits: Identifying fraud before transaction completion. Capacity for continuous adaptation to new patterns of behavior. Reducing financial losses and strengthening confidence in the system. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Results: The system began identifying suspicious transactions in less than 1 second, allowing for real-time alerts—even before the transaction was completed—and giving partner institutions the chance to act preventively. This ultra-fast response was crucial for protecting users and maintaining the integrity of the system, especially in a context of exponential growth of Pix. In simulations using historical data, it was estimated that the feature would have prevented up to R$ 1.5 million in losses, proving its potential for a direct impact on clients' financial results. But the benefits go beyond mitigating losses. The solution added strategic value to the portfolio of the banking software development company, which now offers not only a management tool, but also an intelligent and proactive security infrastructure. The new feature has enhanced the perceived value of the platform, increasing its competitiveness in the market and reinforcing the brand's positioning as a benchmark in innovation and anti-fraud technology within the Pix system. The combination of technical performance and concrete results has solidified the functionality as a true competitive differentiator. Gabriel Casara reinforces: “It’s very rewarding when we can deliver solutions that generate real and immediate value for the client, solving concrete problems with a direct impact on results. That’s exactly the kind of challenge that drives us.” Impacts on operations: Automated identification of suspicious transactions in milliseconds. Reduce financial losses with real-time fraud alerts. Strengthening the value proposition of banking software with a new layer of security. Technological advancement and integration: Implementation of an unsupervised model adapted to different customer profiles. Transaction processing with real-time inference Seamless integration with banking infrastructure without compromising Pix's timeline. Technologies used The solution was designed using several AWS technologies, including: AWS Services Sagemaker S3 Languages, Libraries and Frameworks Python Conclusion: In this case, artificial intelligence was not just an ally: it was the true engine of innovation. By combining unsupervised machine learning with a robust cloud architecture, the company was able to develop a solution that meets the most critical requirements of the financial sector: accuracy, speed, and scalability. With the ability to identify suspicious transactions in milliseconds and a potential prevention rate that could have avoided up to R$1.5 million in fraud, the functionality goes far beyond an extra layer of security. It significantly improves the user experience, protects millions in assets, and strengthens the developer company's core product, which now stands out in the market for offering an intelligent, real-time anti-fraud solution. More than just solving a technical problem, the application of AI here transformed an operational bottleneck into a strategic asset, and that's what makes this type of innovation so valuable: its ability to transform complexity into a concrete competitive advantage. How about creating a case study like this for your company? Let's schedule a call? Learn about some Use Cases . About BlueMetrics BlueMetrics was founded in 2016 and has already delivered over 200 successful projects in the areas of Data & Analytics, GenAI, and Machine Learning for more than 90 companies in the United States, Brazil, Argentina, Colombia, and Mexico. It employs a proprietary project management methodology, blue4AI, and a multidisciplinary team focused on delivering solutions to real-world business challenges.

