When a company decides to adopt Artificial Intelligence, one of the first steps is an internal assessment: are the company’s data reliable and fit for purpose?
High-quality datasets are the real fuel that powers Artificial Intelligence models. However, many organizations are still not fully aware of the value of the data they generate every day through internal processes and interactions with customers and suppliers.
Beyond the state of their data, companies must also address a broader set of questions related to the objectives they aim to achieve through AI adoption, such as developing data-driven products, improving commercial relationships, optimizing operational costs, and strengthening marketing and human resources management.
Artificial Intelligence for Italian companies
The report Artificial Intelligence for the Italian System, published by Confindustria (the Federation of Italian Industry) collected a range of real-world Artificial Intelligence applications already in use by Italian companies, asking them to share what they have learned from applying AI within their business processes.
From the case studies gathered, five key guidelines have emerged, reflecting the first-hand experience of companies that have already embarked on the journey of Artificial Intelligence adoption.
Five key guidelines for adopting AI in Italian SMEs and enterprises
1) Data quality for enterprise artificial intelligence: why it’s critical
Without reliable, complete, and well-organized data management, Artificial Intelligence cannot fully deliver its value within a company. As a result, the models and algorithms in use risk producing inaccurate or unreliable outcomes.
The first step for any organization moving toward AI adoption is to establish a structured data collection process, followed by data cleaning activities and the cataloguing of information using dedicated tools.
This process requires investment in skills and training, raising awareness among employees about data quality, its strategic importance, and the need to continuously and consistently feed these systems with high-quality data.
2) Skills, data culture and organization: a company-wide transformation
The adoption of Artificial Intelligence within a company is not limited to technical or IT teams alone. AI must become part of the organization’s DNA, supported by a clear AI skills strategy and an effective change management plan capable of guiding the transformation.
This often requires reorganizing and rethinking existing processes, starting from the people who experience them on a daily basis, and investing in employee training to overcome initial challenges and resistance.
The key lies in fostering dialogue between technical teams and operational roles, under the guidance of management. Managers play a crucial role as a bridge between technology and day-to-day operations, translating business needs into concrete AI use cases.
3) Platforms and tools: starting on the right foot
To effectively use Artificial Intelligence in business, companies should invest early in digital platforms, IoT sensors, and data collection and integration systems capable of building high-quality, traceable, and complete datasets.
Developing an internal team is an effective way to build the necessary in-house AI skills. However, it is equally important to consider collaboration with a broader ecosystem of partners, including technology providers, domain experts, and specialized startups, to accelerate project kick-off and the deployment of AI solutions into production.
In many cases, introducing Artificial Intelligence within an organization also drives the standardization of documentation and the structuring of information and access procedures. This not only makes algorithms more operational, but also strengthens internal management processes across multiple business areas.
4) Implementing Artificial Intelligence step by step
In business, AI is a tool that supports decisions and processes ultimately managed by people. The experiences collected in the report point to a clear recommendation: it is best to begin adopting AI through well-defined, small, and focused pilot projects (proofs of concept, PoCs).
The most effective approach to adopting the right tools is agile: prototype quickly, test, and iterate.
Positive results—such as improved efficiency or fewer errors—tend to emerge progressively, thanks to ongoing optimization and incremental adjustments over time.
5) The benefits of human oversight in Artificial Intelligence
Artificial Intelligence delivers its best results in business when it is supported by human oversight.
By automating repetitive tasks, teams can focus on higher-value activities such as strategy and business development, improving the overall effectiveness of AI-driven projects. Even within automated workflows, human-in-the-loop control remains essential: reviewing proposed decisions and ensuring that business processes align with organizational objectives and regulatory requirements, while still delivering tangible benefits in terms of productivity and work quality.
AI in support of the Italian system
By giving a voice to Italian companies, the Confindustria report, analyzes the state of digital transformation across the Italian productive system, highlighting opportunities, challenges, and levels of maturity in the adoption of Artificial Intelligence.
Through concrete use cases and first-hand accounts of AI deployment in business, the report provides valuable insights to inspire organizations looking to start or accelerate the digitalization of their production processes.
IT4LIA AI Factory offers a services portfolio designed to facilitate the adoption of Artificial Intelligence by companies, startups, research organizations, and public administrations, turning vision and inspiration into operational solutions.
Schedule a call with our AI and HPC experts to turn your innovation strategy into reality and place AI at the core of your project’s growth.
Giovanni Bognetti, Silvio La Torre (CONFINDUSTRIA)
