AI and machine learning (ML) have become cornerstones of fintech

 AI and machine learning (ML) have become cornerstones of fintech, driving innovations across various domains in 2025. Here are the key areas where AI is revolutionizing fintech operations and decision-making:

1. Enhanced Risk Management: AI and ML models analyze massive amounts of data, detecting patterns that would be impossible for humans to identify. This allows fintech companies to predict and mitigate risks in real time, reducing exposure to fraud and credit default. AI-driven credit scoring systems have become more accurate, allowing financial institutions to assess risks more holistically.

2. Automated Decision-Making: AI streamlines decision-making processes by automating routine tasks such as loan approvals, customer verification, and transaction monitoring. This automation enables faster processing times, reducing customer friction and freeing up human resources for more complex tasks.

3. Personalized Financial Products: AI's ability to analyze user behavior and preferences allows fintech companies to offer highly personalized financial products and services. Machine learning algorithms create tailored investment portfolios, personalized loan products, and customized insurance plans based on the unique needs of individuals and businesses.

4. Fraud Detection and Prevention: With the rise of digital transactions, fraud has become a significant concern in fintech. AI systems are revolutionizing fraud detection by monitoring vast datasets, identifying unusual patterns, and flagging potentially fraudulent activities in real time. These systems continuously learn and adapt to new threats, making fraud prevention more effective over time.

5. Customer Service and Engagement: AI-driven chatbots and virtual assistants are reshaping customer service in fintech. These systems handle queries 24/7, provide personalized advice, and help customers manage their finances more effectively. The increased use of natural language processing (NLP) ensures that interactions feel more human-like and responsive.

6. Algorithmic Trading: AI and ML have taken algorithmic trading to new heights. By processing vast amounts of market data, these algorithms make faster and more informed trading decisions. AI helps predict market trends and optimize trading strategies, giving fintech firms a competitive edge in the stock and cryptocurrency markets.

   
   
   

7. Regulatory Compliance: Regulatory technologies (RegTech) powered by AI help fintech companies stay compliant with ever-evolving regulations. AI systems can automatically track changes in financial laws, identify areas of non-compliance, and ensure that companies adhere to legal standards, thereby reducing the risk of penalties and enhancing trust with regulators.

8. Blockchain and Smart Contracts: AI is playing a significant role in enhancing the security and efficiency of blockchain technology. In fintech, AI-driven smart contracts automatically execute transactions when predefined conditions are met, eliminating the need for intermediaries and ensuring transparency and security in financial agreements.

In 2025, fintech operations in India and globally are no longer just about processing data but about deriving actionable insights that inform better business decisions. AI's continuous learning capability ensures that fintech firms can stay agile, innovative, and customer-focused in an increasingly competitive market.

Exploring Transformation with AI in Fintech

AI in Fintech is redefining the financial industry, driving an unprecedented digital transformation in the sector. 

This technological revolution has given rise to the Fintech ecosystem , where financial technologies merge with AI to offer innovative digital services. 

In this article, we will explore how AI in Fintech is transforming the financial sector, the benefits it brings and the challenges it poses, as well as its role in preventing credit fraud and delinquency. 

We will also analyze the key segments within the Fintech industry, the importance of its diversity, current trends, and discuss the emergence of neobanks and their differentiation from traditional banks.

Benefits and Challenges of the Financial Technology Revolution and AI in Fintech

The financial technology revolution, driven by AI in Fintech, has brought with it a series of benefits and challenges for the financial sector.  Some of these are:

1. Process automation

AI enables the automation of routine and repetitive tasks, leading to greater operational efficiency and time savings.

2. Improvement in decision making

AI algorithms can analyze large volumes of data in real time, providing valuable information for more informed and accurate financial decision-making.

3. Customization of services

AI enables the personalization of financial services, adapting to the individual needs of customers and improving the user experience.

4. Fraud detection

AI is effective in the early detection of suspicious activity and in preventing credit fraud, thus protecting financial institutions and customers.

However, this revolution also presents challenges, such as the need to address data privacy issues , train staff in technical skills , and adapt to a constantly changing business environment.

Advantages of AI in Fintech compared to traditional credit assessment methods

AI in Fintech plays an essential role in credit assessment compared to traditional methods.

Credit assessment is a fundamental aspect of the financial sector, and AI in Fintech has introduced significant improvements to this process compared to traditional methods. 

