In the past two years, generative artificial intelligence has experienced exponential growth, radically transforming business activities. This technology supports data analysis, conceptual, and manual work through specialized chatbots and advanced robots. It is crucial for executives and entrepreneurs to understand how to integrate AI into their business vision. The recommended approach is agile and progressive, starting with areas such as marketing and communication. Continuous staff training and efficient data management are essential for effective implementation.
In the past two years, artificial intelligence, particularly generative AI, has seen exponential growth, with technological advancements occurring weekly and the introduction of the first practical solutions for the business context. This technology represents a true "industrial revolution" as it not only offers isolated innovations but has the potential to completely reorganize productive activities, both conceptual and manual. In the business realm, artificial intelligence supports companies in data analysis, spanning from the financial sector to maintenance, while generative AI facilitates conceptual work through chatbots specialized in business needs. Additionally, it contributes to manual labor through a new generation of robots that not only perform repetitive operations with precision but also interact with human operators, carrying out tasks that require reasoning and responses to verbal commands.
Given this complexity and the potential immediate and future repercussions, it is crucial that companies approach this transition phase with awareness. This awareness must start with management, CEOs, and entrepreneurs, who need to understand how this new technology can be integrated into their business vision and production model.
In my role as a business consultant, I dedicate myself to raising awareness among executives, entrepreneurs, and CEOs about the potential of generative artificial intelligence. To this end, I have developed an approach that clearly and meaningfully explains the structure of a large language model (LLM), the core of generative AI platforms like ChatGPT or Gemini. To make the concept more accessible, I compare the operation of these models to that of a company, thus facilitating the understanding of their dynamics and potential.
Let us imagine, therefore, that a large language model (LLM) is a company specialized in text processing. This company, which we will call "Language Leaders Inc.", consists of various departments and specific roles, each with well-defined tasks. All work synergistically to transform textual inputs into coherent and useful outputs.
Text Processing Company: "Language Leaders Inc."
Language Leaders Inc. is organized into two interdependent departments. The first department is dedicated to training and staff development, a fundamental aspect to ensure the company's productivity and efficiency. The second department focuses on actual production, applying the skills acquired by the employees to achieve the company's objectives.
Training and Development Department of "Language Leaders Inc."
Within the company "Language Leaders Inc.," the Training and Development Department plays an indispensable role. Here, a dedicated group of professionals is responsible for preparing and training the company team, ensuring that every employee, whether a "Token" or a "Neuron," is well-informed and equipped from day one.
Training begins in the Data Archiving sector, where the "Knowledge Archivists" gather and catalog information from a multitude of sources, such as digital libraries, article databases, and web directories. This data forms the core set of resources that the Tokens will use in their daily work, while the Neurons learn to process and interpret this information to perform complex analyses and generate accurately calibrated responses.
Moving on to the Text Analysis team, we find the "Text Analysts," true masters in the art of deconstructing documents into basic elements such as words, phrases, or individual characters. Each element is meticulously assigned to a Token, allowing it to acquire and specialize in specific portions of knowledge. Similarly, the Neurons are trained to refine their ability to process information based on their specializations, ensuring perfect synergy between analysis and text generation.
At the heart of operational action, the "Configuration Technicians" serve as trainers for both Tokens and Neurons. These technicians devote their time to perfecting the skills of both entities, ensuring they become increasingly precise and reliable in their data processing work. Through progressive training programs, Neurons and Tokens are continuously guided towards improvement, much like a team leader develops the capabilities of their group.
The "Attention Coordinators," true company strategists, evaluate and prioritize the information processed by the Tokens while setting tasks and goals for the Neurons based on the relevance of the data to ongoing projects. These coordinators ensure that every team member operates optimally and in line with the company's objectives.
Lastly, we encounter the "Optimization Experts," high-level consultants who further refine the skills of Tokens and Neurons. Through an intensive process called fine-tuning, these professionals specialize the team members on targeted data sets, enhancing their ability to respond effectively and accurately to specific scenarios.
For example, consider that "Language Leaders Inc." has trained its Tokens and Neurons with phrases like "The cat is on the mat" and "The dog plays with the ball." During their training, the Tokens and Neurons have learned the essential characteristics of animals and objects and have assimilated how these entities interact with each other. Thanks to this preparation, they can generate new textual content that is not only grammatically correct but also logically coherent and meaningful.
At "Language Leaders Inc.," the Training and Development Department is crucial to ensuring that every Token and Neuron can effectively perform their roles, thus contributing to the overall success of the company in the field of text generation.
Productive sector
Pre-processing of raw material
In the context of natural language processing, texts represent the essential raw material for the process of language analysis and generation, similar to the raw materials used in industrial production.
In the imaginary company Language Leaders Inc., texts arrive as raw materials. The first phase is tokenization, which breaks down the text into smaller units. For example, tokenizing the phrase “Elaborazione del linguaggio naturale” divides the text into its components: "Elaborazione", "del", "linguaggio", "naturale". Each word or symbol represents a component of the raw material, which is assigned to a Token worker within the company, specifically trained for that type of text segment. This worker is able to match the text segment with a small package of information learned during training in the Training and Development Department.
