Generative AI

Generative Artificial Intelligence (GAI) is a branch of artificial intelligence that focuses on developing models and algorithms capable of generating new and original content. Through machine learning and optimization, these systems can autonomously create texts, images, music, and other types of content. GAI uses techniques like Generative Adversarial Networks (GANs) and transformer-based language models to produce realistic and high-quality results.

GAI has the ability to adapt to the specific preferences and needs of customers. By training models with relevant data, AI can generate personalized and relevant content for each user, enhancing customer experience and increasing satisfaction.

By leveraging data analysis and customized solutions through generative AI, companies can offer more engaging and relevant experiences, ranging from product recommendations to precise audience segmentation. In terms of operational efficiency, internal processes can be optimized, leading to cost reductions, more effective resource management, and greater efficiency in critical areas like supply chain and logistics.

Organizations that maximize their use of data and AI solutions will be well-positioned to make informed decisions and adapt quickly to a constantly changing business environment.

Additionally, the adoption of these technologies further expands the possibilities of data analysis, enabling companies to perform predictive and prescriptive analyses, which means they can not only understand past events but also anticipate future trends and take proactive measures.

The main challenge is cultural. Adopting these technologies requires a new way of thinking about how companies deliver value to their customers and employees.

First, a culture focused on data exploitation is required. Today, several generative AI technologies are available to both companies and their competitors. Therefore, the competitive advantage lies in how a company uses its specific data in conjunction with generative AI to provide new experiences, identify new business opportunities, and improve processes. Related to this, companies that best leverage these technologies are those prepared to experiment quickly and at a reasonable cost. Due to the non-deterministic nature of AI, the best way to approach AI initiatives is through hypothesis generation, repeatable experimentation, hypothesis validation, or pivoting. It’s a learning journey in itself, strongly data-driven, which gives “data-centric” companies an advantage over those that are not.

Lastly, while significant progress has been made, much work remains in democratizing the knowledge needed to implement AI-based solutions. Strong data science expertise can make the difference between highly successful projects or failures (with no learning along the way). Therefore, we encourage companies to continue building internal data science capabilities, with a focus on the company’s reality, and also to seek external expert support when needed.

Some uses where this technology can be capitalized include:

Content Generation: Automatic and efficient creation of coherent and relevant texts such as articles, product descriptions, or accurate and precise responses.

• In retail, it can be used to deploy promotions and discounts aimed at a specific audience and with a determined language.
• In the field of auditing and document management, it can be used for translating existing content into multiple languages and generating summaries of existing regulations.
• In contact centers, it can create personalized messages tailored to different audience segments and optimized to maximize impact and conversion.

Document Management: It answers user queries based on large volumes of stored and indexed information.

• In Human Resources, it can be used to process resumes, job application forms, and other hiring-related documents or for the automatic analysis of skills, work experience, references, and comparing profiles with job requirements.
• In logistics and transportation, it can be applied to manage large volumes of supply chain-related documents to optimize inventory management, shipment tracking, and billing processes.
• In legal areas, it can search and extract information from digitized legal documents using keywords or identify specific clauses, legal terms, and case summaries to streamline document review and facilitate information search.

Conversational Assistants with Real-Time File Processing: They can answer specific questions about information stored in documents, audios, and images. Applicable in various industries:

• Education: To summarize recorded classes or for teachers to review students’ academic history.
• Healthcare: To read studies or reports and expedite patient medical history analysis.
• Security: To detect potentially dangerous elements through video analysis.
• Banking: To process documents directly from their images or to detect potential fraud through document forgery or identity theft.
• Digital Marketing: To detect customer emotions in interactions and improve the experience or assist in planning, executing, and tracking campaigns.

Nubiral has a business unit entirely dedicated to implementing AI, Machine Learning, Deep Learning, GPT, and other Generative AI-related technologies, aligned with the company’s vision to become a leader in these technologies in Latin America. For medium and long-term projects, Nubiral consistently invests in building capabilities around Artificial Intelligence, which allows us to have the experience and knowledge today to support clients in adopting these technologies and applying them to specific use cases that generate real value for their organizations. At Nubiral, we have been working on a strategic talent development program in data and AI technology, enabling us to incorporate these new capabilities organically into our talent development plan. Today, we have a Generative AI Center of Excellence with professionals dedicated exclusively to these new technologies.

Within the Generative AI technology that Nubiral applies, there is the Large Language Models (LLM) technology. In this regard, at Nubiral, we have trained ourselves in the different options available today to implement a solution with these characteristics. These include developing solutions using Azure OpenAI service with GPT as its core language model, Amazon Bedrock, and AI21’s model, as well as implementing various models available within Hugging Face, such as LLaMA. An example of using this type of technology involves developing conversational assistants that can interact with users in natural language, providing comprehensive personalized attention. To make this possible, we must combine LLM technology with all the user information stored in databases, allowing the assistant to offer truly personalized attention to each user.

At Nubiral, we adopt a responsible approach to implementing Generative AI, ensuring that the developed solutions meet the highest ethical and privacy standards. We work to minimize biases in algorithms and ensure transparency in the use of AI.

First, we must automate how we capture, store, analyze, and consume our data. Reduce points of failure, automate, digitize, centralize data, and empower people. It’s not just about data, but data + process + culture = success. It’s about making sure everyone sees the same picture, in its same version. Finally, connect the dots, break down silos, and have all the data in one place, like a data lake, for example. Companies that adapt to the current rules of the game, break the cultural barrier, and undertake digital transformation have achieved positive results internally and in their customer interactions.

In any transformation process, leaders are responsible for deeply understanding the organizational and operational context from which they start, so that the implementation plan is grounded in the company’s reality. This will allow for a realistic identification of areas with the greatest potential to apply new technologies, create realistic and consistent plans, and minimize potential friction between the current situation and the desired new one. Our vision is that all organizations, in general, should approach the use of these technologies holistically, not with the intention of replacing the human factor, but as intelligent assistants that take on different roles in our processes, whether as a tutor for incorporating new knowledge, an assistant helping with arduous tasks that require reading, analyzing, and synthesizing large volumes of text, or a co-pilot in software solution development that allows for increased agility and precision in implementation.

AI can be an excellent factor for increasing team productivity, incorporating new knowledge, and simplifying the context in which organizations carry out their activities.

To start a Generative AI project with Nubiral, contact our specialists. We will assess your business needs and design a personalized roadmap for implementing Generative AI, tailored to your objectives and resources. Don’t hesitate to contact us to discover how we can help you innovate and grow!