Generative AI is transforming the way companies create content, develop products and make strategic decisions.
Unlike traditional artificial intelligence models, which only classify or analyze data, generative AI is capable of producing texts, images, codes, sounds and even 3D models from learned patterns, generating original results adapted to the context.
In recent years, the advancement of this technology has gained momentum with the power of cloud computing, making it possible to train and run complex models at scale, without the need for robust local infrastructure.
According to Gartnergenerative AI was one of the most talked about innovations of 2023, with the potential to impact more than 70% of companies by 2025.
In this article, we’ll explore what generative AI is, how it works in practice, its applications in the cloud, its role in cybersecurity and the direct impacts for businesses of different sizes and sectors.
What is Generative AI?
In objective terms, generative AI is an artificial intelligence system trained to identify patterns, contexts and complex relationships in data and, based on this knowledge, produce new content that simulates or expands on what already exists.
This learning takes place on the basis of huge data sets – such as texts, images, audios and codes – which allow the model to understand the structure, style and logic of the information, creating original and contextualized results.
With this, generative AI is able to create realistic, coherent results adapted to different purposes. In practice, this means that instead of just answering a question or classifying data, the technology can, for example ..:
- Write complete reports based on the data provided.
- Create customized advertising designs.
- Generate product prototypes based on user-defined requirements.
This versatility explains why generative AI is at the heart of innovations in sectors such as marketing, health, engineering and entertainment.
With this definition, it’s clear why generative AI is becoming a strategic tool for companies looking to innovate quickly and at scale.
According to an estimate by McKinseythe economic impact of generative AI could reach US$4.4 trillion a year globally, boosting productivity and accelerating the development of new businesses.
How does Generative AI differ from other types of AI?
Although generative AI is part of the artificial intelligence ecosystem, it stands out for its creative nature.
In more traditional models – such as discriminative AI or predictive AI – the focus is on classifying information, identifying existing patterns and predicting outcomes.
Generative AI, on the other hand, doesn’t just respond or recognize: it creates. Examples:
- Discriminative AI
Main objective: to classify or recognize data.
Example application: detecting fraud in bank transactions.
- Predictive AI
Main objective: to anticipate results or trends.
Application example: forecasting stock demand.
- Generative AI
Main objective: to create new and original content.
Application example: generating personalized advertising images
In addition, generative AI is more dependent on large-scale models and requires advanced computing power, something made possible today by cloud computing.
How does Generative AI work?
Through advanced machine learning models, generative AI can understand complex patterns in data and create new content based on this knowledge.
Main Generative AI models
These models are trained on large volumes of information, extracting essential characteristics to generate original and coherent results. Each model has a specific approach and application. Check it out!
Transformer-based models (LLMs – Large Language Models)
By using attention mechanisms, these models are able to capture context much more efficiently than previous architectures. Well-known examples are GPT (from OpenAI), Gemini (from Google) and their derivatives.
In the business context, LLMs can be used to automate report creation, customer service via intelligent chatbots and even generate software code.
These models are trained with billions of words and can produce coherent and contextualized texts, which facilitates communication and speeds up internal processes.
Generative Adversarial Networks (GANs)
GANs are made up of two neural networks that compete with each other: one generating network creates content and the other, a discriminator, evaluates the quality of this content, promoting dynamic learning and constant improvement.
This dynamic allows GANs to produce extremely realistic results. In the corporate world, GANs are used to generate realistic images and videos for marketing, create virtual product prototypes, among other applications.
Variational Autoencoders (VAEs)
VAEs, on the other hand, are models that learn to represent complex data in reduced latent spaces, which allows for the generation of new data similar to the original, but with creative variations.
They are widely used in data compression tasks, image reconstruction and the generation of new content. In the business sector, VAEs are applied to create realistic data simulations that help train other AI models, improve digital manufacturing processes and optimize recommendation systems.
For example, a manufacturing company can use VAEs to generate a variety of equipment failure scenarios, improving the predictive capacity of its preventive maintenance systems.
The role of cloud computing in the evolution of Generative AI
One of the main drivers behind the advancement and large-scale adoption of generative AI is cloud computing. cloud computing. Generative models, especially those based on transformers and GANs, require enormous computing power to train and operate.
This need goes beyond the limits of traditional on-premises infrastructures, making the cloud an indispensable solution for companies wishing to exploit the full potential of this technology.
Providers such as Wevy, AWS etc. offer robust platforms that allow companies to access scalable processing, storage and artificial intelligence resources on demand.
A Wevyfor example, is the first Brazilian cloud computing multinational specialized in business, serving several clients in Latin America with advanced cloud solutions, cybersecuritydata, artificial intelligence and managed IT services.
By using these cloud services, organizations can reduce infrastructure costs, accelerate the development of solutions based on generative AI and guarantee high availability and security.
In addition, the cloud offers flexible environments for experimentation, testing and continuous deployment, facilitating the innovation cycle.
According to a report by Gartnerthe combination of generative AI and cloud computing is expected to grow at a compound annual growth rate (CAGR) of more than 40% by 2027, with companies increasingly migrating their workloads to cloud environments to take advantage of this technology.
Applications of Generative AI in Cloud Computing and Cybersecurity
In addition to its creative capacity and relevance in cloud computing environments, generative AI is also gaining prominence in cyber security.
Companies from all sectors are adopting these solutions to automate complex processes, anticipate threats and optimize operations, taking advantage of the scalable and flexible power of the cloud.
