In recent years, Artificial Intelligence (AI) has gained a prominent role in the Italian economic and social landscape. According to data provided by the Artificial Intelligence Observatory of the Politecnico di Milano, the AI market in Italy reached 760 million euros in 2023, with a 52% growth compared to the previous year. This figure highlights the acceleration of initiatives in this sector, particularly among large companies, which account for 90% of investments. However, the adoption of AI is still uneven, with small and medium-sized enterprises (SMEs) struggling to keep up, contributing only 18% of currently implemented AI projects.
AI Adoption and Use in Italy: A Fragmented Picture
One of the most significant aspects that emerges from the study is the marked disparity in AI adoption between different types of companies. Large companies are the ones investing the most and implementing AI solutions, while SMEs, due to economic and organizational constraints, are slower in their digitalization journey. Six out of ten large companies have already launched at least one AI project, compared to just 18% of SMEs. Generative AI, although representing only 5% of the market (38 million euros), is beginning to find significant applications, especially in areas such as data processing and predictive modeling.
This disparity in adoption is also reflected in the strategies and projects undertaken by companies. The study shows that 22% of large companies already have a clear AI strategy, while an additional 13% are beginning to implement one. On the contrary, many SMEs are still in the exploratory phase or do not consider AI a strategic priority. Companies in the industry/manufacturing macro-sector are those most frequently activating AI strategies or projects, with 46% already active in this area, compared to 39% in other sectors.
Another critical element is the available economic resources. Large companies have greater financial capacity, allowing them to allocate significant funds towards acquiring new technologies and developing AI solutions. As a result, 90% of total AI investments in Italy are made by large companies, leaving a reduced share for SMEs and Public Administration. Moreover, SMEs are often limited by a lack of internal expertise, which makes it challenging to adopt advanced technologies such as AI without significant external support.
Geographic distribution plays an important role in the uneven adoption. Thirty-eight percent of the companies interviewed are based in the northwest of the country, while only 14% are located in the center and 15% in the south. This geographic gap further contributes to a non-uniform distribution of technological innovation and AI-related opportunities, with the more industrialized areas benefiting from greater resources and skills.
The lack of training and specific skills represents another obstacle to the widespread adoption of AI. Forty-seven percent of Italian companies are investing in internal personnel training to overcome this barrier, but only 16% are actively hiring new specialized professionals. Companies in the industrial sector appear slightly ahead compared to other sectors in terms of acquiring new skills and technologies, with 74% of manufacturing companies considering it necessary to upgrade their capabilities to make the most of AI's potential.
In conclusion, the picture that emerges is one of a two-speed adoption, where large companies manage to benefit more from the opportunities offered by AI, while SMEs continue to struggle with economic, organizational, and training barriers. To bridge this gap, structured support is needed, including economic incentives, training programs, and collaboration initiatives between large companies and SMEs. Only in this way can a more equitable and widespread adoption of AI technologies be ensured within the Italian business fabric.
Main Areas of AI Application
The applications of AI in Italian companies are diverse, with a particular emphasis on process automation and data analysis. According to the KPMG-IPSOS survey, 46% of companies are using AI to automate processes to improve efficiency and productivity, while 39% leverage data processing models to foster product innovation. Another 37% of the companies surveyed use AI for sales data analysis and to develop predictive models that support strategic planning activities.
In addition to these areas, other interesting applications of AI are emerging, such as customer data analysis to create personalized marketing campaigns, an aspect mentioned by 26% of companies. These solutions make it possible to develop more effective and targeted marketing strategies, increasing conversion rates and improving return on investment. Moreover, 23% of companies have implemented chatbots that can interact with customers, answering questions and providing information in real time. This application not only improves customer service but also reduces the operational costs associated with managing support centers.
Analyzing transaction patterns to identify suspicious activity and potential fraud represents another important area of application, chosen by 21% of companies. AI's data processing capabilities allow for the detection of anomalies and suspicious behaviors more quickly and accurately than traditional analysis methods, thereby contributing to corporate security and the prevention of financial fraud.
Another sector in which AI is applied is systems for voice, image, and video recognition, adopted by 11% of companies to enhance security. These tools are used in areas such as access control, video surveillance, and monitoring of production activities, contributing to improved overall security and operational efficiency.
