In Brazil, the consumer profile is changing, and with it, the demands regarding the shopping experience. It is no longer enough to offer a good product or service; what really matters now is how companies relate to their customers from the first contact to after-sales.

In a scenario where interactions are increasingly instant, consumers seek experiences that go beyond simple exchanges of products and services, demanding quick, relevant, connected, and personalized responses. For companies, this presents a challenge, as it is necessary to create an integrated and aligned strategy that ensures a seamless and flawless experience.

In light of this reality, a global concept called 'Universal Customer Experience (UCE)' has emerged: an innovative and integrated approach that is gaining increasing traction in Brazil.

“Universal Customer Experience is a methodology that aims to unify all processes, stages and technologies involved in the relationship with the consumer, with the objective of providing a consistent, fluid and personalized experience at each point of contact”, explains Alberto Filho, CEO of Poli Digital, a developer of technologies for centralizing and automating customer service channels.

According to Filho, UCE is an approach that, unlike fragmented models, seeks total integration of interactions, making the customer journey continuous, without interruptions or communication failures, regardless of the channel used, be it a website, Instagram, Whatsapp, etc.

"This concept aligns with a growing reality in the market: customer loyalty is a key factor for business growth," comments the CEO of Poli Digital. According to data from Investopedia, 65% of Brazilian companies' revenue comes from loyal customers, demonstrating the positive impact of a successful customer journey experience. Additionally, according to a KPMG survey, 86% of loyal consumers recommend brands that serve them well to friends and family, expanding the company's reach and creating a word-of-mouth effect that enhances the acquisition of new customers.

However, to achieve the Universal Customer Experience, more than just implementing technological solutions is required. Although tools like chatbots, virtual assistants, and automation systems are essential for optimizing and streamlining processes, the true essence of UCE lies in creating an experience that is both personalized and seamless at every stage of the customer journey. The integration between the company's departments is the secret to ensuring that every interaction, whether in customer service, sales, or post-sales support, is aligned with the core strategy of providing a unique and personalized experience.

Alberto Filho emphasizes that "the implementation of innovative technologies, no matter how advanced they are, does not solve all problems on its own. To provide a successful customer journey, all departments must work in an integrated manner, understanding the importance of each touchpoint and using data intelligently to continuously improve the experience, from acquisition, through service, sales, and also the post-sale experience." This implies treating each customer interaction as an opportunity to strengthen the relationship and deliver real value, based on information and behaviors collected in real time.

For companies seeking not only to meet but to exceed their customers' expectations, the Universal Customer Experience becomes a mandatory strategy. It enables a faster, more efficient, and personalized service, not only improving the customer journey but also generating greater brand loyalty and, consequently, driving growth and continuous innovation.

“By adopting this concept, companies not only create a competitive edge, but also build a solid reputation, where consumers feel valued and understood at every stage of their journey. In an increasingly dynamic market, UCE can be the key where each interaction reflects a genuine commitment from the company to the satisfaction and loyalty of its consumers”, he concludes.

We are only at the beginning of 2025 and the CEO of the technology company Nvidia, Jensen Huang, has already stated that the“the era of agentic AI has arrived”. TheGoogleandOpenAIrecently announced new agency models planned for launch in 2025, and40%of large Brazilian companies plan to integrate agentic AI systems into their operations this year.

How is agentic AI different from its predecessors, and what could the tech buzzword of 2025 mean for Brazilian consumers? The analysts are expecting radical changes, but the final results may be more subtle than expected.

"Artificial intelligence is an extremely exciting new stage in the development of intelligent systems," comments Krishna Tammana, Chief Technology Officer at Gupshup. "But, as with all advanced technologies, it's easy to get caught up in technical jargon." Instead, make a comparison with the size and scale of the shift from the phone to the smartphone in the late 2000s. "If what we are using now, generative AI, is a single application, then agentic AI is like using a smartphone," says Krishna.

Generative AI, like the older cell phone models, is basically a single-use application. You open it, interact with it, and close it until you need it again, the executive continues. If we compare it to a smartphone, which can contain many apps at the same time, that's what agentic AI looks like in relation to generative AI. It's no longer about a single app, but an entire smartphone full of various apps, all interacting with each other at the same time.

The AI agent was also created to perform tasks autonomously, make decisions, and adapt to user information and environmental changes. And, although generative AI has seen use cases for consumers and businesses, this next evolutionary step will likely impact professional life more than personal.

