9 tendencias de IA que impulsan la innovación de las apps

9 AI trends driving app innovation

AI is revolutionising the business world as we know it. Thanks to it, organisations enjoy more competitive advantages, operational cost savings, improved user experience, continuous innovation… This innovation has reached the apps sector. Therefore, it is relevant to talk about the innovations that arise in apps thanks to Artificial Intelligence.

From product optimisation, to customer service, to security and maintenance, thousands of smart apps are being developed thanks to AI.

Do you want to know how AI is transforming apps? Discover the 9 most impactful trends in this article.

Trend 1: Low-code/No-code Development

AI makes programming easier for all types of users, with more or less experience, through Low-code/No-code technology. This is an AI trend that helps users to create applications, websites and workflows without prior programming experience.

Low-code/No-code tools automate and streamline the development phase of a project, making it easier to create apps without requiring extensive IT knowledge. Moreover, such platforms adapt to the user’s preferences, style and feedback. This helps developers to be much more efficient over time. This is very interesting for companies that need to develop solutions constantly and quickly.

Trend 2: Conversational AI

Artificial intelligence can create realistic conversations between humans and machines. Conversational AI-enabled applications allow users to communicate by text or voice as they would with another person. This is due to machine learning to understand and generate natural language responses.

In this way, costs are reduced, productivity is increased, a better customer experience is offered by providing personalised assistance at any time, problems are solved quickly, additional information and suggestions can be offered… Among other things. A clear example of conversational AI could be a translation application that enables real-time communications during multilingual video conferences.


Generative AI facilitates the creation of new content, such as text, images or code, by training on existing data. It uses learning and neural networks to analyse the patterns and structure of input data in order to generate new content with similar characteristics.

Today, generative AI drives a variety of intelligent application experiences that include text, voice, code and images. For example, you could create an app that summarises complex financial documents, a medical app that generates images showing the future progression of a disease, or an AI assistant that provides information from a company’s websites to speed up research.


Predictive analytics involves using data to forecast future events. So a company’s CEO can accurately plan and strategise.

It uses data analytics, machine learning, AI and statistical modelling to detect patterns that indicate future behaviours and outcomes. This process involves collecting and analysing past and present data, and then using different methods to detect trends, connections and unusual patterns. With these capabilities, predictive models can be used to create intelligent apps that measure the likelihood of certain events or outcomes and suggest next steps.

In the medical field, a company could use predictive analytics to innovate an app that analyses medical records to predict health outcomes and prescribe possible treatments.

Trend 5: Cybersecurity

Artificial intelligence has become an essential aid in cyber security. It is the perfect partner for safeguarding online systems against attacks by cybercriminals.

By learning and processing natural language, AI helps monitor and analyse behavioural patterns to detect all types of cyber threats in real time. It also improves the management of user access to applications and websites by analysing login attempts using biometrics, multi-factor authentication and behavioural data.

These capabilities are key in industries such as finance, insurance, e-commerce and healthcare. These are highly sensitive data markets.


AI hyperautomation, as the name suggests, aims to automate everything possible in an organisation, both in terms of data and processes. Hyper-automation collects and analyses data from different sources, such as sensors, databases or user input, with greater precision and speed than a human being could. But it doesn’t stop there, it also applies to the automation and optimisation of business processes, identifying inefficiencies and opportunities for improvement. This makes hyper-automation a great tool for intelligent applications that handle large and complex data sets. By automating repetitive and routine tasks and streamlining business processes, hyper-automation allows employees to focus on more strategic and higher-value tasks, thereby improving the organisation’s efficiency and productivity.


In this context, we must highlight the role of Process Mining. This adds a plus of efficiency and precision to hyper-automation. Process Mining makes it possible to discover, supervise and improve business processes by extracting knowledge from the data available in corporate information systems. For example, it is very valuable for enterprise resource planning (ERP) systems. Why? Let us explain:

  • Uncovering hidden processes: As we know, ERP systems manage a number of interconnected processes. Some of these may not be explicitly documented. Process Mining is therefore crucial in this respect because it reveals hidden sub-processes and provides a complete overview of the entire process landscape.
  • Identification of bottlenecks: it pinpoints the points where delays occur. For example, slow approval cycles, delays in inventory management that affect production, bottlenecks in payment processing, etc.
  • Audits: ERP is crucial for regulatory compliance and financial reporting. In that respect, it ensures that processes adhere to policies and regulations, facilitating audits and reviews.
  • Continuous improvement: thanks to Process Mining, monitoring is 100% guaranteed. Companies will be able to track changes to assess their impacts, make the necessary changes and ensure continuous improvement. This is achieved by collecting and analysing event data from corporate information systems. Every time a change is made to a process, an event is generated and recorded in the system. Process Mining can collect this event data and use it to create a visual model of the process, which shows how it is running in real time. Once this model has been created, organisations can use it to assess the impact of changes made.

