Starting from the end of 2022, we have officially entered the era of generative AI. Researchers, analysts, and journalists are daily debating its medium- and long-term effects, fueling a debate between those who praise its impact on business, data-driven transformation, and society as a whole, and those who fear its consequences.
On one side, some see generative AI as an ally to accelerate scientific research, develop innovative solutions, and improve working conditions, while critics warn about the potential negative impact on employment and express ethical concerns.
But what is the reality? And more importantly, what is the true opportunity for economic actors?
Generative AI: an opportunity worth twice Italy’s GDP
Analysts have been pondering the economic impact of generative AI for over a year. Despite recognizing that the phenomenon is still in its infancy, the enthusiasm is undeniable. On one hand, generative AI has come to represent 5% of the spending on artificial intelligence solutions in Italy in just one year—a remarkable value considering its recent origin; on the other hand, analysts estimate that globally, generative AI “could generate additional annual economic value between $2.6 trillion and $4.4 trillion.” To put this in perspective, Italy’s GDP is just over $2 trillion.
The scope of the phenomenon justifies these figures: while it is believed that 75% of the value could come from just four areas (R&D, marketing and sales, customer operations, and software engineering), it is also true that companies have already identified hundreds of use cases, demonstrating a phenomenon that can drastically change the way we work, do business, and create wealth.
The promises: automation and human augmentation
To define the opportunities of generative AI, it is enough to consider that, according to analysts, “current generative AI and other technologies have the potential to automate work activities that consume 60% to 70% of employees’ time.” Automation, which leads to the macrocosm of operational efficiency, is undoubtedly a key driver for technology investments. It is no coincidence that more than half of business leaders believe that GenAI can lead to significant savings as early as 2024.
The phenomenon is not limited to the simple automation of routine and rule-based activities, for which effective technologies like Robotic Process Automation (RPA) already exist. Gartner, in fact, emphasizes how generative AI can be used to autonomously perform entire business and IT processes, as well as “augment human capabilities,” a factor that would significantly increase productivity levels. Moreover, even a superficial use of ChatGPT is enough to understand its capabilities in assisting professionals, without, however, proposing to fully replace human abilities. It is precisely on this new human-machine relationship that, in all probability, the future of work will be built.
Gartner also talks about revenue opportunities and risk opportunities related to generative AI. In the first case, the focus is mainly on the accelerated development of new products and services, but one could add the creation of engaging and interactive shopping experiences, boosting sales through automation (virtual assistants), or personalization of offerings. Regarding risk management, generative AI can support companies in terms of compliance (e.g., in the ESG area) and cyber risk, by analyzing immense volumes of data and proposing reports and solutions in real time.
The challenges of Generative AI: lack of skills and strategies
The promises of generative AI explain the interest surrounding the topic. Many companies see AI as a key factor in gaining a competitive advantage and are not willing to give ground to competitors.
It is not surprising that investments are high; however, it is observed that many companies are proceeding with (too much) caution, creating fertile ground for more agile and modern operators. This category of observers extends to 90% of companies.
The reasons? Clear: inability to define investment priorities, limited skills, and lack of a responsible artificial intelligence strategy, that is—borrowing Microsoft's definition—a well-defined approach "to the development, evaluation, and distribution of AI systems in a safe, reliable, and ethical manner."
How to develop a Generative AI Strategy
The scope of the phenomenon does not help companies embrace it quickly. Generative AI involves all industrial sectors, all company structures, and all roles; as mentioned, there are hundreds of use cases in experimentation or production, and the feeling of having seen only the tip of the iceberg is strong.
Implementing generative artificial intelligence (generative AI) in a company cannot happen randomly; a dedicated strategy is indeed essential, taking into account specific goals and the peculiarities of each business. A strategy directs individual initiatives toward systemic value, reduces risks, and facilitates adoption, another central theme when the change is significant.
To this end, Gartner comes to the aid of companies by providing guidelines to develop their own GenAI Strategy, borrowed from the 10% of companies that have already implemented AI solutions in various processes and business units. In particular, analysts focus on four pillars:
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AI Vision
The company defines how generative AI should help it achieve its goals. By defining them, it becomes easier to fund the right projects and use cases that align with the overall vision. Notably, the KPIs to measure the success of initiatives are defined here.
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AI Value
The goal is to highlight strategic issues that could hinder the achievement of objectives and limit the value (ROI) of individual projects. Certainly, the solutions, responsibilities, and actions to be implemented must then be identified.
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AI Risk
Any digital transformation initiative requires careful management of reputational and regulatory compliance risks. Additionally, generative AI has specific risks such as biases, i.e., results influenced by human prejudices present in the training data of the models, and so-called hallucinations.
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AI Adoption
Not all projects can be implemented. It is therefore necessary to carry out a scoring that takes into account the feasibility of projects, including technical evaluations such as data availability and skills, and their business value, considering the metrics identified in the first phase.
And we are only at the beginning
Although generative AI is a new technology, it has already demonstrated potential capable of shaping the future of organizations. Predictions indicate that AI investments, of which generative AI is a segment, could reach $200 billion by 2025, a figure that underscores the disruptive impact of this technology.
To fully seize the opportunities, organizations must act promptly by adopting a strategic and considered approach. On one hand, it is essential to recognize the pioneering nature of these initiatives and the need to proceed with caution to manage their risks; on the other, it is crucial to adopt a forward-looking vision, recognizing its central role in shaping the future of companies and society.
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