Introduction
Generative AI (GenAI) represents a technological breakthrough that is transforming the way companies operate and innovate. This technology enables the autonomous creation of content, predictive models, and intelligent solutions, expanding human capabilities and automating complex processes. However, the rapid and often uncontrolled entry of GenAI brings with it a significant risk: shadow AI.
AI: A value revolution against shadow risk
- Shadow risk manifests when AI tools are adopted without clear policies or governance, generating potential vulnerabilities in business infrastructures and processes.
- The absence of control can compromise data security, regulatory compliance, and operational effectiveness.
- GenAI requires a structured approach to transform from a risk to a competitive advantage.
Importance of GenAI in the current context
- The massive spread of GenAI has changed the digital expectations of millions of users and workers.
- Gartner estimates that by 2027 this technology will support up to 60% of knowledge workers’ activities.
- In Italy, over half of large companies have started experimenting with or adopting generative tools, indicating the recognized strategic value.
Evolution of the Value of AI in Companies
- AI is no longer an option but an essential requirement to compete in the global market.
- Companies must integrate AI sustainably, developing clear strategies and effective governance to maximize value.
- The transition from isolated projects to organized strategies is key to fully leveraging the potential of generative AI.
Conscious management of GenAI thus becomes a central element to mitigate shadow risks and enhance artificial intelligence as a driver of growth and innovation.
Current Scenario of Artificial Intelligence
The AI market in 2025 shows significant growth, reaching an estimated value of over 1.2 billion euros, with an increase of 58% compared to the previous year. This figure reflects the growing interest of companies in artificial intelligence solutions, especially in the field of GenAI. The main driving force comes from large enterprises, which represent the engine of AI adoption in business.
Levels of AI Adoption in Companies
- 65% of large companies are actively experimenting with generative AI solutions.
- 53% have already purchased GenAI-based tools, integrating them into various business processes.
- Only 3% of companies have structured AI strategies, meaning detailed and coordinated plans to leverage artificial intelligence at the organizational level.
- 39% of businesses work on isolated projects, sporadic initiatives without an overall vision or centralized governance.
- The remaining 13% have not yet started any AI project.
Differences Between Structured Strategies and Isolated Projects
Companies with well-defined AI strategies show a greater ability to:
- Integrate AI into key processes.
- Manage risks associated with uncontrolled use of technology.
- Achieve concrete results in terms of efficiency and innovation.
On the other hand, companies that adopt isolated projects often face difficulties such as:
- Lack of coherence between different initiatives.
- Difficulty in monitoring and governing the adopted tools.
- High risks associated with so-called “shadow AI”, i.e., technologies implemented without official supervision.
The difference between a structured approach and a fragmented one determines success in AI adoption within companies and directly impacts competitive capacity in the Italian market. Large companies are leading this change, while SMEs still show significant delays in starting AI projects.
The evolution of the AI market requires a cultural leap towards more mature strategies capable of supporting the effective and safe implementation of generative technology. According to a recent report on artificial intelligence, the sector is experiencing significant growth, highlighting the urgent need for a transition towards more structured and integrated AI strategies in SMEs.
The Risk of Shadow AI and Effective Governance
The risk of shadow AI represents a concrete and growing threat to Italian and international companies. It manifests when generative artificial intelligence tools are adopted within organizations without formal control, bypassing established IT structures and governance processes. This phenomenon creates a fragmented operational environment with potentially serious consequences:
- Loss of control over sensitive data: unauthorized tools can access, process, or store critical information without supervision.
- Vulnerability to cybersecurity threats: uncertified apps or internally developed ones without adequate standards increase the risk of attacks and breaches.
- Non-compliance with regulations: the absence of clear policies exposes companies to legal penalties and reputational damage.
Managing the risk of shadow AI involves the rigorous adoption of AI policy and compliance. Companies must define precise rules for:
- The responsible use of generative tools.
- The protection of personal and business data.
- The traceability of automated decisions.
These policies not only reduce vulnerabilities but also promote operational transparency, facilitating internal audits and verifications.
An effective governance of artificial intelligence requires an integrated approach that involves:
- Clear definition of roles and responsibilities for the management of AI technologies.
- Continuous monitoring of the adopted tools, with particular attention to those spontaneously introduced by users.
- Continuous training of personnel to ensure awareness of the risks associated with the misuse of GenAI.
- Implementation of control frameworks that ensure alignment between technological innovation and regulatory requirements.
Companies that invest in structured governance transform generative AI from a hidden risk into a competitive advantage, mitigating the dangers associated with shadow AI and safely and sustainably enhancing their technological investments.