Once the text segment is matched to the Token workers, they transmit their information to the next department, composed of a team of specialist analysts called Attention Heads.
Attention Heads: Team of Specialized Analysts
Each attention head functions like a team of specialized analysts who examine various aspects of the information provided by the tokens, ensuring an accurate and multidimensional assessment of the details.
Each team analyzes the data from unique and specific perspectives, such as temporal context, relationships between meanings, and grammatical connections, contributing to a deeper and more detailed understanding of the information.
Question Office (WQ Matrix)
This sub-team within each attention head is responsible for formulating relevant questions from the data provided by the Tokens. In practice, it transforms this information into pertinent questions that guide the analysis process.
Key Office (WK Matrix)
The Key Office identifies the information that answers the questions posed by the Question Office. This allows for filtering and selecting the crucial data for a deep understanding of the context.
Value Office (WV Matrix)
This sub-team transforms key information into data ready for further processing, generating values that represent useful and synthesized information.
Synthesis Office (WO Matrix)
The Synthesis Office collects and combines the results from the various attention head teams, ensuring that the unified information is coherent and ready for further processing. This team integrates the different analyses into a single cohesive view, resolving any contradictions and ensuring the complementarity of the information.
William Enhancer - Specialist in Information Expansion (W1 Matrix)
William Enhancer, as a specialist in information expansion, is responsible for analyzing the combined reports provided by the Synthesis Office. His main task is to identify sections that require deeper insights and additional details, aiming to transform the initial report into a more comprehensive and valuable informational resource for subsequent analyses.
The first phase of the process involves a thorough evaluation of the synthesis report, during which William determines how to expand the existing information. He employs advanced techniques to enhance the data features, identifying more complex relationships and adding new dimensions to achieve a more detailed representation of the information.
During the integration and transformation of the data, William further refines the features, applying advanced techniques that improve the representation of complex relationships among the information.
Once the enrichment is complete, William prepares a detailed report intended for other departments for further processing. This report, structured clearly and organized methodically, facilitates the understanding and utilization of the enriched information.
William Enhancer’s work is essential for information enrichment within the company. Without his intervention, the combined reports might lack the necessary depth for a comprehensive understanding. William ensures that all information is detailed and structured, ready to support the subsequent phases of the decision-making process, thereby contributing to the overall operational efficiency of the company.
Simon Reducer – Expert in Information Synthesis and Simplification (W2 Matrix)
Simon Reducer is a specialist in the synthesis and reduction of information. Working for Language Leaders Inc., his crucial task is to transform the information already processed by William Enhancer, his colleague specialized in data enrichment, into even more refined and concise outputs. This operation allows for the presentation of only the essential elements, indispensable for effective decision-making.
Once he receives the enhanced information from William, Simon begins a careful evaluation aimed at identifying the key elements of the content. This step is strategic for separating relevant details from superfluous ones, a necessary process for creating a clean and incisive final product. After this selection, Simon proceeds with the condensation of the informational material. He eliminates any redundant details, focusing exclusively on the core points. During this process, he ensures that the relevance and meaning of the information are never sacrificed or distorted.
Alongside the reduction of information, Simon carries out meticulous data formatting. He restructures and organizes the information in such a way as to maximize its effectiveness and ease of use. Through this reorganization, the data is optimized to ensure a fast and smooth decision-making process, free of complications. The final product of this scrupulous work is a streamlined, coherent report that is ready to be easily consulted, representing an added value for further analysis and decision-making.
From Neuron to Neuron: Cycles of Continuous Processing and Refinement
In the ecosystem of Language Leaders Inc., Neurons are a team of highly specialized workers, divided into workgroups known as decoding layers. Each Neuron within a group receives the "Simon Reducer" report and transforms it through precise processing, generating useful and contextualized outputs. This iterative process ensures that the information is continuously improved and refined.
The processing carried out by the Neurons primarily includes linear operations, such as handling the received information with specific parameters. This process generates new representations of the information, which are then further processed by Alice the Activator. Alice adds more complex and sophisticated processing, which is essential for recognizing intricate patterns, selecting, and enhancing important information.
The process repeats through different groups of Neurons, starting again when the Attention Heads of the next group analyze the output of the previous group. Subsequently, a colleague of William Enhancer further refines this information, which is then synthesized by a colleague of Simon Reducer within the same group. The output of Simon Reducer's colleague is finally processed within the same group of Neurons, completing the processing cycle at that level. Once this cycle is completed, the resulting processing is transferred to the next group of Neurons, where the cycle repeats. This process continues sequentially, group after group. The output of each group becomes the input for the next, creating a chain of transformations that continuously refines the information.
The importance of the Neurons lies in their ability to perform accurate and value-adding transformations, constantly improving the quality and precision of the information. Their efficiency helps maintain a smooth workflow and reduces the likelihood of errors, ensuring that the final information is of high quality and ready for further processing and decision-making.
Product Finalization and Distribution
In "Language Leaders Inc.," the "Product Finalization and Distribution Department" plays a crucial role in ensuring that the final outputs are ready for use and comprehensible. Think of them as artisans who transform raw data into valuable and usable information.