Some of the key advantages of AI in credit assessment include more comprehensive data analysis, more accurate predictions, increased speed, and reduced bias .

AI can analyze a wide variety of data, including unstructured information such as social media and online behavior , providing a more complete picture of an individual's or company's credit profile.

AI algorithms can identify patterns and trends in credit data, leading to more accurate predictions about repayment ability and credit risk.

In turn, they can evaluate credit applications quickly and efficiently, speeding up the approval process and improving the customer experience.

Another very important aspect of AI in Fintech is that it can reduce bias in credit assessment by relying on objective data rather than subjective judgments.

We will also explore how AI in Fintech becomes a key ally in preventing credit fraud and delinquency.

The role of AI in Fintech in preventing fraud and late payments

AI in Fintech plays an essential role in preventing credit fraud and delinquency in the financial sector. Some ways it addresses these challenges include:

• Detection of anomalous patterns : AI can identify unusual spending patterns or atypical customer behavior, which may indicate potential fraud.
• Payment history analysis : You can also analyze payment histories and predict when delays are most likely to occur, allowing financial institutions to take preventative measures.
• Identity verification : AI can improve identity verification through facial and biometric recognition, reducing the possibility of impersonation.
• Real-time monitoring : This technology also allows continuous monitoring of transactions in real time, facilitating the immediate detection of suspicious activities.

Combining these approaches helps protect both financial institutions and customers from potential fraud and defaults.

Main segments or areas of activity within the Fintech industry

The Fintech industry encompasses a wide range of segments or areas of activity, each with its own importance and focus. We will discuss these key segments and their contribution to financial innovation.

Digital payments

Digital payment solutions allow users to make electronic transactions, seamless money transfers, and online payments conveniently and securely. Notable examples include digital wallet apps and online payment platforms.

Peer-to-Peer (P2P) Loans

Peer-to-peer lending platforms connect borrowers and investors directly, eliminating the need for traditional financial intermediaries. This streamlines the lending process and can offer more competitive interest rates.

Investment management

AI has become a crucial component in investment management, enabling the creation of customized portfolios and data-driven investment strategies.

Crowdfunding

Crowdfunding platforms allow companies and projects to raise funds from a broad base of individual investors online.

Insurtech

Technology has revolutionized the insurance industry , enabling more accurate risk assessment, automated claims processing, and personalized policies.

Regtech

Financial regulation is becoming increasingly complex, and Regtech uses technology to help financial institutions comply with regulations efficiently and accurately.

Importance of segment diversity in the AI ​​ecosystem in Fintech

The diversity of Fintech segments is essential for several reasons, such as continuous innovation, attention to different needs, ecosystem resilience, and collaboration opportunities .

Furthermore, this diversity fosters competition and constant innovation. Each segment seeks to develop unique and improved solutions, which benefits both consumers and businesses.

Each one focuses on addressing specific financial needs. This allows individuals and businesses to access solutions tailored to their individual requirements, from digital payments to investments.

The diversity of segments reduces dependence on a single market niche, making the Fintech ecosystem more resilient to economic fluctuations .

Finally, collaboration between different segments can lead to more comprehensive and attractive solutions for customers. For example, collaboration between digital payments and investment management can offer a complete financial experience.

Examples of Current Trends in AI in Fintech and the Financial Industry

AI in Fintech continues to drive current trends in the financial industry, promoting collaboration and constant adaptation.

The Fintech industry is highly dynamic and constantly evolving. Some current trends include:

• Open banking : Open banking promotes collaboration between financial institutions and Fintech companies by allowing users to securely share their financial data with third parties. This leads to greater innovation and more choices for consumers.
• Cryptocurrencies and blockchain : Cryptocurrencies and blockchain technology continue to be areas of growth in the industry, with applications ranging from cross-border payments to non-fungible tokens (NFTs).
• Artificial Intelligence and Machine Learning : AI and machine learning continue to play a fundamental role in the industry, improving decision-making, fraud detection, and service personalization.
• Financial sustainability : Sustainability has become an important focus in the industry, with Fintech companies offering sustainable investment options and tools to assess environmental impact.

Neobanks: Their definition and distinction from traditional banks in the era of AI in Fintech

Neobanks, or digital banks, are financial institutions that operate exclusively online, without a physical presence . They differ from traditional banks in several ways:

Absence of physical branches

Neobanks do not have physical branches, which allows them to reduce operating costs and offer financial services more efficiently.