Imagem gerada por IA AI-generated summary: This article explores how data, machine learning, and GenAI are transforming automotive retail by integrating operational efficiency, personalization, and new customer experiences. It highlights case studies such as automated inspections by Amazon/UVeye, real-time production adjustments at GM, virtual assistants and predictive modeling on platforms like Zoomcar and Seez, the use of GenAI in vehicle design (Ferrari), and strategic applications in marketing, predictive maintenance, and smart manufacturing. It also presents a case study from BlueMetrics showing how the same GenAI architecture used in an e-commerce case can be adapted to the automotive sector, offering 24/7 service, contextual recommendations, and scalability, resulting in faster sales, a better customer experience, and a higher conversion rate. In recent years, global retail has been transformed by the strategic use of data and artificial intelligence. In segments such as e-commerce, fashion, and consumer goods, Machine Learning (ML) and Generative AI (GenAI) solutions are already part of everyday life, ensuring personalized recommendations, dynamic pricing, marketing automation, and intelligent customer service. This movement is also strongly impacting the automotive retail sector, a field historically marked by long, in-person, and complex journeys. Digitalization and advances in AI technologies are shortening paths, simplifying interactions, and redefining the experience of buying and using vehicles. Imagine a customer arriving at the dealership: they've already been exposed to a personalized campaign, pre-selected vehicles in a virtual showroom, spoken with an intelligent assistant who understood their color and budget preferences, and found their test drive ready at the exact time. Meanwhile, algorithms anticipated demand, adjusted prices in real time, and simulated preventive maintenance scenarios. This scenario is not fiction: it is already supported by concrete applications that are shaping the future of the sector. Let's look at some examples. Operational efficiency: automated fleet inspections and predictable production. Amazon, although outside the direct automotive retail market, offers an inspiring case: UVeye's AVI technology performs automated van inspections using high-resolution cameras and ML, reducing the check time from five minutes to just one. In manufacturing, General Motors applies AI in its Factory Zero plant for real-time adjustments to the production line, predictive maintenance, and personalization based on consumer preferences. This operational intelligence, when migrated to automotive retail, can reduce costs and increase process reliability. GenAI and intelligent assistants in customer service and sales. On the front end, the impact is equally significant. Zoomcar, in India, integrated GenAI and ML (Google Vertex AI and Gemini) to optimize the booking journey, simplify vehicle onboarding, and enhance safety. Seez, a startup from the United Arab Emirates, offers a complete package for dealerships: Seezar: GPT-powered chatbot for complex queries; SeezPad: omnichannel platform; SeezBoost: dynamic and targeted marketing; SeezNitro: predictive modeling of prices and inventory. These tools illustrate how GenAI can scale customer service and sales while maintaining consistency and personalization. GenAI in automotive design and customer experience. Major brands are also exploring generative AI in more creative areas. Ferrari uses GenAI to accelerate design and improve customer service, without sacrificing the brand's exclusivity. In the academic field, research with GANs (generative adversarial networks) shows advances of up to 43.5% in predicting attractive designs, making the prototyping process faster and more efficient. Data Mastery: Predictive Intelligence, Marketing, and Smart Manufacturing In addition to transforming operations and customer service, artificial intelligence has taken on a strategic role in the automotive industry, supporting everything from long-term planning to the creation of more personalized customer experiences. Knauf Automotive is a good example of this. The company uses computer vision in production lines to automatically detect defects and predict failures before they happen. This allows for significant efficiency gains, as problems can be corrected preventively, avoiding waste and reducing costs associated with rework or recalls. At the sectoral analysis level, S&P Global Insights highlights that AI already permeates the entire automotive supply chain, from powertrain development to predictive maintenance, and also includes the personalization of the consumer experience. This panorama shows how data and algorithms are not just support tools, but central pieces in the innovation process. IBM has also invested heavily in solutions applied to the automotive retail sector. These applications include the use of GenAI in advanced driver assistance systems (ADAS), the creation of digital twins for project optimization, the development of personalized marketing strategies, as well as predictive maintenance and customer support tools. Finally, a survey conducted by DigitalDefynd highlights the convergence between data and AI in some of the world's leading automakers. Tesla stands out for its use of advanced algorithms for autonomous driving; Stellantis relies on multilingual voice interfaces that facilitate interaction between driver and vehicle; and Nissan applies AI to accelerate research and development processes. These examples reinforce the idea that competitiveness in the automotive sector increasingly depends on the ability to