OpenAI, Gemini, Claude, Perplexity, Copilot, ElevenLabs… these names have become so familiar that we've stopped thinking of artificial intelligence (AI) as something new and started seeing it as something we should already be using to “keep up.”
Although historians trace the origins of artificial intelligence back to the 1940s, you could say the real boom began in the 2020s, when various companies released their technologies or platforms to the public. Here are a few key dates:
- ChatGPT: launched by OpenAI on November 30, 2022.
- Perplexity: the AI-powered conversational search engine launched in December 2022.
- Gemini: Google made its initial announcement on December 6, 2023.
- Copilot: Microsoft officially announced it on September 21, 2023.
These are some of the best-known names, but many other tools and solutions are available, especially when it comes to enterprise AI. To give just a couple of examples, at BluePixel we work with Dialogflow to develop AI-powered chatbots, and we use Amazon Personalize, an Amazon Web Services (AWS) service that generates real-time product or content recommendations, to deliver a better shopping experience on the eCommerce sites we develop for our clients.
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Given the popularity of artificial intelligence and the fact that it's being implemented in all kinds of business solutions—from creating chatbots that answer frequently asked questions to using algorithms that detect cyberthreats in real time—we wanted to explore 16 industries making the most of the technology that, as IBM puts it, “enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.”
Join us as we explore each one…
Administration
One of the biggest applications of artificial intelligence isn't in the videos or images you see on Instagram or TikTok. It's in spreadsheets, approval workflows, and shared files on platforms like Drive, AWS, and Microsoft Teams.
Administration is often one of the first areas where companies see tangible results from artificial intelligence because so much of the work revolves around information, documentation, coordination, and decision-making. In other words, many administrative tasks were already taking place in digital environments before AI became popular, making the transition to intelligent tools relatively natural.
According to McKinsey's The State of AI report, organizations are primarily using artificial intelligence to support customer service, operations, marketing, sales, and product development—all functions closely tied to administrative processes.
What's interesting is that adoption isn't limited to large corporations. More and more small and medium-sized businesses are introducing AI assistants to automate routine activities that previously took hours of work each week. From writing reports to analyzing large volumes of data, AI is helping teams spend more time on strategic work and less on repetitive tasks.
In the projects we've seen at BluePixel, the use cases tend to fall into four broad categories:
Productivity and Workflows
Generating meeting notes, executive summaries, task lists, and automated follow-ups has become one of the most common use cases. Tools like Copilot, Gemini, and ChatGPT can turn entire meetings into structured documents in a matter of minutes.
AI-powered assistants are also helping coordinate calendars, automate internal approvals, generate documents, and speed up planning processes that previously required constant manual intervention.
Internal and External Communication
Drafting emails, announcements, executive reports, and messages for coworkers and colleagues has become an everyday practice in many organizations.
In addition, conversational agents and automated support systems can handle common questions, provide internal support, and improve the experience for both employees and customers.
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Data Analysis
Perhaps one of the most significant changes is happening in how companies interact with information.
Today, AI can be used to clean databases, detect anomalies, identify behavioral patterns, generate visualizations, and produce forecasts based on historical data.
This is especially valuable for organizations that generate large amounts of information but don't always have dedicated data science teams.
In fact, one of the most interesting advances is that today's tools let people query information using natural language. Instead of building complex queries or advanced formulas, users can simply ask about trends over a given period and receive answers supported by automated analyses.
Administrative Support and Compliance
Another use case with enormous potential is reviewing documents and processes related to regulatory compliance.
Organizations are using AI to identify inconsistencies in contracts, check internal guidelines, analyze regulatory documentation, and detect risks that traditionally required manual reviews.
In highly regulated industries or areas such as finance, healthcare, and insurance, these capabilities are significantly reducing review times and improving responsiveness during audits.
Agriculture
If someone had predicted ten years ago that farmers would use artificial intelligence to decide when to plant, fertilize, or harvest, it probably would have sounded like an exaggeration. Today, it's a reality.
Modern agriculture generates enormous amounts of information from sensors, weather stations, drones, satellite imagery, and connected machinery. Artificial intelligence turns all that data into practical decisions that help improve productivity and reduce waste.
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One of the best-known applications of artificial intelligence is precision agriculture. Using predictive models, companies can identify specific areas of a field that need water, fertilizer, or special attention instead of treating areas with entirely different needs in exactly the same way.
