Artificial Intelligence
Ethical AI: Can We Trust Artificial Intelligence in the Workplace?
3 modern AI approaches to consider for your custom software
Companies looking to integrate artificial intelligence into their custom software often face confusion: with machine learning, generative AI and agentic AI, how do you know which is best suited to your needs?
In 2025, understanding these approaches becomes strategic. Too many initiatives fail not for lack of ambition but because of the wrong technology choice. Conversely, a well-chosen approach can transform your operations: automation, predictive analytics, advanced personalization and immediate productivity gains.
In this article, we present three modern AI approaches for your custom software. Through concrete examples, a comparison table and practical guidance, you’ll be better equipped to choose the solution that will maximize your impact.
Understanding modern AI approaches for your custom software
To choose the right AI technology, it’s essential to understand the difference between generative AI, agentic AI and machine learning. Here is a clear and accessible explanation of the modern AI approaches for your custom software. What is machine learning? How do you choose an AI for custom software? These questions are at the heart of this article.
Machine learning
Machine learning identifies patterns in historical data to make predictions or classifications. Unlike generative AI, it does not create new content; rather, it learns from your specific data to recognize trends invisible to the human eye.
Machine learning excels at fraud analysis and anomaly detection, trend and sales forecasting, and analyzing user and consumer habits. It can predict customer churn risk and optimize experiences via continuous A/B testing. Machine learning also transforms raw data into intelligent reports, segments data (for example, by administrative region in Quebec), extracts and structures unstructured data, and powers hyper-personalization systems.
A machine learning system can predict customer churn by analyzing purchasing behaviour. Another can detect suspicious transactions in real time. In custom software, machine learning excels at turning raw data into informed strategic decisions. The difference between generative AI and machine learning is fundamental: one creates, the other analyses and predicts.
Generative AI
Generative AI creates entirely new, original content from pre-trained models. It generates text, images, video, code or summaries based on your instructions.
Generative AI excels at producing technical and administrative reports, processing and improving text, and creating images and video. It can also convert text to speech, automatically generate captions, and summarize long content to speed human comprehension. Machine translation is another area where it shines.
ChatGPT as a conversational agent and DALL·E for visuals are the best-known examples. In custom software, you could integrate generative AI so users automatically generate product descriptions, reducing production time and manual errors.
Agentic AI
Agentic AI goes beyond generation: it makes autonomous decisions and executes actions. These systems combine language understanding with the ability to plan, navigate complex systems and perform multi-step tasks without constant human intervention.
Agentic AI automates manual and multi-step processes, manages resource planning and provides decision support based on business rules. It also excels at automated quality control on production lines, content moderation and sentiment analysis. Conversational agents for customer service are a popular application.
An AI agent can analyse sales data, identify trends and automatically recommend price or inventory adjustments. Another could sort customer files, prepare purchase orders or coordinate complex workflows without human supervision.
Comparison table of the 3 AI types
AI type | Description | Main use cases | Practical examples |
|---|---|---|---|
Machine learning | Algorithms that learn patterns from existing data | Sales forecasting, fraud detection, customer segmentation, predictive maintenance, A/B testing, hyper-personalization | A system that predicts churn risk, a tool that detects suspicious transactions |
Generative AI | Creates entirely new, original content from pre-trained models | Report generation, text processing, image and video creation, text-to-speech, captions, summaries, translation | ChatGPT, DALL·E, Claude |
Agentic AI | Autonomous software agents that make decisions and execute complex actions | Automation of multi-step processes, resource planning, decision support, quality control, content moderation, conversational agents | An agent that plans a trip, an assistant that manages your email, a virtual coach |
How to choose the right type of AI
The choice depends on four key factors:
1. Your problem type
If you need to create content, prioritize generative AI. To automate complex processes, agentic AI is your best option. If you need to make predictions based on historical data, choose machine learning.
2. The quality of your data
AI performs well when your data is high quality. If your data is fragmented or incomplete, start with a data-cleaning and structuring project before integrating AI.
3. Your budget and timeline
Existing APIs like ChatGPT or Claude offer quick, lower-cost implementation. A custom model trained on your data provides greater accuracy but at a higher cost.
4. Your security and compliance obligations
In Quebec, Law 25 imposes strict rules on data protection. Ensure your AI solution meets these requirements before deployment.
According to an analysis published in 2025 by independent technology innovation firms, organisations that choose the right type of AI based on their specific needs achieve a 40% higher ROI than those taking a generic approach. Enterprise AI examples show that a prudent selection among these AI types is decisive for success.
Conclusion
These three modern AI approaches for your custom software address very different needs: content creation, intelligent automation or predictive analytics. Understanding their respective strengths is essential to maximise your investments and select the technology truly aligned with your business objectives.
In the projects we run in Quebec, the organizations that achieve the best results are those that adopt a structured approach: a clear business problem, actionable data, then the choice of the most appropriate AI type. In many cases, combining several AI types is what maximises impact, improves operational performance and accelerates decision-making.
AI is not a technological fad. It is a strategic lever redefining productivity and the value of custom software. Organisations that make informed decisions today about modern AI approaches for their custom software will have a durable competitive advantage in the coming years.
Ready to choose the right AI technology for your custom software? Talk to an Exolnet expert. We analyse your needs, evaluate your data and recommend the most cost-effective AI approach for your organisation.
FAQ
What is the fundamental difference between generative AI and machine learning?
Machine learning recognizes patterns in existing data to make predictions or classifications. Generative AI is a form of machine learning specialized in creating new content (text, images, video).
Can agentic AI operate without human supervision?
Agentic AI automates repetitive tasks and rule-based decisions, but it works best in collaboration with humans. Although it can operate autonomously, it is strongly recommended to review its outputs to achieve better results and reduce risk. Complex strategic decisions remain the responsibility of humans.
Which modern AI approach for custom software is the easiest to integrate?
Generative AI via APIs (like ChatGPT) is generally the easiest and fastest to integrate. Machine learning and agentic AI require more preparation and higher-quality data.
Which type of AI generates the best ROI for custom software?
It depends on your context. Machine learning often delivers quick ROI if you have quality data. Generative AI offers immediate gains. Agentic AI offers the greatest long-term potential.
