User and Entity Behaviour Analytics (UEBA) is a broad branch of data analytics that attempts to build a normal behavioural profile in order to detect anomalous events. Among the techniques used to detect anomalies, Deep Autoencoders constitute one of the most promising deep learning models on UEBA tasks, allowing explainable detection of security incidents that could lead to the leak of personal data, hijacking of systems, or access to sensitive business information. In this study, we introduce the first implementation of an explainable UEBA-based anomaly detection framework that leverages Deep Autoencoders in combination with Doc2Vec to process both numerical and textual features. Additionally, based on the theoretical foundations of neural networks, we offer a novel proof demonstrating the equivalence of two widely used definitions for fully-connected neural networks. The experimental results demonstrate the proposed framework capability to detect real and synthetic anomalies effectively generated from real attack data, showing that the models provide not only correct identification of anomalies but also explainable results that enable the reconstruction of the possible origin of the anomaly. Our findings suggest that the proposed UEBA framework can be seamlessly integrated into enterprise environments, complementing existing security systems for explainable threat detection.

AI (Artificial Intelligence) is the new human-machine that understands customers better than anyone did before! The aim is nothing less than establishing a bridge between technology and its users by being more human-like. AI is being built to make life and businesses easy and convenient, and;eCommerce is one area that's already feeling the AI magic! A report by Boston Consulting Group (BCG) shows those retailers who have implemented personalization strategies, gain almost 6 to 10%; a rate that is two to three times faster than other retailers who haven't adopted the same strategy. In fact, AI is predicted to boost profitability rates by almost 59% in the wholesale and retail industries by the year 2035!

Digital transformation today is still about organization and standardization, but it's also about automation. In fact, post-Covid-19, it will be much more about automation than functional standardization. While enterprise applications vendors (like SAP) and ERP vendor enablers (like UiPath) are investing heavily in automation, the most automated companies will move past their enterprise applications to functionality that's increasingly automated outside of older application architectures.

If customer experience is the center of digital transformation, customer relationship management (CRM) must be central to managing that experience. But mentioning the term "CRM" in your meeting room often leads to groans of disgust rather than coos of excitement. Indeed, most companies have a love/hate relationship with their customer management software. It allows them to keep in touch with the people keeping them in business. But in most cases, it's sluggish, time-sucking, and confusing--not words you'd like to describe the tech most central to your company's success.

If customer experience is the center of digital transformation, customer relationship management (CRM) must be central to managing that experience. But mentioning the term "CRM" in your meeting room often leads to groans of disgust rather than coos of excitement. Indeed, most companies have a love/hate relationship with their customer management software. It allows them to keep in touch with the people keeping them in business. But in many cases, it's sluggish, time-sucking, and confusing--not words you'd like to describe the tech most central to your company's success.

If customer experience is the center of digital transformation, customer relationship management (CRM) must be central to managing that experience. But mentioning the term "CRM" in your meeting room often leads to groans of disgust rather than coos of excitement. Indeed, most companies have a love/hate relationship with their customer management software. It allows them to keep in touch with the people keeping them in business. But in many cases, it's sluggish, time-sucking, and confusing--not words you'd like to describe the tech most central to your company's success.

A few years back, cloud computing transformed customer management, giving every small and medium business access to unified data and communication platforms without the need to make heavy investments in IT infrastructure and staff. This time around, the next revolution in the space is being driven by artificial intelligence algorithms that help businesses automate customer outreach and make optimal use of data. Beneath the surface of the roiling sea of data we're generating hide exceptional business and sales opportunities. But the problem is there's now more information available than limited human resources and legacy tools can handle. Fortunately, making sense of data, both structured and unstructured, is something that artificial intelligence is becoming increasingly proficient at.

A few years back, cloud computing transformed customer management, giving every small and medium business access to unified data and communication platforms without the need to make heavy investments in IT infrastructure and staff. This time around, the next revolution in the space is being driven by artificial intelligence algorithms that help businesses automate customer outreach and make optimal use of data. Beneath the surface of the roiling sea of data we're generating hide exceptional business and sales opportunities. But the problem is there's now more information available than limited human resources and legacy tools can handle. Fortunately, making sense of data, both structured and unstructured, is something that artificial intelligence is becoming increasingly proficient at.

While we're still least decades away from human-level synthetic intelligence--AI that will match the human brain in reasoning and decision making--machine learning algorithms, computer vision, natural language processing and generation (NLG/NLP), and other forms of narrow artificial intelligence are proving to be the best complement for human activity. Growbots, a lead generation platform, uses machine learning algorithms to automate the prospecting process and marketing campaigns. The platform integrates with SalesForce and uses AI to enhance and automate email marketing, creating tailored emails for customers, scheduling campaigns and sending follow ups at opportune times. Amelia is a virtual customer assistant that uses natural language processing to understand customer queries and provide answer based on data gathered from previous interactions and the company knowledge base.

A few years back, cloud computing transformed customer management, giving every small and medium business access to unified data and communication platforms without the need to make heavy investments in IT infrastructure and staff. This time around, the next revolution in the space is being driven by artificial intelligence algorithms that help businesses automate customer outreach and make optimal use of data. Beneath the surface of the roiling sea of data we're generating hide exceptional business and sales opportunities. But the problem is there's now more information available than limited human resources and legacy tools can handle. Fortunately, making sense of data, both structured and unstructured, is something that artificial intelligence is becoming increasingly proficient at.