Artificial Intelligence (AI) continues to dominate the business world. However, despite significant investments and initial enthusiasm, many companies are not as prepared as they thought to adopt and fully integrate AI. This is the main finding of the Cisco AI Readiness Index 2024, a survey that evaluates the readiness of organizations to adopt AI across six key pillars: Strategy, Infrastructure, Data, Governance, Talent, and Culture. The survey involves nearly 8,000 business leaders globally and provides a realistic picture of the challenges companies face.
Overview of the Results
The Cisco AI Readiness Index 2024 reveals a complex situation: fewer than one in seven companies are classified as "Pacesetters" (leaders in AI adoption), a decrease compared to the previous year. Companies are divided into four readiness levels: Pacesetters (absolute leaders), Chasers (moderately prepared), Followers (with limited preparation), and Laggards (poorly prepared). The percentage of Pacesetters has fallen to 13%, while Followers are the largest group, representing 51% of the total.
Another significant finding concerns investment levels. About 50% of companies reported allocating between 10% and 30% of their IT budget to AI, highlighting a strong financial commitment despite the challenges. However, almost 50% of respondents say they have not seen the expected results from their AI investments, indicating that many projects have not delivered tangible improvements in efficiency or automation.
Moreover, it emerged that only 38% of companies have clearly defined metrics to measure the impact of AI initiatives, suggesting that a lack of adequate evaluation processes may contribute to the perception of disappointing results.
About 59% of the companies interviewed stated that they have a maximum of one year to implement their AI strategy, or they risk losing their competitive advantage. This figure underscores the perceived urgency to accelerate AI adoption, despite the difficulties encountered.
Support from top management is also declining: only 66% of boards and 75% of executive teams express support, down from 82% last year. The pressure to adopt AI mainly comes from the highest levels, with 50% of companies citing the CEO and their team as the main promoters of AI adoption.
Six Pillars of Corporate AI Adoption
1. Strategy
Strategy is the pillar with the highest level of readiness, with 76% of companies classified as Pacesetters or Chasers. Nearly all organizations (95%) claim to have a well-defined or developing AI strategy. The top priority for AI adoption is cybersecurity, with 42% of companies already implementing advanced protection systems. Additionally, 27% of companies indicate that AI is an absolute priority for budget allocation, with no significant variation from last year. The willingness to invest is one of the characteristics that distinguishes Pacesetters from other companies.
2. Infrastructure
Infrastructure readiness has slightly decreased compared to last year. About 68% of respondents say their infrastructure is only moderately ready to adopt AI technologies. The main challenges include a lack of adequate computing power and limited scalability and flexibility of existing infrastructures, with 54% of companies reporting issues in this area. Furthermore, 78% lack confidence in the availability of sufficient computing resources to support AI workloads. The growing demand for Graphics Processing Units (GPUs) is another relevant aspect, with 79% of companies requiring additional GPUs to support future workloads.
3. Data
Data is fundamental to AI success, but fewer than one-third (32%) of organizations feel truly ready in this regard. The main difficulties concern data fragmentation, with access still being problematic for 82% of companies. The integration of analytics tools with AI platforms is also a significant obstacle, with 73% of companies reporting difficulties in this area. An additional 64% stated that there is room for improvement in tracing data origins, while 80% of companies continue to face issues in data preprocessing and cleaning for AI projects.
4. Governance
Governance readiness has decreased this year, partly due to the evolving global regulatory landscape on AI. Only 35% of organizations claim to have a good understanding of global data privacy standards. Furthermore, only 29% have regular controls in place to monitor and correct biases in the data used by AI. A lack of expertise in governance, law, and ethics has been reported by 51% of organizations as one of the main barriers to improving their governance readiness.
5. Talent
The lack of talent is one of the main barriers to AI adoption. Only 31% of companies report having talent with a high level of AI preparedness. To address this challenge, 40% of organizations are investing in training existing staff, while 56% rely on contracts with external suppliers to fill the gaps. Additionally, 45% of companies indicated that the lack of adequately skilled talent is one of the main obstacles. Furthermore, 48% of respondents emphasized that the growing competition to attract qualified professionals is contributing to increased costs.
6. Culture
Corporate culture is perhaps the most complex pillar to tackle. Only 9% of companies fall into the Pacesetters category regarding cultural readiness, while the number of Chasers has dropped from 40% to 31%. Cultural resistance is evident, with 30% of companies reporting resistance to AI adoption by employees. Furthermore, the receptiveness of boards has dropped from 82% to 66%, indicating a significant decline in enthusiasm for AI adoption at the highest levels. Support from leadership teams has also dropped to 75%, signaling increased difficulty in gaining widespread commitment within organizations.
Challenges and Recommendations in Italy
The Italian situation presents some peculiarities compared to the rest of the global landscape analyzed by the Cisco AI Readiness Index 2024. In Italy, the level of readiness of companies for AI adoption appears even more uneven, with a significant concentration of organizations falling into the "Followers" or "Laggards" category. Indeed, only 10% of Italian companies are classified as "Pacesetters," well below the global average of 13%. This figure reflects slow progress in adopting AI technologies, partly due to poor digitalization and delayed infrastructure modernization that characterize many Italian companies.
