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ARTICLE | JANUARY, 20

Must-watch tech trends shaping 2025

By Patagonian content team

The tech world isn’t just evolving; it’s sprinting. In 2025, businesses across sectors must embrace innovation or risk falling behind. This post delves into the most impactful tech trends, specifically tailored to industries where Patagonian boasts deep expertise.

Table of contents

In 2025, the cloud computing landscape is shifting toward Retrieval-Augmented Generation (RAG) services. Cloud providers, recognizing the limitations of traditional large language models (LLMs), are now focusing on integrating external knowledge sources to enhance AI accuracy and reliability.

First, let’s take a look at how LLMs operate:

Large language model diagram

Disadvantages of LLMs

Despite their capabilities, LLMs often face challenges related to factual accuracy, generating plausible yet incorrect content (hallucinations) due to their reliance on extensive but potentially outdated datasets. Additionally, they typically lack citations, which makes information verification difficult.

The solution

RAG addresses these challenges by connecting LLMs to external knowledge bases, such as company wikis, research papers, or real-time data feeds. Integrating up-to-date external sources into the generation process can improve accuracy, relevance, and reliability.

For example, a customer service chatbot powered by RAG could access a company’s knowledge base to answer specific product questions or resolve customer issues with greater accuracy. Major hyperscalers are expected to launch RAG-related solutions, addressing issues like AI hallucinations and accuracy concerns, and setting a new standard for cloud services.

How do RAGs work?

RAG systems ‘retrieve’ relevant information from external sources based on the user input. They then ‘augment’ the user input with this newly retrieved information. Finally, they pass all this combined data to an LLM to ‘generate’ a more accurate, up-to-date response, which can include citations to the original external sources. For a deeper dive into RAG, check out our CTO’s post.

RAG infraestructure diagram
RAG diagram

Advanced retrieval mechanisms:

  • Neural search: moving beyond keyword-based search, neural search will become more prevalent, allowing RAG systems to understand the semantic meaning and context of queries to retrieve more relevant information.
  • Graph-based retrieval: leveraging knowledge graphs to represent relationships between entities and concepts will enable more sophisticated and nuanced information retrieval.
  • Multimodal retrieval: RAG systems will increasingly incorporate multimodal data, such as images, audio, and video, alongside text, enhancing their ability to understand and respond to complex queries.

Agentic AI is a type of AI that’s all about autonomy. This means that it can make decisions, take actions, and even learn on its own to achieve specific goals. It’s like having a virtual assistant that can think, reason, and adapt to changing circumstances without needing constant direction.

Leading the way with conversational agents

Our AI-powered chatbots provide clear human-like answers and avoid common pitfalls such as overwhelming users with information by following conversational design principles. These agents interact with users in natural language, providing information and completing tasks.

Other real-world applications

Agentic AI has numerous potential applications in enterprise settings, including customer support, business process automation (BPA), supply chain management, manufacturing, and finance.
  • Supply chain optimization: agentic AI can revolutionize supply chain management by optimizing inventory, predicting demand, and dynamically adjusting delivery routes in real time.
  • Enhanced cybersecurity: proactive threat detection and rapid response are crucial. Agentic AI systems can analyze network activity, identify vulnerabilities, and autonomously mitigate cyber threats.

Main healthcare predictions

Person-centered experiences

Hyper-personalized care is not just about tailoring treatments; it’s about creating personalized user experiences that empower patients and improve outcomes.
  • Focus on patients: they are at the center of every decision, not just the disease. This includes understanding their health literacy, cultural background, and communication preferences.
  • Empathy and accessibility: design interfaces and experiences that are intuitive, easy to navigate, and accessible to people with diverse abilities.
  • Patient agency: empower patients to take an active role in their care by providing them with the tools and information they need to make informed decisions.

Reducing administrative burdens with AI and automation

By 2025, the life sciences and healthcare sectors are expected to hold 36% of the world’s data. To stay competitive, companies are adopting AI tools, predictive analytics, and IoT to help turn data into useful insights. 

