Archivi categoria: Artificial Intelligence (AI)

RAG on Azure Local: the evolution of generative AI in hybrid environments

In the era of Artificial Intelligence, companies are required to combine computational power with distributed data management, as data is increasingly located across cloud environments, on-premises infrastructures, and edge settings. In this context, Azure Local emerges as a strategic solution, capable of extending the benefits of cloud computing directly into local data centers—where the most sensitive and critical workloads reside. After exploring this topic in the previous article, “AI from Cloud to Edge: Innovation Enabled by Azure Local and Azure Arc,” this new piece focuses on a particularly significant evolution: the adoption of RAG Capabilities (Retrieval-Augmented Generation) within Azure Local environments. Thanks to Microsoft’s adaptive cloud approach, it is now possible to design, deploy, and scale AI solutions consistently and in a controlled manner, even in hybrid and multicloud scenarios. Azure Local thus becomes the enabler of a tangible transformation, bringing generative AI capabilities closer to the data, with clear benefits: reduced latency, preservation of data sovereignty, and greater accuracy and relevance of the generated results.

A Consistent AI Ecosystem from Cloud to Edge

Microsoft is building a consistent and distributed Artificial Intelligence ecosystem, designed to enable the development, deployment, and management of AI models wherever they are needed: in the cloud, on-premises environments, or at the edge.

This approach is structured into four key layers, each designed to address specific needs:

Application Development: With Azure AI Studio, developers can easily design and build intelligent agents and conversational assistants using pre-trained models and customizable modules. The development environment offers integrated tools and a modern interface, simplifying the entire AI application lifecycle.
AI Services: Azure offers a wide range of advanced AI services — including language models (based on OpenAI), machine translation, computer vision, and semantic search — which, until now, were limited to the cloud environment. With the introduction of RAG in Azure Local, these capabilities can now also be executed directly in local environments.
Machine Learning and MLOps: Azure Machine Learning Studio allows for efficient creation, training, optimization, and management of ML models. Thanks to the AML Arc Extension, all these features are now also available on local and edge infrastructures.
AI Infrastructure: Supporting all these layers is a solid and scalable technology foundation. Azure Local, together with Azure’s global infrastructure, provides the ideal environment for running AI workloads through containers and optimized virtual machines, ensuring high performance, security, and compliance.

Microsoft’s goal is clear: to eliminate the boundary between the cloud and the edge, enabling organizations to harness the power of AI where the data actually resides.

What is Retrieval-Augmented Generation (RAG)

Within the unified AI ecosystem Microsoft is building, one of the most impactful innovations is Retrieval-Augmented Generation (RAG) — an advanced technique poised to revolutionize the approach to generative AI in the enterprise space. Unlike traditional models that rely solely on knowledge learned during training, RAG enriches model responses by dynamically retrieving up-to-date and relevant content from external sources such as documents, databases, or vector indexes.

RAG operates in two distinct but synergistic phases:

Retrieve: The system searches and selects the most relevant information from external sources, often built using enterprise data.
Generate: The retrieved content is used to generate more accurate responses, consistent with the context and aligned with domain-specific knowledge.

This architecture helps reduce hallucinations, increase response accuracy, and work with updated and specific data without retraining the model, thereby ensuring greater flexibility and reliability.

RAG on Azure Local: Generative AI Serving On-Premises Data

With the introduction of RAG Capabilities in Azure Local environments, organizations can now bring the power of generative AI directly to their data—wherever it resides: in the cloud, on-premises, or across multicloud infrastructures—without needing to move or duplicate it. This approach roots artificial intelligence in enterprise data and enables the native integration of advanced capabilities into local operational workflows.

The solution is available as a native Azure Arc extension for Kubernetes, providing a complete infrastructure for data ingestion, vector index creation, and querying based on language models. Everything is managed through a local portal, which offers essential tools for prompt engineering, monitoring, and response evaluation.

The experience is designed in a No-Code/Low-Code fashion, with an intuitive interface that allows even non-specialized teams to develop, deploy, and manage RAG applications.

