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Immagine del redattoreAndrea Viliotti

Technology 2024: The Impact of AI

In 2024, the technology sector is at the center of a profound transformation driven by artificial intelligence (AI), accelerating investments and the adoption of advanced technologies by cloud providers, companies, and technology suppliers. Bain & Company's "Technology Report 2024" provides an in-depth analysis of AI's global impact, highlighting how the adoption of generative AI solutions is influencing economic sectors and redefining the dynamics of the global technology market. This article will explore the main findings of the report, the implementation strategies, and the technological implications of AI that are transforming companies and technical infrastructures worldwide.

Technology 2024: The Impact of AI on the Global Sector
Technology 2024: The Impact of AI on the Global Sector

Technology 2024

In 2024, cloud service providers, companies, and technology suppliers significantly increased investments in artificial intelligence, with venture capital continuing to flow into start-ups focused on generative AI. According to the report, the top five cloud computing providers (Microsoft, Apple, Alphabet, Meta, and Amazon) spent around $223 billion on research and development (R&D) in 2023, which was 1.6 times the total U.S. venture capital spending in the same period. These investments have allowed these companies to maintain a controlling position in an evolving market, strengthening their leadership.


In particular, industry leaders are using this capital to develop and integrate increasingly larger and more powerful AI models, enhancing data center processing capacity and efficiency, and pushing towards the adoption of 100-megawatt architectures and beyond. Nvidia, for example, has seen a significant increase in its computing capacity, consolidating its leadership position among GPU suppliers for AI, with a market value that surpassed $3 trillion in the second quarter of 2024.


Investments are not limited to developing cutting-edge hardware but also to creating software infrastructures that allow companies to fully harness AI's potential. Large companies are pushing to create AI ecosystems that are highly scalable and integrable in diverse contexts. This means that the focus is not only on enhancing data centers but also on developing machine learning operations (MLOps) platforms, AI orchestration tools, and modular architectures that enable rapid adaptation of AI models to new market demands.


Another key aspect of investments is the growing integration of AI into strategic sectors such as healthcare, finance, and manufacturing. In healthcare, for instance, investments are aimed at creating AI models that can support medical diagnostics, improve treatment personalization, and optimize the management of healthcare resources. In the financial sector, major banks are investing in AI solutions to improve risk analysis, portfolio management, and fraud prevention, while the manufacturing industry benefits from intelligent automation of production processes and predictive maintenance.


These investments are also fostering collaboration between technology companies and academic institutions to promote research and innovation in AI. Major companies such as Google and Microsoft are forming strategic partnerships with universities and research centers to develop advanced solutions, support doctoral programs, and fund scholarships to train AI experts. This synergy between the private sector and academia not only accelerates technological progress but also helps create a highly qualified talent pool needed to sustain the sector's growth.


Furthermore, a significant portion of the investments is aimed at expanding access to artificial intelligence through open-source platforms and development tools, allowing even small and medium-sized enterprises (SMEs) to exploit its potential. Access to pre-trained models and AI-as-a-Service (AIaaS) platforms is democratizing the use of AI, enabling SMEs to innovate and compete globally without incurring the high costs associated with developing proprietary solutions. Amazon Web Services (AWS), for example, has launched several initiatives to provide machine learning tools and AI models to a wide range of companies, offering flexible and customizable services based on the specific needs of each customer.


Another key element of the growing investments is the push for sustainability in AI infrastructures. With the increasing computational capacity needed to train more complex models, large companies are also investing in sustainable solutions to reduce the environmental impact of their operations. Microsoft, for example, has announced the goal of becoming "carbon negative" by 2030, investing in energy-efficient data centers and advanced cooling technologies to reduce energy consumption. These investments not only help mitigate AI's environmental impact but also contribute to improving overall operational efficiency.


Finally, investments in AI are also driving innovation in security technologies. With the increasing use of AI in critical sectors, data security and protection against cyber threats have become top priorities. Companies are investing in AI solutions to improve intrusion detection, incident response, and cyber risk management. These advanced tools enable continuous monitoring of digital infrastructures and rapid response to potential threats, ensuring a level of protection appropriate to the challenges posed by the current technological landscape.


Cost Reduction and Productivity Increase

The adoption of AI is not limited to the creation of new products but is also transforming traditional business processes, allowing for significant cost reductions and productivity increases. According to Bain & Company, AI-driven automation could reduce the time dedicated to manual responses in contact centers by 20%-35% and reduce the time spent on content creation for marketing by 30%-50%. Moreover, the use of AI in software product development has allowed companies to reduce the time required for coding activities by 15%.