Patagonian is committed to delivering exceptional person-centered and data-driven healthcare experiences. Our at-home glaucoma monitoring solution empowers patients to actively participate in their care through an intuitive website and mobile app, all while adhering to FHIR standards for data management. This innovative solution combines user-centered design, conversational design, and advanced technologies to improve patient outcomes and enhance the overall quality of care.

The role of advanced technologies:

  • Computer vision analyzes medical images (X-rays, MRIs, CT scans) to detect anomalies, improves diagnostic accuracy, and streamlines data analysis. It also plays a crucial role in the efficient processing of vast amounts of medical records through optical character recognition (OCR).
  • AI-powered predictive diagnosis integrates artificial intelligence and data analytics. This way, healthcare providers can anticipate patient needs, proactively address potential health issues, and improve overall patient outcomes.
  • RAG will play a crucial role in personalized medicine, enabling doctors to access and analyze patient data more effectively to provide more accurate diagnoses and treatment plans.

Learn more about some of our innovative healthcare solutions:

Energy innovation outlook

AI-powered Oil & Gas: management & forecasting

AI is becoming crucial in energy management, with predictive analytics enabling precise forecasting of energy demands. AI solutions tailored for the energy sector lead the charge, optimizing generation, storage, and distribution while exploring green energy solutions.

Real-world applications for oil and gas

  • Predictive maintenance and equipment reliability:
    IoT sensors embedded in energy infrastructure collect data on equipment performance and operating conditions.
  • Data Lakes: the data is consolidated into data lakes for analysis. Leveraging data collected throughout the workflow (exploration, drilling, etc.) enables informed decision-making at every stage. Our data-driven optimization of well handover processes is a clear example of an oil and gas application.
  • AI-driven analytics: AI algorithms analyze this data to detect anomalies, predict equipment failures, and schedule maintenance. This minimizes downtime, reduces operational costs, and improves overall grid reliability.

AI can predict the likelihood of equipment failure, allow for timely maintenance, and prevent costly and disruptive outages: Real-time monitoring of pumps and blenders.

Sustainability and green tech

There is a growing focus on sustainable technology solutions to address environmental challenges, including renewable energy, energy-efficient systems, and eco-friendly materials

AI is accelerating the transition to renewable energy sources. AI algorithms can optimize the operation of solar and wind farms by predicting weather patterns and adjusting operations accordingly to maximize energy output.

Innovation in energy storage and battery technology

Advances in battery technology, such as solid-state and flow batteries, are making renewable energy storage more viable. These innovations promise to enhance the integration of solar, wind, and tidal energy into grids, addressing the challenges of intermittency. Additionally, Argentina is pioneering the tokenization of lithium, a strategic resource for clean energy development, allowing investors to access fractional shares through blockchain technology.

Combating fraud with AI and cybersecurity

The financial sector is undergoing a significant transformation driven by the convergence of AI and cybersecurity. By analyzing vast amounts of data, these technologies can identify and prevent fraud in real time. Techniques like anomaly detection, behavioral analysis, and real-time transaction monitoring are being employed to identify suspicious activities and thwart cyberattacks.

  • Enhanced security posture: financial institutions are investing heavily in AI-powered security solutions. Gartner predicts that cybersecurity spending in the financial sector will reach $212 billion by 2025, driven by stricter regulations and the increasing sophistication of cyber threats.
  • Developing a skilled cybersecurity workforce: the successful implementation of AI-driven security solutions requires a skilled workforce, strategic planning, and a commitment to continuous improvement. Finding the right talent to address these advancements will be key for cybersecurity.

RAG technologies for fintech

  • Personalized financial advice: RAG can analyze vast amounts of financial data, including market trends, individual financial goals, and risk tolerance, to provide personalized financial advice and recommendations.
  • Enhanced decision-making: By accessing and analyzing real-time information and market data, RAG systems can empower financial institutions to make more informed and data-driven decisions.
  • Improved customer experience: RAG-powered chatbots and virtual assistants can provide 24/7 customer support, answer complex financial queries, and assist with tasks such as account management and transaction processing.

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