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Chanel redefined luxury with Microsoft cloud. By using data and AI, it transformed the client experience. The best part? They didn’t have to start from scratch, because: 1️⃣ They understood the power of data.  2️⃣ They had a rich heritage of craftsmanship.  3️⃣ The right technologies were already in place. Through this partnership, Chanel has not only enhanced client personalization but also improved operational efficiency—all while preserving their legacy. Here’s how they did it: 1️⃣ First, they adopted Microsoft Fabric to create a decentralized data platform, making sales, inventory, and customer trends accessible to everyone, from advisors to the CEO. 2️⃣ Then, they built a data mesh using Azure, Power BI, and Azure Synapse to ensure seamless data sharing while maintaining strict security and access controls. 3️⃣ Azure OpenAI powers generative AI, helping personalize client experiences with tailored product recommendations and improving marketing and sales aftercare. 4️⃣ To increase productivity, Chanel’s teams use Copilot for real-time translations and daily task automation, reducing manual effort and increasing efficiency. 5️⃣ Lastly, AI-powered clienteling apps now help store advisors deliver personalized service, and knowledge-based search tools provide easy access to crucial data for decision-making. And after all this, here's what Chanel's team had to say: “We chose Microsoft Fabric as the foundation of this platform, driven by its ability to implement a data mesh approach,” says Philippe Baumlin, CIO Corporate. “We've crafted a technical blueprint that specifies the Microsoft technologies to be used, from Azure to Power BI,” adds Olivier BARBONNAT, CIO Europe. This shows how Chanel is setting an example of using data and AI to drive innovation while preserving their tradition. Now, just a quick question! Is your organization making the most of AI? 👇💬 P.S. Follow Simform for more insights on AI and digital transformation. #GenerativeAI #AI #Microsoft #Luxury #Simform

We never imagined we would be talking about this kind of innovation in healthcare. As of today, critical equipment in operating rooms can predict malfunctions before they happen. What's fascinating is... (For those not interested in the tech details, feel free to skip—but this is a huge leap for healthcare! 🎉) 1. IoT sensors are continuously monitoring life-saving equipment. From ventilators to heart rate monitors, these sensors track vibration and temperature, providing real-time insights. 2. The data flow is powered by Azure IoT Hub and Data Factory. These tools work together to seamlessly ingest and orchestrate massive amounts of sensor data for analysis. It’s fully automated, capturing every data point without delay. 3. Anomaly detection is driven by Azure Databricks and machine learning. When irregularities appear in the data, healthcare teams receive instant alerts through Power BI, catching issues before they impact patient care. This technology is setting a new standard for reliability in critical medical environments. Wondering how IoT and predictive maintenance can drive similar transformations in other fields? Let’s dive into it—share your thoughts below! #HealthcareInnovation #IoT #Azure #HealthcareTech #Simform

7/10 vendors still rely on manual methods for patient scheduling! Resulting in misaligned calendars & wasted productivity. But here, if AI had been leveraged, things could have been way better with benefits like: - Automation - Personalization - Predictive capabilities This is how you can build such an AI-powered system! 👇 It all starts with a strong foundation in data management. 1. Data Collection & Integration First, integrate with tools your team already uses – Google Calendar, Microsoft Teams, Slack, etc. API connections ensure that all scheduling data flows smoothly into one place. This is the backbone of AI scheduling. 2. AI Model Development The key here is deep learning, which enables the AI to learn from historical scheduling patterns, preferences, behaviors, and bottlenecks. Additionally, incorporating reinforcement learning can allow the model to continuously improve its scheduling accuracy based on user interactions and feedback. 3. Conflict Detection & Resolution This is where AI’s predictive capabilities come in. Using real-time data analysis, the AI detects potential conflicts before they happen and suggests alternatives, automatically adjusting schedules without the need for manual intervention. 4. User Behavior Adaptation Building a smart system requires designing for adaptive learning. The AI should be able to analyze user behaviors and adjust scheduling preferences over time – knowing, for example, that John is more productive in the mornings, or that team meetings happen best on Thursdays. 5. Real-time Notifications & Feedback To complete the loop, you’ll need a system for real-time communication. The AI should instantly notify teams about changes, conflicts, or upcoming deadlines, syncing across devices and tools seamlessly. 6. Scalable Cloud Infrastructure For a system that scales with growing teams, you need a cloud-based infrastructure. Think AWS or Azure for elastic computing power – the AI needs to handle scheduling for 10 people as easily as 1000. Cloud makes this possible. AI-powered scheduling systems can be built using readily available tools, but their value multiplies when customized to your team’s exact needs. P.S. Follow Simform for more AI insights. #tech #aitools #scheduling #simform 

