{"id":6020,"date":"2026-09-04T09:29:08","date_gmt":"2026-09-04T07:29:08","guid":{"rendered":"https:\/\/britishdailynews.co.uk\/?p=6020"},"modified":"2026-09-04T09:29:10","modified_gmt":"2026-09-04T07:29:10","slug":"diskover-the-artificial-intelligence-anticipating-the-future-of-industry","status":"publish","type":"post","link":"https:\/\/britishdailynews.co.uk\/?p=6020","title":{"rendered":"Diskover: The Artificial Intelligence Anticipating the Future of Industry"},"content":{"rendered":"\n

Founded in 2019, Diskover develops advanced artificial intelligence solutions for manufacturing and business processes. From predictive maintenance to its SMeCo generative platform, CEO Riccardo Di Nisio<\/strong> outlines a model that integrates data, automation and human capital within the evolving framework of Industry 5.0.<\/p>\n\n\n\n

Dr Di Nisio, Diskover was founded in 2019 with a very clear mission: to bring artificial intelligence into companies. What did that mean in practical terms back then, and what does it mean today?<\/strong><\/p>\n\n\n\n

In 2019, talking about artificial intelligence in a business context still meant, in most cases, operating on a largely theoretical or experimental level. Our ambition, by contrast, was extremely practical: we wanted to embed AI into everyday operational processes, turning it into a decision-making tool rather than a purely academic exercise.<\/p>\n\n\n\n

Today, that approach has evolved. It is no longer simply about introducing algorithms, but about building genuine intelligent architectures that connect data, people and processes. Artificial intelligence thus becomes an invisible yet central infrastructure, continuously guiding business decisions.<\/p>\n\n\n\n

You chose manufacturing as your first area of application. What was the strategic rationale behind this decision?<\/strong><\/p>\n\n\n\n

Manufacturing is where data can be translated most directly into economic value. Every inefficiency translates into a cost, while every improvement contributes to margins. This enabled us to demonstrate the impact of our solutions very quickly.<\/p>\n\n\n\n

Moreover, manufacturing is characterised by considerable operational complexity: machinery, production cycles and environmental variables. This makes it an ideal environment in which to apply predictive models and generate tangible competitive advantages.<\/p>\n\n\n\n

Looking more closely at the technology, what exactly do your predictive maintenance and predictive quality systems do?<\/strong><\/p>\n\n\n\n

We operate on two levels. On the one hand, we monitor the condition of industrial equipment through sensors and high-frequency data acquisition systems; on the other, we use machine-learning<\/em> algorithms to interpret these data and identify anomalous patterns.<\/p>\n\n\n\n

This enables us to anticipate potential failures or deviations in production quality before they occur. In practical terms, companies no longer simply react to problems: they anticipate them. The result is a significant reduction in machine downtime, greater production stability and a substantial decrease in waste and rejects.<\/p>\n\n\n\n

You have also developed proprietary and patented solutions. How important is research to this journey?<\/strong><\/p>\n\n\n\n

Research is fundamental. Our solutions, such as Rewind technology<\/strong>, were developed precisely out of the need to adapt theoretical models to real-world industrial environments, which are often extremely complex.<\/p>\n\n\n\n

It is not simply a matter of applying existing algorithms, but of developing frameworks<\/em> capable of operating effectively across heterogeneous production environments, with imperfect data and variables that are difficult to control.<\/p>\n\n\n\n

Working with major players, both in Italy and internationally, has enabled us to refine these technologies and make them scalable.<\/p>\n\n\n\n

Many SMEs still perceive predictive maintenance primarily as a cost. Why is that?<\/strong><\/p>\n\n\n\n

It is largely a cultural issue. There is often a tendency to focus on immediate costs without adequately considering risk and opportunity cost. Unplanned machine downtime can have an enormous financial impact, yet it is often perceived as an occasional event.<\/p>\n\n\n\n

Predictive maintenance, by contrast, is an investment that dramatically reduces this uncertainty. What is needed, therefore, is a change in mindset: a shift from a reactive approach to one that is preventive and strategic.<\/p>\n\n\n\n

In 2023, you integrated generative AI into your solutions. What made this transition possible?<\/strong><\/p>\n\n\n\n

The availability of a structured data foundation. Without organised and reliable data, generative AI risks producing results of limited practical value. We already had a well-established infrastructure in the predictive field.<\/p>\n\n\n\n