AI-powered systems can also analyze drone images to detect pests, diseases, or nutrient deficiencies before they're visible to the human eye. This makes it possible to intervene early and reduce losses.
This Deutsche Welle documentary shows how technology is being used to process thousands of images to assess a crop field and identify pests and diseases:
In addition to these examples, climate prediction algorithms help farmers plan production cycles more effectively and make better-informed decisions in response to weather events that could affect yields.
As the world's population continues to grow and pressure on natural resources increases, agriculture is likely to remain one of the industries where artificial intelligence has the greatest impact in the decades ahead.
Food and Beverages
The food and beverage industry is using artificial intelligence across virtually the entire value chain, from production to the end consumer's experience.
On the operational side, companies use predictive models to forecast demand, optimize inventory, and reduce waste. This is particularly important when dealing with perishable products, where an inaccurate estimate can lead directly to financial losses.
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In manufacturing, AI is helping identify quality issues, optimize recipes, monitor production lines, and even predict equipment failures before they cause disruptions.
On the consumer side, the applications are just as interesting. Digital platforms, loyalty programs, and recommendation systems use artificial intelligence to personalize offers, promotions, and shopping experiences.
Amazon Personalize, for example, can generate real-time recommendations based on users' past behavior and preferences, a technology already widely used in eCommerce and loyalty programs.
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Arts, Entertainment, and Recreation
Perhaps no industry has sparked as much conversation about artificial intelligence as content creation. Every week brings new AI-generated videos, songs created from prompts, or images that look as though they were taken by professional photographers but were actually created on a computer.
However, reducing AI's impact to amusing images or viral videos would be missing the bigger picture… In the entertainment industry, AI is already involved in audiovisual production, animation, dubbing, content localization, visual effects, personalized experiences, and even recommendations for movies, TV series, and music.
Netflix, Spotify, and YouTube are probably among the best-known examples. Millions of people interact daily with algorithms that analyze behavior, identify patterns, and suggest content based on individual interests.
Here's a short explanation of how Netflix's algorithm works:
Meanwhile, tools like ElevenLabs, Runway, and Midjourney are transforming creative workflows. What's interesting is that most professionals don't use these solutions to replace human work entirely but rather to speed up specific stages of production.
Looking ahead, we'll see increasingly personalized experiences in which content dynamically adapts to each user, from video games to streaming platforms.
Real Estate
For years, the real estate business was driven by human expertise: agents knew which areas had the greatest potential, developers analyzed growth trends, and investors made decisions based on market research, site visits, and a great deal of professional intuition. Artificial intelligence is changing that dynamic.
Today, platforms can analyze thousands of variables simultaneously to estimate property values, project appreciation scenarios, and identify investment opportunities far faster than a person could. Companies like Zillow have used machine learning models to build automated valuation tools, while various proptech startups are using AI to analyze demographic trends, urban mobility, supply and demand, access to services, and even local economic indicators.
What's interesting is that the impact doesn't end with buying or selling properties. AI is also finding a place in property management and operations. For example, smart buildings use algorithms to optimize energy consumption, monitor critical equipment, and schedule maintenance before costly breakdowns occur. This predictive capability is especially valuable in corporate complexes, shopping centers, and large-scale residential developments.
The customer experience is evolving, too. AI-enhanced virtual tours, automatically generated property descriptions, and virtual assistants that answer questions about listings are shortening response times and improving the property search experience.
Visit some of the most popular real estate websites and you'll find search tools that let you filter properties by location, price, size, amenities, and many other criteria:
What comes next looks even more interesting. Analysts at PwC and Deloitte agree that the future of real estate will be increasingly tied to the ability to analyze and predict behavior. In other words, the most successful projects may be those that not only understand where a city is growing but can also anticipate where it will grow next.
Construction
Few industries face as many factors that can affect a project as construction. Delays, scope changes, supply chain problems, weather conditions, cost overruns, and safety risks are all part of everyday life on a construction site. It's hardly surprising, then, that artificial intelligence is quickly gaining ground in the sector.
When people think of AI in construction, they often imagine autonomous machinery or robots putting up structures. Although these technologies exist, most of the value today comes from something far more practical: helping companies make better decisions.
