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How Machine Learning Can Help In Process Improvement


How Machine Learning Can Help In Process Improvement

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Over the next few years, we can fully expect the use of Machine Learning and AI to become mainstream as more organisations discover its many applications. IDC has estimated that by 2019, 40% of digital transformation initiatives will use AI services and by 2021, 75% of enterprise applications will use AI. The possible applications could range from insights to predictions to recommendations on running the business better. As a result, machine learning can help bring about several process improvements too.

Here are some examples:

Superior Customer Service

AI and machine learning can allow organisations to studyhistorical customer service data, current engagement

patterns and use natural language processing to significantly improve the quality of responses to customer queries. Chatbots are increasingly gaining favour, as customers become more comfortable with them. Human intervention is required only to handle complex cases or exceptions, that too only until the time that the bot is able to understand and internalise these situations. This offers an opportunity to provide superior customer service at much lower costs.

In addition, the machine learning algorithm can study transaction patterns and customer behaviour to do a social sentiment analysis. This can help determine which customers are at the highest risk of leaving, thereby helping to map retention strategies. It can also help improve profitability based on buying patterns.

AI insights can also help in predictive maintenance, by anticipating machine failures and putting systems in place to address them. This way, organisations can better plan for service disruptions etc. so that customer inconvenience is minimised.


Perhaps one of the biggest strengths of machine learning is its ability to automate several labour and time intensive tasks. One great example is in recruitment. For an average job opening, hiring managers need to sift through hundreds of resumes to shortlist a suitable candidate. This is a difficult and lengthy process if done manually. However, with the right algorithms, this process can be highly simplified since the software can evaluate thousands of resumes and shortlist based on the credentials that the company values. Apart from the massive amount of time saved, this also helps human bias that often inadvertently creeps in during the recruitment process.

Finance is another area that offers immense scope for automation using machine learning and AI. Machine learning algorithms can learn from existing processes to recognise patterns and exceptions.

Insight Based Decision making

AI can offer useful and actionable insights to facilitate faster and better decision making. For example, it can help create much more targeted marketing campaigns by tracking data on non- traditional parameters such as the impact of placement of logos in digital advertisements or the number of product mentions. This aids better establish relationships between the action and impact; thereby resulting in more effective campaigns.

AI can also help ensure smoother supply chains by using contextual data on suppliers to predict any disruptions, thereby enabling better planning.It can also help detect any anomalies in patterns, both based on internal and external data, thereby helping in early fraud detection.

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