IoT and IIoT: Challenges and Oportunities – Interview with Antonio Grasso

IDboxRT had a unique opportunity to talk to one of the leading authorities in digital transformation, Antonio Grasso, who has more than 37 years of experience working on numerous IT projects.

An entrepreneur, technologist, founder and CEO of the Italian startup Digital Business Innovation Srl, Antonio is considered one of the leading experts in digital transformation and researchers in the field of Artificial Intelligence and the Internet of Things. With his ethical vision of digital transformation, Antonio aims to help spread the concept of digital transformation about new, emerging technologies and their use to ensure a better future.

In this interview, we talked about the differences between loT and IIoT, their main characteristics and the potential impact on the manufacturing world, we also learned from Antonio what the future holds for industrial IoT technologies and how to prepare now for the challenges posed by the digital revolution.


IDboxRT: What is the Internet of Things (IoT) and how is it different from the Industrial IoT (IIoT)?

Antonio Grasso: The Internet of Things is a paradigm that describes a world of connected devices (devices that are connected to a network and exchange information) that make the concept of a cyberphysical world real. The main differences between the IoT and the IIoT are in the necessary requirements and expected features.

While IoT mainly refers to domestic devices and things that are used in a consumer environment (a connected car, a connected refrigerator or a smartphone are considered IoT), IIoT – as the name implies – are connected devices that are used in a manufacturing environment to automate production, monitor processes or collect and process data in close proximity to the device.

This infographic will help you understand the key differences between IoT and IIoT:

IDboxRT: How can the IIoT change the world of manufacturing? What are the main challenges and opportunities in this area?

Antonio Grasso: One of the greatest opportunities is the collection of data for processing by new algorithms that exploit the probabilistic capabilities of AI. In fact, IIoT devices, in addition to controlling the transition of signals from the physical world to the cybernetic world, collect data that can then generate new types of insight not previously available.

“IIoT devices, in addition to controlling the transition of signals from the physical world to the cybernetic world, collect data that can then generate new types of insight not previously available.”

Beyond this undeniable advantage, there is also a multifaceted aspect of IIoT devices that can benefit both product development and process improvement. As the following infographic shows, IIoT is present in both scenarios.

IDboxRT:  To meet evolving customer expectations and support the growing demand for services, what key features should manufacturers be looking for in industrial IoT software?

Antonio Grasso: As devices in manufacturing are constantly growing in sophisticatedness, you cannot think to plug and play a device or software without thinking about the architecture. Designing the correct architecture helps you to implement devices and software in the right way.

Starting from considerations about what are my customer needs and what are my objectives could heavily accelerate the implementation. Just think about the layers you need to configure for a mid-sophisticated architecture, could give you a vision of it as you can note from the infographic:

Let me say that the right implementation strictly depends on your industry. For example, if you are in the logistics industry, you should start thinking about connecting your trucks. This is a simple model of what you can manage/gather when implementing IIoT for the logistics and transportation industries:

Thus, determining the right software is not easy. It is better to start with your business model, your strategy, and your goals, and then build a team – perhaps using external experts – to develop your implementation.

IDboxRT: How do the IIoT monitoring solutions benefit the industry?

Antonio Grasso: First, familiarize yourself with the basic concepts of the Internet of Things, as in this infographic:

It all starts with data. Your goal is to manage and interpret the data generated by hardware devices. So, in my opinion, the first thing to do is to define the IIoT operating system, which is just the beginning for managing the incredible flow of data generated by IIoT devices.

“It all starts with data. Your goal is to manage and interpret the data generated by hardware devices. So, in my opinion, the first thing to do is to define the IIoT operating system, which is just the beginning for managing the incredible flow of data generated by IIoT devices.”

Then you have to get to know machine learning in order to analyze the huge amount of data and create insights useful for making business decisions.

The benefits are enormous and tend to be related to your business model. If I have to list some significant ones, I think the ones mentioned in this image are the right ones:

IDboxRT: In conclusion, what does the future hold for industrial IoT technologies, could you give an approximation of what we should expect in the context of the manufacturing industry?

Antonio Grasso: Many technologies are coming together to create value for manufacturing. Imagine infusing AI and Edge Computing into IoT devices would bring intelligence to perform inference locally, without transferring data to servers or the cloud. Many have coined a new term: AIoT to refer to the strict interconnectivity between the two.

But watching communications closely, I think 5G private networks will increase connectivity like never before. Low latency, extreme bandwidth and increased security will create a new production model out of augmented and virtual reality as the new blue-collar companion.