Soon, we will have a workday where the most time-consuming and repetitive tasks are performed autonomously, freeing up more time for us to focus on higher-level strategic, creative, and social functions. In our personal lives, agentic AI can also materialize as a helpful assistant that can place orders and reservations, organize a weekend social schedule, or even manage our finances, for example. However, since no one knows how close we are to large-scale artificial general intelligence, experts are looking for pioneering sectors to lead the way.

"The first sectors to implement agential AI do so behind the scenes in their operations," explains Tammana. "The financial sector is seeing results in fraud detection, doctors and pharmacists focusing on data analysis, research, and development, and manufacturers are creating intelligent robots, and even entire factories, that can operate with minimal human intervention," he observes.

Unless they work in these specialized fields, the daily life of Brazilians may not feel an immediate impact from agentic AI, except in one key area. "At Gupshup, the AI agent handles a range of requests, but one of the most advanced is managing customer interactions," Krishna continues. Therefore, I wouldn't be surprised if, this year, consumers start to notice a difference when talking to customer support agents.

These models are trained with company-specific data, with integrated protection barriers and human agents available for transfer at any time. Therefore, Gupshup's chief technology officer tells consumers to watch out for the shift in interactions between chatbots and humans for a smoother service. "The fact that you don't realize who is human and who is AI means we did a great job. But if the agent you're talking to is experienced, helpful, proactive, and available 24/7, then this could be your first interaction with an AI agent," he explains.

In Brazil, a partnership with a major national fashion brand highlights the emerging landscape of agentic AI. Gupshup is among the first conversational AI platforms to launch an AI agent in Brazil for the retailer Reserva. The brand's AI agent offers users a unique shopping experience, with AI available directly on WhatsApp. Users can search for products by sending text or voice messages, and even describe the characteristics of a person they want to gift. This was done on Mother's Day, when the user had the chance to detail what their mother was like and thus find the best gift to celebrate the occasion.

In this way, the AI understands and recommends the products that best fit the description provided by the user, who can easily check the available products through a native WhatsApp carousel model, having access to more information about the product, or even being redirected to the website to complete the purchase.

The idea ofartificial intelligence(AI) is not new, but recent advances in related technologies have turned it into a tool used by all of us daily.The growing importance and proliferation of AI are both exciting and potentially alarming, as the foundations of many AI platforms and resources are essentially black boxes controlled by a small number of powerful corporations.

Large organizations like Red Hat believe thateveryone should have the ability to contribute to AI. Innovation in AI should not be restricted to companies that can afford massive amounts of processing power and the data scientists needed to train those machines.large language models(LLMs)

Instead, decades of open-source experience in software development and collaboration with communities enable everyone to contribute and benefit from AI, while helping to shape a future that meets our needs. There is no doubt that an open source approach is the only way to realize the full potential of AI, making it safer, more accessible, and democratized.

What is open source?

Although the term "open source" originally refers to a software development methodology, it has expanded to encompass a more general form of work that is open, decentralized, and deeply collaborative. The open source movement now goes far beyond the world of software, andthe open source wayhas been embraced by collaborative efforts around the world, including sectors such as science, education, government, manufacturing, healthcare, and more.

Open source culture has somefundamental principles and valuesthat make it effective and meaningful, for example:

• Collaborative participation
• Shared responsibility
• Open exchanges
• Meritocracy and inclusion
• Community-driven development
• Open collaboration
• Self-organization
• Respect and reciprocity

When open source principles form the basis of collaborative efforts, history shows that incredible things are possible. Some important examples range from the development and proliferation of theLinuxas the world's most powerful and ubiquitous operating system until the emergence and growth ofKubernetesand containers, in addition to the development and expansion of the Internet itself.

Six advantages of open source in the age of AI

There are numerous benefits to developing technologies through open source, but six advantages stand out among the others.

1. Increased speed of innovation

When technology is developed collaboratively and openly, innovation and discovery can happen much more quickly, unlike closed organizations and proprietary solutions.

When work is openly shared and others have the ability to build upon it, teams save a huge amount of time and effort because they don't have to start from scratch. New ideas can expand the projects that came before. This not only saves time and money, but also strengthens results because more people work together to solve problems, shareinsightsand review each other's work.

A broader and more collaborative community is simply able to achieve more: promoting people and connecting expertise to solve complex problems and innovate more quickly and effectively than small, isolated groups.