structure, analyze, and transform data into actionable intelligence. Imagem gerada por IA Our experience: leading e-commerce + GenAI, a case applicable to automotive retail. Context BlueMetrics supported a well-established e-commerce company in the corporate gifts segment, which operates through three online platforms focused on connecting suppliers and buyers. In a sector marked by high complexity, with thousands of products and many specific demands, personalized service and operational scalability became competitive imperatives. This approach is fully adaptable to the automotive retail sector, where the vast variety of models, configurations, and customers with unique needs demands equally flexible and contextual solutions. Problem: personalization and limited service. The client faced significant limitations: Service was only available during business hours, which left gaps in customer support. High reliance on the individual knowledge of the service providers, which caused delays, errors, and inconsistency in recommendations. Product category data had low semantic richness and lacked contextual structure (for example, which models fit each use case), making automated recommendation difficult. Proposed solution BlueMetrics has deployed a GenAI-based solution, consisting of: Automatic enrichment of product category data using Large Language Models (LLMs); Building a contextual knowledge base , allowing product information to be accessible with relevance and accuracy; A contextual virtual assistant , capable of operating 24/7, interpreting complex requests, and guiding the customer with assertive recommendations. In an automotive retail context, the same architecture would allow, for example, potential buyers to receive recommendations based on usage (family, city, highway), budget, desired features, and even financing options, in an immediate and personalized way. Results The implementation generated significant impacts: Continuous automated service, going beyond the restrictions of business hours; Reducing reliance on the tacit knowledge of service agents, leading to more reliable and consistent recommendations; Increased semantic accuracy in category data, with contextualization appropriate to customer usage scenarios; Operational efficiency and scalability, with more customers served simultaneously and with higher quality. In the automotive retail sector, these results translate into faster sales, a better customer experience, reduced rework, and a higher conversion rate—without requiring a proportional expansion of the workforce. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Conclusion: Accelerate with data and AI in automotive retail. The case studies presented, from Amazon and its automated inspections, to GM, Zoomcar, Seez, Ferrari, and academic studies, show that data, ML, and GenAI have the potential to redefine efficiency, sales, and the customer journey in automotive retail. Today, these technologies are more than a differentiator: they are an essential, strategic resource. If your company is looking to build a smart sales and customer experience ecosystem in the automotive retail sector, let's talk . We can accelerate this journey together. Or, if you believe that examples like this apply to your company, even if it doesn't belong to this sector, let's schedule a conversation . Our focus is on delivering real solutions to our clients that solve real problems. Learn about some Use Cases .

Standardization in credit analysis Automation with predictive models Scalability. with data-driven decisions Imagem gerada por IA AI-generated summary: Seeking greater precision and speed in credit granting, a large Brazilian real estate developer adopted a machine learning-based solution developed by BlueMetrics. The predictive model was trained using the company's own historical data, considering variables such as income, marital status, and number of children, to automatically classify the default risk of new applicants. The solution, integrated with AI and supported by AWS technologies, eliminated subjectivity in the process, reduced rework between departments, and enabled faster and more informed decisions. The result? A potential 46% reduction in default rates. Overview In a highly competitive real estate market, ensuring that credit is granted to the right clients can make all the difference. This was precisely the challenge that one of Brazil's largest real estate developers faced and overcame by using artificial intelligence applied to credit analysis. Our client, a real estate company that sells residential units, also operates as a financing provider, granting direct credit to buyers. With the increase in sales volume and the growth of operations, the need to revise the credit granting model became evident, as it was previously poorly standardized and highly dependent on the subjective assessment of analysts. “Working on problems like this is extremely motivating for us because they are strategic for the business and allow us to apply our expertise in a practical and measurable way,” says Luciano Rocha, commercial director of BlueMetrics. “Furthermore, we know that a well-organized data structure makes all the difference when developing AI solutions that truly deliver value, and data expertise is precisely one of our greatest differentiators.” Market context: High competitiveness in the real estate sector. Increasing volume of loan applications The need for quick and assertive decisions. High risk of default. Manual processes that are susceptible to human error. Problem: How can we optimize customer service and provide accurate answers regarding financing? As we saw above, in addition to