Focus on user experience

Neobanks typically prioritize user experience, offering intuitive mobile apps and personalized services.

Competitive rates

Due to their lower cost structure, neobanks can often offer more competitive fees and attractive interest rates compared to traditional banks.

Greater agility

Neobanks are known for their agility and ability to launch new services and features quickly in response to customer needs.

Automated technology to handle 43% of work by 2030: Report

Automated technology to handle 43% of work by 2030 

According to the World Economic Forum's "Future of Jobs Report 2025", the UAE is expected to experience significant job market disruptions, ranking 11th globally in terms of anticipated changes. The report predicts that by 2030, 43% of work tasks in the UAE will be handled by autonomous technologies. This shift is a part of a broader trend where businesses are increasingly integrating automation and AI to enhance efficiency.

In response to these anticipated disruptions, 28% of UAE employers plan to upskill their workforce to adapt to these technological changes. Upskilling will likely focus on equipping workers with the necessary skills to work alongside AI and automation technologies, as well as to take on roles that require human creativity, judgment, and strategic thinking.

This report highlights the accelerating pace of automation and the need for businesses and governments to prepare the workforce for these changes, ensuring that workers can transition to new roles and remain relevant in an evolving job market.

World Economic Forum's "Future of Jobs Report 2025" as it pertains to the UAE.

Here are some of the key takeaways:

• High Level of Automation: The UAE is poised for significant automation, with 43% of work tasks projected to be handled by autonomous technologies. This signifies a rapid shift in how work is performed.
• Focus on Upskilling: Recognizing the need for a skilled workforce in this changing landscape, a significant portion of employers (28%) are prioritizing upskilling initiatives. This proactive approach is crucial to ensure that the workforce remains competitive and adaptable.
• Importance of Human Skills: The report implicitly emphasizes the importance of human skills that cannot be easily replicated by machines, such as critical thinking, creativity, and emotional intelligence. These skills will be highly valued in the future of work.   
• Need for Workforce Adaptation: The report serves as a strong reminder of the urgent need for individuals and governments to prepare for the future of work. This includes investing in education and training programs that equip individuals with the skills necessary to thrive in an increasingly automated world.

Overall, the report provides valuable insights into the evolving nature of work in the UAE and highlights the importance of proactive measures to ensure a smooth and successful transition to an increasingly automated future.

The Future of Work: Automation and its Impact on Employment

Understanding technological advancements and their influence on the job market

In the modern world, technology plays an increasingly important role in our lives, including how we work. This article explores how automation is changing the future of work and what impact it is having on employment.

The Rise of Automation

Automation, the process of making a system or process operate automatically, has been a topic of growing interest in recent years. This is largely due to the rapid development of technologies such as artificial intelligence (AI) and robotics, which are changing the way we work and live.

Impact of Automation on Employment

Automation has the potential to increase efficiency and productivity, but it also raises concerns about its impact on employment. According to a report by the McKinsey Global Institute , up to 800 million jobs could be automated by 2030.

Benefits and Challenges of Automation

While automation has the potential to displace some jobs, it can also create new opportunities. Jobs in areas such as data analytics, software engineering, and robotics are on the rise. However, these jobs often require advanced technical skills, which poses challenges in terms of education and training.

Preparing for the Future

It is vital that we prepare for this changing future. This includes investing in education and training to help workers acquire new skills, as well as developing policies that protect workers and promote a just transition to a more automated world.

Mercedes-Benz’s Virtual Assistant uses Google’s conversational AI agent

Mercedes-Benz’s Virtual Assistant uses Google’s conversational AI agent

It's worth noting that Google Cloud's new " Automotive AI Agent " platform promises to "continue conversations and provide reference information" during users' journeys, and the first car announced to feature it is the new Mercedes CLA. That car has the next-generation MB.OS operating system with an enhanced MBUX virtual assistant.

It's worth noting that when Mercedes unveiled this at CES 2024, it didn't specify which of the company's LLMs it was running on. Meanwhile, the existing MBUX voice assistant system, which could handle around 20 commands triggered by "Hey Mercedes," now includes results provided by OpenAI's ChatGPT and Microsoft Bing, but it's not a conversational platform. Mercedes has indicated there are plans to implement this updated system in "more models" running the older voice assistant, but didn't specify which ones.

