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AI and Public Administration: towards Intelligent Governance
What role does AI play in Public Administration
AI is emerging as a driver of change for Public Administration, fostering a structural transformation of public services. It offers the opportunity to make the relationship with citizens more efficient and innovative, introducing new management and interaction models.
Its potential impact lies in its ability to make public administration more effective, transparent, and accessible to all.
What are the benefits of integrating AI into Public Administration
Among the main benefits stemming from the integration of AI into public services, we find:
- Optimization of services and cost reduction:
Public Administrations can direct AI investments towards the automation of repetitive tasks related to institutional services and basic administrative functions. This makes it possible to free up resources to be allocated to higher value-added activities, contributing to the improvement of the quality of services offered to citizens. - Transformation of public service management:
AI is profoundly changing the ways public services are delivered and managed. By automating complex and repetitive processes, AI significantly reduces the operational workload, increases efficiency, and improves the accuracy of operations, minimizing human errors. - Enhancement of predictive and decision-making capabilities:
AI strengthens the ability of public administrations to analyze data in an advanced way, improving the quality of decisions and making them more timely and well-informed. Thanks to the identification of recurring patterns and emerging trends, AI makes it possible to anticipate citizens’ needs and plan targeted interventions, with a more strategic use of resources. - Personalization of services:
Through intelligent data analysis, AI enables Public Administration to adapt services to the specific needs of citizens, offering more personalized and effective experiences. In this scenario, AI Agents play a key role by providing immediate assistance and real-time responses, making interaction with the PA more direct, intuitive, and satisfying for the user. - Strengthening of transparency and public accountability:
Artificial intelligence can contribute to administrative transparency by automating data dissemination and simplifying the analysis of institutional activities. It allows citizens to access clear and understandable information, fostering greater awareness and transparency. - Combating corruption and promoting integrity:
Thanks to its ability to process large volumes of data, AI represents a strategic tool in preventing corruption. Real-time analysis of information flows allows the identification of potential irregularities and timely intervention, promoting a proactive approach based on transparency, control, and legality.
An example of AI application for the PA
Artificial intelligence-based tools offer a strategic contribution to the automation of complex and repetitive processes within public services, with particular relevance in document management. AI allows the optimization of workflows related to mandatory institutional services and administrative activities, significantly improving operational efficiency and access to information.
A concrete example of application is the automation of Certified Email (PEC) management: some PAs have adopted an AI-based system capable of automatically processing incoming PECs. These solutions enable the registration of applications and communications by detecting keywords, classifying documents, and associating them with the correct files, optimizing processing times and significantly reducing the risk of error.
AI Agent Margot for Public Administration
Margot is an intelligent agent based on artificial intelligence, designed to support daily activities and dynamically adapt to operational needs, seamlessly integrating into existing workflows. It is an advanced solution, created to simplify complex processes and increase efficiency.
Thanks to its modular architecture, scalability, and ease of configuration, Margot represents a strategic innovation that can be easily implemented even in the context of public administration, effectively responding to the new challenges and needs of the sector.
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RAG (Retrieval-Augmented Generation): What it is and how it enhances AI
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is an artificial intelligence (AI) process designed to optimize the responses of a traditional large language model (LLM). To achieve this, it references an external and reliable knowledge source, in addition to the data used during training, before generating a response. When providing answers, a RAG system draws from specific knowledge bases defined by the developers of the AI agent.
This technology combines the ability to retrieve relevant information from external sources with AI-generated text, improving accuracy and relevance. It enables organizations to leverage the power of LLMs by integrating their own documents, files, or web pages into the response process, ensuring more reliable and personalized results.
Practical Applications of RAG (Retrieval-Augmented Generation)
The practical applications of Retrieval-Augmented Generation (RAG) are expanding, thanks to its ability to integrate specific and up-to-date knowledge directly into decision-making and communication processes.
Some of the most relevant use cases include:
- Customer Support
RAG enables the development of intelligent chatbots capable of accurately responding to customer inquiries by drawing from manuals, FAQs, and company documentation. This reduces problem resolution times, enhances support efficiency, and ensures accurate, context-aware responses - Information Generation
This technology allows for the extraction of relevant data from business documents, annual reports, and other internal sources, facilitating quick access to strategic information and improving overall comprehension.
At the enterprise level, RAG allows organizations to leverage AI by grounding generative results in their specific business knowledge, delivering relevant and value-added responses.
