11 AI trends transforming business software tools in 2026
What are the top AI trends transforming business software tools in 2026?
Artificial intelligence (AI) is a highly transformative technology that’s changing the way we work, live, and interact. That’s why AI has seen a massive surge in popularity over the last decade.
According to a report by Statista, global AI software revenue is expected to grow from $10.1 billion in 2018 to $126 billion by 2025. This is a compound annual growth rate (CAGR) of 37.3% over seven years.
The same report also states that the number of active AI applications has increased 270% over the last four years. In 2019, there were only 1000 AI applications in use. Today, there are over 3700 active AI applications.
Let’s take a look at the top AI trends transforming business software tools in 2026.
1. AI democratization
AI’s impact on business software is not just about adding new features. It’s also about making software more accessible to everyone in your organization. AI is helping to democratize access to data and advanced tools.
AI-powered software can help you find the information you need more quickly, even if you’re not an expert in data analysis. It can also help you use that data to make better decisions, even if you’re not a data scientist.
As AI becomes more prevalent in business software, it will be important to make sure that everyone in your organization has the skills they need to use these tools effectively. That means providing training and support, and it also means choosing software, such as modern employee feedback tools that is designed with accessibility in mind.
2. AI-powered software development
AI is also making it easier for developers to create and improve software. For example, AI can be used to automatically write and test code, making the development process more efficient. AI can also be used to analyze user data with ai insights and feedback to identify areas for improvement, and to automatically make changes to the software.
In the future, we can expect to see AI being used in more and more aspects of the software development process, from initial concept to final product. This will make it easier and faster for developers to create and improve software, and will also lead to the creation of more advanced and powerful software tools.
3. Explainable AI
AI models are becoming more accurate at predicting outcomes and suggesting actions. However, the next frontier for AI in business is to make the technology more understandable.
AI models can be very complex, and even the data scientists who build them can’t always explain how they work. That’s why many business leaders are hesitant to adopt AI. They don’t want to base decisions on a “black box” that they can’t understand.
Explainable AI (XAI) is an emerging field that aims to make AI more transparent. XAI tools provide insights into how AI models make decisions and recommendations, so business leaders can have more confidence in the technology.
4. AI engineering
AI engineering is a relatively new discipline that combines AI with software engineering. It’s a set of practices, principles, and tools that help developers standardize and scale their AI models and applications.
AI engineering is a cross-functional discipline that involves data scientists, machine learning engineers, and software engineers. The goal is to streamline the process of developing, deploying, and managing AI models in production environments. For companies running self-hosted eCommerce platforms, AI engineering helps standardize how AI models are deployed, maintained, and scaled within complex, custom-built infrastructures.
AI engineering is a critical capability in modern business software tools. It allows developers to build and deploy AI models more efficiently and with less risk. Teams can also manage and maintain AI models more effectively over time, ensuring that they continue to deliver accurate and relevant results.
5. MLOps
Machine learning operations, or MLOps, is a set of practices that aims to standardize and streamline the process of developing machine learning models. It’s a relatively new concept that has been gaining traction in the AI community as more and more companies look to incorporate AI into their products and services.
MLOps borrows from the principles of DevOps, a set of practices that seeks to improve collaboration and communication between software developers and IT professionals. In the context of machine learning, MLOps aims to improve collaboration and communication between data scientists and the teams that will be using and deploying the models they develop.
MLOps is a critical part of the machine learning lifecycle, which includes data collection and preparation, model development and training, model deployment and monitoring. By standardizing and streamlining these processes, MLOps can help companies develop and deploy machine learning models more quickly and efficiently.
6. Data fabric and data mesh
Data fabric and data mesh are two architectural approaches to data management that are becoming more important as companies work with more and more data. Data fabric and data mesh both provide a way to manage and access data from multiple sources and systems.
Data fabric provides a unified architecture that allows data to be accessed and used in a consistent and efficient way. Data mesh, on the other hand, is a more decentralized approach that allows individual teams to manage their own data.
Data fabric is a more traditional approach to data management, while data mesh is a newer and more flexible approach. Both approaches have their benefits and drawbacks, and the right approach for a company will depend on its specific needs and goals.
7. Mainstream AI
AI is no longer a niche technology. It’s now a mainstream part of the software that businesses use to manage their operations. The more businesses use AI, the more they’ll come to expect it from all of their software tools.
The rise of AI in business software means that companies will have to compete on their ability to use it to create value. The market for AI in business software is expected to grow by 25.3% per year and reach $997.9 billion by 2028. That means there’s a huge opportunity for software companies that can effectively incorporate AI into their products.