AI technologies are seen to enhance human cognitive activities, with an impact that goes beyond simple automation. Indeed, AI is employed to support strategic decision-making (30%) and improve customer service (28%). The ability to process large amounts of data quickly and accurately allows managers to make more informed decisions, thereby improving the quality of business strategy and adaptation to market changes.
These applications demonstrate that AI is not just a tool for improving operational efficiency but has the potential to transform how companies interact with customers, develop products, and manage their internal operations. Integrating AI into various corporate areas is enabling Italian companies to create new business models centered on innovation and personalized offerings.
Challenges for AI Adoption
Despite the enthusiasm surrounding AI adoption, there are numerous challenges that companies must face. One of the main challenges is cultural transformation, indicated as the greatest obstacle by 37% of the companies surveyed. In addition, the need to upgrade employee skills is a significant problem for 34% of companies, followed by the implementation of new production and operational processes (33%). Only 22% of companies perceive implementation costs as a crucial problem, suggesting that the main difficulties lie more in managing change than in mere financial aspects.
The challenges related to AI introduction are not limited to the technological aspect but also involve the ability to manage cultural transition and organizational change. Finding the right combination of human capital and technological capital is identified by 27% of companies as a significant challenge. This balance is crucial for fully exploiting AI's potential without alienating workers. Additionally, compliance with regulatory and legislative implications is perceived as an obstacle by 13% of companies, highlighting the need to adhere to an ever-evolving legal framework that often imposes stringent constraints on AI use.
An important aspect is the need for clear and structured governance for AI adoption. Indeed, 37% of companies believe that defined governance models are necessary to ensure that AI is developed and used responsibly and reliably. Internal staff training, indicated by 59% of companies, emerges as the most relevant factor for overcoming employee resistance and fostering AI acceptance. Only 19% of companies foresee significant difficulties in gaining employee acceptance, while 57% believe there will be no significant obstacles, suggesting a certain optimism regarding the ability to integrate AI without generating strong resistance.
The issue of skills also plays a crucial role in managing challenges. Employee training and retraining are seen as indispensable tools for tackling change: 47% of companies are investing in staff training to develop AI-related skills, while only 16% are hiring new specialized figures. However, the standardization of skills, due to the adoption of generative AI technologies, could lead to a reduction in differences between high- and low-performing workers, making the workforce more homogeneous but less differentiated in distinctive abilities.
In conclusion, the challenges for AI adoption are manifold and require an integrated approach that combines technological, training, and organizational aspects. Companies must not only invest in technological infrastructures but also develop a change management plan that involves staff and creates a corporate culture ready to embrace innovation. This path, although complex, is a necessity to remain competitive in an increasingly technology-driven economic environment.
Benefits and Future Prospects
The benefits of adopting artificial intelligence in Italian companies go beyond mere productivity gains and cost reductions. According to the study, three out of four companies believe that AI can significantly improve the internal economic situation of the company. This figure rises to 81% among companies that have already activated AI projects, demonstrating a high degree of confidence in the long-term benefits that these technologies can bring. The positive economic impact of AI is particularly felt in large companies, where 77% of respondents expect improvements, compared to 69% of SMEs.
AI adoption is also profoundly influencing corporate leadership models. According to 95% of respondents, AI will allow managers to assume a more strategic role, freeing them from operational and routine tasks and allowing them to focus on decisions with a greater impact on the company. This change, which leads to a redistribution of responsibilities within the organization, can contribute to improving overall efficiency and making the company more agile and responsive to market dynamics.
Another benefit concerns AI's ability to foster a more collaborative and innovation-centered work environment. AI technologies are indeed seen not just as automation tools but as "augmentation" tools, technologies that enhance human capabilities. This vision is particularly important in a Human Innovation perspective, where people remain at the center of the transformation process, and AI becomes a partner to enhance human contributions within the organization.
In terms of innovation, only 13% of companies have reported AI's ability to innovate products and services as a main benefit, suggesting that the full potential of AI in this area is not yet fully exploited. However, 23% of companies indicated an improvement in acquiring and managing new customers as an important result of adopting AI solutions. This shows that AI can also play a significant role in business growth, not only by optimizing internal processes but also by directly contributing to improving market performance.