Key Benefits

Data Privacy and Compliance: Sensitive data remains within corporate and jurisdictional boundaries, allowing the model to operate securely and in compliance with regulations.
Reduced Latency: Local data processing enables fast responses, which are crucial in real-time scenarios.
Bandwidth Efficiency: No massive data transfers to the cloud, resulting in optimized network usage.
Scalability and Flexibility: Thanks to Azure Arc, Kubernetes clusters can be deployed, monitored, and managed on local or edge infrastructures with the same operational experience as the cloud.
Seamless Integration with Existing Environments: RAG capabilities can be directly connected to document repositories, databases, or internal applications, enabling scenarios such as enterprise chatbots, intelligent search engines, or vertical digital assistants—natively and without invasive infrastructure changes.

This capability represents a fundamental element in Microsoft’s strategy: to make Azure the most open, extensible, and distributed AI platform, capable of enabling innovation wherever data resides and transforming it into a true strategic asset for the digital growth of organizations.

Advanced RAG Capabilities on Azure Local

The RAG capabilities available in Azure Local environments go beyond simply bringing generative AI closer to enterprise data—they represent a comprehensive set of advanced tools designed to deliver high performance, maximum flexibility, and full control, even in the most demanding scenarios. Thanks to continuous evolution, the platform is equipped to support complex and dynamic use cases, while keeping quality, security, and responsibility at the forefront.

Here are the main advanced features available:

Hybrid Search and Lazy Graph RAG (coming soon): The combination of hybrid search with the upcoming support for Lazy Graph RAG enables the creation of efficient, fast, and low-cost indexes, providing accurate and contextual responses regardless of the nature or complexity of the query.
Performance Evaluation: Native evaluation pipelines allow structured testing and measurement of RAG system effectiveness. Multiple experimentation paths are supported—helpful for comparing different approaches in parallel, optimizing prompts, and improving response quality over time.
Multimodality: The platform natively supports text, images, documents, and—soon—videos. By leveraging the best parsers for each format, RAG on Azure Local can process unstructured data located on NFS shares, offering a unified and in-depth view across various content types.
Multilingual Support: Over 100 languages are supported during both ingestion and model interactions, making the solution ideal for organizations with a global presence or diverse language requirements.
Always-Up-to-Date Language Models: Each update of the Azure Arc extension provides automatic access to the latest models, ensuring optimal performance, enhanced security, and alignment with the latest advancements in generative AI.
Responsible and Compliant AI by Design: The platform includes built-in capabilities for managing security, regulatory compliance, and AI ethics. Generated content is monitored and filtered, helping organizations comply with internal policies and external regulations—without placing additional burden on developers.

Key Use Cases of RAG on Azure Local

The integration of RAG into Azure Local environments delivers tangible benefits across several sectors:

Financial Services: in the financial sector, RAG can analyze sensitive data that must remain on-premises due to regulatory constraints. It can automate compliance checks on documents and transactions, provide personalized customer support based on financial data, and create targeted business proposals by analyzing individual profiles and preferences.
Manufacturing: for manufacturing companies, RAG is a valuable ally for enhancing operational efficiency. It can offer real-time assistance in problem resolution through analysis of local production data, help identify process inefficiencies, and support predictive maintenance by anticipating failures through historical data analysis.
Public Sector: public administrations can leverage RAG to gain insights from the confidential data they manage. It’s useful for summarizing large volumes of information to support quick and informed decision-making, creating training materials from existing documentation, and enhancing public safety through predictive analysis of potential threats based on local data.
Healthcare: in the healthcare sector, RAG enables secure handling of clinical data, delivering value across multiple areas. It can support the development of personalized treatment plans based on patient data, facilitate medical research through clinical information analysis, and optimize hospital operations by analyzing patient flow and resource usage.
Retail: in the retail sector, RAG can enhance customer experiences and streamline business operations. It is effective for creating personalized marketing campaigns based on purchasing habits, optimizing inventory management through sales data analysis, and gaining deeper insights into customer behavior to refine product and service offerings.