Below, we explore the main applications that are driving corporate digital transformation.
Cybersecurity automation with Generative AI
Information security is one of the biggest challenges for companies in the digital age. Generative AI works to automate incident responses, generate scripts to contain attacks and create systems capable of detecting unusual patterns in network traffic, which makes malicious actions more difficult.
“When it comes to cybersecurity, time is everything. Generative AI allows us to analyze huge volumes of logs and events in real time, creating correlations that humans would take hours or days to find. In addition, it can generate hypotheses and attack scenarios, anticipating the attacker’s movements. In practice, this reduces detection time and speeds up the response, because it already suggests actions and countermeasures almost immediately.”
Mozart Marin – Data/IA Architecture and Product Manager at Wevy
Predictive analytics and enterprise data management
Cloud computing combined with generative AI allows companies to carry out much more accurate and comprehensive predictive analysis. Generative models can synthesize large volumes of data to generate actionable insights, predict market trends and identify growth opportunities.
With Wevy, it is possible to power these analyses through its scalable and secure cloud infrastructure, which offers optimized environments for advanced data processing, facilitating the integration of generative AI into business workflows.
“At Wevy, we combine generative AI with a distributed cloud architecture to process
streaming data. This means that we can train and update models
predictive analytics in near real time, using corporate data in a secure and scalable way.
Generative AI steps in to enrich these forecasts, creating alternative scenarios, explaining results and helping leaders make faster, more informed decisions.
It’s an integrated journey of AI, data and cloud to generate competitive advantage.”
Mozart Marin – Data/IA Architecture and Product Manager at Wevy
Creating advanced virtual assistants and chatbots
One of the most visible uses of generative AI in the cloud is the development of virtual assistants and chatbots capable of interacting in a natural and personalized way with customers and employees.
These systems can understand the context, answer complex questions and even perform administrative tasks automatically.
Optimizing IT infrastructure with Generative AI
Generative AI also helps to optimize IT infrastructure, proposing automated configurations for servers, networks and storage systems.
Based on historical data and operational patterns, the technology generates recommendations for load balancing, resource allocation and failure prevention.
Benefits and challenges of Generative AI
The adoption of generative AI in companies brings a series of benefits that directly impact operational efficiency, innovation and competitiveness.
However, it also presents major challenges, especially in relation to ethical and governance issues, which must be managed to ensure the responsible use of technology.
How does Generative AI improve operational efficiency?
By automating tasks that previously required significant time and human effort, generative AI boosts operational efficiency.
This includes automatic report generation, content creation, digital prototyping and even software code development.
Companies that adopt generative AI are able to speed up internal processes, reduce manual errors and free up teams to focus on strategic activities.
In addition, the scalability and flexibility that come with modern AI solutions ensure that operations can adjust to the demands of the business in real time, providing agility and cost savings.
Main challenges and ethical concerns
Despite the benefits, generative AI also raises significant challenges that need to be addressed by companies. The main ones include:
- Algorithmic bias: generative models can reproduce and amplify biases present in the training data, negatively impacting automated decisions.
- Privacy and security: the use and generation of sensitive data requires strict protection and compliance policies, especially in regulated sectors.
- Transparency and explainability: the “black box” of AI models makes it difficult to understand how certain decisions or content are generated, creating barriers to auditing and trust.
- Misuse: the ability to generate realistic content can be exploited for fraud, deepfakes and spreading disinformation.
A robust ethical approach includes regular audits, human review, constant updating of models and clear usage policies.
According to experts, this balance is essential to ensure that generative AI is a sustainable and reliable competitive differentiator.
The future of Generative AI and its impact on the technology market
Generative AI is at the heart of the technological transformations that will shape the future of business.
With continuous advances in models, computing capacity and integration with cloud computing, the potential for innovation is immense.
Companies that anticipate these changes will be better placed to gain a competitive advantage and lead their markets.
Emerging trends and future innovations
Over the next few years, generative AI is set to evolve on several fronts:
- Multimodal models capable of combining text, images, audio and video to generate increasingly rich and interactive content.
- Real-time customization, allowing solutions to adapt dynamically to user behavior and preferences.
- Integration with emerging technologies such as metaverse, augmented reality (AR) and blockchain, expanding the possibilities for application in sectors such as entertainment, health and finance.
- Explainable AI (XAI) to increase transparency and confidence in the results generated, meeting regulatory and ethical demands.
How companies can prepare to adopt Generative AI
To prepare for the effective adoption of generative AI, companies need to:
- Invest in a scalable and secure cloud infrastructure, guaranteeing the capacity to train and run advanced models.
- Develop internal skills in data science, AI and technological governance to keep up with the evolution of models and their applications.
- Implement clear ethics and security policies, ensuring the responsible use of technology and mitigating risks of bias and misuse.
- Establish strategic partnerships with suppliers specializing in AI and the cloud, who offer continuous support and technological updates.
A Wevy acts as a strategic partner for companies in Latin America, offering robust solutions in cloud computing, artificial intelligence and cybersecurity.
With a high-performance infrastructure and managed services, Wevy facilitates the integration of generative AI into businesses, accelerating digital transformation safely and efficiently.
Talk to Wevy and transform your business with Generative AI
Do you want to accelerate innovation in your business with generative AI? Discover Wevy’s solutions, which combine cutting-edge technology, security and specialized support to help your company make the most of this technological revolution.
Get in touch now and find out how Wevy can be your partner on the journey to the future of artificial intelligence and cloud computing.