75% of hospitals risk sacrificing patient care due to outdated systems. That’s a serious risk we can't overlook. We’ve seen organizations like Shriners Children's dealt with similar challenges: - Disconnected clinical notes - Delays in diagnosis - Inefficient workflows That’s why they partnered with Fractal and Microsoft Azure to transform patient data management. Here’s how they did it: 1️⃣ First, they migrated all clinical notes to Azure Blob Storage, so unstructured data became easily accessible in one place, eliminating the need for manual searches. 2️⃣ Then, they use Azure Storage Service Encryption to ensure security during migration, making sure only authorized personnel could access sensitive patient data. 3️⃣ To further enhance data access, they built ShrinersGPT using Azure OpenAI Service. It enables researchers to quickly search clinical notes independently. 4️⃣ By integrating NLP, clinicians could now ask direct questions about symptoms or conditions and get real-time, and accurate answers for better decision-making. 5️⃣ Microsoft Entra ID was implemented to manage access control across teams. This ensured only authorized users could retrieve sensitive patient data and protect privacy. 6️⃣ Lastly, Azure Monitor Log Analytics allow clinicians and researchers to run log queries and optimize system performance to ensure constant access to accurate data. The outcome? Clinicians and researchers now access patient data independently using ShrinersGPT. This reduces reliance on IT teams and speeds up workflows. Is your organization making the most of AI to solve real challenges? Let’s know in the comments 👇 💬 P.S. Follow Simform for more AI insights. #GenerativeAI #azure #simform #healthcare #AI 

Financial institution needs modern credit risk scoring system. We’ll explain how you can build it for yourself using Azure Databricks: BACKGROUND: Credit risk scoring is the backbone of smart financial decisions. Accurate predictions aren’t just about the right algorithms—they depend on powerful, efficient data processing. Traditional systems struggle with large datasets and lack real-time insights. Azure Databricks changes the game. With our expertise in modern analytics, we’ve seen how integrating Databricks with Azure services creates a scalable, real-time credit risk scoring system that continuously enhances accuracy and drives better decisions. 1. Real-Time Data Integration Traditional systems face delays and outdated data. With Azure Event Hubs and Data Factory, you can ingest real-time transactions, credit history, and reports, ensuring your models always use fresh, actionable data. 2. Efficient Data Processing Not all data is immediately useful. With Azure Databricks and Apache Spark, you can organize raw data into Bronze, Silver, and Gold layers using Delta Lake, optimizing it for accurate model predictions. 3. Predictive Risk Models at Scale Your credit scoring models must scale. MLflow with Azure Databricks handles the full ML lifecycle—training, tuning, and deploying models for real-time, accurate credit default predictions. 4. Actionable Insights for Decision Makers A system's value lies in actionable insights. With Power BI and Azure Synapse, executives can visualize credit risks and spot high-risk loans, enabling faster, smarter decisions. 5. Continuous Monitoring and Governance Credit risk constantly evolves. Azure Purview and Monitor provide governance, data lineage, and real-time monitoring, ensuring a secure, high-performing system that meets financial regulations. Next steps… Explore how your financial institution can adopt Azure’s modern architecture for credit risk scoring. It’s time to ensure your models are real-time, scalable, and accurate—helping you make smarter, faster lending decisions. #CreditRisk #Azure #DataAnalytics #FinTech  #Simform

97% of medical imaging data goes unused. Why? Because traditional systems rely on manual interpretation! Causing more delays and errors. This is where AI comes in. It helps healthcare organizations to analyze imaging data in real-time to boost diagnostic accuracy, speed up decisions, and reduce errors by 30%. Helps healthcare organizations to: - analyze imaging data in real-time - boosts diagnostic accuracy - reduce errors by 30% - speed up decisions Here’s how AI transforms medical image analysis: 1. Identify the exact medical problem AI should solve, such as detecting breast cancer or classifying lung diseases like pneumonia or tuberculosis from X-rays. 2. Collect diverse and high-quality datasets like HIPAA and GDPR while ensuring privacy compliance. Anonymizing data and accurate labeling are key to build a reliable AI model. 3. Match the AI model to the task. Convolutional neural networks (CNNs) like ResNet effectively detect fractures or classify lung diseases. 4. Use pre-trained models like ResNet or Vision Transformers (ViT) to fine-tune with medical data. This approach minimizes misdiagnosis, reduces development costs, and saves time. 5. Seamlessly embed AI within healthcare systems like PACS. This integration can cut diagnosis times by 25%, accelerating decision-making and enhancing diagnostic accuracy. So, the bottom line is that AI in medical imaging opens up unused data. If it's built on a strong foundation and smoothly integrated into workflows, this can enhance diagnostics and improve patient outcomes. Are you using AI in your medical imaging process yet? 👇 💬 P.S. Check out our pinned comment where we shared some similar work related to this use case. #microsoft #azure #healthcare #dataengineering #simform

