How Machine Learning Improves Business Processes

Many of the leading organizations today are using the power of Artificial Intelligence (AI) to automate the decision-making process. Machine learning and deep learning, the two subsegments of AI are also reaching deep into the enterprise. They are enabling automation of many tasks that are now done manually, creating greater efficiencies, and reducing errors. Also, machine learning improves business processes by offering valuable new insights into the massive amounts of data being generated. According to IDC, by the year 2021, 75% of digital transformation initiatives will use Artificial Intelligence services.

The possible applications could range from insights to predictions to recommendations on running the business better. As a result, ML can help bring about several process improvements too.  Systems using AI and ML capabilities can play a vital role in making businesses less reactive and more proactive. They can enable businesses to make better decisions for the future and improve the customer experience.

This blog post will discuss the power of machine learning in improving business processes.

Impact of Machine Learning on Business Processes

Enhancing Customer Experiences:- Reaching more people and ensuring their purchasing loyalty is a never-ending race. To better execute this, many leading organizations use ML as a significant tool in the process. For example, information Facebook users leave on their profiles are collected and analyzed with the help of ML. Based on users’ age, gender, location, and behavioral history on the platform, Facebook creates personally customized sponsored posts and ad suggestions. Predictions made by ML focused on customer service, are also used by hospitals and clinics. They can analyze information about ER layout, staff information, department charts, and patient data.

Besides, ML algorithms can study transaction patterns and customer behavior to do a social sentiment analysis. Using ML capabilities, businesses can determine which customers are at the highest risk of leaving, and can accordingly map retention strategies.

Fraud Detection:- According to TechFunnel, on average, businesses lose 5% of their total revenues each year due to fraud. Machine Learning algorithms can play an important role in combatting fraud. They can track data and apply pattern recognition to identify abnormalities in the data. ML algorithms are highly effective for risk management teams to detect fraudulent activities in real-time, preventing attacks before getting started. Applying these algorithms to cybersecurity efforts and using AI’s capability accurately identifies threats, allowing companies to address those, beforehand.

Improved Recruitment Processes:- Hiring best-qualified applicants for job openings is a difficult process for hiring managers. Many times, a company will receive dozens, even hundreds of applicants for one job opening. Sifting through this immense volume of applicants is a very daunting and time-consuming process. With ML, the process of sourcing, attracting, screening, assessing, and matching candidates is automated. ML uses the data collected from various sources such as social media, employee history, employer information, and other pertinent details. It eliminates time and financial constraints from the recruitment and hiring process.

Finance: Accruals:- Machine learning enables finance teams to cut through the myriad factors when determining bonus accruals. The team looks at the current headcount salaries and bonus plans and tries to forecast all KPIs in compensation plans. Based on that, finance managers try to calculate the most accurate accrual. However, accuracy often ends up being a matter of luck more than anything else. Deploying AI solutions, we could leave this calculation to a machine that uses all available system data and predictive analytics capabilities. It can generate unbiased accrual figures, leaving finance teams with more time during closing periods. They can focus on other activities that require human review and judgment.

Telecommunications:- Telecommunication companies have massive amounts of data. But, until recently, they did not have the tools to do anything or extract insights from this data. Machine learning is enabling telecom companies to build new revenue models, address consumer needs better, and reduce operational costs. Telecom businesses are using machine learning to improve internal process such as optimizing mobile tower operations.

Machine learning has made considerable strides in language understanding and speech recognition through chatbots and virtual assistants. It can efficiently manage customer issues for common connectivity issues or even deploy a technician in extreme situations. With this, telecom businesses can save a huge on costs and provide much faster service to their customers.

Conclusion

Machine learning is improving business processes in many ways. From providing improved customer experiences to providing a more comprehensive fraud detection system, it is changing the way businesses conduct their operations. As ML and AI progress, companies and customers will both benefit more from what they offer.

Why choose Oodles for Artificial Intelligence Services

With Oodles, you too can avail the benefits of artificial intelligence to provide your customers with an exceptional experience. Our AI team develop next-gen AI applications to give you an edge over your competitors. They perform a thorough analysis of your business to identify the areas of improvement through AI. Based on your requirements, we build advanced AI and Machine Learning applications to streamline your business processes.

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