Furthermore, AI is changing the competitive landscape, promoting the adoption of new, more scalable, and data-oriented business models. AI-driven companies tend to break down traditional silos, becoming more integrated and capable of continuously collecting, analyzing, and using data to improve decision-making processes and service quality. This approach not only improves internal efficiency but also enables companies to be more agile in adapting to market changes and to gain a sustainable competitive advantage.
From a skills perspective, AI adoption requires a significant investment in employee training. However, it is not just about developing technical skills but also about promoting soft skills such as problem-solving and critical thinking, which are essential for fully exploiting AI's potential. The ability to adapt and understand the interaction between technology and human processes is crucial for long-term success. Companies are therefore promoting specific training programs to ensure that staff are prepared to manage these new technologies and integrate them effectively into business processes.
Impact on Business Models
AI adoption is also having a significant impact on business models. Sixty-four percent of the companies interviewed believe that AI will change their business model, with particularly relevant effects in production (indicated by 59% of respondents), sales (32%), and personnel organization (29%). This transformation will require a rethinking of business strategies and operating models, including greater collaboration between functions and the adoption of more agile ways of working open to innovation.
According to the KPMG-IPSOS report, AI is becoming the core of companies' operating models, representing a true paradigm shift that changes the way of doing business. AI is no longer considered just a tool to improve efficiency or reduce costs but has become the universal engine that drives business transformation. This change implies a break from traditional silos and a reorganization towards greater scalability and flexibility. For example, AI-driven companies like MyBank, Amazon, and Zara have shown that integrating AI into operational processes not only improves service quality but also allows for the creation of a new value creation model, based on continuous learning from data collected in real-time.
This transformation goes beyond simple technological application, as it redesigns how companies interact with markets and consumers. According to 95% of respondents, the introduction of AI will lead to a more strategic role for managers, freeing them from more operational tasks and allowing them to focus on creating new growth opportunities. AI indeed enables a better understanding of customer needs and supports the adoption of new business models oriented towards open innovation, in which collaboration with external partners and the collection of real-time insights become central to business success.
Moreover, AI pushes companies to rethink their alliance ecosystem. The creation of strategic partnerships, even with companies from different sectors, represents one of the main opportunities for growth and differentiation from competitors. An AI-driven company can indeed offer more personalized products and services, increasing overall efficiency and leveraging an extended ecosystem of skills and resources.
Conclusions
The adoption of artificial intelligence represents a turning point for Italian companies, but only conscious leadership can ensure its full potential is harnessed. Strategic AI management requires an integrated vision that goes beyond mere technological fascination, shifting the focus from technology to the concrete goal of solving business problems. AI is not an end but a means to enhance organizations' decision-making and operational capabilities. However, many initiatives fail due to a disconnect between business objectives and technical design, often exacerbated by inadequate infrastructure and poor-quality data.
For Italian SMEs, which struggle to keep up with large companies, AI integration must not be seen as a luxury but as a competitive necessity. This requires targeted public incentives and a collaborative culture between large companies and SMEs to share skills and resources. AI technology adoption can be transformative, but only if continuous training and the building of robust digital infrastructures are invested in.
A critical area is data management. The quality and relevance of corporate data are not just technical issues but strategic decisions. Organizations must treat data as an asset, investing in data engineering and infrastructure capable of supporting scalable AI projects. Companies that do not adopt this vision risk losing competitiveness, remaining trapped in cycles of failure due to structural deficiencies.
Corporate leadership must also address the cultural change necessary to integrate AI into decision-making processes. AI democratization requires inclusive governance that balances technological innovation and human capital. This means creating collaborative environments where technicians and executives work together to translate strategic needs into practical technical solutions. Only leadership capable of listening and learning can build trust in AI and harness its potential without falling into unrealistic expectations.
Finally, it is crucial to shift the focus from immediate AI applications to its strategic role as a catalyst for new business models. AI offers the opportunity to redesign business processes for greater scalability, agility, and personalized offerings. This does not only imply adopting technology but building a new business ecosystem capable of generating value through continuous learning from data.
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