As AI becomes more mainstream, it will also become more accessible to businesses of all sizes. This will help level the playing field and make it easier for small and medium-sized businesses to compete with larger companies.
8. Generative AI
Generative AI is a type of AI that can generate large amounts of data quickly. It’s used in a variety of applications, from creating realistic images using AI image prompts to generating music and even writing code. Tools like Picsart’s image generator demonstrate how AI can create high-quality images from scratch, making it easier for creators and developers to experiment with visual content. Beyond content creation, Generative AI models are also being used to power features within conversational intelligence platforms, such as automatically generating post-call summaries and identifying action items from long meeting transcripts.
Generative AI can be used to create training data for other machine learning models. This is especially useful when there isn’t enough existing data to train a model effectively.
For example, if you wanted to create a machine learning model to identify different types of birds in images, you would need a large dataset of labeled images of birds. But creating such a dataset manually would be time-consuming and expensive.
With generative AI, you could create a large dataset of synthetic bird images to use as training data. This would allow you to train your model more quickly and effectively.
9. Generative adversarial networks
Generative adversarial networks (GANs) are a type of unsupervised learning model that pits two neural networks against each other in a cat-and-mouse type of game. One network generates data, and the other network tries to determine if the data is real or fake.
The generator network’s goal is to create data that is indistinguishable from real data. The discriminator network’s goal is to get better and better at determining whether the data it sees is real or fake. This process continues until the generator network is creating data that is virtually indistinguishable from real data.
GANs have a wide variety of applications, from creating deepfakes to generating realistic images and even helping scientists create new molecules. In the context of business software, GANs can be used to generate synthetic data for training machine learning models.
One of the biggest challenges in building machine learning models is getting your hands on enough high-quality data to train the models. This is especially true for models that require a large amount of labeled data. GANs can be used to generate synthetic data that can be used to supplement real data, making it easier and more cost-effective to train machine learning models.
10. No-code AI and AutoML
The trend of making AI more accessible to business professionals and non-technical users is a common theme across many of the trends on this list.
No-code AI and AutoML are two ways that AI developers are working to make machine learning models more accessible to people who don’t have a background in data science or software engineering.
No-code AI platforms allow users to build and train machine learning models using a visual interface, without having to write any code. This makes it possible for business professionals to create their own custom AI models to analyze data, make predictions, and automate tasks.
AutoML, short for automated machine learning, is a set of tools and techniques that automates the process of building, training, and optimizing machine learning models. With AutoML, data scientists and machine learning engineers can build and deploy AI models faster and with less manual effort.
Both no-code AI and AutoML are making it easier than ever for businesses to take advantage of the power of AI, without having to hire a team of data scientists and machine learning engineers.
11. AI-driven security
Cybersecurity is a growing concern for businesses, and the number of potential threats is growing. AI can help businesses monitor and secure their networks by identifying patterns and anomalies that could indicate a security threat.
AI-driven security tools can help businesses identify and stop potential threats before they become a problem. These tools can also help businesses quickly respond to security incidents, reducing the potential impact on their operations.
AI-driven security tools are becoming increasingly important for businesses of all sizes. As the number of potential threats continues to grow, businesses need to be able to monitor and secure their networks more effectively. AI can help businesses do just that.
12. Prompt-based AI workflows for growth and marketing tools
One of the most exciting AI trends shaping business software in 2026 is the rise of prompt-based workflows, where users can create complex systems simply by describing what they want in plain language.
Instead of manually configuring rules, logic, and integrations, business users can now rely on AI to generate entire workflows from a single prompt. This trend is already transforming areas like analytics, customer support, and marketing automation—and it’s increasingly visible in referral and loyalty software.
For example, modern referral tools like ReferralCandy are beginning to use AI to help businesses design and launch referral programs from a simple prompt, such as describing their ideal customer, reward structure, and growth goals. This shift toward “intent-based” building is also seen in productivity tools; for instance, many startups leverage a Notion discount to access advanced “AI Agents” that can build entire project databases and automated workflows from a single conversational prompt. The AI can then suggest program mechanics, incentives, messaging, and even placement recommendations—dramatically reducing setup time and complexity.
As prompt-driven interfaces become more common, business software will shift away from rigid configuration screens toward conversational, intent-based creation. This will empower non-technical teams to launch sophisticated growth initiatives—like referral programs—faster, with less friction, and with better alignment to business goals.
Conclusion
AI is evolving rapidly, and it’s important to keep up with the latest trends to ensure your business remains competitive. By staying informed about the latest AI trends, you can make more strategic decisions about how to use AI to improve your business processes and meet customer needs.