The Digital Twin will become an Intelligent Twin, opening up new scenarios for virtualizing even more complex devices, such as engines, to achieve unprecedented simulation capabilities that will improve products and services.

Overall, I think it’s important to note that financial return on investment is something manufacturers should consider before setting out to upgrade their manufacturing process. Here is some interesting information that shows the expected return on investment (ROI) from IoT projects.


We would like to thank Antonio for taking the time to have a conversation with us at IDboxRT.  We certainly learned a lot and hope this information helps our community as well.

You can find more information about the protagonist of the interview and his work in the digital field here:

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If you’re looking for a powerful monitoring solution for your industry, don’t hesitate to Contact us to gain more insights from our IDboxRT experts!

User interface: the key factor in interacting with software and applications

The more complex technology becomes, so do our needs. Two of the essential needs we seek, especially when it comes to business processes, are speed and efficiency. While we meet the need for speed and efficiency, we have realized that there is also another need: entertainment. We want to be entertained, to have interaction during our engagements. The user interface fills that void since our lives have been inundated by digitization. We may not realize how common and crucial its functions are. One thing is for sure: user interfaces make our lives much easier. In this blog, we have discussed the topic of user interfaces with a very basic approach, as this topic can easily become very detailed and technical.

What is user interface?

The term is self-explanatory: user interface (UI) is a tool/object that helps people interact and operate more efficiently and easily. In other words, it enables human interactions to occur with computers, software, applications and websites. There are two aspects that occur when humans begin to interact with the user interface: the ability to command efficiently and easily and to receive instant feedback that aids the decision making process for the operating human.

User interfaces are created with multiple layers, with the goal of appealing to users’ senses, such as touch, sight and hearing. These layers help humans expend as little effort as possible to achieve the expected results when interacting with it. Layers are supported by various devices to increase and facilitate the level of interaction, such as keyboard, mouse, microphone, touch screen, camera and many more.

Why is it important to have user interfaces?

In addition to facilitating and increasing the efficiency of human interaction, user interfaces play a key role in terms of the functionality of computers, software, applications or websites. Functionality is often measured by the speed and responsiveness of the user interface. It is also important to consider the user experience when designing and focusing on the functionality of user interfaces.

Below, we highlight important components of user interfaces. These components provide valuable information about the goals of user interfaces. However, user interfaces, in most cases today, contain multiple components.

Information architecture: Its goal is to provide an easy process, bringing out the most important information with minimum effort for the user. Information architectures can be built in hierarchical, sequential or matrix form. They also include tooltips, icons, progress bar, notifications, message boxes and modal windows.

Visual design: When it comes to visual design, color, contrast, typeface and video play a crucial role in maximizing user appeal. While the goal is to create the attraction, it is also considered mission critical to make the user continue the interaction as much as possible.

Input design: These controls allow users to interact with computers, software, applications and websites to complete the desired action. They include check boxes, radio buttons, drop-down lists, list boxes, buttons, toggle buttons, text fields and date fields.

Navigation components: As you can understand, they are placed in order to provide easy and efficient navigation for the user to interact with. Breadcrumb, slider, search field, pagination, slider, labels and icons are common examples.

User interface on the IDboxRT platform:

The IDboxRT platform is based on state-of-the-art technology and the latest IT security implementations with the ultimate goal of bringing efficiency, security and ease to your production.

Our platform is the ideal place to view, control, compare and analyze everything related to your business processes. We provide all the necessary information with an in-depth view. Moreover, the in-depth view is applied for each component level.

IDboxRT is a platform in constant development and evolution. Depending on the specific needs of the company, the platform can be adapted to the configuration of the operation.

Please contact us for more information on the updates from the IDboxRT team of experts.

What does it take to scale the IoT?