The impact of AI does not stop there. Back-office processes, such as human resources management and finance, are benefiting from the automation of repetitive tasks, enabling companies to allocate human resources to higher-value-added activities. For example, automating payroll and accounting processes has reduced errors and improved process speed, increasing overall efficiency and reducing operational costs.

Integrating artificial intelligence into business workflows is also changing supply chain management, improving demand forecasting accuracy and optimizing inventory management. Advanced AI systems can analyze vast volumes of historical data, providing more precise forecasts and helping reduce waste while increasing logistical efficiency. An example is automation in distribution centers, where robots and AI algorithms work in synergy to manage storage, picking, and packaging quickly and effectively, minimizing human intervention and optimizing the entire logistics process.


AI is also transforming customer service, enabling more efficient and personalized handling of customer requests. AI-based chatbots, integrated into contact centers, can answer a wide range of common questions quickly and accurately, reducing the need for human intervention. This not only reduces operational costs but also improves the customer experience by providing timely and relevant responses.


AI-powered automation is also transforming workforce management. AI tools can optimize the recruitment process by analyzing resumes, identifying the most promising candidates, and even conducting preliminary interviews. This type of automation reduces the workload of the HR team, speeds up the hiring process, and ensures that the most qualified candidates are selected efficiently. Additionally, AI can be used to improve employee performance management by identifying areas for improvement and suggesting personalized development paths for each individual.


Another area where AI is having a significant impact is predictive maintenance. In manufacturing and industrial sectors, adopting AI solutions to monitor equipment conditions and predict potential failures is enabling companies to reduce unplanned downtime and improve operational efficiency. Sensors and machine learning models are used to analyze real-time data from machinery, identifying anomalies and alerting operators before a failure occurs. This proactive approach not only reduces maintenance costs but also increases equipment lifespan and optimizes the entire production cycle.


However, the real challenge for companies is not only to implement the technology but also to redesign processes to derive maximum value from AI. In many cases, the adoption of artificial intelligence requires radical changes in workflows and operational methods, involving hundreds or thousands of employees. According to the report, the most effective examples of implementation are those that adopt a holistic approach, in which automation is integrated with a review of business procedures.


A key aspect of this holistic approach is actively involving employees in the transformation process. Successful companies are those that invest in the training and upskilling of their workforce, making them an integral part of the change. Integrating AI into workflows should not be seen as merely replacing human activities but rather as an opportunity to enhance existing capabilities and introduce new ways of working that foster collaboration between machines and people.


A practical example of this human-machine collaboration is the use of AI to support sales teams. AI tools can analyze customer data, identify behavior patterns, and provide personalized suggestions on sales strategies. This allows salespeople to focus on higher-value activities, such as relationship building and negotiation, increasing the effectiveness of sales campaigns and improving overall performance.


Finally, AI is also enhancing companies' ability to make data-driven decisions. Advanced analytics platforms, powered by artificial intelligence, enable managers to access real-time information, identify emerging trends, and make more informed and strategic decisions. This type of predictive analysis is particularly useful in sectors like finance and retail, where the ability to anticipate market trends and adapt operational strategies can make the difference between success and failure.


AI Sovereignty and Technological Geopolitics

Another critical aspect addressed in the report concerns the growing AI sovereignty at a geopolitical level. Governments worldwide, including India, Japan, France, Canada, and the United Arab Emirates, are investing billions of dollars to develop national computing infrastructures and domestic AI models. This "de-globalization" of technology is creating sovereign technological blocks, where the need to protect data privacy, ensure national security, and incentivize local technological ecosystems has become a strategic priority.

The formation of national AI ecosystems is contributing to the increasing fragmentation of the global technology landscape. While some countries aim to build autonomous infrastructures to reduce dependence on foreign actors, others are focusing on international collaborations to ensure access to the most advanced technologies. Balancing protectionism and cooperation is one of the main challenges that governments will face in the coming years.


A significant aspect of this race towards AI sovereignty is the economic implications. Countries that succeed in developing robust and independent domestic AI infrastructures have the opportunity to reduce their technological dependence on other nations, strengthening their economic and geopolitical position. This shift is leading some governments to actively support national industries through tax incentives, subsidies, and funding programs for AI research and development. These policies aim not only to build internal competencies but also to create a competitive edge over rival economies.


China, for example, is a major player in this scenario. The Chinese government has allocated substantial resources to develop artificial intelligence infrastructures, with the declared goal of becoming a world leader in AI by 2030. This strategy includes investments in supercomputers, support for tech start-ups, and the creation of special economic zones dedicated to technological innovation. China's success in this field could alter the global balance of power, leading to increased competition between rival technological blocks.