This department takes the information processed by the last group of Neurons and converts it into clear texts, detailed reports, intuitive graphs, well-organized tables, and other visualizations that simplify understanding. It's not just about presenting the information but making it accessible and useful for anyone who uses it.
One of the key functions of the Department is the customization of the output. Each result is tailored to meet the specific needs of various users or departments, ensuring that the information is always relevant and targeted.
The quality of the output is another fundamental aspect. Before any information is distributed, it undergoes rigorous checks to ensure its accuracy and compliance with corporate standards.
And it doesn't end there. After distribution, the department gathers feedback from users to further improve the quality of the output. This continuous improvement cycle ensures that the company never stops progressing.
The Product Finalization and Distribution Department at "Language Leaders Inc." is essential for the effective and high-quality presentation of information. This department closes the processing cycle, ensuring that every result is ready to make a difference.
Critical Issues in the Business Context of "Language Leaders Inc."
One of the potential critical issues concerns the insufficient training of the employees at "Language Leaders Inc." Let's consider an example where the company receives raw material, in our case a text, whose topic is either not covered or covered in a generic way in the training provided to the company's employees.
Suppose a request demands specific details about the application of new legislative regulations on cryptocurrencies in a country like Liechtenstein or Malta. These regulations might have just been released or updated and contain very technical details regarding fiscal aspects, compliance, or blockchain technology that are not present in the Training and Development Department's programs at "Language Leaders Inc."
As a result, the final product released by the "Product Finalization and Distribution Division" of "Language Leaders Inc." might provide a generic response about cryptocurrency regulations, based on commonly discussed information or data available in large datasets provided by the company's "Knowledge Archivists." This may fail to capture the nuances or technical details of the new laws or specific laws of a country, leading to a response that, while grammatically correct, is inadequate in terms of information or even misleading.
The problem will become apparent after the distribution of the finished product, as customer feedback will highlight issues related to the quality of the product provided by the company.
In this case, "Language Leaders Inc." can address and resolve the issue by having its Training and Development Department acquire the missing documentation regarding the new legislative regulations on cryptocurrencies and integrate it into the department's training programs.
Similar problems arise when the "Knowledge Archivists" manage sources that contain inaccuracies, false information, or distinctive cultural aspects of populations or ethnicities that, by their nature, differ and can create cultural conflicts.
Of course, these are just two examples of the typical critical issues faced by companies like "Language Leaders Inc.," and, as in all companies, there are many more critical issues. However, these two issues are the ones that most significantly impact the quality of the company's production products.
Implementation and Management of Generative Artificial Intelligence in Companies: Strategies and Challenges
In our "virtual" journey inside the workings of the company "Language Leaders Inc.," a metaphor to explain the operation of language models like ChatGPT or Gemini, we have explored only a few aspects of these systems. The fundamental element to understand is that within these generative artificial intelligence platforms, there is no genie in a bottle or an oracle capable of answering any question. Instead, there exists an extremely complex logical-mathematical structure, so intricate that some of its functionalities remain mysterious even to the developers themselves.
The approach of "Language Leaders Inc." is clearly statistical. Translating this aspect into daily human experience, we could consider it an experience-based approach. There are no mathematical structures capable of providing certain answers to questions posed to LLMs. The answers they provide depend on the organization of the algorithm and its training, that is, the "experience" of the LLM we are querying.
This aspect helps us understand why, when we ask the same question to generative AI platforms, the answers we get are always different: sometimes only slightly, other times substantially. This happens because the statistical approach adopted by "Language Leaders Inc." is not deterministic.
To clarify, if we need to predict the fall of an apple from a branch, we can use Newton's equation, which allows us to predict exactly the position of the apple second by second. However, in the world of knowledge, there is no deterministic formula like Newton's for the fall of bodies. Therefore, the statistical approach, which we can compare to an experience-based approach, is mathematically rigorous and scientific but remains probabilistic.
When deciding to adopt a generative AI platform within the company, the first question to ask is whether this platform has the necessary and sufficient "experience" to meet the company's needs.
If we want to provide an initial assessment of the adequacy of a generative AI platform with generic training, such as OpenAI's ChatGPT or Google's Gemini, we will discover that for simple support tasks within companies, the training might already be sufficient. However, as we delve into specific and highly professional tasks, it becomes clear that the support offered by platforms with generic training is not always able to fully meet the company's needs. This is mainly related to the nature of the training of these platforms and thus their "experience."
Therefore, for companies intending to adopt generative AI platforms, it is essential to consider from the outset the further training of these platforms with the information and specifics of their own organization. This step, which may seem relatively simple, is actually one of the most complex, due to the often distributed and compartmentalized (siloed) nature of corporate data and information.
To structurally apply generative AI within companies, it is essential to update the management of corporate data. This aspect represents one of the greatest costs, both in economic terms and time.
Another crucial question for a company intending to adopt generative AI platforms is who to put in charge of the project. Naturally, IT professionals are often seen as the most suitable candidates for this role. However, analyzing the experiences of companies that have already embarked on this path, it emerges that the most suitable figure to manage the introduction of these technologies is someone with an in-depth knowledge of the company's organization.