The combination of artificial intelligence, drones, IoT sensors, and Building Information Modeling (BIM) platforms is making it possible to monitor construction sites in real time, compare progress against original schedules, and spot deviations before they become major problems.
Some organizations are using computer vision to analyze drone images and detect safety risks. Others are implementing predictive models that help estimate potential delays based on historical information, material availability, and the performance of similar projects.
Something interesting is happening on the financial side as well. Algorithms are starting to help estimate construction costs more accurately, identify budget deviations, and even assess the likelihood of certain projects succeeding before construction begins.
According to McKinsey, construction remains one of the industries with the greatest opportunities for productivity improvements. This helps explain why many of the sector's major digital transformation initiatives now have a significant artificial intelligence component.
Education
Artificial intelligence is forcing the education sector to revisit questions that seemed far off just a few years ago:
- How do we assess learning when students have access to intelligent assistants?
- How can teaching be personalized for classes of hundreds or thousands of students?
- How can educators take advantage of these tools without losing the human side of education?
The use of technology and artificial intelligence is being discussed by technology experts such as IBM, as well as decision-makers and policymakers. One recent example is the news that the use of digital devices and cell phones in elementary and secondary schools in Mexico will be banned starting November 3, 2026.
What's interesting is that AI isn't just transforming learning. It's also changing how educational institutions operate.
On the academic side, adaptive learning systems can personalize experiences according to each student's pace and performance. Instead of having everyone move forward in exactly the same way, these platforms can identify areas for improvement, recommend specific content, and even adjust the difficulty of exercises and activities.
Companies like Duolingo are among the most visible examples of this trend. The platform uses algorithms to adapt exercises, reinforce knowledge, and optimize the learning experience based on the individual behavior of millions of users.
But education doesn't end in the classroom. Universities and training centers are using artificial intelligence to automate administrative processes, improve communication with students, analyze dropout indicators, and optimize institutional resources.
The biggest challenge is probably not technological but pedagogical. The debate is no longer about whether students will use AI, because they clearly will. Instead, the conversation is increasingly focused on how to teach students to use these tools critically, ethically, and productively.
Finance and Insurance
If there's one industry that was working with artificial intelligence long before ChatGPT became popular, it's probably finance.
Whenever an institution flags a suspicious transaction, assesses credit risk, or analyzes millions of records to identify unusual behavior, there's a good chance an AI model is working behind the scenes.
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For decades, banks, insurers, and financial services companies have relied on vast amounts of information. What's changing now is the speed and sophistication with which that data can be processed.
One of the best-known examples is fraud detection. Visa, Mastercard, and numerous financial institutions use algorithms that analyze thousands of signals in fractions of a second to identify unusual activity. What once required manual reviews can now be done almost in real time.
Insurers are also finding new applications: some companies use AI to review claims, analyze documentation, or generate preliminary estimates of damage from photographs. In certain cases, processes that once took days can be completed in minutes.
However, perhaps the most interesting transformation is happening in the user experience. AI-powered financial assistants are starting to help individuals and businesses better understand their spending, plan budgets, and make decisions based on more complete information.
Finance has always been a data-driven industry. The difference is that the data is now more accessible, easier to interpret, and more actionable than ever before.
Hospitality
Hospitality has historically been a people-centered industry, as hotels, restaurants, and tourism services depend on understanding needs, anticipating expectations, and creating memorable experiences.
Paradoxically, that need for personalization makes artificial intelligence an especially good fit for the sector.
Today, many hotel chains use algorithms to analyze occupancy patterns, dynamically adjust rates, and optimize revenue. Marriott, Hilton, and other global organizations use predictive analytics tools to better understand demand and maximize the performance of their properties.
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But the most visible impact for consumers is often in the service experience. AI-powered chatbots can manage reservations, answer frequently asked questions, or assist guests before, during, and after their stay. Platforms also use recommendation systems to suggest personalized activities, experiences, and services.
A similar shift is happening in restaurants and food chains, where artificial intelligence helps predict reservation and consumption demand, optimize inventory, and reduce waste—a particularly important consideration when dealing with perishable products.
What's interesting is that the future seems to be moving toward hyperpersonalized experiences, where systems will be able to anticipate preferences and adapt services almost in real time:
Information Industry
If we had to choose one industry that isn't just using artificial intelligence but is also helping build it, it would probably be the information industry. This includes software companies, telecommunications, digital services, technology platforms, media outlets, and organizations whose primary raw material is knowledge.
