If you work with software today, AI is already part of your reality. It influences how your products are built, how data is processed, and how decisions are made across your systems.
A few years ago, AI adoption meant experiments and pilot projects. In 2026, it means production systems, real users, and measurable results. Businesses are no longer deciding whether to use AI. They are deciding how to use it in a way that is reliable, scalable, and aligned with your business goals.
This shift is reflected in the market itself. The global AI market reached $638.23 billion in 2025. In 2026, this growth has turned AI into a core technology for modern software engineering and a standard part of software products and engineering workflows.
The conversation around AI has also changed. Instead of worrying about replacement, teams focus on collaboration. AI supports engineers, analysts, and product teams by reducing routine work, improving accuracy, and helping them move faster. It complements human expertise rather than replacing it.
In this article, you will find the AI trends that matter in 2026 and see how they apply to real software products and business use cases.
AI Trends to Consider in 2026
Using insights from our collaborations with partners and customers in Europe and North America, as well as analysis of the market situation, we’re thrilled to share our top AI trends and predictions for 2026. We also explore the business returns these emerging trends can deliver.
Open-source AI models for project elevation
One of the major AI trends is the growing adoption of open-source pre-trained models. According to the research, the open-source AI market is expected to reach $54,7 billion by 2034, with applications in verticals such as finance, education, and healthcare.
Open-source models can be used for real-time data analysis, enhancing productivity and cost efficiency. They are freely available for modification, which contributes to fostering a collaborative environment that drives innovation and technological advancement. Let’s see how open-source AI models contribute to the faster development of intelligent systems:
First, they provide ready-to-use frameworks and tools, eliminating the need to build software from scratch. As a result, these models save developers time and effort, significantly speeding up the development process.
Second, open-source AI models promote collaboration by enabling engineers to contribute to codebases and suggest improvements. This trend fosters global knowledge sharing, where developers can work together and exchange solutions.
Third, open-source AI models support continuous learning, allowing developers from diverse backgrounds to share knowledge and skills, which contributes to the faster evolution of AI technologies.
Here is the list of open-source AI frameworks that are more than simple tools and help unlock the full potential of artificial intelligence:
TensorFlow: This framework is flexible and extensible and supports Python and JavaScript. It supports the construction and deployment of machine learning models, providing a rich set of tools and robust community support. TensorFlow is widely used in production environments as well as for experimentation.
PyTorch: This framework offers an intuitive interface, facilitating debugging and building deep learning models. Its integration with Python libraries enables efficient model training and rapid software development prototyping.
JAX: This framework is designed for high-performance machine learning and numerical computing. It is widely used for advanced AI research and large-scale model training. JAX supports automatic differentiation and runs efficiently on GPUs and TPUs, making it a strong choice for building and optimizing modern AI models.
The popularity of open-source AI tools and community platforms demonstrates the growing maturity of AI frameworks and open-source ecosystems.
Multimodal generative AI for intelligent information processing
One of the AI trends to adopt is multimodal generative AI, which is designed to perform multiple tasks, such as image processing and speech recognition, simultaneously. According to Grand View Research, the global multimodal AI market size is projected to reach $10,89 billion by 2030, showing a market growth of 36,8%.
One of the driving forces behind this growth is that multimodal tools can support a broader range of tasks compared to language-centric models. They can process and combine audio, text, and images within a single system.
We suggest some top multimodal AI tools that can expand your scope of AI applications:
OpenAI’s GPT-Multimodal: A powerful model that processes and generates text, images, and audio. You can build apps that understand user photos, answer questions about images, or combine text and visual outputs. GPT multimodal AI is well-suited for chat systems, assistants, and interactive user experiences.
Hugging Face Transformers: A rich ecosystem of open-source models that handle text, vision, audio, and more. It includes CLIP, BLIP, and other multimodal architectures. The platform allows teams to fine-tune models and integrate them into software stacks using Python or JavaScript.
Meta AI’s LLaVA: Large multimodal models that combine vision and language understanding. They are designed to interpret images with context, answer questions about visual input, and support creative AI workflows. They are commonly used for analytics, search, and visual reasoning applications.
Customization of enterprise AI for a tailored experience
Another AI trend gaining momentum is custom enterprise artificial intelligence, which understands business-specific data and delivers personalized insights. What’s the secret behind custom AI models? First, custom AI focuses on domain-specific intelligence. Second, these models are often easier to integrate, govern, and manage within enterprise systems.
Companies are adopting customized enterprise AI for enhanced security and better performance. With custom AI, companies can safeguard their data and achieve superior outcomes with fewer errors. Additionally, with this tailored solution, they can make more accurate predictions and data-based decisions.
This AI trend is propelled by the following factors. Firstly, businesses are used to artificial intelligence and understand its potential. That’s why they recognize the need for a tailored experience. Secondly, large volumes of enterprise data require customized AI models to unlock the full potential of information. Finally, customization empowers businesses to build AI solutions to address their unique requirements and issues.