How can the IoT marketplace deliver on its promise? Let’s take a look at what it will take to succeed from an enterprise IoT customer perspective. Companies that have been successful in large-scale IoT adoption take five basic steps:

  1. Identify who owns the IoT in the organization. Currently, many organizations do not have a clear IoT owner, and decision-making is scattered across functions, business units, and levels. Companies that have been successful in large-scale IoT implementations are addressing this situation by appointing a clear owner who can be representative of different functions and roles.
  2. Design for scale from the start. IoT should be based on business outcomes. Too often, enterprise customers get carried away with technology and focus only on pilot projects, causing many enterprise customers to suffer as a result.
  3. Invest in technical human resources. IoT technicians are in short supply. Hiring engineers and data scientists is important, but for organizations to be on the cutting edge, they also need to upskill their current employees in data science.
  4. Change the entire organization, not just IT. Too often, IoT implementation is seen as a technology project run by IT rather than a business transformation. Technology alone will never be enough to unlock the potential of the IoT and deliver maximum value. Instead, the underlying operating model and workflow of the business must be redefined.
  5. Monitor your processes. In general, IoT-based remote asset and process monitoring allows companies to reduce the time it takes to detect, analyze and fix problems with critical assets and processes. In addition, they can optimize human resources, improve the security of operations and reduce operating costs.

The IoT market is growing rapidly. Growth may be slower than expected, but that’s not because of a lack of confidence or belief in the impact this technology can have. Rather, we believe that operational factors are holding the market back. As we have seen, there are nuances at the level of customization and use clusters. This applies not only to growth, but also to tailwinds and headwinds. For the IoT to reach its potential, companies and their customers must address these issues.

Want to know more about this topic? Contact us and our experts will be happy to answer all your questions! 🙂

The 3 technology trends that will transform the world

What distinguishes the fourth industrial revolution from previous ones is the convergence and interaction of several technological trends at the same time. Here are some of the main technological trends that that will permanently change our businesses and our lives.

Trend 1: Everything connected and smart

You’re no doubt familiar with the Internet of Things (IoT). IoT refers to the growing number of smart, connected devices and objects capable of collecting and transmitting data.

In the future, everything that can be connected will be connected. Not just devices and products – although this is certainly a key factor for businesses – but also the spaces in which we live and work. From smart, connected factories and offices to entire smart cities, the spaces around us will be increasingly equipped to track what is happening and act accordingly.

Trend 2: The datification of the world

Ubiquitous computing and IoT are bringing huge amounts of data being generated every day. But along with this machine-generated data, we humans also generate an enormous amount of data during our daily activities, a process that shows no signs of slowing down.

The good news is that companies can use this data to develop better products and services, improve business processes, make decisions and even create new revenue streams. However, companies also need to be aware of the risks associated with data, particularly data privacy and security.

Trend 3: Artificial Intelligence (AI)

All the data generated is an important tool for artificial intelligence, which has taken an incredible leap forward in recent years.

The result for businesses is that, as our interactions with machines become increasingly intelligent, customers will expect all types of products and services to have some form of AI capability.

Now, following all of these trends, we understand that we are entering an era of rapid and continuous evolution, where multiple technology trends combine and feed into each other to produce major changes. For businesses, this means that the days of incremental technology upgrades are gone forever. Continuous change is the way of the future.

If you want to know more about how technologies can improve your business processes, contact us and our best experts will inform you.

Web 3.0: What is it and how does it differ from Web 2.0?

What is Web 3.0?

Although Web 2.0 may seem like an advanced approach to the Internet, it still has many drawbacks. What about the security of your personal data? With Web 2.0, trusted institutions take control of user data, especially because of the need for trusted intermediaries. If two parties want to make a transaction but do not know and trust each other, they will have to resort to the services of trusted intermediaries. However, the intermediary has control over the storage and management of the data, thereby increasing its power over the users. In addition, centralized power has never led to success in times of crisis, which requires decentralization.

Web 3.0 is a promising improvement over Web 2.0, especially given the major transformations in terms of infrastructure. The third generation of the Web, also called the semantic Web, uses an improved metadata system. The metadata system helps structure and organize all types of data, making them readable for humans and machines. The main benefit associated with Web 3.0 is almost the best twist on the differences between Web 2.0 and Web 3.0. Web 3.0 eliminates the need for centralized intermediaries and introduces the universality of information.

How revolutionary is Web 3.0?


Understanding the comparison between Web 2.0 and Web 3.0 should also focus on the uniqueness of Web 3.0. The third iteration of the Web is a formidable response to Web 2.0’s shortcomings with an emphasis on innovative technology. Web 3.0 uses artificial intelligence to enable machine-to-machine interaction as well as advanced analytics. In addition, Web 3.0 uses a decentralized network to bring data under the control of the owners. As a result, users are able to own their data as well as determine how it is distributed. In addition, the differences between Web 2.0 and Web 3.0 are improvements in user privacy and security. Web 3.0 uses encryption and distributed ledger technology to address trust issues that were evident in Web 2.0.