This depends on the main characteristic of artificial intelligence, particularly generative AI, of being a transversal technology that will inevitably affect all company departments, even if not immediately. Therefore, only someone with a holistic vision of the company can understand how this technology can contribute to the development and improvement of the organization.
Another important aspect concerns the goal of generative AI platforms, which is not strictly technological. Unlike universities, where the goal is to demonstrate that a certain technology or scientific vision works, in companies, it is necessary to implement these technologies to ensure the best possible economic results. For this reason, the overall vision and interests of the company remain central.
Therefore, to effectively manage the adoption of generative AI platforms, it is crucial to entrust the project to someone who has a deep understanding of the company's structure and needs, as well as a comprehensive vision of the cross-sectional potential of these technologies.
Another crucial aspect is how to proceed to implement generative AI within companies. From the experiences of companies that have already embarked on this path, it clearly emerges that the recommended approach is an agile one. This implies a project that evolves along the way but must be seen as a comprehensive and not partial project. It is advisable to start with the most easily implementable areas, which typically for companies are marketing and communication activities. In these areas, the experience of generative AI platforms is already sufficiently advanced to provide effective support.
Despite starting with the areas where the use of these platforms is most immediate and where the economic return is already measurable, it is essential to adopt a general approach that learns from previous experiences. If the company starts with the marketing department, all acquired knowledge must be capitalized to redesign the overall project of AI introduction. Using a recursive method, similar to that of LLMs, the implementation of the generative AI project in the company becomes more efficient.
If you think there is a unique and prepackaged solution to introduce generative AI into your company, the likelihood of error is very high. Another mistake could be waiting for the evolution of generative AI in the software platforms already in use in the company. This approach is valid only for companies that use a single software platform to manage all business activities, making it natural to wait for new software versions.
However, if your company manages different software platforms dedicated to various activities, the introduction of generative AI offers the advantage of unifying the data processed by the different platforms under a single interface for certain types of operations. However, it is important to note that in this case, the data management issue previously mentioned reemerges.
Once decided how to introduce generative AI within the organization, one of the most important aspects to consider is the training of personnel.
The skills necessary to use generative AI platforms are mainly divided into two categories.
The first competence is computational, which translates into the ability to break down a complex problem into various steps. This skill is not exclusive to programmers or those who perform conceptual work, but is also applicable to more practical tasks. For example, in managing a vegetable garden, one must prepare the soil, identify planting or transplanting areas, irrigate, weed, and help the plants grow. These simple steps allow for a functional garden. Similarly, computational ability allows one to break down a complex goal into smaller, manageable steps, facilitating the use of generative AI platforms.
The second competence is critical, which is the knowledge of the specific domain in which one operates. Since the answers provided by generative AI platforms are not deterministic but probabilistic and based on the platform's own experience, it is inevitable that some answers are not perfectly aligned with the set goals. Therefore, it is essential that users have a good understanding of the specific context in which they apply AI, to be able to critically evaluate the answers and adapt them to their needs.
Once the implementation of AI within companies has begun, it is crucial to understand that it is a job that will never end. To clarify the concept, consider the difference between acquiring standard software or technology and adopting generative AI. While standard technology, once installed and started, mainly requires technological maintenance and training of new personnel, generative AI is an entity in continuous evolution.
As discussed in the previous parts of this article, generative AI is in constant evolution, both in its training and in the algorithms that process the information. Its statistical and non-deterministic nature makes constant monitoring of the quality and relevance of the responses provided by the platforms to the assigned tasks indispensable.
Regarding cost management, the experiences of pioneering companies in the sector show that for every euro invested in the initial implementation of the platform, three euros will be necessary for its continuous maintenance and updating. This means that investment in generative AI does not end with its initial adoption, but requires a constant commitment to ensure that the technology remains effective and relevant over time.
Examples of what LLMs are used for in companies
Let's now look at some concrete examples of how companies are implementing AI, and in particular generative AI, within their organizations, both in conceptual work and in manual work through intelligent robotics.
Uses of LLMs in Conceptual Work
Data Analysis and Management
Company: JPMorgan Chase
JPMorgan Chase is one of the leading global financial institutions, operating in over 100 countries. It offers banking, financial, investment, and wealth management services to private clients, businesses, and governments.
JPMorgan Chase is adopting generative artificial intelligence tools with a cautious and disciplined approach. The company has filed for the trademark "IndexGPT," a project that employs generative AI for various business purposes, including consultancy and financial software. However, JPMorgan has specified that it will not fully adopt these tools until all issues related to data security and ethics are resolved.
Additionally, the company already has over 300 AI use cases in production, employed for activities such as risk management, marketing, and fraud prevention. This reflects a significant commitment to AI adoption, while maintaining a particular focus on responsible and secure implementation of the technology.
Automated Customer Service
Company: American Express
American Express is a leading multinational financial company in payment services and travel solutions. It offers credit cards, banking products, and expense management for customers worldwide.