Conclusion

The integration of RAG capabilities within Azure Local environments marks a significant milestone in the maturity of distributed Artificial Intelligence solutions. With an open, extensible, and cloud-connected architectural approach, Microsoft enables organizations to leverage the benefits of generative AI consistently—even in hybrid and on-premises scenarios. RAG capabilities, in particular, allow advanced language models to connect with the contextual knowledge stored in enterprise systems—without compromising governance, security, or performance. This evolution makes it possible to create intelligent, secure, and customized applications across any operational context, accelerating the time-to-value of AI across multiple industries. Azure Local with RAG represents a strategic opportunity for businesses that want to govern Artificial Intelligence where data is born, lives, and generates value.

AI from Cloud to Edge: Innovation Powered by Azure Local and Azure Arc

In the era of Artificial Intelligence, which is significantly transforming business models, the adoption of local and distributed infrastructures is crucial for managing specific and mission-critical workloads. In this context, Azure Local emerges as an innovative solution capable of bridging the gap between cloud and edge computing, delivering applications, data, and AI services exactly where they are needed. This article will explore real-world scenarios where Azure Local, combined with Azure Arc, enables real-time data processing “at the source” and the deployment of advanced AI solutions. We will also delve into the new Azure AI services designed for Azure Local, focusing on maximizing the potential of on-premises data.

Real-World Scenarios of Local and Distributed Infrastructure with Azure Local

In the following sections, we will examine concrete examples that demonstrate how Azure Local, in synergy with Azure Arc, effectively addresses the needs of distributed infrastructure, ensuring low latency, security, and operational continuity across various business and industrial contexts.

Figure 1 – Real-World Scenarios for Local and Distributed Infrastructure with Azure Local

Local AI Inferencing

In many situations, analyzing data in real-time directly at the edge (e.g., within a retail store or an industrial facility) provides significant advantages in terms of latency and reduced bandwidth usage. Azure Local enables on-site data processing, eliminating the need to transfer all data to the cloud before performing critical analyses. Here are some examples:

Retail Loss Prevention: With AI integrated locally, suspicious behaviors and potential thefts can be identified in real-time, allowing retailers to act immediately and reduce losses.
Smart Self-Checkout: Video surveillance and visual analysis facilitate automatic item recognition, improving customer experience and reducing wait times.
Pipeline Monitoring: In sectors like oil & gas, real-time video monitoring of infrastructure helps detect anomalies and leaks, reducing environmental risks and ensuring timely interventions.

Operational Continuity in Mission-Critical Environments

The ability to ensure business continuity during network or power outages is a crucial aspect. With Azure Local, robust systems can be implemented to preserve operations even when cloud connectivity is limited or unavailable. Examples include:

Factory and Warehouse Operations: Production and inventory management cannot stop; having a local solution ensures that production lines and management systems continue functioning despite network disruptions.
Stadiums and Event Venues: Services like security, ticketing, and lighting must remain operational to safeguard both spectator experience and safety.
Transport Hubs: Constant operation of ticketing systems, scheduling, and communications is essential for passenger flow and safety in large transit hubs.

Control Systems and Near Real-Time Processing

Some industrial, financial, and healthcare environments demand extremely low response times to avoid errors, ensure safety, or maximize performance. Azure Local, combined with Azure Arc, can meet these latency requirements:

Manufacturing Execution Systems (MES): Continuous synchronization and monitoring of production machinery optimize processes and minimize downtime.
Industrial Quality Assurance (QA): Immediate quality checks and verifications identify defects before they reach the final stage of production, increasing compliance and reducing waste.
Financial Infrastructures: Low-latency transaction processing and rapid risk assessment are critical for market competitiveness and stability.

Regulatory Compliance and DDIL Connectivity (Disconnected, Degraded, Intermittent, Limited)

For many organizations (governmental, military, or those operating critical infrastructures), data protection and secure management, even in the absence of reliable connectivity, are top priorities. Azure Local supports the need for on-premises data and control:

Government and Military Sectors: Data confidentiality is paramount, requiring local management to ensure continuous access even in compromised network scenarios.
Energy Infrastructures: The stability of distribution networks and control of pipelines and refineries require resilience under limited connectivity conditions, while adhering to stringent regulations.

