Historical background: when AI entered business

The first uses of AI in companies began in the 1960s with expert systems, programs designed to imitate human reasoning. Early examples include DENDRAL (1965) and MYCIN (1972), which showed how computers could support human decision-making. In the 1980s, thanks to personal computers, big firms like IBM and General Electric started using AI for production planning and financial analysis. In the 2000s, the rise of big data, machine learning, and cloud computing made AI more accessible. Even small and medium-sized enterprises began using AI to improve marketing, logistics, and decision-making.

Benefits of AI in companies

AI helps businesses save time, reduce errors, and make smarter choices. One clear advantage is automation: machines can handle repetitive work such as data entry or invoice management. This allows employees to focus on creative and strategic tasks. Machine learning improves decision-making by analysing data patterns invisible to humans. For example, Stitch Fix, an online clothing company, uses AI to personalise product recommendations and optimise stock, reducing costs and improving customer satisfaction. Similarly, Catalytic uses AI to automate administrative tasks and reduce human mistakes.

AI also predicts market trends and detects problems before they happen. In manufacturing, Fast Radius uses AI to monitor production and cut waste. Another key benefit is customer experience: natural language processing helps companies understand customer opinions and adapt services quickly.

Consequences and challenges

AI adoption also has consequences. Automation can change job structures, reducing some job positions but creating new ones in programming, data analysis, and AI management. To work well, companies must change culture and organisation, encouraging teamwork between data experts and business managers. Ethical issues are also important: AI systems can make biased decisions if trained on poor data. That is why transparency and regulation are essential.

AI also requires investment in digital infrastructure and skilled staff. Maintenance and monitoring are continuous needs. Finally, companies must protect customer trust and data privacy while innovating responsibly.

Future perspectives

The future of AI in business looks promising. Over 60% of companies already use some form of AI, and this number will keep growing. Generative AI is one of the most dynamic fields, able to create text, images, and ideas from simple prompts. Cognitive automation, which combines AI with business process analysis, helps companies learn and adapt automatically.

AI is also transforming finance, logistics, and marketing through predictive analytics and personalisation. However, ethical and legal frameworks are needed. The European Union’s AI Act, expected in 2025, aims to ensure transparency and accountability. The most successful companies will combine technology with social responsibility, using AI not just for profit but to build fairer and more sustainable organisations.

Conclusions

AI has deeply changed how businesses work. From its early beginnings to today’s advanced systems, it has become a key part of innovation and competitiveness. The future of AI will depend on how companies balance technology, ethics, and human skills. Used wisely, AI can become a lasting engine of economic and social progress.

References

Tobin, S., Jayabalasingham, B., Huggett, S., de Kleijn, M., & Lawlor, B. (2019). A brief historical overview of artificial intelligence research. Information Services & Use, 39(4), 291-296.

Radanliev, P. (2024). Artificial intelligence: reflecting on the past and looking towards the next paradigm shift. Journal of Experimental and Theoretical Artificial Intelligence.

Bonadimani, L. (2023). IA Calling: Intelligenza artificiale. Guida essenziale per PMI. Filosoft Srl.

Majakovskij, V. V. (2025). Strumenti per l’innovazione e l’apprendimento, n. 4: Next.