American Express is adopting generative AI to improve customer service. The company has identified around 500 potential applications for this technology and has already initiated some promising trials. For instance, American Express has tested a generative copilot for travel consultants, reducing call handling times by approximately 60 seconds, allowing for faster responses to customer inquiries. This tool will be expanded to more countries by the end of 2024.
Moreover, American Express is exploring the use of generative AI models to support their software engineers, enhancing their efficiency and satisfaction. For these experiments, the company uses both proprietary and open-source models.
Thus, American Express is implementing LLM-based chatbots and other forms of generative AI to increase efficiency and customer satisfaction, maintaining a cautious and controlled approach to mitigate the risks associated with this technology.
Human Resources Management
Company: Unilever
Unilever is an Anglo-Dutch multinational specializing in consumer goods. Founded in 1929, it operates in over 190 countries, producing food, beverages, home, and personal care products.
Unilever is using generative artificial intelligence to automate the initial screening process of candidates in human resources. The company has partnered with Accenture to accelerate and scale generative AI solutions through the AI Horizon3 Lab in Toronto. This lab focuses on various AI projects, including HR management, where AI is used to analyze and select resumes, improving the efficiency of the selection process.
Unilever also uses artificial intelligence for other business purposes, such as trend forecasting and complex data modeling, demonstrating a continuous commitment to integrating advanced technologies to optimize various business operations.
The integration of AI into the recruitment process has led to significant improvements, such as reduced hiring times and increased diversity among new hires. Additionally, Unilever has saved over 50,000 hours in interview times thanks to the automation of the screening process.
Legal Support
Company: Baker McKenzie
Baker McKenzie is a global law firm offering legal and business consultancy services internationally. Founded in 1949, it has a presence in over 40 countries and is distinguished by its multinational expertise.
In 2024, Baker McKenzie continues to use generative artificial intelligence to enhance its legal processes, including the review and generation of legal documentation. The firm has implemented advanced AI technologies to increase efficiency in legal service delivery and develop new services. A notable example is the collaboration with the AI platform SparkBeyond, which led Baker McKenzie to win the AI Innovation Award in 2022 for innovative AI use in the legal sector.
Furthermore, Baker McKenzie has established a dedicated machine learning practice, BakerML, which pilots customized models and AI-based workflows for clients. This includes using large language models (LLMs) to accelerate document review processes and reduce errors.
The firm continues to leverage AI capabilities to improve the efficiency and quality of legal services offered to its clients, consolidating its position as a leader in legal innovation.
Automated Translation
Company: Netflix
Netflix is a global streaming platform offering movies, TV series, and original content. Founded in 1997, it has become a leader in the on-demand entertainment sector, available in over 190 countries.
In 2024, Netflix continues to use generative artificial intelligence for the translation and localization of its content into various languages, implementing several AI projects. These include the automatic translation of video materials, making content more accessible to a global and diverse audience. Netflix's localization strategy goes beyond simple translation and includes transcreation, adapting content to resonate with the cultural context of the target audience.
These efforts demonstrate Netflix's commitment to using advanced artificial intelligence technologies to expand its global market reach, improving user experience through more efficient and accurate translations and localizations.
Research and Development
Company: Pfizer
Pfizer is one of the leading global biopharmaceutical companies, committed to the development, production, and distribution of innovative drugs and vaccines to improve health and well-being.
In 2024, Pfizer continues to use generative artificial intelligence to accelerate and optimize research and development processes in the biomedical field. The company collaborates with AWS, leveraging AI platforms such as Amazon Bedrock and Amazon SageMaker for numerous use cases, including the generation of scientific and medical content, the creation of initial drafts of patent applications, and the identification of new therapeutic targets in oncology. This approach saves time and resources, improving overall efficiency in the research and development of new drugs.
Pfizer is also employing supercomputing and machine learning models to screen millions of compounds in the search for new drugs. A significant example is the development of the oral treatment for COVID-19, PAXLOVID™, where the use of these technologies has significantly reduced the time required to bring new drugs to market, while improving the quality and accuracy of drug effect predictions.
In summary, Pfizer's use of large language models (LLMs) and other generative AI technologies is transforming the way the company conducts biomedical research. This allows for the generation of new scientific hypotheses and accelerates the drug development process, contributing to improved operational efficiency and the quality of treatments offered.
Supply Chain Optimization
Company: DHL
DHL is a multinational leader in logistics, specializing in international shipping, express couriers, and supply chain management, with a vast global network and innovative solutions for businesses.
In 2024, DHL is implementing artificial intelligence to optimize warehouse and distribution flow management. The company has launched pilot projects using predictive models and optimization algorithms to improve order fulfillment rates and prevent errors. These models leverage AI to automate workflows and allocate resources more efficiently, increasing resilience and reducing operational costs. DHL is also using AI for last-mile delivery route optimization, improving the accuracy of shipment arrival forecasts and optimizing courier routes in real-time.
Additionally, DHL is exploring visual picking technologies and collaborative robotics to increase productivity and reduce errors in warehouses. The company has partnered with Robust.AI to develop and implement an innovative fleet of warehouse robots, such as "Carter," a collaborative mobile robot designed to improve material handling in warehouses.