Azure’s Adaptive Cloud Approach

Microsoft’s adaptive cloud approach, enabled by Azure Arc, helps organizations unify hybrid, multicloud, and edge infrastructures within Azure. With Azure Arc, the same cloud-native experiences and capabilities—such as security, updates, management, and scalability—can be extended anywhere, from on-premises data centers to distributed locations.

Figure 2 – Adaptive Cloud Approach

Azure Local, connected to the cloud through Azure Arc, enables:

Operating and scaling distributed infrastructure via the Azure portal and the same APIs.
Running fundamental compute, network, storage, and application services locally, choosing hardware from the preferred vendor.
Strengthening the security of apps and data with Azure technologies, protecting them against advanced threats.

A key feature is the presence of Azure Kubernetes Service (AKS), Microsoft’s managed Kubernetes solution. On Azure Local, AKS can be configured and updated automatically, providing everything needed (storage drivers, container images for Linux and Windows, etc.) to support containerized applications. Moreover, each cluster is automatically enabled with Azure Arc, allowing integration with services like Microsoft Defender for Containers, Azure Monitor, and GitOps for continuous delivery.

Figure 3 – Bring Azure Apps, Data, and AI Anywhere

New Azure AI Services with Azure Local and Azure Arc

On-Premises Data Search with Generative AI

In recent years, generative AI has made significant strides, driven by the introduction of language models (like GPT) capable of interpreting and generating natural language text. Public tools like ChatGPT work well for general knowledge queries but cannot address questions about private business data on which they have not been trained. To bridge this gap, the concept of Retrieval Augmented Generation (RAG) was introduced, a technique that “enhances” language models with proprietary data, enabling more advanced and customized use cases.

Within the Azure Local framework, Microsoft has announced a new service that brings generative AI and RAG directly to the edge, where the data resides. Within minutes, organizations can deploy (via an Azure Arc extension) everything needed to query their on-premises data, including:

Small and large language models (SLM/LLM) running locally, with support for both CPUs and GPUs.
An end-to-end data ingestion and RAG pipeline that keeps all information on-premises, with RBAC (Role-Based Access Control) ensuring secure access.
An integrated tool for prompt engineering and result evaluation to optimize model settings and performance.
APIs and interfaces aligned with Azure standards, facilitating integration into enterprise applications, plus a preconfigured UI for immediate service use.

This feature is now available in private preview for Azure Local customers, with Microsoft planning to expand availability to other Arc-enabled platforms in the near future.

“Edge RAG”: The Local Retrieval-Augmented Generation Ecosystem

This new service, known as “Edge RAG”, integrates seamlessly into the Azure ecosystem and supports various input components, such as:

Azure AI Search: Provides document search and indexing functionality, enabling quick identification of relevant content within large datasets.
Azure OpenAI: Offers advanced AI models (like GPT) capable of generating, understanding, and summarizing text in natural language.
Azure AI Studio: A platform for developing and managing AI assets (datasets, models, pipelines) centrally.

Together, these components power an integrated flow—from data ingestion to inference and result presentation via chat or other development interfaces. This enables the creation of chatbots, knowledge discovery tools, and other AI-driven solutions that leverage internal business data in a secure, customizable, and compliant environment.

Deploying Open-Source AI Models via Azure Arc

Another key feature of Azure AI is the availability of a catalog of AI models tested, validated, and guaranteed by Microsoft. These models are ready for deployment and provide consistent inference endpoints. This functionality is now extended to the edge, where Microsoft makes selected models available directly from the Azure portal:

Phi-3.5 Mini (language model with 3.8 billion parameters)
Mistral 7B (language model with 7.3 billion parameters)
MMDetection YOLO (object detection)
OpenAI Whisper Large (speech-to-text recognition)
Google T5 Base (automatic translation)

These models can be deployed in just a few steps on an Arc AKS cluster running on-premises. Most models require only a CPU, but Phi-3.5 and Mistral 7B also support GPUs for enhanced performance in intensive inference scenarios.