Everything about the Mercedes-Benz virtual assistant

It's crucial to note that the new MBUX virtual assistant will feature four "personality traits," including natural, predictive, personal, and empathetic. You can also ask it questions to gain more clarity and get what you need.

Google's new AI agent is custom-designed for automotive use and leverages Google Maps data to find points of interest, search for restaurant reviews, provide recommendations, answer follow-up questions, and much more. Google says that MBUX virtual assistant users will have access to near real-time Google Maps updates. It also states that the agent can handle complex, multi-turn dialogues.

The agent uses Gemini and runs on Google Cloud's Vertex AI development platform, designed to help businesses develop AI experiences.

January 13, 2025 – Mercedes-Benz and Google Cloud announced the expansion of their strategic partnership to introduce new conversational capabilities to the MBUX Virtual Assistant, powered by Google Cloud’s new Automotive AI Agent.

Built using Gemini on Vertex AI, Google Cloud’s Automotive AI Agent is specially tuned for the automotive industry and can reference information from Google Maps Platform to give users more detailed and personalized conversational responses about navigation, points of interest and more.

Google Maps Platform provides Mercedes-Benz owners with information about 250 million places around the world, and the map is updated nearly in real time, with over 100 million updates made to the map each day. With the enhanced search and navigation experience, users can converse naturally with the MBUX Virtual Assistant and get answers to questions like: “Could you guide me to the nearest fine-dining restaurant for a unique culinary experience?”. Users can also ask follow-up questions, like: “Does the restaurant have good reviews?” or “What is the chef’s signature dish?” and the MBUX Virtual Assistant can respond with accurate, up-to-date information and display navigation details through the vehicle’s native interface. The new experience will be available in the MBUX Virtual Assistant, with Mercedes-Benz’s new CLA series later this year.

Google Cloud’s Automotive AI Agent will also enable the MBUX Virtual Assistant to handle complex, multi-turn dialogue and can retain memory of conversations, which means users can continue conversations and reference information throughout their drives.

Mercedes-Benz’s virtual assistant, MBUX (Mercedes-Benz User Experience), has integrated Google's conversational AI technology to enhance its capabilities. This collaboration allows MBUX to provide more advanced natural language processing and understanding, making the in-car experience more intuitive for users.

With the integration of Google's AI, Mercedes-Benz aims to offer more natural and responsive voice commands, improving functions like navigation, media control, and personalized assistance. This enhancement enables the virtual assistant to better understand and predict user needs, creating a seamless and user-friendly experience.

 Mercedes-Benz's latest MBUX Virtual Assistant, introduced in the new Mercedes CLA at CES 2024, incorporates Google Cloud’s Automotive AI Agent platform. This platform is designed to enhance the driving experience by supporting continuous, multi-turn conversations and referencing information throughout the journey.

Unlike the older version of MBUX, which could process around 20 voice commands (like “Hey Mercedes”) and relied on OpenAI’s ChatGPT and Microsoft Bing for search results, the new system is far more advanced. It’s built on Google Cloud's Vertex AI development platform and powered by Google's Gemini language model. The upgraded MBUX Virtual Assistant is capable of handling complex conversational queries, providing nearly real-time Google Maps updates, restaurant reviews, recommendations, and more. Its ability to process multi-turn dialogues means it can maintain context over multiple interactions, making it much more dynamic and intuitive.

The assistant's new design includes four distinct personality traits: natural, predictive, personal, and empathetic, enhancing its ability to offer more tailored, human-like responses. It also improves upon clarity by asking follow-up questions when needed to ensure accuracy in its responses.

Google CEO Sundar Pichai emphasized the transformational potential of these AI-driven "agentic" capabilities in the automotive industry, suggesting this is just the beginning of a more personalized, intelligent in-car experience. While the new system is being launched with the next-generation MB.OS operating system in the CLA, Mercedes plans to roll out this advanced assistant to additional models in the future. However, specific models haven't been named yet.

What are Google's big plans for AI

What are Google's big plans for AI

While major internet platforms like Facebook and Twitter continue to make changes that significantly impact people who spend their time on mobile devices, Google is keeping pace. According to the Financial Times, the tech giant plans to introduce generative artificial intelligence into its advertising in the coming months to offer users more relevant and personalized ads , along with other features related to optimizing the user experience with Bard and other solutions developed by the company . 