selling residential properties, the developer also offers its own financing, which increases its profit margin but also increases financial risk. The credit analysis process was conducted manually, based on criteria that varied among analysts, including even subjective factors such as their mood that day or pressure to meet targets. This lack of standardization generated inefficiency, conflicts between the commercial and financial areas, and hindered risk control. In a market with high default rates and short decision-making deadlines, the company urgently needed a more objective, reliable, and scalable approach to assess credit risk consistently and quickly. “We have seen this type of challenge repeated in various sectors: a lot of data available, but little strategic use,” highlights Luciano Rocha. “That’s where our experience helps companies transform all this potential into concrete initiatives.” Operational limitations: Manual and time-consuming credit analysis process Dependence on the individual judgment of analysts. Lack of standardization in decisions Conflicts between areas due to differences in assessments. Business limitations: High risk of default. Difficulty in scaling the operation safely. Impact on customer experience due to delays. Critical decisions influenced by subjective factors. Technological limitations: Lack of an automated model for risk analysis. Lack of integration between data and company departments. Lack of a consolidated history of previous decisions. Low capacity to generate insights from available data. The solution: Machine Learning in credit analysis Imagem gerada por IA To overcome this challenge and generate real value, BlueMetrics developed a machine learning-based classification model capable of predicting, based on variables such as income, marital status, and number of children, whether a loan applicant would have a higher or lower propensity to default. The model was trained using the company's own historical data and integrated with an artificial intelligence agent that performs the query in real time. As soon as a new credit request is received, the system automatically assesses the risk, generating a risk score to support the analysts' decision. Important: ultimate control remains in the hands of the human team, but now with the support of objective and consistent data. The architecture was built with scalable AWS technologies, such as Amazon SageMaker, ensuring performance, reliability, and flexibility for operational growth. Gabriel Casara, CGO of BlueMetrics, adds: “We have already delivered around 200 data and AI projects to more than 90 clients in Brazil, the United States, and Latin America. And it is precisely this track record that allows us to offer speed in solutions, security in delivery, and a total focus on results.” Immediate benefits: Reducing the workload of analysts Standardization of analyses and elimination of subjectivity. Automated customer service and credit analysis 24/7 Greater agility and assertiveness in decision-making. Strategic gain: Potential reduction in default rates by up to 46% Structured database for future marketing and credit initiatives. Supporting decision-making with reliable historical data and forecasts. Possibility of scaling the operation safely and efficiently. Key features of the solution: Integration with AI agent for real-time responses. Total transparency and control on the part of the analysts. Implementation using robust and scalable AWS technologies. Model trained with real data from the business itself. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Results: The solution achieved 92% accuracy in classifying good payers, making the process more reliable and significantly reducing rework between departments. Standardization brought clarity, reduced internal conflicts, and improved operational efficiency. According to simulations using historical data, the company achieved a potential reduction of up to 46% in delinquency, in addition to a significant gain in the speed of credit decisions. Furthermore, the model began generating valuable insights for the marketing team, which started targeting campaigns based on customer profiles most likely to pay on time. This created a virtuous cycle of efficiency and prevention, with a direct impact on the profitability of the operation . Technologies used The solution was designed using several AWS technologies, including: AWS Services Sagemaker S3 Lambda DynamoDB API Gateway Languages, Libraries and Frameworks Python Conclusion: This case demonstrates how the application of artificial intelligence in the real estate sector can go far beyond automation. By bringing predictability, agility, and intelligence to the credit granting process, the company was able to reduce risks, make more strategic decisions, and scale its operations safely. More than just a one-off improvement, the project represented a leap in the organization's analytical maturity. "It's very gratifying to see a solution generate real and immediate value for the client, solving a concrete problem with a direct impact on results," concludes Luciano Rocha. "That's what we strive for in every project we deliver." How about creating a case study like this for your company? Let's schedule a call? Learn about some Use Cases . About BlueMetrics BlueMetrics was founded in 2016 and has already delivered over 160 successful projects in the areas of Data & Analytics, GenAI, and Machine Learning for more than 70 companies in the United States, Brazil, Argentina, Colombia, and Mexico. It has its own methodology and a multidisciplinary team focused on delivering solutions to real-world business challenges.

