Starting with an excursus on technological evolution and economic impact data, we will take an in-depth look at some sectors that are particularly involved in change and then conclude by describing an AI transformation model that can support Italian companies in taking advantage of technologies by paying attention to the specificity of business processes and the training and accompanying needs of people.

Although in digital tools and processes these two types of AI are increasingly integrated, it is important to be aware of the underlying technological differences in order to make informed choices about where to direct one’s investments and to understand their potential impact on work processes.

The concept of Artificial Intelligence originated in computer science to denote research and applications aimed at developing machines and programs that can simulate the human mind. It is a very broad concept, so to clarify its actual impacts on business, it is appropriate to know the categories that most characterize this field:

Now that we have explored the history and map of Artificial Intelligence, we have more tools to understand what technology can be useful in addressing different business needs and, in particular, what is the role of GenAI and Predictive Analytics in increasing process efficiency.

Robotic Process Automation (RPA) is a technology that has been popular for years in companies for automating complex workflows to reduce errors and increase efficiency. More recently it is being integrated with Machine Learning leading to the emergence ofHyper Automation.

This approach makes it possible to automate not just individual tasks but entire business processes, leveraging platforms such as Microsoft Power Automate to improve efficiency, reduce operational costs and expand the range of tasks that can be automated.
The integration of automation solutions integrated with AI has a positive impact on several levels (Cambridge Open Academy Report):

One of the main innovations introduced by the latest generation of artificial intelligence is the ability to interact through conversational chatbots. These capabilities integrated into organizational contexts for customer service and internal users has already yielded significant results in terms of efficiency and satisfaction:

However, an important aspect to consider when planning to introduce this technology into the enterprise is to make sure that conversational chatbots have been created to reduce the risks of errors and hallucinations. In particular, Chatbots must be integrated with Retrieval-Augmented Generation (RAG) technologies, which combine generative AI with access to specialized information databases. RAG chatbots access business and market data and ensure an accurate response based on up-to-date information. From these technologies, it is then possible to create multimodal conversational interfaces capable of handling real-time voice interactions up to true Digital Human

Generative AI can be used in the context of communication and marketing not only to support sales through conversational chatbots but also to improve content creation processes particularly in areas where high volume is required, such as social media and ecommerce.
Certainly an important role is conversational shopping through RAG chatbots in ecommerce to guide customers in product selection and answer their questions in natural language. This type of interaction improves not only the conversion rate but also the overall user experience.

A second type of application, however, concerns multimedia content. Particularly in retail, GenAI is used to create realize images of new product collections more efficiently by combining traditional shooting techniques with the use of diffusion models for image generation.

Predictive analytics can be used to anticipate future events and optimize strategic decisions with an impact not only in terms of efficiency but also in terms of safety.
In the financial world, it is being used to develop advanced trading advisors who can analyze real-time market data, predict economic trends and identify investment opportunities while reducing risk.

In the logistics sector, it improves warehouse management by predicting product demand, optimizing inventory and anticipating possible disruptions in the supply chain, increasing efficiency and reducing costs.
In addition, predictive analytics is crucial in the field ofEnvironmental and Structural Health Monitoring, where it is used to monitor structures such as bridges, buildings and critical infrastructure, predicting potential failure or damage due to natural events or deterioration, thus helping to improve safety and prevent disasters.

As we have seen, artificial intelligence is a very large and evolving field that can be integrated within organizations with different goals, from pure efficiency to securing complex contexts such as mobility infrastructure.

Given the complexity of this transformation, organizations must approach change with a method that takes into account all key dimensions: technological opportunities, process and interaction design, regulatory aspects, and the impact on people.

The journey toward AI-based transformation therefore requires multidisciplinary skills that integrate in-depth knowledge of individual technologies, user experience design, regulatory compliance, and human skills development, enriched by a cross-cutting process of PoC, testing, and scaling that ensures continuity and adaptability. Proof of Concept allows you to start small and verify the potential of solutions; testing allows you to collect data and optimize; and scaling ensures that what works is applied at scale, ensuring positive and lasting impacts. This approach allows the feasibility of solutions to be verified in a controlled manner, adapted through targeted experimentation, and finally scaled up with confidence forlarge-scale adoption.

Technology is the first pillar, where it all starts with acareful assessment of processes and data. Only by thoroughly understanding what works, what can be improved and how, can an effective automation process be undertaken. Next, by integrating advanced technologies such as text generation using language models (LLM and RAG) and predictive analytics based on machine learning and deep learning, organizations can leverage AI to gain competitive advantages. This path culminates in advanced solutions, such as multi-agent systems, digital twins and digital humans, that enable the enterprise to become increasingly autonomous and resilient.

The second crucial aspect in the ongoing transformation is related to user experience (UX), which is the bridge between technology and the people who use it. Assessing user experience is essential to understanding how people interact with new tools and ensuring thattechnology adoption is not only effective but also intuitive and accessible. This is achieved by building consistent design systems and a focus onaccessibility, ensuring that the user experience is affordable for everyone.

The third pillar deals with compliance and legal aspects. The introduction of AI into the enterprise must take place in a context of regulatory compliance, starting with an assessment of GDPR compliance and identification of AI-related ethical risks. Defining clear corporate policies on the use of artificial intelligence is an essential step to mitigate risks and promote responsible adoption of new technologies, making companies ready to meet the requirements of an ever-changing regulatory environment. Learn more about the regulatory aspects of AI transformation in the Guide to Business Compliace in the Age of Artificial Intelligence. Where to start to be compliant and respect privacy and confidentiality.

Finally, people are the beating heart of any transformation. Preparing them for change means cultivating an AI-oriented mindset by providing training workshops and co-design opportunities where resources can actively contribute to transformation. Staff involvement is then supported with training sessions, coaching and constant monitoring so that each individual feels part of the growth journey.