Artificial intelligence is one of the most significant technological trends of our time. It is not only changing business but also sparking global discussions about its efficiency, environmental impact, and social consequences. In recent years, companies and even governments have been investing heavily in AI development while trying to cope with challenges related to regulation, infrastructure, ethics, and environmental impact.

The MIM:AGENCY team has analyzed several studies and is ready to present its findings.

Moving from hype to the real value of artificial intelligence

Generative AI I has become the center of attention due to its capabilities in content creation, data analytics, and process automation. It simplifies complex tasks such as personalizing customer interactions and automatically analyzing large amounts of information. According to S&P Global research, 88% of organizations are already exploring the possibilities of Generative AI, and 24% of companies are actively integrating it into their business processes. Its application is focused on customer support, data management, and personalized marketing. 

However, companies are now focusing more on realizing real benefits rather than experimenting. The introduction of AI provides an increase in efficiency by reducing the time for data processing and automating routine processes. For example, in the financial sector, AI is used for risk analysis and fraud prevention, and in retail to optimize supply chains and inventory management. 

However, businesses face the question of its economic feasibility due to the high cost of implementation and the need for a large amount of computing resources. Investors are increasingly paying attention to whether this technology can provide real value and not just be a fashionable trend. Despite large investments, including $20 billion in Generative AI startups in 2024, the issue of monetizing such solutions remains open. Many companies seek to reduce AI costs by using cloud computing and modular models. This will allow scaling systems without excessive load on the internal infrastructure. 

It is expected that in 2025, AI strategies will focus on integration with existing systems and reduction of computing costs.

AI infrastructure development

Infrastructure is extremely important in the implementation of artificial intelligence. For AI to function efficiently, companies need to upgrade their data centers, improve data processing, and find new scaling solutions. This is especially true for GPU computing, which is becoming a limitation in the further development of AI systems. 

A recent S&P Global study shows that 83% of organizations predict an increase in AI workload in the next two years, and two-thirds expect significant changes in infrastructure to support these needs.

Energy costs are rising in many organizations, raising concerns about the environmental impact of AI. According to a Deloitte study, data centers that serve AI are consuming more and more energy, and their CO₂ emissions are increasing. It is expected that by 2028, data center electricity consumption will increase by 28-44%, forcing companies to look for alternative ways to reduce the load on energy systems. In this regard, businesses are starting to invest in green AI strategies that involve the use of energy-efficient models and alternative energy sources.

We should also mention the growing use of cloud solutions for AI computing. About 46% of companies use GPU clouds to train AI models, and 32% actively implement specialized computing cloud services. In this way, companies reduce the cost of their own infrastructure and speed up the processing of large amounts of data. Another trend is the development of edge AI, a technology that allows processing AI data directly on devices, reducing the load on central servers and ensuring faster response to requests.

Despite all the benefits of AI infrastructure, companies face serious challenges. The main ones are data security, privacy, and the complexity of regulatory requirements that are constantly changing in different jurisdictions. It is also important for businesses to implement effective AI model governance policies to avoid bias in algorithms and improve transparency.

Artificial intelligence in the legal sector

The legal sector has also been transformed by AI, with algorithms being used to automate routine tasks, prepare contracts, analyze legal documents, and even make legal decisions. For example, law firms are increasingly using AI to quickly analyze court decisions and predict the outcome of court cases. This reduces preparation time and reduces costs for clients.

However, privacy and regulatory issues remain. The AI Act introduced in the EU defines new standards of security and ethics in the use of AI, while the US and China continue to seek a balance between innovation and government control. An important issue is the potential risks of algorithmic bias, as AI can reproduce existing discriminatory practices that are present in historical legal data. This calls into question the impartiality of judicial processes and requires the creation of more transparent mechanisms to control the activities of AI in the legal system.

Artificial intelligence is used in the field of alternative dispute resolution and mediation, where algorithms help in negotiations and finding compromise solutions between the parties. Companies dealing with large volumes of legal documentation are actively using natural language processing (NLP) technologies to automatically detect anomalies in contracts and identify potential legal risks.