Notable features of Web 3.0


You can build a stronger foundation for understanding “What’s the difference between Web 2.0 and Web 3.0?” by focusing on Web 3.0 features. Here are some of the most important features of Web 3.0 that will help distinguish it from Web 2.0.

  • Web 3.0 uses artificial intelligence to deliver the right results at a faster pace along with access to real-time information.
  • Web 3.0 also allows users to tap into the potential of 3D images and graphics.
  • Web 3.0 has a Semantic Web functionality. This means that Web 3.0 can support understanding the meaning of words. As a result, machines and people can easily find, share and analyze information on Web 3.0.
  • Web 3.0 also offers important features such as improved privacy and security.
  • Web 3.0 uses improved authorization mechanisms using distributed ledger technology and encryption to protect user identity and data.

If you want to learn all about how Web 3.0 and related technologies can improve your business processes, contact us and our top experts will inform you!

What is the best way to successfully define, scale and lead Digital Transformation?

DEFINE A CLEAR DIGITAL STRATEGY
Planning a company’s digital transformation from the outset involves being able to see the big picture. You also need to be clear about your desired outcomes. Define your objectives from the outset. Decide what you want to achieve and make sure you understand why the organization needs to transform. Depending on your current and planned methods of operation, the following may be decisive factors:

  • Reduced supply costs
  • Increased sales volume
  • Reducing waste or duplication
  • Increasing profits
  • Attracting new customers
  • Catching up with or surpassing competitors
  • Implement new technologies

In summary, start by assessing the current state of your business. Consider the requirements of your employees and systems, as well as potential areas for improvement.

CALCULATING ROI BEFORE STARTING IOT PROJECTS
A digital transformation should include an assessment of how much the proposed technology will drive revenue growth. A return on investment (ROI) calculation provides a feasibility check prior to

SELECTIVE TECHNOLOGY INVESTMENT
Choosing the right tools and technology is critical. There are many digital transformation trends being discussed in the marketplace, but technology must serve the business, not the other way around. Choosing the best strategy for the IoT journey remains a challenge. Before deciding on an expensive IoT implementation and choosing the direction of your journey, examine your options and seek advice on choosing the best technology and systems for your business.

IT/OT INTEGRATION FOR IOT SOLUTIONS
Integrating information technology (IT) and operational technology (OT) is no easy task, but with the right approach, it can deliver unprecedented growth. By implementing the right IoT solutions, you can successfully bridge the gap and create an intelligent, interconnected foundation to improve business processes and add value to your data. By integrating IT and OT systems, you can unlock the power of your data to generate accurate predictions and make informed decisions.

To ensure that your digital transformation efforts do not stall, it is important to have a trusted partner to guide you on your digital transformation journey. Each digital journey is individual and should be tailored to the client’s needs.

By checking off the different aspects of this list, you will be on your way to a successful digital transformation. If you would like to discuss digital transformation readiness in more detail, please contact us and we will put you in touch with our experts.

Cantabria in Digital Transformation

David Vilasack, IDboxRT manager at CIC, participated last month, September 28, in the conference “Cantabria in the Digital Transformation” organized by Ascentic and inaugurated by the president of Cantabria, Miguel Angel Revilla.

The manager’s intervention was aimed at talking about the experience of the IDboxRT monitoring platform in Industry 4.0, focusing on one of its main success stories: Bosch, whose project with the tool involves the monitoring of thousands of signals between its Spanish plants in Madrid, Barcelona and Aranjuez. Among the benefits obtained by this customer, we can highlight the creation of calculations and subsequent consumption reports to evaluate energy losses, or even the acquisition of data from CO2 sensors for personnel control (COVID-19) and reports for HR and Medical Service on the status of the different rooms of the plant for CO2 control (COVID-19).

In addition, Vilasack spoke about the key technological pillars in Industry 4.0, on which the strategy and operations of any company in its digital transformation process must be based. Among them, he highlighted augmented reality, cybersecurity, Big Data, the cloud, also known as cloud computing, an essential axis since 90% of companies will partially or totally migrate to cloud environments in the next three years.

Artificial intelligence was another of the elements analyzed, as business environments will become much more intelligent, forming a clear cohesion between machines and humans. They will be more efficient, effective and competitive spaces due to the automation of processes, so companies will increase their production and productivity.

7 Tips for Hotel Energy Management

With integrated functionality, controls, sensors, and connected energy management platform capabilities, hotels can reduce energy consumption by 20-50% (depending on facility and location) and lower operating costs.