These initiatives demonstrate DHL's commitment to integrating advanced artificial intelligence technologies to improve the efficiency and reliability of the supply chain. Additionally, DHL has updated the myDHLi platform with a virtual assistant based on generative AI, improving visibility, control, and operational efficiency for its customers.
Content Marketing
Company: HubSpot
HubSpot is a leading company in marketing, sales, and customer service software, offering an all-in-one platform to help businesses grow and manage customer relationships.
In 2024, HubSpot continues to use generative artificial intelligence to create personalized marketing content based on user preferences. The company has developed AI tools, such as the Content Assistant, that help generate ideas for blog posts, marketing emails, and social media content, improving the efficiency and quality of the content produced. These tools allow for the creation of tailor-made content for specific audience segments, optimizing engagement and conversion.
A 2024 report on the state of marketing by HubSpot reveals that 77% of marketers using generative artificial intelligence believe it helps create more personalized content. Additionally, 79% of marketers state that content created with AI performs better than content created without it.
Generative artificial intelligence is also used to predict customer behavior and optimize marketing strategies based on historical data and current trends. This approach enables HubSpot to offer more targeted and effective marketing solutions, better responding to the specific needs of customers.
Customer Sentiment Analysis
Company: X
Company X, formerly known as Twitter, is a social media platform that allows users to send and read short messages called "tweets." Founded in 2006, it is famous for its speed and interactivity.
In 2024, the platform X uses generative artificial intelligence for sentiment analysis. The company employs large language models (LLMs) to analyze and classify the sentiments expressed by users in tweets, identifying whether they are positive, negative, or neutral. This process helps monitor trends in online discussions and provides valuable insights into how a particular topic or brand is perceived.
X uses these technologies to improve its ability to detect changes in user sentiment over time, allowing for the quick identification of significant shifts in public opinion. For instance, the Sprout Social listening platform, integrated with X, allows for the visualization of sentiment summaries and trends over time, helping companies better understand the dynamics of online conversations and respond promptly to user opinions.
Additionally, sentiment analysis via generative AI not only allows understanding if a tweet is positive or negative but also analyzes the context and intent behind user expressions, distinguishing between genuine feedback, sarcasm, complaints, or questions. This approach provides companies with more detailed and actionable insights to adapt their marketing and communication strategies.
Thus, X is using generative artificial intelligence for sentiment analysis and monitoring trends in online discussions, providing advanced tools for interpreting user emotions and opinions.
Training and Development
Company: Coursera
Coursera is an online learning platform that offers courses, specializations, and certifications on various topics, developed in collaboration with leading universities and companies worldwide.
In 2024, Coursera continues to use generative artificial intelligence to enhance the learning experience for its users. The platform has introduced the "Coursera Coach," a virtual assistant powered by generative AI, which answers students' questions and provides personalized feedback. This coach can quickly summarize video lessons and suggest specific resources to help students better understand the concepts covered in the courses.
Additionally, Coursera is implementing AI-assisted course creation functionalities. These tools can auto-generate course content, such as the overall structure, readings, assignments, and glossaries, based on simple inputs provided by human authors. This significantly reduces the time and costs required to produce high-quality content.
These innovations demonstrate Coursera's commitment to using AI to improve the learning experience, making courses more accessible and personalized for students worldwide.
Uses of Intelligent Robotics in Manual Labor
Flexible Automation in Production
Company: Fanuc
Fanuc is a Japanese multinational company and a leader in the production of industrial robots, numerical controls (CNC), and machine tools. Founded in 1956, it is renowned for its innovation and advanced automation.
In 2024, Fanuc continues to leverage artificial intelligence to enhance its robotic solutions, especially in handling non-uniform objects on the assembly line. The collaboration with NVIDIA has led to the development of advanced robotic systems that utilize artificial vision and machine learning for the identification and manipulation of objects of various sizes and shapes without the need for predefined programming. This approach allows the robots to adapt in real time to variations in the production process, improving flexibility and operational efficiency.
Fanuc has demonstrated these capabilities at various industry events and trade shows, such as Automate 2024 and CES 2024, showcasing how their robots can perform complex tasks like recognizing and handling mixed parts using 3D vision sensors and AI algorithms. These robots can identify and pick up objects in challenging lighting conditions, thereby enhancing productivity and reducing downtime.
In summary, Fanuc integrates AI into its robotic systems to offer flexible automation solutions that improve productivity and dynamically adapt to the variable needs of production without the need for manual reprogramming.
Autonomous Warehouse Management
Company: Ocado
Ocado is a British company specializing in online grocery retail. It employs advanced automation and robotics technology to manage its warehouses and customer deliveries.
In 2024, Ocado continues to use artificial intelligence and advanced vision systems to autonomously manage its warehouses. The company has developed highly sophisticated robots capable of navigating, collecting, and sorting products autonomously within their distribution centers. These robots operate in a 3D grid system called the "Hive," where they communicate with each other to optimize the movement and picking of items, significantly reducing order processing times.
Ocado also utilizes robotic arms equipped with vision systems and artificial intelligence, capable of handling a wide range of products regardless of their shape and size. These robots can identify and manage items without the need for predefined programming, dynamically adapting to the variations in assigned tasks.