Introducing generative AI enabled us to take a major step forward: not only predicting outcomes, but also generating content, automating processes and directly supporting operational activities.<\/p>\n\n\n\n

SMeCo is the result of this evolution. What exactly is it?<\/strong><\/p>\n\n\n\n

SMeCo is a platform that integrates predictive and generative AI with a specific objective: to orchestrate the complexity of corporate information.<\/p>\n\n\n\n

Companies today use multiple systems \u2014 ERP, CRM and management platforms \u2014 that generate fragmented data. This entails a considerable amount of manual work to collect, process and interpret information. SMeCo sits between these systems, creating coherence across them, automating workflows and delivering information that is already structured and ready to use.<\/p>\n\n\n\n

You describe SMeCo as an \u201corchestrator\u201d rather than simply as software. Why is this distinction important?<\/strong><\/p>\n\n\n\n

Because we do not add another layer of complexity; we reduce the complexity that already exists. We do not ask companies to replace their existing systems. Instead, we enable those systems to communicate with one another.<\/p>\n\n\n\n

In this sense, SMeCo becomes a central point of synthesis, simplifying access to data and transforming information into action.<\/p>\n\n\n\n

The human-in-the-loop model is central to your approach. How does it work in everyday practice?<\/strong><\/p>\n\n\n\n

During the night, the system performs a series of tasks: it analyses data, generates reports, prepares documents and supports commercial and administrative activities.<\/p>\n\n\n\n

When employees arrive at work, they find a dashboard<\/em> containing tasks and information that have already been processed and preliminarily validated by the system. Their role is to review, refine and approve the output.<\/p>\n\n\n\n

This makes it possible to shift human work away from repetitive activities towards tasks requiring greater cognitive value, judgement and expertise.<\/p>\n\n\n\n

There is also the issue of corporate knowledge. How does SMeCo address this?<\/strong><\/p>\n\n\n\n

One of the main challenges companies face is that know-how is often implicit, dispersed among individuals and difficult to transfer. SMeCo makes it possible to structure this knowledge: every interaction, every validation and every document contributes to enriching a shared data repository.<\/p>\n\n\n\n

In this way, individual expertise becomes a collective corporate asset, reducing dependence on specific individuals and strengthening organisational resilience.<\/p>\n\n\n\n

Could we say that this approach anticipates Industry 5.0?<\/strong><\/p>\n\n\n\n

Yes, because it places collaboration between people and machines at the heart of the model. It is no longer simply about automation, but about intelligent integration.<\/p>\n\n\n\n

Technology alone, however, is not enough. Companies need to develop a genuine culture of AI governance<\/strong>: understanding when to use artificial intelligence, how to use it and for what purposes.<\/p>\n\n\n\n

What is the main challenge facing Italian companies today?<\/strong><\/p>\n\n\n\n

Awareness. Many companies have access to advanced technologies but have yet to develop a strategic vision for how to use them effectively.<\/p>\n\n\n\n

This is why training is also a major part of our work: helping people understand the value of data and enabling them to use it effectively.<\/p>\n\n\n\n

Ultimately, what is Diskover\u2019s distinctive contribution?<\/strong><\/p>\n\n\n\n

Our value lies in our pragmatic approach. We do not introduce technology for technology\u2019s sake; we develop solutions built around companies\u2019 real-world data.<\/p>\n\n\n\n

Our goal is to guide businesses through a profound transformation: turning data into decisions, decisions into processes, and processes into lasting competitive advantage.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

Founded in 2019, Diskover develops advanced artificial intelligence solutions for manufacturing and business processes. From predictive maintenance to its SMeCo generative platform, CEO Riccardo Di Nisio outlines a model that integrates data, automation and human capital within the evolving framework of Industry 5.0. Dr Di Nisio, Diskover was founded in 2019 with a very clear […]<\/p>\n","protected":false},"author":3,"featured_media":6021,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=\/wp\/v2\/posts\/6020"}],"collection":[{"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6020"}],"version-history":[{"count":1,"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=\/wp\/v2\/posts\/6020\/revisions"}],"predecessor-version":[{"id":6022,"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=\/wp\/v2\/posts\/6020\/revisions\/6022"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=\/wp\/v2\/media\/6021"}],"wp:attachment":[{"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6020"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6020"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/britishdailynews.co.uk\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6020"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}