Regarding infrastructure, Italy shows an even more marked shortage in terms of computing resources and scalability. About 63% of Italian companies report significant difficulties in ensuring the availability of GPUs and other computing resources needed to support AI workloads, and 70% say that their infrastructure is not flexible enough to adapt to growing needs. This aspect significantly limits the ability of Italian businesses to compete internationally, where AI-ready infrastructure is considered a fundamental requirement to accelerate innovation.
In terms of talent, the lack of specific skills is particularly acute in Italy. Only 28% of Italian companies say they have adequately trained personnel for AI adoption, while 47% rely on external providers to fill skill gaps. This figure highlights a strong dependence on external partners, which could limit the ability to develop internal, sustainable, strategic skills in the long term. To address this situation, 35% of Italian companies have started investing in training and reskilling programs for staff, but the scale of these efforts remains limited compared to actual needs.
AI governance in Italy is also weak, especially regarding compliance with emerging European regulations. Only 30% of Italian companies claim to have a good understanding of data privacy and security standards imposed by the GDPR and more recent regulations such as the European Union's AI Act. This puts Italian companies in a vulnerable position regarding ensuring compliance with legal requirements, with the risk of incurring sanctions or not being able to fully exploit the opportunities offered by AI due to regulatory constraints.
To address these challenges, recommendations for Italian companies include a greater focus on infrastructure upgrades and adopting scalable cloud solutions that can provide adequate computing capacity without requiring significant initial investments. Additionally, it is crucial to encourage collaboration between companies, universities, and research centers to foster the development of local AI skills, reducing reliance on external suppliers. Finally, increased attention to governance and regulatory compliance can help Italian companies improve their readiness, ensuring that AI adoption occurs responsibly and in line with European standards.
Promoting an innovation culture is another crucial step for Italy. Many Italian companies still show some cultural resistance to AI adoption, often seen as a threat rather than an opportunity. Encouraging experimental use of AI, providing training support, and promoting internal success stories could help reduce these resistances and create a more favorable environment for technological innovation.
Conclusions
AI adoption in Italian companies reflects a landscape where the complexity of challenges intertwines with the urgency of transformation. The Cisco AI Readiness Index 2024 clearly shows how many organizations find themselves in a phase of stagnation, where initial enthusiasm has clashed with the harsh reality of infrastructural, cultural, and strategic preparedness. However, despite the digital lag compared to the industrialized world, Italy has a unique asset: the skills of its workforce and widespread entrepreneurship, particularly in micro-enterprises and SMEs, which represent a globally competitive excellence. This element must become the core of a national strategy that transforms the advent of AI into an opportunity to digitize the country's distinctive skills, creating AI capable of differentiating Italy in the global industrial and manufacturing landscape.
First of all, the fact that only 10% of Italian companies are classified as "Pacesetters" highlights a problem of strategic vision and systemic investment. This cannot be seen merely as a lack of resources but rather as a weakness in long-term thinking in a global context. AI offers the opportunity to enhance and amplify Italy's artisanal and specialized skills, creating a technological ecosystem that reflects local excellences. In this context, the Italian model could emerge not as an emulation of large digital economies but as an innovative and distinctive reinterpretation.
AI governance in Italy presents a problem of compliance with European regulations but, above all, of ethical and responsible management of emerging technologies. Only 30% of Italian companies claim to fully understand privacy and security standards. This figure is more than a technical gap; it is a missed opportunity for companies to position themselves as leaders in an increasingly regulated environment. Integrating legal and cultural expertise from Italian SMEs into AI governance could represent a distinctive element and a competitive advantage. The ethical and tailored approach, characteristic of "made in Italy," can become a mark of quality even in the technological field.
A particularly delicate issue is talent. Dependence on external suppliers and the limited availability of specific skills in the Italian market reflect a structural crisis that requires innovative solutions. It is not enough to invest in training; it is necessary to digitize and capitalize on the unique capabilities of the Italian workforce, adapting them to AI paradigms. The Italian model of production chains, based on deep specialist knowledge and unparalleled operational flexibility, could be amplified by the introduction of AI, making Italy a global hub of industrial and artisanal innovation.
The cultural problem, often attributed to internal resistance, must also be reconsidered. The perception of AI as a threat must be reversed, communicating it as an opportunity to preserve and enhance the country's productive and cultural identity. Business leaders must act not only as technological promoters but as guardians of a transformation that combines tradition and innovation. An approach that places people and their know-how at the center could generate a corporate culture where AI becomes a tool to elevate human value.
In conclusion, Italy can transform its digital lag into a unique opportunity, building an AI model that not only digitizes but also enhances the distinctive competencies of its companies. This is not about chasing the model of technological giants but about creating an alternative vision where technology serves to enhance what makes Italy unique. This approach could not only bridge the gap with more advanced economies but position Italy as a reference point for AI that integrates tradition, innovation, and responsibility.
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