Machine learning continues to be a transformational technology innovation, impacting multiple business sectors. Some companies leverage machine learning models to glean actionable information from their business data. Others use ML-powered chatbots to perform basic customer service functions, allowing human CSRs to focus on more value-added tasks. In short, it makes the modern business more efficient, more nimble, and ultimately more profitable.
While many companies develop new machine learning applications from scratch, a massive amount of legacy software exists throughout the business world. Integrating machine learning models into these older applications remains a challenging proposition. However, making this effort ensures these organizations still have the ability to compete with businesses already embracing AI.
With a goal of integrating machine learning into your company’s legacy business applications, check out these insights. Understanding the best use-cases of this approach ensures your team efficiently manages this effort. In an always competitive business landscape, expect other companies in your business sector to be considering a similar approach. In the end, the long-term success of your business might depend on machine learning.
Embedded machine learning models and algorithms provide the means to analyze the user behavior of your company’s applications in real time. This capability is a boon for your organization’s SecOps team, as abnormal usage patterns might be a sign of cybercrime or another form of fraudulent activity. After discovering this behavior, the application sends an alert to a cybersecurity engineer for additional investigation. Depending on the nature of the activity, automatically restricting access for that user also remains an option.
All told, adopting this machine learning use case boosts the efficiency of your cybersecurity team, which becomes critical when considering the demand for SecOps talent across the global business community. It also illustrates a common benefit gained by integrating machine learning in a legacy application: improving the efficiency of employees. Any business in the financial sector needs to consider adopting behavior-based machine learning analysis for cybersecurity.
Of course, leveraging behavioral analysis in a legacy application also provides benefits beyond a strong SecOps posture. Consider a machine learning model embedded in an application performing this analysis to be used as input to another model functioning as a recommendation engine. This approach generates recommendations based on user activity helping to drive engagement and sales.
Music streaming services leverage this approach, as do video streamers, like Netflix, HBO Max, and more. It also makes perfect sense for modern retailers hoping to improve sales. Grocery stores currently benefit from ML-derived recommendations to offer digital coupons based on user activity.
As noted earlier, AI-powered chatbots remain a common use-case for companies looking to modernize legacy applications and processes. These are able to leverage a recommendation engine to analyze the user’s history and account data. It takes into account its findings as well as parsing customer questions to recommend a course of action to meet their needs. It’s a machine learning project that can drive engagement and potentially increase loyalty. It also frees up human CSRs for more complex tasks.
Mapping apps remain very popular on mobile devices. Notably, Apple Maps and Google Maps both leverage reasoning algorithms powered by machine learning to help users choose the most opportune route between two locations. The real-time processing horsepower of machine learning comes into play as these apps take into account current traffic, construction, weather, and road conditions when determining the best route to take.
Integrating this real-time route optimization into a company’s supply chain application also makes perfect sense. It improves the efficiency of the shipping process, helping to save time as well as any wear and tear on the business’s shipping fleet. Expect profitability to improve as a result.
Related to that last scenario, predictive analytics remains another AI-based capability worth implementing in a supply chain app. This type of deep data analysis supports a host of intriguing use-cases. For example, the previously mentioned supply chain app analyzes vehicle usage data to determine when maintenance or even replacement is the most efficacious. This same concept also applies to factory equipment, farm machinery, or anything requiring regular service.
Financial companies also benefit from adding ML-powered predictive analytics functionality to their legacy applications, especially in the area of automated trading. Once again, it allows human traders to become more efficient, since machine learning provides the computing horsepower to quickly analyze the historical data of securities and stock markets. These financial analysts now have the time to work with technical professionals on training and testing the ML models used in their securities applications.
If you want to learn more about adding the power of machine learning to your company’s legacy applications, connect with the experts at NineTwoThree. We boast a keen mix of technical chops and business acumen as well as extensive experience crafting machine learning software solutions. Schedule some time soon to discuss the possibilities of a fruitful partnership with us.
