MACHINE LEARNING FOR EXCELLENCE IN BUSINESS

Many of the leading organizations are experimenting with more-advanced uses of artificial intelligence (AI) for digital transformation and are using machine learning-based tools to automate decision processes. Many factors need to be analyzed while building effective machine learning practices in enterprises requires transformational approaches to everything from how the products and services meet the needs of customers to how the business operates.

There are many challenges while applying any new technology to business. Early adopters have to overcome a lack of resources and understanding, deal with untested and nascent methodologies, and suffer potential risks to existing operations. Considering the impact of transformative digital technologies in many manufacturing organizations such as those represented by big data analytics machine learning, and the Industrial Internet of Things (IIoT), the challenges get amplified by the rating pace of change, acceleration rate, and broad impact of these technologies across the enterprise.
 
Machine learning is enabling organizations to optimize processes and expand their top-line growth while increasing customer satisfaction and improving employee engagement.
 
FOLLOWING ARE SOME CONCRETE EXAMPLES OF HOW AI AND ML CREATE VALUE IN COMPANIES.

Personalizing customer service experience

One of the most exciting areas of opportunity is the potential to improve customer service while lowering costs. By combining the saved historical customer service data, algorithms, and natural language processing, that continuously learn from various interactions, customers get high-quality answers by asking questions. While the algorithms analyze previous data to learn what to do next time, customer service representatives can step in to handle exceptions.

Improving customer loyalty and retention

Companies analyze customer transactions, actions, and social sentiment data to shortlist and identify customers who are planning to switch or are at risk of leaving. Organizations personalize the end-to-end customer experience and to optimize “next best action” strategies through the obtained data which is combined with profitability data of the company. For example, young adults often move to other carriers while coming off their parents’ mobile phone plans. Telcos can, therefore, use machine learning to predict or anticipate such behaviour and make offers based on the individual’s usage patterns, customized as per their needs before they defect to competitors. 

Hiring the right people

Shortlisting qualified candidates have been the most difficult part of the job of recruiters and as per many surveys, corporate job openings pull in a lot of résumés for a particular job. The software can quickly look through thousands of job applications and shortlist candidates who have the skillsets, credentials and are most likely to achieve success at the company. There should be proper monitoring to avoid reinforcing any human biases implicit in prior hiring. But software can detect highly qualified candidates who might have been overlooked as they didn’t fit traditional expectations and combat human bias by automatically flagging biased language in job descriptions. 

Automating finance 

AI can exceed “exception handling” in various financial processes. For example, a person must sort out which order the payment corresponds to if any payment is received without an order number on it, and then determine what to do with any excess or shortfall. AI significantly increases the number of invoices that can be matched by monitoring existing processes and learning to recognize different situations, automatically. This frees up finance staff to focus on strategic tasks and helps organizations to reduce outsourcing to service centres. 

Measuring brand exposure

Automated programs can identify logos, people, products, and much more. For example, in a basketball game, corporate sponsors get to see the return on investment of their sponsorship investment with detailed analyses, including the duration, quantity, and placement of corporate logos through advanced image recognition which tracks the position of brand logos that appear in video footage of a sporting event.

Detecting fraud

Approximately 5% of revenues are lost due to fraud in any typical organization. Machine-learning algorithms can use pattern recognition to spot exceptions anomalies, and outliers by building models based on social network information, historical transactions, and other external sources of data. This helps in detecting and preventing fraudulent transactions in real-time and also many frauds which were previously unknown. For example, to recognize fraudulent behaviour, banks can use historical transaction data to build algorithms.  

Predictive maintenance

Anomalies in the temperature of a train axle can be detected through machine learning that indicates that it will freeze up in the next few hours. The train can be diverted to maintenance before it fails and passengers can be transferred to a different train instead of hundreds of passengers being stranded in the countryside, waiting for an expensive repair  

Smoother supply chains

Contextual analysis of logistics data can be done by machine learning to mitigate and predict supply chain risks. Algorithms can sift through news feeds and public social data in multiple languages to detect, for example, a fire can be detected in a remote factory that supplies vital ball bearings, used in a car transmission. 
 
MACHINE INTELLIGENCE COULD SOON BE COMMONLY USED IN MANY OTHER AREAS LIKE: 

CAREER PLANNING 

What additional education and work experience should they obtain, and in what order if a person with an engineering degree wishes to run the division someday? Recommendations will help employees choose career paths that lead to satisfaction, high performance, and growth. 

DRONE- AND SATELLITE-BASED ASSET MANAGEMENT

Regular external inspections of commercial structures can be done through drones equipped with cameras and they can detect new cracks or changes to surfaces, with the images.

RETAIL SHELF ANALYSIS

A sports drink company can see whether in-store displays are at the promised location, the product labels are facing outward, and the shelves are properly stocked with products through machine intelligence coupled with machine vision  
The potential of machine learning is enormous as it enables an organization to reimagine end-to-end business processes with digital intelligence. Therefore, many organizations are investing heavily in adding AI to their existing applications to create net-new solutions for the ever-changing world. 

 
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