Gen AI in BFSI holds immense potential. Yet many organizations remain cautious. WHY? Because they often overlook the importance of technology quality and actionable insights. Industry leaders emphasize: The true value of Gen AI lies in the confidence it instills in decision-making. According to recent findings: 📈 The global Gen AI market in BFSI is projected to grow from $1.38 billion in 2024 to $13.57 billion by 2032, representing a CAGR of 33.1%. 📈 Over 41.58% of the market share is held by North America as of 2023. 📈 40% of financial institutions are ready to adopt Gen AI solutions this year. Here are 4 key reasons why your organization should embrace Gen AI now: 1. Transformative Operational Efficiency Every other day, you need documents like financial reports, investment summaries, market analyses, etc. Gen AI can automate these with extreme accuracy, so leverage it. 2. Personalized Customer Interactions: Your customers expect tailored experiences. AI-powered chatbots deliver personalized support 24/7, making each interaction unique and valuable. 3. Enhanced Compliance and Risk Mitigation: Compliance can be a constant challenge. Gen AI can monitor transactions and flag anomalies in real-time, reducing errors and ensuring adherence to regulations. 4. Innovative Product Development: Financial markets evolve quickly. Gen AI can help you create new products and dynamic pricing strategies, keeping you competitive as customer needs change. So, is your organization ready to dive into the Gen AI revolution in BFSI? It’s time to assess your AI readiness and invest in technologies that will keep you at the forefront of industry advancements. 👉 What steps are you taking to integrate Gen AI into your operations? Let’s share insights! #GenerativeAI #BFSI #AIFintech #AIinBanking #TechInFinance #AIAutomation #Simform

You can't track data from 10+ sources manually. Neither your outdated system can if there are 50 sources. But, some companies are able to solve these challenges. How? - By using Azure Cloud. Backstory: Parexel, a global provider of clinical development services, had no consistent method for consolidating, viewing, and collecting data. They simplified their data landscape and incorporated Microsoft Azure solutions. And this reduced all of their complexities. Here's what their strategic adoption looks like: 1. Azure Databricks: They consolidated 100+ data sources into a unified data lake, eliminating manual tracking and improving data governance. 2. Power BI: Real-time reporting powered by Power BI replaced manual report generation, speeding up decision-making across teams. 3. Azure Automation: Automated workflows boosted productivity by 30%, allowing teams to focus on innovation rather than hunting for data. The end result? - 85% lower data engineering costs - 70% faster data product delivery - Significantly enhanced clinical trial management Bottomline: Solutions are available. You just need to partner with the right partner who can guide you through the proper tech implementation steps to streamline operations and position your organization for growth. How are you transforming your data strategy? Let’s hear your thoughts below 👇 #microsoft #azure #databricks #healthcare #dataengineering #simform

Hospitals have been struggling with outdated manual processes. (And it's risky.) That’s why many are turning to automation to create something better and safer. You could scroll past this… OR you could check out how these healthcare challenges are being tackled with advanced technology such as Azure. Here’s what’s happening: - IoT sensors installed on sterilization machines monitor temperature and pressure 24/7, integrated into the hospital's current setup. - Azure IoT Hub collects live data and pushes it to the cloud, with pre-configured modules simplifying integration. - Azure Data Factory transfers this data through the hospital’s existing pipelines to Azure Data Lake for secure storage. - Azure Databricks processes the data for machine learning models, automating data prep within existing workflows. - Then, Azure Machine Learning monitors the system in real time, detecting any irregularities and speeding up response times. - If a problem arises, Azure Managed Endpoints instantly sends alerts to existing Power BI dashboards and mobile apps. This ensures the team gets immediate notifications when there’s an issue, enabling them to respond swiftly. Result? This setup massively cuts the risk of sterilization failure, making sure equipment is safe to use and removing the need for unreliable manual checks. Now, how about you? How would real-time monitoring fit into your operations? Share your thoughts below! 👇 #Healthcare #IoT #Azure #Healthcare #Simform

Still, 1 in 3 cloud migrations FAIL! Cloud adoption has surged. And we’ve seen how poorly the migrations are managed. After partnering with a new client, we first understand the business requirements and purpose of cloud adoption. Only then will we proceed with further steps, ensuring that every project gets completed flawlessly without any errors. With the help of our cloud experts, we have curated a list of steps that explain why the failures happen. Swipe through to discover the underrated secrets behind successful cloud migrations. 👇 #cloud #cloudmigration #legacysystem #simform