Trend 7: AI simulation

This type of simulation uses artificial intelligence to create realistic models of physical systems in the virtual world. It allows users to virtually explore different configurations, models and material components before applying them to solve real problems.

On the one hand, in the pharmaceutical industry, this technology is used in smart apps to design molecules for new drugs. On the other hand, in the energy sector, it drives solutions that use drilling data and geological factors to simulate wells and reservoirs. A simple way to optimise the entire supply chain operations. With these simulation applications, you can virtually model: demand, supply, inventory, logistics and distribution of your products.

Trend 8: Content creation

Today, many organisations are using smart applications to create content. Microsoft Copilot or Chat GPT have become the preferred companions for creating engaging content for websites and social media profiles. Thanks to them, the process of generating articles and posts is potentially accelerated.

For example, any organisation could create a smart app that generates content tailored to specific topics, audiences and objectives. It could also optimise the design, layout and subject line of the newsletter to increase open and click-through rates.

Trend 9: Machine learning

Machine learning or Machine Learning is a branch of AI that allows machines to learn and improve their speech based on experience. To do this, they use algorithms and statistical models to analyse and extract patterns from large volumes of data. As more and more data is processed, Machine Learning models are able to make more accurate predictions and/or decisions. Thanks to this system, organisations can obtain faster results by analysing large volumes of data efficiently.

Machine Learning has applications in a wide variety of sectors. We present them below:

  • Infrastructure and construction: helps predict when equipment or infrastructure is likely to fail, enabling proactive maintenance and avoiding costly downtime. It also improves planning and scheduling by analysing patterns in historical project data.
  • Healthcare and pharmacy: it analyses large volumes of healthcare data and provides valuable information that assists clinicians in diagnosis and treatment. As a result, patient care is greatly enhanced.
  • Finance and insurance: ensures the detection of fraudulent transactions, optimises investment portfolios and provides personalised financial advice.
  • Food and beverage: Finally, it helps companies better understand their customers, predicts purchasing trends and personalises offers and recommendations.


In this context, it is important to highlight the crucial role played by Azure Machine Learning. This Microsoft cloud service not only accelerates the lifecycle of machine learning projects, but also offers the following key advantages:

  • Efficient model creation and training: Azure Machine Learning enables developers to efficiently create and train machine learning models, accelerating development time and improving model quality.
  • Simplified model management: Makes it easier to deploy and manage models across multiple work areas, reducing complexity and improving operational efficiency.
  • Security and compliance: Provides a secure environment for running machine learning workloads anywhere in the world, ensuring compliance with security and privacy regulations.
  • Transparency and accountability: Promotes the development of explainable models, providing transparency and accountability in data-driven decisions. This is especially important in a world where the ethics of AI are increasingly relevant.
  • Team collaboration: facilitates team collaboration through shared notebooks, computational resources, serverless computing, data and environments.
  • Model development for fairness and explainability: develops models that are fair and explainable, which is crucial for meeting compliance and audit requirements.
  • Familiar interface: users can use familiar interfaces such as Azure Machine Learning Studio, Python SDK, Azure CLI and Azure Resource Manager REST APIs.
  • Support for open source platforms: users can also create a model in Azure Machine Learning or use a model built from an open source platform such as PyTorch, TensorFlow or scikit-learn.

In short, Azure Machine Learning is a powerful tool that can help organisations realise the full potential of Machine Learning.

Azure, a leader in smart application development

Azure solutions help businesses use AI to create and modernise all kinds of innovative, intelligent apps. The data is clear. A study commissioned by Forrester highlights that the use of Azure-powered solutions offered significant advantages for rapid and efficient app development.

  • Up to 1.5 months faster time to market for new applications.
  • Up to 25% less application downtime.
  • Up to 25% more developer efficiency.

The trends we have seen are revolutionising the way organisations develop applications and deliver innovative solutions to users. What new changes will AI bring in the future? We’ll keep digging!