For years, these companies have faced an enormous challenge: the amount of available information is growing far faster than humans can process it. Artificial intelligence is emerging to address precisely that problem.
Today, it's used to classify content, summarize documents, translate information between languages, detect cybersecurity threats, tag digital assets, moderate posts, and, of course, generate content. However, the real transformation is happening in how we search for and access knowledge.
Tools like Perplexity, ChatGPT Enterprise, Gemini, Copilot, and Claude are changing how organizations interact with their document repositories. More and more companies are implementing RAG (Retrieval-Augmented Generation) systems, which let users query corporate information in natural language without having to navigate hundreds of folders, PDFs, or databases.
At BluePixel, we've seen a significant rise in interest in AI agents capable of answering questions about internal processes, technical documentation, or operational information. The goal isn't just to save time but to democratize access to organizational knowledge.
According to Stanford's AI Index report, organizational AI adoption has reached historic levels and continues to expand into virtually every sector of the economy, particularly benefiting information- and knowledge-based organizations.
Manufacturing
When it comes to artificial intelligence in industry, manufacturing is often one of the most concrete and easiest examples to measure.
Unlike industries where the impact may be felt through productivity or user experience, AI in manufacturing can translate directly into cost savings, less waste, and higher output.
A classic example is predictive maintenance. Traditionally, companies carried out preventive maintenance on a set schedule or reacted when a machine broke down. With artificial intelligence, they can analyze vibrations, temperature, energy consumption, and other variables to detect early warning signs of failure before operations are interrupted.
This is particularly relevant considering that an unexpected production-line shutdown can result in millions in losses for some organizations.
Computer vision is also transforming quality control, as camera-equipped AI systems can inspect products at speeds beyond human capabilities, consistently identifying even the smallest defects.
According to the Stanford AI Index report, China continues to lead the world in industrial robot installations, while AI-powered automation continues to expand across manufacturing operations worldwide.
What's interesting is that the future seems to be moving toward increasingly autonomous factories, capable of adjusting production, material consumption, and maintenance almost in real time.
Retail
If there's one industry where artificial intelligence interacts with millions of people every day without them even noticing, it's probably retail.
Whenever a shopper receives a product recommendation on Amazon, sees a personalized promotion, or discovers an item that seems to have been selected specifically for their interests, there's a good chance an algorithm is working behind the scenes.
However, the real transformation isn't limited to the customer experience, as many of the most valuable applications happen behind the scenes.
For decades, one of retail's biggest challenges has been forecasting demand. Too much inventory drives up costs; too little results in lost sales. Artificial intelligence is helping close that gap with predictive models that analyze historical behavior, market trends, seasonality, and even external variables like weather or local events.
At the same time, organizations are using AI to optimize pricing, identify purchasing patterns, segment audiences, and improve loyalty programs.
At BluePixel, we see this all the time in projects involving digital platforms and eCommerce: personalization is no longer a differentiator. It's an expectation.
┈ Here are the retail companies we've worked with
The World Economic Forum highlights that organizations are using AI to improve productivity and the quality of work, something particularly evident in sectors where customer interactions happen at scale.
Healthcare and Social Assistance
Every year, thousands (or millions) of hospitals, clinics, laboratories, and medical professionals generate massive amounts of information. Medical records, medical images, tests, patient histories, and scientific literature are growing at a rate that simply exceeds our ability to analyze them in full. This is where artificial intelligence is finding some of its most promising applications.
One of the best-known use cases is support for medical image analysis. Various systems help identify patterns in X-rays, MRI scans, and CT scans, allowing specialists to prioritize studies and supplement their diagnoses.
Pharmaceutical research is also being profoundly affected. Organizations like DeepMind have demonstrated that AI can accelerate complex scientific processes related to molecular biology and drug discovery.
But perhaps the most immediate applications are in day-to-day operations: optimizing medical schedules, automating administrative tasks, managing hospitals, and assisting with documentation.
Need to optimize a platform for the healthcare sector? Contact us
The World Health Organization has noted that these technologies have the potential to expand access to medical services and support clinical decision-making when implemented responsibly.
It's important to remember that, at least for now, AI primarily serves as a support tool rather than a replacement for doctors or specialists.
