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I’ve been thinking a lot about how companies deal with aging software systems. My team still relies on an app built more than 10 years ago, and while it “works,” it’s painfully slow and doesn’t integrate with newer tools. I’m curious how other businesses approach modernization. Do you simply rebuild from scratch, or are there smarter ways to update what already exists? And how does AI or machine learning even fit into this kind of process?
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That’s a really good question. From what I’ve seen, most companies don’t throw everything away and start fresh, because that usually costs too much and disrupts daily work. Instead, they take a step-by-step modernization approach: migrating parts of the system to the cloud, replacing outdated components, and then gradually layering on new features. AI and ML often come in at that stage—for example, adding predictive analytics to a logistics tool or using machine learning models to help a healthcare platform identify anomalies in medical imaging. I’ve worked with a mid-size manufacturing firm that kept their ERP core but added AI-driven demand forecasting, which completely changed their supply chain efficiency. A good place to see how companies handle this is here: Custom software development company: Blackthorn Vision—they share examples of industries that adopted both modernization and AI solutions. What really struck me is that it’s not just about new tech, it’s about making sure the upgrade fits the actual workflow of the people using it every day.