Ocado's innovative approach to warehouse automation leverages artificial intelligence to improve operational efficiency, allowing robots to operate with a high degree of autonomy and flexibility. Furthermore, Ocado has introduced new technologies such as the series 600 bot, which is lighter and more efficient, and automated loading systems that reduce labor costs and increase productivity.
These innovations not only enhance productivity and reduce downtime but also facilitate the scalability and installation of technologies in smaller warehouses closer to customers, improving the overall efficiency of the supply chain.
Advanced Precision Agriculture
Company: Blue River Technology
Blue River Technology, a division of John Deere, develops precision agriculture solutions based on artificial intelligence. It utilizes computer vision and machine learning to optimize crop management.
In 2024, Blue River Technology continues to use advanced artificial intelligence and computer vision in its robots to improve agricultural efficiency and reduce the use of chemicals. Their See & Spray platform, developed in collaboration with John Deere, employs high-resolution cameras and advanced computing modules to identify and spray herbicides only on weeds, thereby saving up to 90% of herbicides compared to traditional methods. This system leverages convolutional neural networks trained on millions of images to quickly distinguish between crops and weeds.
The technology developed by Blue River is highly precise and adaptable, reducing herbicide usage and increasing crop yields. The robots can dynamically adjust to changing field conditions without the need for predefined programming, ensuring a sustainable and efficient approach to precision agriculture.
Advanced Collaborative Robotics (Cobots)
Company: Universal Robots
Universal Robots is a leading company in the production of collaborative robots (cobots). Founded in 2005, it develops innovative solutions for industrial automation, enhancing productivity and safety.
In 2024, Universal Robots continues to integrate artificial intelligence into its collaborative robots (cobots) to improve path planning and 3D picking solutions. Through collaboration with NVIDIA, Universal Robots has developed cobots that can adapt their movements and actions based on the behavior and position of human operators, increasing efficiency and safety in industrial applications.
The integration of the NVIDIA Isaac platform and the Jetson AGX Orin module has enabled Universal Robots to achieve path planning 50-80 times faster than traditional solutions. These cobots can now perform complex tasks such as autonomous inspection and piece picking without the need for predefined programming, making industrial processes more flexible and adaptable.
These innovations reflect Universal Robots' commitment to transforming cobots from simple tools into intelligent partners that enhance productivity and creativity in the workplace, allowing for more natural and safer interaction with human operators.
Environmental Data Collection Automation
Company: Saildrone
Saildrone, Inc. is a US-based company headquartered in Alameda, California, specializing in the design and operation of unmanned surface vehicles (USVs) powered by wind and solar energy. These marine drones are used to collect real-time oceanographic and atmospheric data, supporting scientific research, seafloor mapping, and maritime safety. Founded in 2012, Saildrone is known for its environmentally friendly technologies and ability to perform long-duration missions in extreme ocean conditions.
In 2024, Saildrone continues to use autonomous marine drones equipped with advanced artificial intelligence to collect oceanographic and atmospheric data in extreme weather conditions. Saildrone’s drones, such as the Surveyor model, are designed to conduct ocean monitoring and mapping missions using a combination of advanced sensors, radar, automatic identification systems (AIS), and machine learning software to provide detailed and real-time situational awareness.
These marine drones can operate autonomously for extended periods, collecting essential data for hurricane forecasting, ocean floor mapping, and environmental monitoring. Saildrone’s technology enables the measurement of various meteorological and oceanographic variables, making them valuable tools for scientific research and maritime safety. For instance, the drones have been used to track hurricanes in the North Atlantic and to map unexplored ocean areas, contributing to the understanding of marine ecosystems and the management of ocean resources.
Saildrone drones are powered by solar and wind energy, making them highly energy-efficient. Additionally, their ability to operate autonomously and continuously makes them ideal for long-duration missions in extreme environments. The Saildrone Mission Portal control platform allows for real-time monitoring and mission management, providing high-quality data accessible 24/7 through dedicated APIs.
Saildrone has also collaborated with NVIDIA to integrate advanced AI technologies, further enhancing the data collection and analysis capabilities of its drones. This collaboration has led to the development of AI vision applications for image analysis and object detection, improving navigation and real-time data collection.
In summary, Saildrone uses advanced AI technologies to enhance the efficiency and accuracy of environmental data collection, significantly contributing to the understanding of marine ecosystems and the management of ocean resources.
Advanced Kitchen Robots
Company: Moley Robotics
Moley Robotics is a company that develops autonomous and intelligent kitchen robots. Their flagship product, the Moley Robotic Kitchen, can autonomously cook gourmet meals, combining advanced robotics and artificial intelligence.
In 2024, Moley Robotics continues to use artificial intelligence in its advanced kitchen robots, such as the X-AiR model. This system combines precision robotics and AI software to prepare meals from an extensive library of recipes, adapting to user preferences and continuously improving culinary techniques through real-time data collection.
Moley's kitchen robots, including the X-AiR and B-AiR models, use advanced robotic arms to perform a range of complex culinary tasks, such as pouring, mixing, cooking on induction stoves, and cleaning up after preparation. These systems rely on 3D recordings of professional chefs' culinary preparations, allowing the robots to precisely replicate the movements and techniques of chefs.