On the other hand, the European Union has adopted a different approach, focusing on creating a regulatory framework that ensures the ethical development and use of AI, with particular attention to data protection and citizens' rights. The European AI program, known as "Digital Compass," also provides significant investments in AI infrastructure and skills development, but with an emphasis on collaboration among member countries and respect for shared values. This approach aims to create a robust technological ecosystem capable of competing with American and Chinese giants without compromising transparency and privacy principles.


In the United States, the focus remains on technological leadership through the private sector, supported by substantial public funding for defense and security. Large tech companies like Google, Microsoft, and Amazon continue to play a dominant role in AI development thanks to their ability to attract global talent and invest in large-scale infrastructure. However, the U.S. government is also taking steps to ensure that American leadership in AI is sustainable in the long term by promoting policies that encourage innovation and collaboration between the public and private sectors.


Another crucial issue related to AI sovereignty is managing strategic resources, particularly semiconductors. Semiconductors are fundamental for the data processing required by AI, and the ability to produce them locally has become a priority for many governments. The global shortage of semiconductors has highlighted the vulnerability of international supply chains and has prompted governments to invest billions of dollars in building national chip factories. The competition for control of semiconductor production resources has thus become a key element in the race for AI sovereignty.


An important aspect also concerns the use of AI in the military domain. Several countries are developing AI capabilities for military applications, such as autonomous drones, defense systems, and cyber warfare. This is leading to a technological arms race with profound geopolitical implications. The ability to develop and use advanced AI technologies for military purposes can provide a significant strategic advantage, making AI sovereignty a matter of national security. The United States and China are currently leading this race, but other countries, such as Russia and India, are also increasing investments in this sector.


Despite efforts to create national AI ecosystems, the report emphasizes how large global companies like Google and Microsoft still have a significant competitive advantage due to their operational scale and ability to sustain high R&D costs. Training a state-of-the-art AI model, for example, can cost over $100 million, an investment that only a few companies worldwide can afford.


This dynamic creates a significant gap between countries and companies that can afford to invest in cutting-edge technologies and those that must rely on open-source solutions or strategic collaborations. The implications of this gap will be felt not only at the technological level but also economically and socially, with potential impacts on global competitiveness and wealth distribution.


The growing AI sovereignty is also fostering new forms of international collaboration. For example, countries with advanced AI expertise are beginning to form strategic alliances to share knowledge and resources. The AI Partnership for Defense initiative between the United States, United Kingdom, Canada, and Australia is an example of how countries are trying to join forces to develop advanced technologies and maintain a competitive advantage over other blocks. Similarly, the European consortium Gaia-X aims to create a secure and reliable data platform to support the development of European AI solutions, reducing dependence on large American and Chinese tech companies.


In conclusion, AI sovereignty and technological geopolitics are fundamental aspects of the global competition for control of future technologies. The ability to develop and use AI independently is set to redefine international relations and influence the economic and political power of countries in the coming decades. The race for AI sovereignty is not just about technology but also about controlling resources, ensuring national security, and being able to guarantee the sustainable and equitable development of artificial intelligence globally.


Market Opportunities and Technological Innovations

The report estimates that the potential market for AI-based products and services will reach between $780 and $990 billion by 2027, with an annual growth rate between 40% and 55%. The main areas of growth include not only hardware and infrastructure but also software platforms, SaaS solutions, and integrated AI services. Moreover, innovations in AI are transforming not only cloud computing systems but also the so-called "edge computing," thanks to the use of smaller and optimized language models for local devices, thereby reducing operational costs and improving data security.

Today's innovations are not only about technological advances but also the integration of artificial intelligence within already established business models. AI is indeed driving significant evolution in many industrial sectors, with applications ranging from production process optimization to predictive maintenance, allowing companies to improve efficiency and operational quality.


This adoption of AI is opening new market perspectives, particularly in the field of personalized solutions, with models tailored to meet specific customer needs in various areas. For example, in precision agriculture, AI makes it possible to monitor fields and optimize the use of resources such as water and fertilizers, resulting in higher and more sustainable crop yields. In the energy sector, AI supports the optimal management of energy distribution, demand regulation, and fault prediction in networks, contributing to more effective and environmentally friendly resource management.


