Interview with Mauro Bogliani – Recycling and automation

A. Technology and Automation 

1 . Recycling technologies today combine process automation, industrial digitalisation, and sustainability-focused engineering. How do these technological areas work together to improve efficiency in recycling plants?

Efficiency comes from combining machines with smart software. Process automation (PLC/SCADA) controls physical operations, while digital systems convert sensor data into useful information. Sustainability engineering improves energy use and reduces waste in washing and extrusion. Without alignment, transformation does not speed up; it becomes “Noise” and a cost without clear value.

2. Among these areas—automation of recycling processes, smart manufacturing systems, and industrial data analytics—which is currently creating the most significant transformation in the recycling industry?

The real transformation lies in Industrial Data Analysis. Although robotics is fascinating, it is the ability to analyse, in real time, the purity of the material and the performance of production lines that is changing the business model, moving from a “reactive” to a “predictive” and highly precise form of recycling. It is not enough to simply collect data; the organisation must clearly define who, or what system, has the authority to adjust recycling parameters in real time. The shift to “Smart Manufacturing” fails when decision-making authority remains fragmented.

 B. Future Technology and Impact 

3. Looking ahead, which technology do you think will have the greatest impact on the industry in the next five years?

Hyperspectral machine vision, supported by deep learning, is a technology that sees and analyses materials in much greater detail than traditional systems. It can detect differences that are not visible to the human eye or to standard sensors. Because of this, it can separate plastics that today look the same, such as different types of polypropylene or complex multi-layer materials that are normally very difficult to process.

This capability means that mechanical recycling can achieve a much higher level of quality, approaching that of new, virgin material rather than producing lower-grade output. However, this technology will only make a real difference if the whole system around it is properly designed. There must be a clear architecture that maintains data accuracy and consistency from the beginning to the end of the recovery process. If the data becomes incomplete, inconsistent, or disconnected at any point along the line, the technology's benefits are reduced, and its overall impact is limited.

4. Many companies are experimenting with AI but struggle to scale it across the organisation. What do you think will distinguish the organisations that succeed from those that do not?

The real difference will be in Data Governance. Companies that fail tend to treat artificial intelligence as a separate, isolated project. In contrast, companies that succeed integrate AI into their day-to-day operations. They not only invest in algorithms but also in cleaning and organising their data and training plant staff to work effectively with machines.

Companies that can scale have built a structure that keeps identity, capital, and algorithms aligned. This means the organisation knows its values, investment strategy, and technology, all of which are consistent. In these cases, AI can act as a central nervous system that connects everything and supports coherent decisions across the business, rather than a set of disconnected local solutions that do not work well together.

C. Intelligent Automation Congress – Milan 

5. During these two days at the Intelligent Automation Congress in Milan, what idea or discussion stood out the most to you?

Many topics were discussed at varying levels of depth and complexity. What stood out to me was the large number of companies active in this market and the speed at which they operate and introduce new, innovative solutions. The pace is high, and the level of activity shows how quickly this space is evolving.

From my perspective, I take away two key ideas. The first is the need for awareness of how technology is used, meaning that organisations must understand not only what the technology can do but also how and why they apply it. The second is the importance of clear governance during this phase of rapid technological acceleration, so that growth and innovation remain controlled, aligned, and sustainable rather than fragmented or difficult to manage.

6. What is the main message organisations should take away from this event about the future of AI and intelligent automation?

Companies must carefully analyse their internal processes and, if needed, revise them before thinking about how AI can be used as a support tool. AI should not be the starting point. The starting point is understanding how the organisation works today, where the weaknesses are, and where improvements are really needed.

The use of new technologies must be scaled to each company's real needs. This means avoiding a one-size-fits-all approach and focusing instead on what creates value in a specific context. The adoption of AI should lead to clear, concrete, and measurable results, not just technical activity or isolated improvements.

Only in this way can AI enter organisations with quality and purpose. Otherwise, it risks remaining a set of small experiments or local performance gains that do not connect with each other. In those cases, the company as a whole does not move forward, even if some parts appear to improve.