Although Google initially used AI to generate simple ads aimed at driving online purchases, the strategy now appears to be much more ambitious. In this initiative, generative AI is positioned as a key element for expanding the possibilities of attracting future buyers . 

With the goal of capturing the public's attention through creative content, AI aims to create campaigns more focused on people's needs, so that advertisers can better segment their target audience based on data collected from their browsing history. In this article, we tell you everything you need to know about Google's plans for artificial intelligence . 

 

Google incorporates generative AI 

Initially, Google's proposal to integrate generative artificial intelligence into its privacy features was not well received by its employees , who were concerned that the technology could provide inaccurate information . In response to these concerns and fears, Google assured the Financial Times that it expects to implement measures to prevent such errors with its artificial intelligence .

However, Google executives still don't fully understand how their artificial intelligence works because machine learning capabilities far exceed expected limits. As a result, former Google security chief Arjun Narayan expressed concern about the potential dangers of using AI to write news stories . He also emphasized that this risk is real because AI is inherently inaccurate and therefore cannot lend credibility to the message it conveys. 

Among the various risks of applying artificial intelligence to news generation is the challenge of training the model to be tailored to the specific purpose and reflect the truth. Despite these concerns, news services have been incorporating AI to enhance their product offerings, create more content, and personalize services for readers . While this will undoubtedly have positive results in attracting potential customers, it raises concerns about the potential for disseminating false information without human oversight .

Launched primarily to compete against GPT chat and new content generation technologies, Bard, the AI ​​created by Google, is a chatbot capable of generating diverse content and accessing vast amounts of information. However, software industry leaders fear that in the future, even artificial intelligence could spiral out of control , making it extremely difficult to verify the sources from which the technology draws its information. For this reason, AI is not the answer to all human needs. 

Google is making significant strides in artificial intelligence (AI) for 2025, focusing on the development and integration of its Gemini AI model across various platforms and services. CEO Sundar Pichai has outlined ambitious plans to introduce new AI products and features in the coming months, aiming to reach 500 million users with the Gemini AI model and app.

Key Developments:

• Gemini AI Integration: Google plans to integrate the Gemini AI model into multiple products, enhancing user experiences across its ecosystem. This includes updates to Google TV, enabling users to search for content and ask questions without the need to say "Hey Google."

• Automotive AI Collaboration: In collaboration with Mercedes-Benz, Google is integrating its conversational AI agent into the next-generation MB.OS operating system. This integration aims to provide drivers with a more interactive and personalized experience, leveraging Google Maps data for real-time updates and recommendations.

• Advancements in AI Research: Google DeepMind is forming a new team to develop "world models" capable of simulating physical environments. This initiative targets applications in video games, movies, and realistic training scenarios for robots and AI systems, aligning with Google's ambition to achieve artificial general intelligence (AGI).

• AI-Powered Search Enhancements: Google plans to introduce significant changes to its search engine in 2025, aiming to enhance its capability to address more complex queries. Users can expect substantial improvements early in the year, reflecting a profound transformation in AI.

The Artificial Intelligence is also capable of reading the history

 The Artificial Intelligence is also capable of reading the history

From the papyrus of Herculaneum to lost languages. A greater revolution within the great revolution, never seen before.

New tools based on Artificial Intelligence (IA) are making it possible to read old texts.

    One of the texts that from the Herculaneum papyruses found in the eruption of Vesuvius in 79 AD, fragile enough to be unrolled, passing through the vast archive of the kings of 27 Korean kings who lived between the 14th century and the beginning of the 20th century, continues proceeding tables of Crete of the 2nd millennium BC, exculpations with the complicated writing called Lineal B.

    The AI ​​is revolutionizing the sector and generating cantidades of data never before seen, as the Nature magazine points out in an analysis published on the web.

    One of the most important results that is obtaining knowledge of neural networks - models composed of artificial neurons and inspired in the structure of the cerebro- has to be found with the Herculaneum papyrus.

    Thanks to the international competition Vesuvius Challenge, which will take place in 2023, in which more than 1,000 research groups will participate, it is possible to first decipher not only the letters and words, but also entire extracts of carbonized texts.