Another area of innovation is conversational AI and the development of advanced virtual assistants. These tools are becoming increasingly sophisticated, capable of understanding the context of conversations and responding naturally, improving the interaction between users and digital systems. These technologies are finding applications not only in customer service but also in education, healthcare, and other areas where human interaction is essential. Virtual assistants are becoming a key contact point for many services, making interaction with digital technologies more accessible and intuitive.


The entertainment sector is also undergoing a transformation thanks to AI. Artificial intelligence systems are used to create personalized content, such as movie and music recommendations based on user tastes, or to generate new forms of entertainment, such as video games that adapt dynamically to the player's preferences and skills. Additionally, AI is employed to improve the production of digital content by automating special effects creation and enhancing image and video quality.


AI technologies are also opening new horizons in digital health. Beyond diagnostics, AI is used to develop personalized therapies and better manage patients' chronic conditions. Applications such as wearables, combined with machine learning algorithms, allow continuous monitoring of vital parameters and provide real-time information to doctors, contributing to better patient health management. Additionally, AI-enhanced telemedicine platforms enable more effective remote consultations, improving access to care, especially in remote or resource-poor areas.


The mobility and transportation sectors are also benefiting from AI innovations. Autonomous vehicles, supported by deep learning and machine learning algorithms, are becoming increasingly safe and reliable. Cities are implementing smart traffic solutions that use AI to monitor and manage vehicle flow in real time, reducing congestion and improving road safety. These innovations not only improve urban transport efficiency but also contribute to reducing pollution through more efficient route management and energy consumption.


Another emerging sector is decentralized finance (DeFi), where AI is contributing to transforming how financial services are offered. Machine learning algorithms are used to improve risk assessment, optimize investment portfolios, and detect fraudulent activities in digital transactions. DeFi platforms are introducing new models of lending and insurance, made safer and more transparent thanks to artificial intelligence. This combination of technologies promises to democratize access to financial services and reduce costs for end users.

AI is also transforming education and training. Online learning platforms are using artificial intelligence to personalize educational paths, adapting content and teaching methods to the needs and abilities of each student. AI-powered virtual tutoring tools can identify areas where students struggle and provide targeted support, improving learning outcomes and making education more inclusive and accessible.


Moreover, AI technologies are opening new possibilities in e-commerce. AI is improving the online shopping experience through personalized recommendation systems, sales support chatbots, and logistics process automation. E-commerce platforms use artificial intelligence to analyze user behavior and offer product suggestions in line with individual preferences, improving conversions and increasing customer satisfaction.

AI is also entering the field of art and creativity, generating visual, musical, and literary works that are redefining the boundaries between human and artificial art. Artists and creators are collaborating with AI algorithms to explore new forms of expression and experiment with unprecedented styles and techniques. This interaction between human and artificial intelligence is creating new categories of works and leading to a revival of interest in art.


Finally, market opportunities in AI also include the emergence of new professions and specialized skills. The growing adoption of artificial intelligence is creating a rising demand for data scientists, machine learning engineers, AI ethicists, and specialists in data governance. These new skills are essential for developing, implementing, and managing AI systems that are not only efficient but also ethical and transparent. Companies are investing in training programs and partnerships with educational institutions to develop the talent needed to sustain the future growth of the AI industry.

In conclusion, the market opportunities and technological innovations related to artificial intelligence are profoundly transforming numerous sectors. From optimizing industrial processes to personalizing services, from education to healthcare, AI is expanding possibilities for economic growth and social improvement.


Preparing for Future Challenges

The report concludes by emphasizing the importance of preparing for future challenges related to the scarcity of semiconductor components needed to support AI growth. With exponentially increasing demand for GPUs, critical component suppliers are expected to increase their production capacity by 30% or more by 2026, with investments estimated between $40 and $75 billion for the construction of new manufacturing facilities.


The availability of semiconductors is just one of the challenges the sector will face. Companies need to build more resilient and diversified supply chains to reduce dependence on specific suppliers and mitigate disruption risks. The globalization of supply chains has made markets vulnerable to geopolitical events and natural disasters, making it crucial to find alternative and strategic solutions to ensure production continuity.


Another crucial element for preparing for future challenges is adopting sustainability policies in AI production. The energy required to power data centers and AI models is considerable, and companies must find innovative solutions to reduce their environmental impact. This includes adopting renewable energy sources, optimizing the energy efficiency of data centers, and using advanced cooling technologies to reduce energy consumption. Specifically, immersion cooling technologies and renewable energies like solar and wind power are gaining popularity as sustainable solutions to support AI growth without significantly increasing the carbon footprint.