The benefits of customized enterprise AI are multifaceted. From enhanced accuracy to streamlined efficiency, tailored AI solutions can provide more precise predictions and insights while freeing up valuable resources. They also help reduce long-term implementation and maintenance costs, delivering a higher return on investment.
Augmented AI for more informed decisions
Augmented AI, or intelligence augmentation, is one of the AI trends aimed at enhancing human intelligence and decision-making. The key idea behind this new AI technology is to integrate AI with human expertise. For instance, this might involve combining data analysis with human judgment to improve outcomes across various verticals.
The value of augmented AI lies in its collaborative approach, as it is designed not to replace humans but to team up with them, providing tools and insights to make more informed decisions. Another strength of this AI technology is that it can analyze large volumes of data and present information. As a result, this saves the time and effort spent on manual data processing.
For example, Amazon Augmented AI enables the integration of machine learning in workflows, allowing humans to control the outcomes while ensuring precision and accuracy. This approach helps enhance human capabilities rather than automate decisions blindly.
Despite the growth of augmented AI, its use cases are already evident with applications in software development. So, here are some examples of where this AI trend will be relevant:
- E-commerce platforms can use augmented AI to predict customer preferences.
- Mobile video games can employ augmented AI to create augmented-reality events and interactive gaming environments.
- Customer support services can use augmented AI to provide virtual assistance based on language processing.
- Fintech companies can apply it for monitoring and identifying stock market patterns.
AI microservices for better scalability
Integrating AI into microservices is another new AI trend embraced by companies today. More and more businesses are using AI microservices to build and maintain large apps more efficiently. The benefits of this AI trend are the following:
Firstly, this is an optimal way to improve scalability, making the system easier to handle traffic and large volumes of data.
Secondly, performance is another benefit of AI microservices. AI algorithms optimize a system, enabling it to process data faster and more efficiently.
Thirdly, AI contributes to the customization of microservices, tailoring them to users’ behavior patterns and preferences.
Finally, AI algorithms enable businesses to make faster and data-driven decisions, providing real-time insights.
AI can be integrated into microservices in the following ways:
- Predictive analytics. Microservices are combined with predictive analytics to provide businesses with data-driven insights, facilitating more informed decisions.
- Image and video processing. AI algorithms enable microservices to recognize visual elements, making them suitable for security apps and other software that requires visual analysis.
- Natural language processing (NLP). Microservices based on NLP can be good at understanding human language, making them ideal for sentiment analysis and chatbots.
NLP and LLMs advancements for better understanding of clients’ needs
In 2026, standalone chat tools are becoming less central, while large language models (LLMs) and NLP solutions embedded into enterprise systems are the prevailing trends. This shift is driven by the use of domain-specific and locally deployed LLMs, which offer better performance and data security.
As businesses face increasingly complex challenges, LLM integration is one of the AI solutions to address them. For example, companies can use an LLM to measure the degree of anger of their clients. So, it’s possible to predict whether they can continue cooperation with this client or they will cancel their service and tend to go to competitors. LLMs will transform customer support and significantly influence buyers’ purchasing decisions.
The advancements in NLP fuel the development of more intelligent virtual assistants and chatbots, enhancing communication between people and machines. Plus, this is a good option for adapting products and services according to customer feedback.
So, both NLP and LLMs represent significant progress in sentiment analysis, enabling the differentiation between positive, negative, and neutral emotions. As a result, this is a good instrument for responding to clients’ needs and preferences.
AI safety and ethics for reliability and transparency
Today, AI is crucial in enhancing safe and ethical business practices. This AI trend stems from the potential dangers and threats associated with its use. Using advanced algorithms and machine learning, AI-based systems can identify suspicious activities related to fraud and money laundering risks. As a result, they can minimize unfair consequences.
Ethical AI solutions are built on the principles of accountability and responsibility, incorporating mechanisms for continuous monitoring and auditing. This approach ensures accuracy and transparency while guarding against biases.
Looking to Integrate AI? Trust this to the Forbytes Team
These AI trends are not abstract future concepts. They represent the current state of AI adoption in 2026. Businesses use them to build scalable systems, optimize operations, and gain a competitive edge.
At Forbytes, we offer AI integration & consulting services to empower your business with AI-based solutions. From business management and data analytics to content generation and customer service, we can integrate AI into your system to enhance its performance, optimize costs, and streamline business processes.
Our priority is to fine-tune AI solutions to make them intelligent, secure, and scalable. This approach helps our clients stay competitive, reach more users, and achieve predictable business outcomes.
If you’re seeking a reliable AI development company, our expert team is ready to help you build AI-powered software. Feel free to contact us to discuss how AI can support your business growth.