    "This moment really reminds me: now I'm experiencing something that will be a historic moment in my field," comments Federica Nicolardi, papyrologist from the Federico II University of Naples who is participating in the competition.

    To obtain the reading of the papy

rus, a virtual rolling technique was developed, which scans the rolls thanks to the X-ray tomography, but each head is rolled and rolled in a flat image.

    Furthermore, the AI ​​distinguishes the carbon-based dye, invisible on the skins because it has the same density of the papyrus on which it rests.

    In February 2024, the $700,000 prize was awarded to three investigators who produced 16 clearly readable columns of text, but the competition continues.

    The next prize of $200,000 will be awarded to the first few who achieve 90% of four papyrus cards.

    This method opens the way to reading other texts that are now inaccessible, such as the hidden ones in the settings of medieval books or in the books that were sent to Egyptian mothers.

    Without counting how hundreds or thousands of papyrus can still be found in the bay of Herculaneum.

    "Everyone would be one of the greatest discoveries in the history of humanity," says Brent Seales, from the University of Kentucky, creator of the Vesuvius Challenge.

    The first great project that demonstrated the potential of AI born at the University of Oxford in 2017 with the aim of deciphering gray inscriptions found in Sicily where many parts were broken.

    The efforts of the investigators produced a red neural called Ithaca, which is freely accessible on the Internet.

    Ithaca can restore the parts that are missing with 62% accuracy, compared to 25% of a human expert, but when the red neural reaches the investigators the accuracy drops to 72%.

    AI is also fundamental in other ways: for example, read one of the largest historical archives in the world, formed by diary records that contain the records of 27 Korean kings written in Hanja, an ancient writing system based on Chinese characters.

    Or, on the contrary, decipher an ancient language from which only a few texts survive, such as the 1,100 proceeding tables of Knossos (Crete), which contain information about shepherds.

    But the enormous amount of data that the algorithms are gradually revealing poses a great challenge: "There are not enough papyrus scientists", says Nicolardi.

    “We will probably try to create a much bigger global community than the current one,” added Seales.

    For experts, the fear that AI can relegate conventional knowledge and skills to a secondary level is unfounded.

    “The AI ​​is making the work of papyrus more relevant than ever before,” says Richard Ovenden, head of the Oxford University Bodleian Library.

What impact does artificial intelligence have on energy demand?

What impact does artificial intelligence have on energy demand?

When discussing business energy management, AI emerges as one of the most important resources, since, through its multiple applications, it allows the extraction and analysis of a large amount of data and information that can help with energy efficiency , the energy transition, and the sustainability of power plants.

 

What is AI in the business environment?

There are many definitions of artificial intelligence, each suited to its intended use and the environment in which it is applied. In the business environment, AI can be defined as a set of algorithms and technologies that allow industrial equipment and systems to learn and make decisions based on large volumes of data . 

Among the “capacities” of AI, one could list: identifying patterns, making predictions, problem-solving, speech recognition, and the ability to use past experiences as a means of learning to correct mistakes and adapt them to new projects. 

AI has evolved rapidly, becoming a fundamental tool for process automation across multiple sectors. In the energy sector, its implementation is changing how companies manage their resources, enabling more accurate and efficient decision-making that will allow them to anticipate future energy needs with increasing precision.

 

AI for businesses: analyzing data on energy consumption

AI's ability to collect and analyze large volumes of energy consumption data in real time, thanks to intelligent systems, allows companies to identify patterns and trends that help them understand more accurately when and where resources are being used most . This constant monitoring will help detect opportunities for savings and efficiency.

The responsible use of energy and natural resources has become a key objective, as well as a requirement, for any organization seeking to reduce costs and improve its reputation with regulatory bodies, consumers, and business partners. Furthermore, effective energy management helps to reduce the carbon footprint and optimize resource consumption, resulting in a more profitable and environmentally responsible production system.

AI algorithms can predict future energy consumption with high accuracy , facilitating better planning. These predictions allow companies to anticipate their needs, adjusting energy use to minimize waste and maximize available resources. This predictive capability is essential for maintaining consistent and sustainable operations.

 

Energy management: AI to manage energy demand

Artificial intelligence allows for real-time monitoring of energy demand, optimizing resource utilization based on current needs . This is achieved through integrated AI systems that track and adjust consumption according to the flow or movement of business activity, ensuring optimal energy use and cost reduction in operational processes.