Furthermore, the growing demand for AI computing capacity poses a significant challenge for network infrastructure. It will be essential for companies to invest in next-generation broadband networks, such as 5G and 6G, to ensure that the vast amounts of data generated by artificial intelligence models can be processed and transferred efficiently. Latency and data transfer speed will be crucial components in supporting applications such as autonomous vehicles, IoT, and other technologies that rely on real-time processing.


Data security is another major challenge companies must address to prepare for the future. With the increasing adoption of AI and the exponential growth of collected data, it becomes essential to ensure the security and privacy of sensitive information. Companies will need to invest in advanced encryption technologies, multifactor authentication, and AI-based security solutions to protect their digital infrastructures from increasingly sophisticated attacks. New standards and regulations will be necessary to ensure that AI adoption is secure and compliant with data privacy regulations.


An additional crucial aspect of facing future challenges is developing an adequate and qualified workforce. The skills required to work with AI are constantly evolving, and companies must invest not only in technology but also in people. It will be essential to create ongoing training programs and professional development opportunities to ensure that workers are prepared to face the changes AI will bring. This is particularly important in a context where competition for talent is increasingly fierce, and the ability to attract and retain the best professionals can make the difference between success and failure.


Collaboration between the public and private sectors will be another key element in overcoming future challenges. Governments will need to collaborate with companies to create economic incentives and appropriate regulations that promote responsible and inclusive AI adoption. Public investments in infrastructure, research and development, and training will be essential to support AI expansion and ensure that the benefits of adopting these technologies are shared globally.


Finally, AI governance will become an increasingly relevant aspect as artificial intelligence assumes a more central role in business and government decisions. Clear rules and principles will be needed for the responsible use of AI, with particular attention to transparency, ethics, and the prevention of biases in AI models. Companies and governments must work together to develop AI governance that promotes innovation while protecting individual rights and minimizing the negative effects of emerging technologies.


Conclusions

To fully grasp the implications of artificial intelligence, we must go beyond the traditional view of efficiency and innovation. AI does not merely represent a new technological tool but the organizing principle of an era that redefines the very concept of business value. Companies must stop thinking of AI as technical support and see it as an active agent of profound change, capable of reprogramming value creation mechanisms and rewriting the very logic of growth and competitiveness. The impact of these transformations risks making some foundational principles of traditional management obsolete: it is no longer about maximizing efficiency on established processes but about continuously redesigning those processes to adapt to rapidly changing scenarios. The future of businesses will be less predictable and more like a dynamic and adaptive ecosystem, where competitive advantages are no longer durable but extremely fluid and momentary.


This paradigm shift is not neutral: those who do not embrace it risk not surviving in a market that is radically transforming. The companies that will emerge are those capable of learning from artificial intelligence, not just using it. They will have to understand how AI can provide insights and knowledge that would never have emerged from historical data alone or from traditional models. Here, AI becomes the means to push organizations to think the unthinkable, anticipate scenarios, markets, and unmet needs, and develop strategies based on unconventional logic.


Furthermore, the adoption of AI entails an underestimated risk: the loss of competitive uniqueness. With artificial intelligence tools increasingly available to a wide spectrum of organizations, the real competitive difference will lie in each company's ability to build internal and external synergies and connections that make AI usage unique and unrepeatable. This means rethinking the organizational architecture to foster a fluid combination of human skills and artificial intelligence, based on models of continuous and bidirectional interaction and learning between people and algorithms.


The geopolitical challenge, in turn, risks making AI a divisive force. Instead of acting as a tool for international collaboration, tensions over technological sovereignty could lead to a fragmented technological world characterized by isolated "AI blocks" bound by protectionist policies. In this context, international companies might be forced to adapt AI algorithms and strategies to increasingly compartmentalized markets—a challenge that risks slowing growth and limiting opportunities to exploit artificial intelligence on a global scale. The solution lies not only in technological compliance with local regulations but in creating scalable and inherently flexible AI platforms designed to adapt to the cultural, regulatory, and market specifics of the various countries in which they operate.


Finally, if AI is to be the engine of a new economy, companies must position themselves as stewards of ethical and inclusive progress. This can no longer be an ancillary issue: the future value of a brand may not derive solely from market success but from its ability to embody and promote values of fairness, sustainability, and privacy respect. Artificial intelligence must become a tool for widespread empowerment, broadening access to opportunities and improving well-being rather than concentrating power in the hands of a few dominant players. Choosing to be a leader in AI today means choosing to guide a change that goes beyond the mere realm of business: a transformation of relationships between business, society, and individuals, redefining the very boundaries of what it means to generate value in the 21st century.



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