Hotel energy efficiency is a journey. A detailed understanding of a hotel’s energy consumption patterns, its carbon footprint and its overall compliance with established goals and objectives can be the key to achieving the right balance of efficiency and guest satisfaction.

It is very important to establish a historical baseline for the facility to understand in detail the patterns affecting energy consumption versus the hotel’s bottom line.

There are a few general tips that energy management experts say hoteliers can implement to achieve efficiency savings.

The Internet of Things allows for smart connectivity, monitoring, control, asset tracking and more at relatively low cost.

1. Install an energy-saving thermostat based on occupancy to reduce the temperature in unoccupied rooms. Make sure the system can interface with the hotel’s property management system. Being able to manage an aggressive energy-saving profile in a room when it is not sold out can have a significant impact on savings, while preventing temperature drifts that may be unwanted by guests when the room is sold out.

2. Use a real-time occupancy signal from the energy management thermostat to turn off lights in empty guest rooms.

3. Ensure that the guest room energy management system can communicate with the building management system. The ability to view data through a single dashboard can be very useful in monitoring and measuring the energy efficiency of the entire facility.

4. Use run-time data from an energy management system to identify heating, ventilation, and air conditioning units that are operating unusually. Apply preventive maintenance to ensure that problems are rectified quickly.

5. Embrace the Internet of Things and its future development. Hotels need to keep their avenues open in the area of connectivity and management – an area that will grow strongly in the next five to seven years. This space is very fluid, and hoteliers need the flexibility to be able to pivot and embrace the changes that have happened and are coming.

6. Investment in staff and quality products are key. With high staff turnover, hotels may not invest as much in staff training, but it’s worth it for the energy management savings. They should study the data and understand what problems you are having. The analysts will recommend how to solve the problem and how to work more efficiently.

A better product may cost more, but hoteliers have to base it on the value the product brings. The link to that value includes guest comfort, staff convenience, guest feedback, etc. Invest in the products and technology they provide.

7. Give guests the opportunity to understand their habits and how they use energy in the guest room. Give them the opportunity to make a difference in their environment. Motivate them to save energy by educating them about their carbon footprint.

WHAT IS INDUSTRIAL ARTIFICIAL INTELLIGENCE?

INTRO

Although Artificial Intelligence (AI) technologies began to be developed several decades ago, several definitions currently coexist that include concepts such as Big Data, data science, machine learning or IoT.

AI is defined as a set of tools and methodologies that allow machines to simulate processes and develop tasks that require applying human intelligence such as decision making, object recognition or speech understanding.

The industrial sector has been immersed for years in its digital transformation in what has been called the fourth industrial revolution or Industry 4.0. There is no doubt that this sector has a high potential to lead the application of AI technologies in the monitoring and optimization of its processes, providing an appropriate scenario to test its benefits, from design and manufacturing processes to the management of the value chain. Thus, large companies such as GE, Siemens, Intel, Funac, Kuka, Bosch, ABB, NVIDIA and Microsoft are investing heavily in the development of new systems to improve their production.

At this juncture, the concept of Industrial Artificial Intelligence (IAI) has recently emerged, which can be defined as the application of AI to the operations, processes and physical systems of a company, so that the behavior of these operations, processes and systems can be monitored, optimized or controlled to improve their efficiency and performance, providing them with greater autonomy. Thus, this concept includes applications related to the manufacture of physical products, production lines and warehouses, or operations related to different processes.

It is an end-to-end system, in which sensors generate data, which are sent, managed and analyzed by means of different algorithms and models that generate decisions in real time and whose results are returned for actual implementation in the actuators.

USE CASES

There are several different use cases related to three categories of applications depending on the degree of automation involved: monitoring, optimization and control.

Monitoring

Industrial processes need to monitor the performance of their systems and products to identify or predict failures and other situations that lead to unsatisfactory results. Some examples that will benefit from IAI technologies are as follows:

  • Quality Control

Companies find it difficult to maintain high levels of quality and comply with regulations and standards due to the current short time-to-market for new products and services and their increasing complexity. On the other hand, consumers expect defect-free products, so companies must avoid the damage that complaints and defective products can do to the brand.

In this context, AI algorithms enable a new form of quality control. On the one hand, algorithms based on image recognition technologies warn production teams in real time about faults in production systems that can reduce product quality (e.g., recipe deviation, changes in raw materials). On the other hand, the implementation of these algorithms allows the prediction of maintenance and planning tasks that minimize the associated risks. Finally, the integration of such algorithms with IoT platforms allows the collection of data on the use and behavior of products during their useful life, information that can be very valuable when making strategic and design decisions.