Azure AI Offerings: From Cloud to Edge

Microsoft’s approach spans the full spectrum of AI capabilities, offering services and tools that can be delivered in the Azure cloud or extended to on-premises and edge environments via Azure Arc. The offering consists of four main pillars:

Application Development
- Azure AI Studio: A development environment for AI applications (e.g., chatbots, virtual agents) with a complete set of APIs and interfaces for seamless AI integration.
AI Services
- Azure AI Language and Model Services: Preconfigured services for NLP, computer vision, and other AI functionalities.
- Solutions like Edge RAG, Video Indexer, and Managed AI Containers for local deployment of “ready-to-use” AI models.
Machine Learning & ML Ops
- Azure Machine Learning Studio: A comprehensive platform for creating, training, optimizing, and managing machine learning models.
- With Azure Arc, ML Ops capabilities can extend to the edge via extensions like the AML Arc Extension, enabling Azure ML tools on on-premises and edge infrastructures.
Infrastructure
- Azure Global Infrastructure: Azure’s cloud foundation, including compute, storage, and networking resources.
- Arc-Enabled Edge Infrastructure: Extends Azure capabilities to data centers or edge devices, managed as if they were cloud resources.

Conclusion

Microsoft’s strategy is built on delivering the best of the cloud “anywhere.” Azure Local epitomizes this vision: a solution that brings all the benefits of the cloud—agility, scalability, security—directly to local environments, meeting the needs for low latency, operational continuity, and regulatory compliance.

Thanks to Azure Arc, organizations can leverage Azure AI services such as advanced language models, Retrieval-Augmented Generation (RAG) pipelines, and ML Ops tools in a hybrid mode. Applications range from factory quality control to retail theft prevention, from critical government data centers to energy infrastructure monitoring.

In a world where data continues to grow exponentially and the need for on-site analysis becomes increasingly urgent, solutions like Azure Local represent the next step toward a new generation of distributed infrastructures. This is how Microsoft meets the challenge of uniting cloud potential with on-premises reality, creating opportunities for innovation and growth across all sectors.

Microsoft Copilot for Azure: how Artificial Intelligence is transforming Azure infrastructure design and management

In an era marked by relentless technological evolution, artificial intelligence (AI) is emerging as a revolutionary force in the cloud computing landscape. At the heart of this transformation is Microsoft, which has recently unveiled Microsoft Copilot for Azure. This innovative solution marks the beginning of a new era in the design, management, and optimization of Azure infrastructure and services. This article provides an overview of Microsoft Copilot for Azure, a true ally for businesses, designed to fully exploit the potential of the cloud through advanced features and AI-guided intuitiveness.

Premise: Copilot’s experience in Microsoft’s Cloud

Microsoft Copilot is a cutting-edge solution in the field of AI-based assistants. It stands out for the use of sophisticated language model algorithms (LLMs) and its perfect integration with Microsoft’s Cloud. This revolutionary tool aims to enhance productivity by facilitating access to critical data and ensuring high standards in security and privacy. Its core is an intuitive conversational interface that simplifies interaction with data and automation, making application creation simpler and more intuitive.

Copilot adapts to different needs: from basic usage that requires minimal effort and customization, to highly customized solutions that require substantial investment in development and data integration.

Figure 1 – Copilot’s Experience in Microsoft’s Cloud

The main ways to take advantage of Microsoft Copilot are:

Adopting Copilot: Microsoft offers various Copilot assistants to increase productivity and creativity. Integrated into various Microsoft products and platforms, Copilot transforms the digital workspace into a more interactive and efficient environment. Among these, Copilot for Azure stands out, which will be examined in detail in this article.
Extending Copilot: Developers have the opportunity to incorporate external data, simplifying user operations and reducing the need to change contexts. This not only improves productivity but also fosters greater collaboration. Through Copilot, it’s easy to integrate these data into common Microsoft products used daily. For example, both companies and ISVs have the ability to develop plugins to insert their own APIs and business data directly into Copilot. By adding these plugins, connectors, or extensions for messages, users can maximize the use of AI capabilities offered by Copilot.
Building your own Copilot: Beyond adoption and extension, it’s possible to create a customized Copilot for a unique conversational experience, using Azure OpenAI, Cognitive Search, Microsoft Copilot Studio, and other Microsoft Cloud technologies. A customized Copilot can integrate business data, access external data in real-time via APIs, and integrate into business applications.