Additionally, the system is user-friendly, featuring a touchscreen interface that allows users to select recipes and customize meals according to their dietary preferences. The robots can also be programmed to follow custom recipes recorded by the users themselves, making the culinary experience highly personalized and interactive.
Moley Robotics recently opened the first luxury robotic kitchen showroom in London, offering visitors an immersive and interactive experience of the advanced capabilities of their kitchen robots.
Robots for Recognition and Handling of Recyclable Materials
Company: AMP Robotics
AMP Robotics is a leader in applying artificial intelligence to recycling technology. It uses advanced robotic systems to automate the sorting and classification of waste, enhancing efficiency and sustainability in the recycling industry.
In 2024, AMP Robotics continues to utilize advanced artificial intelligence to optimize the recycling process through its robots equipped with computer vision. AMP's robotic systems, such as the AMP Cortex™ model, leverage AI to identify and separate recyclable materials with high precision and speed, capable of handling up to 80 items per minute with a 99% accuracy rate.
AMP's AI platform, known as AMP Neuron™, employs deep learning techniques to continuously improve material identification capabilities. This system can recognize and classify various recyclable materials, such as plastics, paper, and metals, based on colors, sizes, shapes, and other distinctive features.
Over time, AMP has expanded its offerings to include complete automation solutions for material recovery facilities (MRFs), such as the AMP Cortex-C, a compact and easy-to-install system designed to fit into limited spaces and optimize sorting operations without requiring costly retrofits or extended downtime.
These robots not only enhance the efficiency of the recycling process but also provide real-time data to optimize recycling center operations, helping to reduce operational costs and increase material recovery rates. AMP Robotics is also expanding its global presence, partnering with clients in Europe and Asia to modernize recycling infrastructure with their advanced AI technology.
Therefore, the use of advanced AI and robotics by AMP Robotics underscores their commitment to modernizing global recycling infrastructure and making the process more sustainable and economically beneficial.
Conclusion
Modern companies must navigate a technological transformation that may seem complex but is essential for staying competitive in today’s market. Artificial intelligence, particularly generative AI, represents one of the most promising and powerful innovations available to executives and entrepreneurs. In recent years, generative AI has demonstrated significant impacts across various sectors, from finance to logistics, healthcare to manufacturing. Its ability to analyze vast amounts of data, generate complex content, and support strategic decisions offers companies unprecedented opportunities to improve operational efficiency and the quality of their products and services.
One fundamental aspect of successfully adopting generative AI is the awareness and preparedness of management. Executives and entrepreneurs must understand not only the technical workings of these technologies but also their potential applications within business processes. It is crucial to start with a clear vision of how AI can be integrated into the business strategy, identifying areas where it can bring the most benefits. This requires a structured approach that begins with staff training and extends to the reorganization of workflows to incorporate new technologies.
Imagine a company as a large organizational machine where every gear must function perfectly to achieve the best results. In this context, generative AI can be seen as a sophisticated optimization system, capable of improving every part of the production and decision-making process. However, to fully exploit these potentials, continuous commitment is needed in terms of model training and updating internal skills. Staff training must be ongoing, with dedicated programs that allow all levels of the organization to familiarize themselves with new technologies and use them effectively.
One of the most emblematic examples is represented by generative AI platforms used for data analysis and human resource management. Companies like JPMorgan Chase and Unilever have shown how adopting AI tools can significantly improve operational efficiency, reduce management times, and increase decision-making accuracy. In the finance sector, for example, AI is used for risk management and fraud prevention, while in human resources, it supports the candidate selection process, reducing the time needed for recruitment and improving diversity and inclusion.
Another sector where generative AI is making a difference is production. Companies like Fanuc and Ocado are implementing advanced robots that use AI to improve the precision and efficiency of production processes. These robots can dynamically adapt to variations in the production process, performing complex tasks such as handling non-uniform objects and autonomous warehouse management. This not only increases productivity but also reduces operating costs and improves the quality of the final products.
Furthermore, generative AI also finds applications in healthcare and security sectors. CMR Surgical uses advanced surgical robots to perform operations with greater precision and customization, improving clinical outcomes and reducing patient recovery times. Knightscope, on the other hand, has developed autonomous security robots that use AI to patrol and monitor designated areas, enhancing public safety and reducing the risks associated with traditional surveillance.
To effectively implement generative AI, companies must adopt an agile approach, starting with the most easily implementable areas and progressively building on these foundations. It is crucial that the introduction of these technologies is led by someone with an in-depth understanding of the corporate organization, capable of comprehending how AI can contribute to the development and overall improvement of the company. This implies not only a strategic vision but also the ability to manage corporate data effectively, ensuring that information is always up-to-date and accessible.
In conclusion, the adoption of generative AI represents a unique opportunity for companies to transform their production and decision-making processes. However, to fully exploit these potentials, constant commitment to staff training, data management, and the definition of a clear and integrated strategy is necessary. Only in this way can companies ensure the effective and sustainable implementation of these technologies, gaining significant competitive advantages in the long term.