Administrative and Support Services
There's a reason so many companies are starting their AI initiatives in administrative departments: that's often where some of the quickest returns can be found.
Processing documents, responding to requests, managing tickets, coordinating activities, and supporting employees all consume enormous amounts of organizational time, and artificial intelligence is helping reduce precisely that burden.
Today, it's used to automate help desks, classify requests, generate documentation, analyze contracts, organize information, and answer recurring questions.
Contact centers are an especially interesting example. AI assistants no longer just answer frequently asked questions; many can interpret context, consult knowledge bases, and escalate complex cases when needed.
Human resources departments are also using these technologies to support recruitment, training, employee onboarding, and knowledge management.
According to McKinsey, individual productivity is one of the areas where organizations are seeing the most consistent benefits from AI adoption.
Professional, Scientific, and Technical Services
The history of science is full of tools that have helped people see further. The telescope, for example, expanded our view of the universe, while the microscope opened our eyes to things too small to see unaided. Today, many researchers believe artificial intelligence is beginning a comparable transformation.
Scientific research produces colossal amounts of information, ranging from academic papers, experimental data, and simulations to studies that are impossible to process entirely by hand.
AI is helping synthesize knowledge, identify hidden relationships between variables, and accelerate discoveries.
This is already happening in disciplines such as chemistry, biology, engineering, and materials development, where researchers use algorithms to analyze complex scenarios, model behavior, and explore possibilities that would be extremely costly using traditional methods.
A similar effect is emerging in consulting and engineering, where organizations use models to generate scenarios, analyze risks, model processes, and speed up research and design activities.
According to the Stanford AI Index, some of the most impressive recent advances involve models matching or surpassing human performance on certain highly complex scientific tasks.
Transportation and Warehousing
If you've ever received a package ahead of its promised delivery date, artificial intelligence may have played a role.
Modern logistics is one of the most complex systems there is, involving vehicles, routes, warehouses, inventory, traffic, fuel, weather, and thousands of constantly changing variables. That's why AI is such a good fit for the sector.
Companies use predictive models to optimize routes, forecast demand, improve resource allocation, and reduce operating costs. In logistics centers, algorithms help determine which products should be stored closest to dispatch areas based on demand patterns.
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Vehicle fleets are also benefiting from intelligent systems that can monitor vehicle performance, fuel consumption, and maintenance needs to reduce risks and improve efficiency.
Amazon is probably one of the most visible examples, but the reality is that virtually any modern logistics chain incorporates advanced analytics and automation components.
What's interesting is that the next stage seems focused on increasingly autonomous operations, from smart warehouses to vehicles with more advanced automated navigation capabilities.
As global supply chains grow ever more complex, the ability to anticipate problems before they occur can provide an enormous competitive advantage.
The Question Is No Longer Whether Your Industry Will Use AI, but How
If there's one thing that's clear after exploring these 16 industries, it's that artificial intelligence has moved beyond being a technology trend to become a real business tool.
We're no longer talking only about chatbots, computer-generated images, or assistants that draft emails, but about systems capable of analyzing millions of data points, identifying patterns invisible to humans, automating complex processes, and supporting better-informed decisions.
When the internet arrived, many companies wondered whether they really needed a website. When smartphones came along, some organizations hesitated to develop mobile apps. The same thing happened with eCommerce. History shows that transformative technologies are often underestimated in their early years before becoming an essential part of everyday operations.
Artificial intelligence seems to be following the same path, but at a much faster pace.
According to Stanford's AI Index Report, organizational AI adoption has reached historically high levels and continues to spread across virtually every sector of the economy. Companies are no longer just experimenting with the technology; many are beginning to see measurable benefits in productivity, automation, operations, and decision-making.
However, there's an important nuance that often gets lost in the excitement. The real competitive advantage doesn't come from using ChatGPT, Gemini, Claude, or any other trending tool. It comes when an organization identifies specific processes where AI can create tangible value.
For some companies, that will mean automating administrative tasks. For others, it will mean optimizing inventory, improving the shopping experience, accelerating scientific research, strengthening fraud detection, or building new AI-powered digital products.
That's why the right question is no longer: “Should we use artificial intelligence?”
The question the most competitive leaders are asking is much closer to: “What specific problem in our business could we solve better with artificial intelligence?”
And the answer will be different for every organization.
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