Microsoft Copilot for Azure: the assistant revolutionizing the design, management, and optimization of Azure infrastructure and services via AI

Microsoft Copilot for Azure is an innovative AI-based tool designed to maximize the potential of Azure. Using LLMs (Large Language Models), Azure’s control plane, and detailed analysis of the Azure environment, Copilot makes work more effective and productive.

This assistant helps users navigate Azure’s numerous offerings, which include hundreds of services and thousands of resource types. It combines data and insights to increase productivity, minimize costs, and provide specific insights. Its ability to interpret natural language greatly simplifies managing Azure, responding to questions and providing personalized information about the user’s Azure environment.

Available directly through the Azure portal, Microsoft Copilot for Azure facilitates user interaction, responding to questions, generating queries, and performing tasks. Moreover, Copilot for Azure provides personalized, high-quality recommendations, respecting the organization’s policies and privacy.

The following paragraphs report the main features for which Microsoft Copilot for Azure can be used.

Performing tasks with improved efficiency

Copilot for Azure is designed to manage a wide range of basic operations that constitute the daily routine in managing Azure environments. These operations, essential for the maintenance and efficiency of architectures in Azure, can often be repetitive and time-consuming. However, with Copilot, it’s possible to manage these basic operations, saving valuable time and reducing the likelihood of human errors.

Interpreting and assessing the Azure environment:

Obtain information about resources through Azure Resource Graph queries.
Understand events and the health status of services.
Analyze, estimate, and optimize costs.

Working smarter with Azure services:

Deploy virtual machines effectively.
Build infrastructures and deploy workloads.
Obtain information about Azure Monitor metrics and logs.
Work more productively using Azure Stack HCI.
Secure and protect storage accounts.

Writing and optimizing code:

Generate Azure CLI scripts.
Discover performance recommendations.
Create API Management policies.
Generate YAML files for Kubernetes.
Resolve app issues more quickly with App Service.

Obtaining specific and detailed information and advice

Within the Azure portal, Copilot emerges as a useful tool for delving into a wide range of Azure concepts, services, or offerings. Its ability to provide answers is based on constantly updated documentation, ensuring users get up-to-date advice and valuable help in solving problems. This not only improves efficiency but also ensures that decisions are based on the most recent and relevant information.

Navigating the portal with greater ease

Navigating the Azure portal, often perceived as complex due to the vastness of services offered, is made simple and intuitive with Copilot’s assistance. Instead of manually searching among the numerous services, users can simply ask Copilot to guide them. Copilot not only responds by opening the requested service but also offers suggestions on service names and provides detailed explanations, making the navigation process smoother.

Simplified management of portal settings

Another notable aspect is Copilot’s ability to simplify the management of Azure portal settings. Users can now confirm or change settings directly through Copilot, without the need to access the control panel. For example, it’s possible to select and customize Azure themes directly through Copilot, making interaction with the portal not only more efficient but also more personalized.

Limitations as of December 2023

As of December 2023, Microsoft Copilot for Azure is in preview and has the following limitations:

Each user has a limit of ten questions per conversation and a maximum of five conversations per day.
Responses that include lists are limited to the first five items.
For some requests and queries, using the name of a resource may not be sufficient; it may be necessary to provide the Azure resource ID.
Available only in English.

Conclusions

Microsoft Copilot for Azure represents a revolutionary turn in cloud computing, leveraging artificial intelligence to significantly transform the management and optimization of Azure architectures. This tool elevates productivity and security, simplifying daily operations, providing detailed analysis, and assisting users in managing the Azure environment. Although we are still at the dawn of this technology, Copilot for Azure represents a significant advancement. This tool not only provides an intuitive and efficient user experience but also lays the groundwork for a future where artificial intelligence and cloud computing will be increasingly interconnected and synergistic.

Rivoluziona la gestione dei costi cloud con l’IA: scopri il nuovo copilot di Microsoft Cost Management!