There is a growing interest in the use of blockchain technologies in combination with artificial intelligence to increase the transparency and security of legal processes. Such solutions can help avoid document forgery and create more efficient contract verification mechanisms. However, despite all the benefits, the development of artificial intelligence in the legal sector requires further regulation to avoid threats to privacy and human rights. 

The impact of generative AI on financial markets

In 2024, a quarter of all tech deals were related to artificial intelligence, and startups working in the field of Generative AI attracted record investments. Nevertheless, the issue of monetization of such solutions remains open, as most AI projects are still focused on improving operational efficiency rather than creating new revenue streams.

Financial companies are actively implementing Generative AI in trading operations, forecasting market trends, and risk management. According to S&P Global, in 2024, more than 40% of financial institutions started using AI to improve analytics and automated decision-making. For example, AI models can analyze huge amounts of market data and create personalized financial recommendations for investors.

An important advantage is the ability of generative artificial intelligence to quickly adapt to market changes and detect financial anomalies. In this way, the risks of fraud and illegal transactions can be reduced. Many banking institutions are implementing AI in the processes of transaction verification, customer credit assessment, and reporting automation.

However, AI in the financial sector also raises concerns among regulators, as automated solutions can pose systemic risks in case of erroneous forecasts or malfunctioning algorithms. According to forecasts, in 2025, AI regulation in the financial sector will be tightened to avoid possible crises due to excessive automation.

Major corporations such as Microsoft and Google are actively implementing AI in their business models, but the ultimate profit from such technologies is still controversial. Investors are closely monitoring AI startups, assessing their ability to achieve stable profitability in the long run. It is expected that in 2025, Generative AI will become not just an analytics tool but also a full-fledged player in the financial markets, changing the approach to trading, investing, and wealth management.

AI trends in 2025

In 2025, artificial intelligence will continue to expand its influence across various industries, including business, finance, medicine, and cybersecurity. Companies are reconsidering their approaches to AI, and integrating it into their strategies more deeply and efficiently. One of the trends is the transition from universal large language models (LLMs) to modular AI systems that adapt to specific tasks and minimize computing costs. Such systems allow to manage business processes more efficiently, automate complex tasks, and provide higher-quality forecasting.

The role of AI in business automation will continue to grow. Generative AI is already actively used in marketing, HR, and production optimization. In particular, in HR, artificial intelligence helps to automatically analyze resumes, predict the effectiveness of candidates, and improve internal communications in companies. It is also widely used in logistics and supply chain management, simplifying demand forecasting and minimizing resource wastage.

Artificial intelligence will play a major role in cybersecurity. Companies are facing increasingly sophisticated attacks on information systems. It is expected that new AI solutions will allow better identification of threats in real time, increasing the level of protection against cyberattacks and fraud. Already, 60% of financial services companies are implementing AI for automated monitoring of suspicious transactions and protection of customer data.

It is worth paying attention to the issue of artificial intelligence regulation. The European Union is adopting new standards, including the AI Act, which defines the rules for the safe use of artificial intelligence in critical areas such as medicine and finance. The United States and China are also expected to revise their regulatory approaches to strike a balance.

As we mentioned above, the development of green AI, an approach aimed at reducing the energy consumption of AI systems, is a pressing issue. It is expected that by 2030, the share of environmentally friendly AI solutions will increase significantly as companies actively invest in sustainability strategies.

Artificial intelligence in 2025 is no longer just an automation tool, but a full-fledged driving force in business. Its effective use will require clear integration strategies, compliance with new regulations, and finding a balance between innovation and ethical aspects of technology.

Conclusion

Artificial intelligence has already changed the way businesses, governments, and social interactions operate. However, its implementation requires a systematic approach, as technological limitations, regulatory requirements, and issues of public trust continue to hamper its development. Generative artificial intelligence is rapidly changing approaches to content creation, data management, and business process optimization, but at the same time poses new challenges for companies in terms of monetization, ethics, and energy consumption. As early as 2025, AI regulation will become a priority for governments seeking to strike a balance between technological progress and user safety.

Sources:

  1. Deloitte – A Study of AI’s Environmental Footprint
  2. InfoTech – AI Trends 2025_CAIG
  3. International Bar Association – AI and the Legal Profession_CAIG
  4. S&P – Global Trends in AI
  5. SPGMI – BigPicture2025_GenAI_CAIG