Another key contribution of AI is its ability to anticipate equipment failures, known as "predictive maintenance," through daily or periodic monitoring of machinery, capturing patterns that could lead to potential failures. This will help plan preventive system maintenance, extend machine lifespan, and avoid disruptions to company productivity, resulting in long-term cost reductions in both energy consumption and equipment replacement.

 

The integration of renewable energies with AI

Companies, through the analysis and efficient management of the energy they generate, can optimize storage and distribution, allowing them to make the most of sources like solar and wind power, whose availability can be variable. This smart management facilitates the transition to a more sustainable energy model .

In addition to analysis and efficient management, automation is another key contribution of AI . Actions such as turning systems on and off based on usage optimize energy consumption without interrupting operations, resulting in reduced operating costs and greater energy efficiency.

This impacts the entire energy supply chain. From production to distribution, artificial intelligence helps optimize every stage of this process. Again, thanks to predictive analytics, companies can anticipate demand and adjust their processes to ensure efficient distribution and reduce energy waste . It also allows for the accurate calculation of return on investment (ROI) in energy efficiency projects. Thanks to the data that AI can precisely obtain on consumption and savings, companies can evaluate the profitability of their investments and, based on that evaluation, make informed decisions and plan initiatives.

 

Implications of AI in companies: resistance to change and lack of knowledge

Implementing artificial intelligence within a company's energy strategies could also encounter some obstacles. Resistance to change from the team, from management to the front lines, as well as a lack of knowledge in the use of these technologies, are some of the main hurdles that the application of AI in industrial processes can face. 

Investing in training and adequate infrastructure is essential to successfully implement an intelligent system. Furthermore, leveraging the expertise and experience of specialized technology partners will further expedite the process of adapting and applying AI within the organization.

Training must be ongoing, as AI is constantly evolving and changing . Today, some of the emerging trends in its use include the development of smart grids that optimize energy flow in real time, the application of blockchain to improve transparency in the energy supply chain, and other advancements.

Data centers, including those that power generative artificial intelligence, are increasingly using electricity. Yet they are expected to account for only a small share of overall electricity demand growth through 2030.

The Price of Magic

Using ChatGPT, Perplexity or Claude, one can only be amazed at the speed of calculation of generative artificial intelligence (AI). This "magic" that seems to reason, search the internet and create content from scratch requires computer data centers to function. And who says computer centers says significant electricity consumption.

Business Logic

Martin Deron, project manager for the Chemins de transition digital challenge, a research project affiliated with the Université de Montréal, notes that a few years ago, the carbon footprint of digital came mainly from the manufacturing of devices such as phones, tablets and computers. “The impact of the data centres where we store our data was less significant in our total digital footprint,” he says. “Also, the companies that own these centres have a business logic. They try to minimize costs, particularly energy costs.”

6%

This dynamic has led to data centers becoming much more efficient. From 2010 to 2018, they increased their capacity by more than 550% worldwide. However, the total energy they consume has only increased by 6%, according to a study published in 2020 in the journal Science . “So even if our digital uses have increased, the carbon footprint of data centers has not increased that much because of innovation and technical improvements,” says Martin Deron. “However, generative AI is challenging this.”

Demand on the rise

The demands for training models, as well as generating new data, require the establishment of more data centers. "And the centers are reaching the limit of available energy. We hear that companies like Microsoft, Google or Amazon are going to launch or restart power plants to produce the electricity they need. Everything suggests that the demand for energy in this sector will increase in the coming years."

By 2030

The world’s data centers account for about 2% of electricity demand today. The International Energy Agency (IEA) projects that data center electricity demand will account for about 3% of the increase in global electricity demand by 2030, partly due to AI. Other uses, such as industrial needs, buildings, electric vehicles, and air conditioning and heating, are expected to account for a much larger share of electricity demand growth.

Local demand

In a recent analysis , 1 Oxford University data scientist Hannah Ritchie noted that data center demand for electricity is highly localized and is likely to affect certain locations more than overall electricity consumption. “For example, Microsoft has made a deal to reopen the Three Mile Island nuclear power plant. But Three Mile Island can only produce 0.2% of the electricity produced in the United States each year, or 0.02% of the electricity produced globally each year,” Ritchie wrote .  “There is still a lot of uncertainty. The demand for energy from AI will increase, but perhaps less than we think.”