What are vector databases in RAG and how do they work?
Vector databases are advanced storage systems designed to store and query vector embeddings—numerical representations of data, such as text or images, that capture their semantic meaning in a multidimensional space.
Here’s how vector databases function within Retrieval-Augmented Generation (RAG):
- Building the Archive: Before an LLM can retrieve external information, it must be processed into a format the system can understand. The first step involves transforming documents, web pages, or other databases into numerical representations called embeddings using specialized language models.
- Optimized Storage: Once created, these embeddings are stored in a vector database, which is designed to efficiently manage large volumes of vector data and enable rapid queries based on semantic similarity.
- Intelligent Search: When a user submits a query, it is converted into an embedding vector using the same language model employed for stored data. The RAG system then compares this vector with those in the database, performing a similarity search to identify the most relevant content for the user’s request.
- Semantic Search: Vector databases play a crucial role in implementing semantic search within RAG systems. Unlike traditional keyword-based searches, semantic search leverages embeddings to understand the meaning and context of queries, returning relevant information even when it does not contain the exact terms used by the user.
Benefits of using RAG over traditional LLMs
The use of Retrieval-Augmented Generation (RAG) offers several significant advantages over traditional large language models (LLMs).
Here are the key benefits of RAG compared to conventional models:
- Higher Accuracy
RAG generates responses based on reliable and up-to-date data sources, significantly reducing the risk of the LLM producing incorrect information. By providing a set of citable and verifiable sources, RAG enhances the trustworthiness of the information delivered. - Up-to-Date Information
Unlike traditional LLMs, RAG allows for the integration of continuously updated content, such as recent research, statistics, or real-time news. Information sources can be modified by adding or removing documents, improving the system’s flexibility and effectiveness. - Greater Control for Developers
With RAG, developers have more control over the generated text output, allowing them to customize and optimize responses based on specific needs. By refining and adjusting the information sources, the system can be tailored to different contexts, ensuring that generated results are relevant and aligned with application requirements.
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Odoo vs Salesforce: 5 reasons to choose Odoo
Salesforce represents one of the historical brands among the CRM systems in the market, while Odoo is a relatively recent player (relatively, because the software’s origin although significantly different from the current product dates back to 2005).
In recent years, Odoo’s growth has surged, primarily due to the multitude of Apps made available to users and its aggressive pricing strategy. In 2021, the company officially joined the ranks of “Unicorns” meaning companies valued at over 1 billion dollars. This is why we can now comfortably compare Odoo and Salesforce.
Odoo and Salesforce: a Comparison
A necessary premise: Salesforce is a CRM software, whereas Odoo is an ERP software that encompasses various modules catering to different business areas (HR, accounting, warehouse, production, etc.).
Indeed, there would be nothing unusual about using both Salesforce and Odoo by integrating them, but let’s get to the point.
[INDICE]
1. Integrated System
Starting with the aforementioned difference, Odoo contains multiple applications (modules) such as CRM, Human Resources, Website, accounting, warehouse, and more – all integrated with each other. This eliminates the need to purchase and configure additional integrations.
2. Agile Customization
One of Odoo’s strengths is undoubtedly its high degree of platform customization, a characteristic likely stemming from the open-source logic around which the software was developed.
Odoo enables customization of the interface, modules, and workflows according to a company’s specific needs without requiring integrations with third-party software.
3. Scalabilità
Odoo’s architecture makes it a highly scalable system. You can start with 2-3 modules and then expand the scope of functionalities, adapting the software to the company’s needs. Unlike Salesforce, which might incur higher costs as a company’s needs grow, Odoo is suitable for startups or, more generally, for companies with evolving and growing structures.
4. Open Source and Accessible Code
Odoo was born and owes its development to the Open source formula. Odoo maintains a Community version (Open source), but it ensures a considerable degree of customization even in the Cloud version. It provides Partners and developers with the odoo.sh platform for code and functionality development and testing before production deployment.
5. Costs
What makes Odoo appealing to many entities is undoubtedly its cost. Odoo Community Edition is free, while the cloud version entails a monthly or yearly fee starting from 11.90€/user/month, along with a range of advantages and functionalities not present in the Community version.
A price point that is undoubtedly highly competitive, especially when considering that you’re not just purchasing a CRM but an ERP software.
