The large-scale launch of generative artificial intelligence and the constant evolution of its potential are transforming business ecosystems. For years, companies have considered GenAI – and AI in general – a key factor for competitive advantage, investing in automation and process optimization projects to obtain concrete and measurable returns on investment.
This phase is giving way to a profound transformation: AI is no longer just a simple accelerator of efficiency but a key element in redefining business operating models. The approach is shifting from circumscribed interventions to pervasive integration, capable of breaking down organizational silos and enabling new modes of work, decision-making, and strategic planning.
In this article, we analyze the true potential of AI in companies today and with an eye towards the future, exploring the opportunities and challenges that leaders must face to transform it into a structural and differentiating asset.
From Data-Driven to AI First: Opportunities and Challenges
In 2024, the Artificial Intelligence market in Italy reached a new record, hitting 1.2 billion euros, with a growth of 58% compared to 2023 (Osservatorio PoliMI). This result is mainly due to the implementations of Generative AI, which in 2023 accounted for 5% of the total value and today for 43%.
The growing adoption of Generative AI solutions indicates a willingness to explore new business opportunities and advanced operating models, moving as quickly as possible – as mentioned – away from the logic of individual projects or siloed experimentation, which limits innovation and systemic transformation. This means rethinking the company from the ground up as a data-driven entity by nature, where AI becomes the engine of innovation, enabling the redefinition of processes, developing a new organizational structure, assisting in decision-making, and amplifying human capabilities.
A systemic transformation brings with it significant challenges. Foremost among these is the ability to implement systemic changes within acceptable timeframes, aligning the transformation with the pace of the market, the existing competitive setups, and customer needs. The relationship with AI itself must then be managed; companies must understand how to integrate it effectively, avoiding fragmented approaches and ensuring that its use generates value. This requires a change in mindset, as adopting new technologies is not enough; resources must be refocused on value-added activities, redesigning roles and skills to centralize and maximize human contribution in an increasingly automated context.
The chapter on challenges concludes with those related to governance, security, and regulatory compliance, which require companies to define clear frameworks for its use. Risk management, algorithm transparency, and data protection become key elements to ensure responsible and sustainable adoption.
The "Reboot" of the Operating Model and Continuous Innovation
To guide companies toward an unprecedented evolution, IBM emphasizes the need for a true reboot of the corporate operating model, which allows AI to be integrated structurally and strategically throughout the organization. For this purpose, it is advisable to consider hybrid deployment models, such as the Hub-and-Spoke approach (used by 63% of executives), which allows balancing central control and governance with the flexibility necessary to foster innovation within individual business units.
In this way, the corporate transformation could embody the paradigm of open innovation, which promotes collaboration and the flow of ideas, resources, and skills both within and outside the organization. This approach aligns with hybrid deployment models, in which the central hub aggregates and manages AI knowledge, tools, rules, and technologies, while the nodes collaborate for a customized adoption of the technology and, above all, to create an ecosystem of continuous innovation, in contrast to traditional paradigms based on individual projects and well-defined phases.
In an open innovation context, individual business units are no longer mere executors but real experimentation laboratories, where every team actively contributes to the continuous improvement of processes and to enhancing corporate competitiveness. An error to avoid in this regard is considering AI as a mere accelerator of existing processes; rather, teams must be encouraged to rethink operating processes, fully exploiting the opportunities offered by artificial intelligence to optimize every aspect of work.
Collaboration, Culture, and Skills: The Key Words of AI Transformation
For a successful reboot of the corporate operating model, the right approach is not enough. It is necessary to build the pillars that support a change which, for many companies, is epochal:
- A working model based on collaboration;
- A cultural evolution that fosters synergy between people and intelligent systems;
- The development of the skills necessary to support and manage the new reality.
Building a Collaborative and Anti-Silo Model
The centrality of the collaborative approach in open innovation has already been mentioned because collaboration among teams, divisions, and professionals is essential in the context of pervasive modernization. Business units can no longer work in isolation, and artificial intelligence cannot be solely the domain of IT, but must also involve the finance department, the business unit, the legal division, marketing, and many other areas, which are required not only to share information and use similar tools, but also to make joint decisions and form multidisciplinary teams, with clear objectives and an equally clear idea of how to achieve them with the help of AI.
A New Human-Machine Relationship
Another fundamental aspect for the success of transformation is the company's ability to evolve culturally. In this context, the goal is to embrace a paradigm that sees AI not as a substitute but as an ally, capable of generating synergy between people and machines. People, with their skills and experience, remain at the center, while IT systems amplify these capabilities, enhancing their impact. To make this evolution possible, companies must actively foster the development of a culture of mutual trust and collaboration between employees and technologies.
Skills and Data Literacy
A key element for corporate evolution in the era of AI is represented by skills. It is not just about technical skills reserved for specialized figures such as data scientists and data analysts but about the widespread ability to use AI strategically to meet business needs: in other words, data literacy. To become truly AI-driven, companies must first become aware of the necessary skills and any gaps to be filled and then invest in training. The latter cannot be seen as a sporadic initiative but as a pillar of corporate culture, integrated into processes and organizational evolution.
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