What role can machine learning play in business operations – Michael Osland

So, what can machine learning do for an organization? Machine learning may make a positive impact on business operations across a variety of functions. Sometimes the benefits are apparent – increasing sales or reducing costs – but other times, it’s much less clear how new technology will affect a specific process or workflow.

Examining the potential applications of machine learning in business operations helps to understand some of the challenges organizations face. The first is that as processes mature, they become more efficient and less adaptable. Automating those processes can yield a very high return on investment – but introducing machine learning into a mature process often requires significant effort and expertise.

Mature processes also generally involve increased performance standards that affect business decisions. In some cases, you can use machine learning to increase speed or reduce errors – even if an organization doesn’t need to make any process changes. However, most organizations will still consider using ML in conjunction with workflow enhancements and automation efforts says Michael Osland. 

The final challenge organizations face when incorporating machine learning is finding the right tools for the job at hand. Typically, there are two major types of machine learning:

Recurrent Neural Networks, which are used for time series analysis and deep learning applications that use neural networks with many layers of interconnected nodes. The most effective machine learning applications often involve integrating both types since they each have unique strengths and weaknesses. 

Can machine learning replace humans – Michael Osland

Machine learning will never replace human decisions; instead, it will augment them by allowing people to devote more energy to high-level assessments. For example, RNNs are helpful in cases where an organization wants to forecast a future event. At the same time, deep learning is better for classifying events and other non-time-related phenomena (such as emotion recognition). This is important: the choice between the two should be based on what’s being analyzed – not which type has been more successful in general.

The next step is determining whether you can use machine learning to improve a business operation. To answer this question, consider the following: 

Is there sufficient data? 

Machine learning requires time series data that are better predictors of future events than past events. For example, it’s possible to use machine learning for sales forecasting with just months of history if it includes information on monthly sales targets and last month’s performance. 

However, years of monthly sales with no target or actual figures would not provide enough data for effective predictions – especially given the volatility introduced by factors like seasonality or new product launches. It doesn’t mean ML isn’t useful in these; it simply means that an organization should collect data relevant to its specific needs. 

Is the historical data in a format that’s conducive to machine learning? 

Some data are better suited for machine learning than others, making it complicated to find the necessary information. For example, suppose an organization wants to analyze customer service requests by user agent type. In that case, it may need to extract records from logs files in various formats before you can use them in a model. This is time-consuming and requires specific expertise; however, once these requirements have been met, organizations will often build their models quickly.