Nell’era digitale, il cloud computing è diventato una componente essenziale per molte aziende, offrendo flessibilità, scalabilità e agilità. Tuttavia, con l’adozione sempre più diffusa del cloud, la gestione dei costi associati è diventata una sfida sempre più complessa e le aziende sono alla ricerca di soluzioni innovative per ottimizzare le loro spese nel cloud. In questo contesto, Microsoft ha introdotto “Copilot” in Cost Management, una nuova funzionalità basata sull’intelligenza artificiale, progettata per aiutare le aziende a navigare in questo panorama complesso. In questo articolo vengono riportate le principali caratteristiche di questa integrazione, che promette di rivoluzionare il modo in cui le aziende gestiscono ed ottimizzano le loro spese per le risorse cloud.

Una visione chiara dei costi con Microsoft Cost Management

Microsoft Cost Management, disponibile direttamente dal portale Azure, offre una visione dettagliata dei costi operativi, permettendo alle aziende di comprendere meglio come vengono spesi i loro fondi. Questo strumento fornisce informazioni dettagliate sulle spese, evidenziando eventuali anomalie e modelli di spesa. Inoltre, permette di impostare budget, condividere i costi tra diversi team e identificare opportunità di ottimizzazione.

L’IA al servizio della gestione dei costi

Con l’introduzione dell’IA in Microsoft Cost Management, gli utenti possono ora fare domande in linguaggio naturale per ottenere rapidamente le informazioni di cui hanno bisogno. Ad esempio, per comprendere una fattura recente, è possibile chiedere una suddivisione dettagliata delle spese. L’IA fornirà una panoramica delle diverse categorie di spesa e del loro impatto sul totale.

Oltre a fornire una panoramica dei costi, l’IA offre suggerimenti su come analizzare ulteriormente le spese. Gli utenti possono confrontare le fatture mensili, esaminare le spese specifiche o indagare su eventuali anomalie. L’IA fornisce anche informazioni dettagliate su eventuali variazioni nei costi e suggerisce azioni correttive.

L’IA integrata in Microsoft Cost Management interpreta le intenzioni dell’utente e recupera i dati necessari da diverse fonti. Queste informazioni vengono poi presentate a un modello di linguaggio avanzato che genera una risposta. È importante notare che i dati recuperati non vengono utilizzati per addestrare il modello, ma solo per fornire il contesto necessario per generare una risposta pertinente.

Prospettive future

Le capacità dell’IA in Microsoft Cost Management sono in continua evoluzione. In futuro, gli utenti potranno sfruttare simulazioni e modellazioni “what-if” per prendere decisioni informate. Ad esempio, potranno esplorare come varieranno i costi di archiviazione in caso di crescita aziendale o valutare l’impatto di spostare risorse da una regione all’altra.

Figura 1 – Esempio di simulazione e modellazione “what-if”

Benefici

L’introduzione dell’AI in Microsoft Cost Management permette di ottenere i seguenti benefici:

Maggiore visibilità e controllo dei costi: con una maggiore visibilità e comprensione dei costi delle risorse cloud, le organizzazioni possono prendere decisioni in modo più consapevole e gestire meglio i loro budget.
Efficienza operativa: l’uso dell’IA per analizzare e interpretare i dati riduce il tempo e lo sforzo necessari per ottenere intuizioni preziose. Inoltre, gli utenti possono porre domande specifiche in linguaggio naturale e ricevere risposte dettagliate, personalizzate per le loro esigenze.

Figura 2 – Esempi di domande

Ottimizzazione: con suggerimenti e raccomandazioni guidati dall’IA, le organizzazioni possono identificare e attuare opportunità di ottimizzazione per ridurre ulteriormente i costi.

Conclusione

L’integrazione di Copilot in Microsoft Cost Management rappresenta un passo avanti significativo nella gestione dei costi del cloud. Con l’aiuto dell’intelligenza artificiale, le aziende ora hanno uno strumento potente per ottimizzare le loro spese e assicurarsi di operare al massimo della loro efficienza. Con l’evoluzione costante dell’intelligenza artificiale, si prospettano ulteriori e interessanti innovazioni nell’ambito della gestione dei costi del cloud e non solo.