Turn AI into a Real Competitive Advantage

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 text, images, music, and other types of content. GAI uses techniques such as generative adversarial networks (GANs) and transformer-based language models to produce realistic, high-quality results.

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

Thanks to data analysis and customized solutions based on generative AI, companies can offer more engaging and relevant experiences, ranging from product recommendations to precise audience segmentation.
In terms of operational efficiency, companies can optimize their internal processes, resulting in cost reductions, more effective resource management, and greater efficiency in critical areas such as the supply chain and logistics.

Organizations that make the most of data and artificial intelligence solutions will be in an optimal position to make informed decisions and adapt quickly to an ever-changing business environment.

Furthermore, the adoption of these types of technologies further expands the possibilities of data analysis, as they enable companies to perform predictive and prescriptive analytics, meaning they can not only understand what has happened in the past but also anticipate future trends and take proactive measures.

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

First and foremost, a culture focused on leveraging data is required. Today, we have several generative AI technologies available, both for the company itself and for its competitors. Therefore, the differentiator—or competitive advantage—will lie in how the company uses its own specific data in conjunction with generative AI to deliver new experiences, identify new business opportunities, and streamline processes.
In this regard, the companies that get the most out of these types of technologies are those that are prepared to experiment quickly and at a reasonable cost. Given the non-deterministic nature of AI, the best way to approach AI initiatives is through hypothesis formulation, repeatable experimentation, hypothesis validation, or pivoting the hypothesis. It is a learning process in itself, one that is heavily data-driven; therefore, “data-centric” companies have an advantage over those that are not.

Finally, while much progress has been made, there is still a great deal to be done in terms of democratizing the knowledge needed to implement AI-based solutions. A strong foundation in data science can be the difference between highly successful projects and failure (with nothing learned along the way). That is why we encourage companies to continue building internal data science capabilities—with an expert eye on the company’s specific context—while also seeking support or expert insights from the technology sector.

Some ways this technology can be leveraged include:

– Content creation. Automatic and efficient creation of coherent and relevant text, such as articles, product descriptions, or efficient and accurate responses.

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

– Records management. It answers user queries based on large volumes of stored and indexed information.

• In Human Resources, you can use it to process resumes, job application forms, and other documents related to hiring, or for the automated analysis of skills, work experience, and references, as well as to compare profiles with job requirements.
• In logistics and transportation, it can be applied to the management of large volumes of supply chain-related documents to optimize inventory management, track shipments, and streamline billing processes.
• In legal settings, to search and extract information from digitized legal documents using keywords or to identify specific clauses, legal terms, and case summaries in order to streamline the review of legal documents and facilitate the search for information.

– Conversational assistants with real-time file processing. They can answer specific questions about information stored in documents, audio files, and images. This applies to numerous industries:

• Education: to summarize recorded classes or to allow teachers to review their students’ academic records.
• Healthcare: to read studies or reports and streamline the analysis of a patient’s medical history.
• Security: to detect potentially dangerous elements through video analysis.
• Banking: to process documents directly from their images or to detect potential fraud involving document forgery or identity theft.
• Digital marketing: to identify customer emotions during interactions and improve the customer experience, or to assist in the planning, execution, and monitoring of campaigns.

Nubiral has a business unit dedicated entirely to implementing solutions focused on AI, machine learning, deep learning, GPT, and other technologies related to generative AI, which is in line with the company’s vision of becoming a leader in these types of technologies in Latin America.

As for medium- and long-term projects, Nubiral is consistently investing in building capabilities in artificial intelligence, which means we now have the experience and expertise to support clients as they adopt these technologies and apply 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 technology and AI, and this allows us to incorporate these new capabilities organically into our talent development plan. Today, we have a Center of Excellence in Generative AI staffed by professionals dedicated exclusively to these new technologies.

Among the generative AI technologies used by Nubiral is Large Language Model (LLM) technology. In this regard, we at Nubiral are training ourselves on the various options available today to implement a solution with these features. These include developing solutions using the Azure OpenAI service and GPT as its core language model, Amazon Bedrock and the AI21 model, as well as implementing various models available on Hugging Face, such as LLaMA.

One example of how this type of technology is used is the development of conversational assistants that can interact with users in natural language, providing comprehensive, personalized service. To make this possible, we must combine LLM technology with all the user information we have stored in our databases; this enables the assistant to provide truly personalized service to each user.

At Nubiral, we take a responsible approach to implementing generative AI, ensuring that the solutions we develop meet the highest ethical and privacy standards. We work to minimize bias in our algorithms and ensure transparency in the use of AI.

First, we must automate the way we capture, store, analyze, and use our data. Reduce points of failure, automate, digitize, centralize data, and empower people. It’s not just data—it’s data + process + culture = success. It’s about making sure everyone sees the same picture, in the same version.
And finally, we need to connect the dots, break down silos, and keep all the data in one place—a data lake, for example. Companies that have adapted to the current rules of the game—those that broke down cultural barriers and embarked on a digital transformation—have achieved positive results both internally and with their customers.

In any transformation process, leaders are responsible for gaining a deep understanding of the initial organizational and operational context, so that the implementation plan is grounded in the company’s reality. This will make it possible to realistically identify the areas with the greatest potential for applying new technologies, develop realistic and consistent plans, and minimize potential friction between the current situation and the desired new state.

Our vision is that all organizations, in general, should adopt these technologies in a comprehensive manner—not with the intention of replacing the human element, but rather as intelligent assistants that take on various roles in our processes, whether as a mentor to help us acquire new knowledge, as an assistant that helps us perform demanding tasks requiring the reading, analysis, and synthesis of large volumes of text, or as a co-pilot in the development of software solutions that enables us to gain agility and precision when implementing them.

AI can be an excellent tool 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’ll assess your business needs and design a customized roadmap for implementing Generative AI, tailored to your goals and resources. Don’t hesitate to contact us to find out how we can help you innovate and grow!