AI software development methodologies: a guide

Published: November 1, 2026

What an AI software development methodology is and where it differs from classic software

According to Rsmart's guide, artificial intelligence is a field of technology that gives machines the ability to think, learn and solve problems the way humans do. These systems examine large data sets, learn from that data and improve their decision processes accordingly. Ekolsoft's article defines software development as the design, development, testing and maintenance of an application. The same article says that developing AI systems usually requires big data and complex algorithms. Rsmart describes the process of an AI project in five steps: 1. Data collection: the system is fed with accurate and sufficient data; the data is compiled from different sources. 2. Model training: the model is trained with the collected data, and machine learning algorithms come into play at this stage. 3. Testing: the model's accuracy and reliability are checked with various methods. 4. Deployment: the trained model is integrated for real-world use. 5. Monitoring and updating: the model's performance is tracked regularly, and the model is renewed when needed. In other words, the work is not over once the model goes live; monitoring and updating are part of the process. According to Rsmart, the most common mistakes in these projects are missing data, wrong model choice and inadequate testing processes, and these can hurt the project's outcome.

The most widely used AI development methodologies

According to Ekolsoft's article, AI development and the Agile method can work together. Agile is an approach that supports flexibility, fast feedback and continuous improvement. In this setting, AI tools can track the project's progress, spot possible problems and bring suggestions to the team. On the other hand, since AI systems demand big data and complex algorithms, teams can use Agile to produce prototypes quickly and process user feedback right away; according to Ekolsoft, this allows AI projects to develop in a more dynamic way. horoz.ai also bases its own way of working on short cycles. The process consists of four steps: 1. The goal, data and constraints are heard, and the problem to be solved is clarified. 2. The approach is tested early on real data with a small, working prototype. 3. Development continues in short cycles, and a working version is shown at the end of each cycle. 4. The system is put into operation, monitored and developed further as needs change. For large language model projects, horoz.ai's LLM integration and software development service covers integrating large language models into existing products and, when needed, building the web application and back end from scratch. On which questions to ask in projects involving personal data, the article published by horoz.ai walks step by step through the KVKK compliance of AI agents under the headings of the data controller role, explicit consent, cross-border transfer and hallucination risk. Rsmart's recommendations apply whichever method is chosen: care about data quality by updating data sets regularly, catch problems early by continuously monitoring the model's performance, and train the team in AI.

How to start your first pilot project this week

horoz.ai's process shows how to enter an AI pilot: first the goal, data and constraints are clarified, then a small, working prototype is tried on real data. To avoid the mistakes Rsmart lists, missing data, wrong model choice and inadequate testing need to be reviewed at this stage. horoz.ai's AI consulting covers jointly determining where AI will produce real value in your business and drawing up a prioritised, workable roadmap. Three topics come to the table in the discovery call: 1. Your goal: which problem you want to solve. 2. Your data: which data you have on hand. 3. Your constraints: which limits the project has to stay within. For the call, writing your need in a few sentences is enough; the next step is decided together.

Frequently asked questions

What are the AI software development methodologies?

Rsmart's guide describes the AI development process in five steps: data collection, model training, testing, deployment, and monitoring and updating. According to Ekolsoft, this process can work in harmony with the Agile method; teams can produce prototypes quickly and process user feedback right away. horoz.ai also follows a four-step process made up of clarifying the problem, an early prototype, short development cycles and monitoring after deployment.

How do you start an AI pilot project?

According to horoz.ai's process, the first step is to hear the goal, the data and the constraints and clarify the problem to be solved. Then the approach is tested on real data with a small, working prototype, development proceeds in short cycles, and a working version is shown at the end of each cycle. According to Rsmart, caring about data quality, continuously monitoring the model and training the team during this process raise the project's chance of success.

What is the difference between working with an AI consultant and working with an agency?

According to horoz.ai's definition, consulting covers determining where AI will produce real value in your business and drawing up a prioritised, workable roadmap. The agency side takes on the work from idea to live system: the need is clarified, a solution specific to you is designed, developed and put into operation. horoz.ai does this work at whatever scale is needed, from a single prototype to an end-to-end product.