  • Predictive maintenance

The continuous maintenance of production machinery represents a major expense in manufacturing processes, so it is important to implement solutions based on AI algorithms that allow predicting future failures in a part, machine or system in order to drastically reduce unplanned downtime and increase the useful life of production systems.

Optimization

IAI-based decision making and planning systems allow users to design plans to optimize a set of business metrics.

  • Process planning

Many industrial scenarios include complex work sequences whose order of execution can significantly impact factors such as cost, time, quality, workloads, supplies or waste. The application of AI-based optimization algorithms allows such sequences to be defined dynamically in real time.

  • Generative design

AI is changing the way products and services are designed. Generative design uses AI algorithms to design new products based on their description, including parameters such as type of material, means of production, budget or time to market. These algorithms analyze different configurations before proposing the best solutions.

  • New market adaptation strategies

The application of AI is not limited to the production plant; its algorithms make it possible to optimize supply chains or help companies anticipate market changes. All this is a great advantage for business management, moving from a reactive approach to a strategic one. Thus, estimates of market demand can be formulated by searching for patterns that relate location, socioeconomic and macroeconomic factors, weather patterns, political status or consumer behavior so that companies can optimize their resources or control inventory.

Control

Finally, control systems are at the heart of industrial process operations and are essential to reap the full benefits of automation. Examples of applications that benefit from AI include the following:

  • Robotics

Traditionally, industrial robots have been explicitly programmed to move between a series of 2D or 3D points and perform specific actions at those points. New approaches such as collaborative robots or co-robots simplify programming by allowing the capture of these points based on the robot’s physical position. However, in both cases, the robot does not detect changes in the environment or in the position of the parts it is manipulating. Therefore, computer vision allows robots not to interfere with people or other robots, and to interact independently.

  • AGV

Autonomous mobile robots (AGVs) are used in warehouses and companies to transport and collect materials thanks to the use of image-based AI algorithms that enable them to understand, map and navigate these environments more efficiently.

CHALLENGES OF INDUSTRIAL ARTIFICIAL INTELLIGENCE

Industrial AI presents a number of challenges that set it apart from other consumer-facing AI applications.

  • Data acquisition and storage

IAI systems rely on data captured by sensors that seek to digitally represent the real world. The implementation of IoT platforms and cyber-physical systems (CPS) has enabled large volumes of data in industrial processes whose acquisition, management and storage has led to different architectures and storage systems.

However, it should not be forgotten that large volumes of data are often captured with a lot of noise, which makes it difficult to acquire and store the data for subsequent analysis. Therefore, advanced simulation techniques including digital twins are used to generate training data under different conditions.

  • Data hybridization

A challenge of IAI is the generation of common formats for heterogeneous data coming from diverse sources (images, videos, dwg plans, …). For this, it will be necessary to develop semi-automatic models that help to structure (e.g. extraction of information from a plan, extraction of tags from an image), homogenize (e.g. spatial/temporal interpolation techniques to move from data in one unit to another) and/or harmonize data (e.g. product matching/record linkage techniques to associate data from different sources).

  • Training

The correct application of IA algorithms is associated with the availability of annotated training data. Capturing this data can be complex in industrial environments, as it is often difficult to detect and reproduce some of the failures or lack of quality of products and services.

  • Regulated environments

Industrial environments must comply with certain standards and regulations that impact their operations, such as product safety, public health, environmental impact or occupational safety. In some cases, regulatory controls can make it difficult to implement AI technologies.

As stated in the European Data Strategy1 published by the European Commission in February 2020, Europe must become a model of a data-empowered society for better decision making in the public sector and private enterprise. To this end, the European Union will promote a legal framework in relation to data protection, fundamental rights, security and cybersecurity. The aim is to generate an ecosystem of trust thanks to a regulatory framework for AI2.

SITUATION IN SPAIN

According to a study by EY for Microsoft, the majority of companies surveyed in Spain (65%) have plans, pilot projects or proofs of concept around AI. However, only 20% have solutions in operation, 12 points below the European average.

The “Strategy for Artificial Intelligence in R&D&I in Spain” includes a section oriented to the application of AI in the Connected Industry. As the document states, Spanish industry represents 13% of the country’s added value and employs 11% of the employed population, so the social and economic impact of AI technologies is essential.