Strategies to Maximize the Value of Artificial Intelligence
The implementation of enterprise AI requires a structured and results-oriented approach. The simple adoption of generative artificial intelligence tools does not guarantee value if there is no clear strategy. Companies must define precise objectives, select technologies compatible with existing processes, and prepare personnel through dedicated training.
Approaches to successfully implement AI in companies
- Preliminary analysis of data and processes: Understand what data is available, its quality, and how business processes can be optimized with AI.
- Identification of high-impact areas: Focus on repetitive, complex, or critical tasks that benefit the most from intelligent automation.
- Targeted pilot projects: Start controlled experiments to test generative solutions in real-world contexts before large-scale deployment.
- Governance and continuous monitoring: Establish roles, policies, and metrics to evaluate performance, risks, and compliance.
Improving work efficiency through the use of generative AI
Work efficiency with generative AI translates into:
- Reduction of time spent on manual and repetitive tasks.
- Decision support based on accurate predictive models.
- Automatic creation of customized content, reports, and analyses.
- Facilitation of internal collaboration through intelligent tools integrated into workflows.
Only a conscious use guided by business objectives allows for the achievement of these tangible benefits.
Methods for sustainably integrating AI into business processes
For effective and lasting integration, it is necessary to:
- Align AI with the strategic objectives of the company, avoiding isolated solutions that generate only spot projects without systemic value.
- Continuous training of employees, so that they can make the most of generative tools, increasing digital skills and adaptability.
- Development of scalable and secure IT infrastructures, capable of supporting variable loads and ensuring data protection.
- Adoption of agile frameworks, to quickly iterate improvements based on operational feedback.
Generative artificial intelligence represents a value revolution against shadow risk.
Only through clear strategies, effective governance, and sustainable integration can this technology be transformed into a solid competitive advantage.
BigProfiles.AI: Innovation in the AI Sector
BigProfiles.AI stands out as a prominent innovator in the field of artificial intelligence, leading technological evolution with cutting-edge solutions. The platform leverages the power of GenAI to transform complex data into strategic insights, providing concrete contributions in managing shadow risk.
Role in predicting shadow risk
BigProfiles.AI plays a fundamental role in the prediction and management of shadow risk through:
- Utilization of advanced generative AI algorithms to identify anomalies and unauthorized activities related to the uncontrolled use of AI.
- Continuous monitoring of AI applications within the company, preventing vulnerabilities and inefficiencies arising from Shadow AI.
- Integration of automated compliance policies, ensuring strict control over processes and data.
First autonomous AI Agent for CRM
BigProfiles.AI introduces the first AI Agent for Customer Relationship Management (CRM) capable of:
- Creating predictive models autonomously without constant human intervention.
- Customizing interactions with customers based on dynamic analysis and real-time updates.
- Optimizing business strategies by anticipating trends and purchasing behaviors.
BigProfiles.AI represents a true value revolution in the field of GenAI, effectively addressing shadow risk and bringing concrete innovation to the business sector. Artificial intelligence thus becomes a reliable and governed tool capable of generating real competitive advantages.
Frequently Asked Questions
What is generative AI and what role does it play in the current context of companies?
Generative AI is a technology of artificial intelligence capable of autonomously creating content, such as texts or predictive models. In the current context, it represents a fundamental element for companies, contributing to improving work efficiency and generating innovative value.
What are the main risks associated with Shadow AI in enterprises?
The risk of Shadow AI refers to the uncontrolled or unauthorized use of AI solutions within companies, which can lead to compliance issues, data security problems, and operational inefficiencies. It is essential to implement policies and effective governance to mitigate these risks.
How are AI adoption strategies in companies evolving towards 2025?
Companies are progressively adopting structured strategies to integrate artificial intelligence into their processes, moving from isolated projects to coordinated plans. This trend is supported by market data that predicts a significant increase in AI adoption by 2025.
How can effective governance help manage the risk of Shadow AI?
Effective governance defines clear policies and compliance processes for the use of artificial intelligence, ensuring that all AI applications are monitored and compliant with corporate regulations. This reduces the risk of Shadow AI and ensures responsible and safe use of the technology.
What strategies can maximize the value of artificial intelligence in companies?
To maximize the value of AI, companies need to implement integrated solutions into their business processes, promote work efficiency through generative AI, and adopt sustainable approaches that balance innovation and risk control, thus enhancing the digital revolution.
What does BigProfiles.AI offer in the field of artificial intelligence and how does it contribute to shadow risk management?
BigProfiles.AI is an innovator in the field of artificial intelligence, specializing in GenAI. It offers advanced solutions such as the first AI Agent for CRM capable of autonomously creating predictive models, helping to foresee and manage shadow risk through intelligent governance tools.