The Dragonfly Segmentation Trainer, which is an advanced machine learning plug-in for image segmentation. This tool provides an opportunity to train a classifier within a sub-sample in an image so that it will learn how to segment the pixels of the whole dataset or other similar datasets. (Photo Credit: Wikimedia Commons)

Machine learning has been one of the top tech new topics in recent months and is now being widely applied to businesses. Briefly, machine learning (ML) is an application of AI (artificial intelligence) that allows systems to learn and improve without being directly programmed. Focussing on the development of computer programs that can access data in order to learn autonomously, machine learning is being used by Google on its AI Platform which is bringing all its services, from data preparation to the training, tuning, deploying, collaborating and sharing of machine learning models.

How does this affect business? For starters, ML today has the ability to compute vast quantities of data and to collect metrics while developing more intelligent algorithms that will be able to perform complex tasks. Take Periscope Data which is invested in taking machine learning and AI to evolve into a deeper evolution of data analysis and access where humans and machines in what is a quickly evolving business culture today. Where real-time intelligence for complex decision-making is crucial for businesses today, that forecasting the performance of the markets in future years will be best accomplished with ML over human force.

There are challenges with the integration of AI within businesses which are often prone resistant to change. For instance, there needs to be a prioritization of IT applications over IT architecture and where companies need to stop separating digital from AI, but instead think of their desegregation. Employee engagement with AI has recently been shown to increase performance and retention. Additionally, AI can function to promote a healthier work environment as TechRepublic recently reported that by analyzing email conversations and biometric data, “companies can more easily promote a sense of belonging among employees, identify red flags, and create an engaging work environment.”

In fact, ML has been used across various disciplines from healthcare to sports and it is showing no sign of slowing down.What is clear from the advantages of using AI within business is that a majority of companies are actively working on a roadmap for handling data (68 percent) but only 11 percent of these companies have completed this task. It is not just how much data but what types of data and the various areas where data is stored. Hence, the models which are the most successful today are those which allow certain tasks to be taken over by AI such that machine learning can learn from and predict consumer behavior where current ML models allow for rapid iteration of data and they deliver quick, reliable data sets.

Learning from the recent trend in business-to-business (B2B) demand generation strategy, the business-to-consumer (B2C) field is jumping in on this model as well through conversational marketing practices. We have all seen these at some point online such as website chat features and chatbots which initiate real-time interactions with customers in order to move them to the next stage. Indeed, businesses are making a better use of employees by allowing what AI is best at and similarly allowing humans to use their skills to advance productivity.

Businesses need to adopt a clear data governance framework where information is handed over to those developing emerging technology and where future endeavors will involve machine learning that can crush datasets in a fraction of the time that teams of humans simply cannot. The use of machine learning will be the true test of survival for businesses of the future.