Talking about artificial intelligence in business means dealing with the clearest technological trend of the moment. Last year was a record year for AI investments, reaching 500 million euros in Italy, growing 32% from the previous year. Currently, according to the Observatory of the Politecnico di Milano, 61% of companies have started at least one AI project, a finding that is also in line with that of McKinsey analysts, who speak of a global adoption between 50% and 60%. A still open topic concerns the creation of tangible value through AI projects, as currently only a small percentage of companies manage to achieve the goal; however, interest, attention, research and investments in artificial intelligence in business are constantly growing.

Artificial intelligence in business as a support for decision-making processes

As an integral part of the Data Science macrocosm, any AI technique represents a valorisation of corporate data. Two main objectives:

• support for business decisions, a topic that flows into business intelligence and involves the use of predictive analysis;
• automation of processes in the key of hyper automation.

Support for decision-making processes is the main application of artificial intelligence in business and is totally transversal with respect to business divisions and the sector. This is the case of sales forecasting based on Machine Learning as well as a potential clinical diagnosis made by a CDSS (Clinical Decision Support System).

In any case, the adoption of AI is a crucial component of a broader digital strategy.It’s a process, not an event: it is necessary to define business objectives, map data sources within complex information ecosystems, acquire data, normalise and value them with descriptive, predictive or prescriptive analysis techniques (the three main types of Big Data Analysis). Following this process, and through a visualisation phase, it is possible to make data-driven decisions that benefit business performance. Additionally, AI presentations can effectively communicate insights derived from these analyses.

Artificial intelligence as a pillar of hyper automation

Another opportunity, still evolving but with great potential, is the adoption of artificial intelligence in business in the key of hyper automation.

Under this profile, hyper automation is positioned as a sort of extension of the previous case. Here, in fact, artificial intelligence does not help business managers make decisions, but has a certain degree of decision-making autonomy and uses it to automate processes that are not necessarily routine. In this sense, the application of AI in business processes overcomes the limitations of Robotic Process Automation (RPA) and configures an Intelligent Process Automation (IPA), which, when transformed into a systemic approach, actually becomes hyper automation. The immediate result is an increase in process efficiency, to which is added the tangible impact of innovation.

In concrete terms, Artificial Intelligence acquires the data and enhances it based on different analysis techniques; then, depending on the result obtained, it opts for a certain type of direct action on the process, obviously within well-defined constraints and without replacing professionals. In this way, it reduces the workload on people because it takes care of all repetitive processes and also activities and process phases that require a certain decision-making capacity, more or less evolved depending on the needs and technical capabilities of the company.

The fields of application are the same as RPA, being in fact its logical evolution: administrative and financial processes represent the area of choice, but they certainly do not subtract themselves from supply chain management, operations and even IT. Last but not least, it should be noted that all vertical sectors can benefit from the adoption of artificial intelligence in business, from finance to healthcare, through manufacturing.