Imagem gerada por IA AI-generated summary: This article explores how artificial intelligence is revolutionizing the tourism and hospitality sector, offering highly personalized and efficient experiences for travelers. Technologies such as Machine Learning and GenAI enable everything from automated travel planning—as in the fictional case of executive Laura—to the intelligent operation of hotels, airlines, and agencies. AI is already applied in predictive personalization, automated customer service agents, content generation, real-time reputation analysis, and logistics optimization. Real-world cases show significant gains in revenue, efficiency, and sustainability. Imagine this scenario: Laura is an executive from São Paulo who decided to take twenty days of vacation. Upon opening the app of her preferred travel agency, entirely developed using GenAI and Machine Learning technologies and powered by real-time data, she didn't need to navigate through dozens of pages or type lengthy searches. Based on her travel history, gastronomic preferences, and date availability, the system recommended a personalized itinerary for southern Italy. All of this considered the climate, suggested local events, took into account cost-effectiveness, and even the ideal type of accommodation for her profile. The entire trip was planned and approved in minutes. Hotel reservations and airline tickets were purchased automatically, with options adjusted according to her schedule and preferences. The hotel concierge was informed that Laura prefers to stay in high-ceilinged rooms with ocean views, as well as a vegetarian breakfast with gluten-free bread options. The whole experience was not only seamless but also intuitive, a direct result of the integration of data, Machine Learning, and GenAI in the tourism and hospitality business ecosystem. We're talking about something that goes far beyond process efficiency or productivity gains: we're talking about exponentially improving the customer experience, minimizing friction and making the experience much more fluid and rewarding. GenAI, Machine Learning, and data are decisively transforming all sectors of the economy. Why wouldn't tourism and hospitality be the same? How AI is transforming tourism and hospitality. 1. Predictive personalization based on Machine Learning Modern companies in the hotel and tourism sector are replacing traditional recommendation models with supervised and unsupervised machine learning systems that analyze large volumes of behavioral data to anticipate customer needs. These predictive models use data such as: Booking history and preferences; Activity on digital channels; Previous reviews and ratings; Profiles of similar travelers. This allows for highly personalized suggestions to be offered even before the customer verbalizes their needs, as in Laura's example. Resorts, tour operators, and B2B travel platforms already use clustering and regression algorithms to predict seasonality, dynamic pricing, and consumer profiles. 2. AI agents in customer service and operations The use of enterprise AI agents, automated with LLM-based conversational flows and integrated into systems such as CRMs, ERPs, and booking platforms, is radically transforming customer service. They not only answer questions but also perform tasks such as: Automatic check-ins and check-outs; Real-time itinerary updates; Logistical reorganization in the face of unforeseen events (such as cancelled flights); Multilingual service with empathy and context. Large hotel chains already use these agents as reception assistants or 24/7 digital concierges, reducing staffing costs and increasing customer satisfaction. Furthermore, corporate travel companies use these agents to manage travel policies and compliance, freeing up human teams to focus on strategic matters. 3. Automated content generation with GenAI Generative AI plays a key role in supporting the creation of customized content for marketing and selling travel packages. Companies in the sector use these models to: Generate unique hotel and itinerary descriptions based on real data; Create email marketing content tailored to each customer's profile; To produce cultural scripts with language adapted to diverse audiences (young people, families, couples, etc.); Translate and localize content with cultural nuances while maintaining brand consistency. The key difference with enterprise-grade GenAI lies in its integration with secure sources and proprietary data. Unlike the generic use of open-source AI, structured companies train models with their own datasets, ensuring quality, accuracy, and legal compliance. 4. Real-time sentiment and reputation analysis Automated analysis of feedback, reviews, and mentions on social media, enabled by NLP (Natural Language Processing), allows hotels and agencies to monitor customer experience in real time. Enterprise AI systems identify negative patterns before they escalate, recommending: Operational corrective actions; Proactive compensation; Adjustments to communication campaigns. Furthermore, this type of analysis feeds into predictive dashboards that cross-reference satisfaction metrics with operational data, guiding strategic decisions focused on Customer Experience (CX). 