2. Democratize access

Open source also democratizes access to new AI technologies. When research, codes, and tools are openly shared, it helps to eliminate some of the barriers that normally limit access to cutting-edge innovations.

THEInstructLabIt's a great example of this premise. The initiative is an independent open-source AI model project that simplifies the process of contributing skills and knowledge to LLMs. The goal of the effort is to allow anyone to help shape theGenerative AI(gen AI), including those that do not have the skills and training in data science normally required. This allows more individuals and organizations to contribute to the training and refinement of LLMs reliably.

3. Enhanced security and privacy

Because open source projects lower the barriers to entry, a larger and more diverse group of contributors is able to help identify and address potential security challenges present in AI models as they are being developed.

Most of the data and methods used to train and tune AI models are closed and maintained by proprietary logic. Rarely do outsiders manage to gain any insight into how these algorithms work and whether they harbor any potentially dangerous data or inherent biases.

If a model and the data used to train it are open, however, anyone interested will be able to examine them, reducing security risks and minimizing platform biases.Additionally, open philosophy contributors can create tools and processes to track and audit the future development of models and applications, allowing for monitoring the development of different solutions.

This openness and transparency alsogenerate trustsince users have the ability to directly examine how their data is being used and processed, so they can verify if their privacy and data sovereignty are being respected. Furthermore, companies can also protect their private, confidential, or proprietary information by using open source projects like InstructLab to create their own fine-tuned models, over which they maintain strict control.

4. Provides flexibility and freedom of choice

While monolithic, proprietary, black-box LLMs are what most people see and think about when it comes to generative AI, we are starting to see a growing push towards smaller, independent, purpose-built AI models.

Thosesmall language models(SLMs) are typically trained on much smaller datasets to give them their basic functionality, and are then further tailored for specific use cases with domain-specific data and knowledge.

These SLMs are significantly more efficient than their larger counterparts, and have demonstrated such good (if not better) performance when used for their intended purpose. They are faster and more efficient to train and deploy, and can be customized and adapted as needed.

And it is largely for this reason that the InstructLab project was created. With it, you can take a smaller open-source AI model and expand it with the data and additional training you desire.

For example, you can use InstructLab to create a customer service chatbot that is highly tailored and developed for a specific purpose, enhancing best practices within the organization. This practice allows you to provide the best of your customer service experience to everyone, everywhere, in real time.

Most importantly, this allows you to avoid vendor lock-in and provides flexibility in terms of where and how you deploy your AI model and any applications built on top of it.

5. Enables a vibrant ecosystem

In the open community, “no one innovates alone, and this belief has persisted since the early months of the community's founding.

This idea will remain valid in the era of AI within Red Hat, a leader in open solutions, which will provide several open source tools and frameworks in the form of theRed Hat AI, a solution with which partners will generate more value for end customers.

A single supplier cannot provide everything an organization needs, or even keep up with the current pace of technological evolution. The principles and practices of open source accelerate innovation and enable a vibrant ecosystem by fostering partnerships and collaboration opportunities between projects and industries.

6. Reduce costs

In early 2025,estimate-it-isThe average base salary for a data scientist in the United States is over $125,000, with more experienced data scientists able to earn significantly more.

There is clearly a huge and growing demand for AI-powered data scientists, but few companies have much hope of attracting and retaining the specialized talent they need.

And really large LLMs are exorbitantly expensive to build, train, maintain, and deploy, requiring entire warehouses full of highly optimized (and very expensive) computing equipment and massive amounts of storage.

Open, smaller, and purpose-built AI models are significantly more efficient for building, training, and deploying. They not only require a fraction of the computing power of LLMs, but projects like InstructLab enable people without specialized skills and experience to actively and effectively contribute to the training and fine-tuning of AI models.

Clearly, the cost savings and flexibility that open source brings to AI development are beneficial to small and medium-sized businesses hoping to gain a competitive advantage from the applications AI can bring.

In summary

To build a democratic and open AI, it is crucial to use the open source principles that made cloud computing, the internet, Linux and so many other open, powerful and profoundly innovative technologies possible.

This is the path that Red Hat is following to enable AI and other related tools. Everyone should benefit from the development of artificial intelligence, so everyone should be able to help determine and shape its trajectory, and contribute to its development. Collaborative innovation and open source are not essential or unavoidable for the future of the discipline.