5. Logistics and operational planning with AI Large hotel groups and tour operators also use AI to optimize the supply chain, room occupancy, staffing levels, and even energy consumption. AI-based systems analyze variables such as: Real-time booking flow; Local events and holidays; Currency exchange rates and price variations; Consumption history by customer profile. With this data, ML models automatically adjust inventory, predict demand peaks, and suggest resource redistributions, improving profitability without compromising service. Next, we will look at some practical applications of these technologies. Imagem gerada por IA Real-world success stories with AI in tourism and hospitality. 1. AI infrastructure in airlines Delta Air Lines implemented predictive models to forecast operational disruptions (such as delays and crew restrictions), reducing cancellations by 12% and streamlining crew redeployment. American Airlines and Google , with Project Contrails, have already started avoiding high-humidity routes to reduce contrails, which are the visible white lines of cloud-like formations that jet aircraft leave behind in the sky, especially at high altitudes. Although contrails are a natural byproduct of jet aviation, they can contribute to global warming by trapping heat in the atmosphere. Furthermore, machine learning algorithms identify anomalies in bookings—reducing fraud—and implement dynamic pricing on ancillary products (such as baggage and onboard coffee), increasing conversions by up to 36% and incremental revenue by up to 10%. 2. Revenue management in hotels Some of the world's largest hotel chains, such as Marriott and IHG , have been using advanced revenue management systems for decades. Marriott uses PCR (price optimization) with live elasticity models, increasing RevPAR, or Revenue Per Available Room, by approximately 14%. IHG, on the other hand, implemented individual response models to rate offers, achieving a 2.7% increase in RevPAR. This fundamental metric in the hotel industry indicates the average revenue generated by each hotel room in a given period, regardless of whether it is occupied or not. More recently, ML-based systems have been integrated into the demand forecasting cycle, room cleaning operations, and staff allocation, reducing operational costs and improving the guest experience. 3. Sustainability and efficiency in operations The IAG group (owner of British Airways and Aer Lingus ) has integrated ML into flight navigation and predictive maintenance systems, optimizing routes and reducing delays. In the hotel sector, the Iberostar chain , in partnership with Winnow, adopted systems with cameras and smart scales to monitor food waste, saving more than 1,100 tons of food in 2023 alone. 4. Customer experience and personalization These same companies are applying AI to improve the customer journey. At airports like Changi , AI and ML support automated baggage screening, biometric recognition, and streamlined immigration processes, reducing queues and improving the experience, as well as reducing paper and energy consumption. 5. Demand and Engagement Forecasting for Online Travel Agencies Platforms like Expedia and Skyscanner use trend and churn forecasting models to anticipate trending destinations and nurture leads with personalized offers, as well as notify users about price drops, predict cancellations, reduce losses from " no-shows ," identify potential churn cases , and launch automated interventions that have increased retention by 30%. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Our experience in this sector Here at BlueMetrics, we have served a large resort chain in Latin America for several years, initially in the areas of data engineering and analytics. More recently, we have been assisting this client on their GenAI and Machine Learning journey, implementing some of the features already discussed in this article. To achieve this, we rely on our proprietary blue4AI method , designed to accelerate project time-to-live (TTL). Our extensive portfolio of over 160 solutions already delivered to more than 70 clients in the United States and Latin America also serves as a facilitator in projects, ensuring more effective results and assertive processes. Conclusion: AI as a strategic lever for tourism and hospitality. From a smoother and more satisfying traveler experience to increased operational efficiency in flights, hotels, and tour packages, it's clear that AI, especially when implemented with agility and professionalism, is a game-changer in this industry and an engine for innovation and competitiveness. In the era of smart tourism, investing in enterprise AI means: Anticipating needs and building traveler loyalty; Optimize revenue, costs, and operations; Ensure regulatory compliance and privacy; Promoting sustainability and ESG; Innovate with speed and safety. If you believe that examples like this apply to your company, whether it's in this sector or not, let's schedule a conversation . Our focus is on delivering real solutions to our clients that solve real problems. Learn about some Use Cases .