AI-Machine Learning Success stories of 2018 that changed society to the better.


The current year is the great phase of digital transformation for every industry like manufacturing, pharmaceutical, hospitals, sporting, telecommunication, banking, finance, and Tours and Travelling. As the innovations in the technology field got a speed due to the awareness of the importance of the data. Now the amount of data generated by the business sectors are valued the most. Data has become fuel for technology innovations, in order to make huge profits by applying the right strategies in the business and to sustain in this competitive world. The company who owns and generates a large amount of data per second and knows how to make profits out of it will stand apart from the competition.

So to sustain in this Artificial Intelligence and Machine Learning age, there’s a need to change the way how businesses used to run in the absence of the importance of big data. And that change which gained big data knowledge with best strategic plans and business operations, making use of Machine Learning and its optimization techniques and also the decision-making process is integrated with the robust and scalable cloud technologies is known as the digital transformation.


Use of AI and Machine Learning changed society to the better. 

Predicting future performance using Historical data:


Rich Hillebrecht is the CIO of Riverbed Technology, who has unique challenges while providing software designed to improve the performance of wide area networks. Riverbed Technology offers products in the networking domain like network performance monitoring/ application performance monitoring, Wi-Fi, wide area network, edge computing. So Hillebrecht mainly focuses on how to use machine learning models to ingest data from multiple sources across the company's supply chain to drive better business insights.

In order to increase the performance of wide area network riverbed might combine order management and some ERP data with the Historical data about weather and some nearby environmental issues to find the data patterns that could predict future performance.

"We want to apply machine learning techniques to process way more data than we normally would have," Hillebrecht tells CIO.com.

Further riverbed use cases could include search machine learning models which can automatically turn the performance configuration and spot cybersecurity threats in the networking."We want to be more predictive in terms of downstream risk in terms of capacity and our ability to fill orders to customers," Hillebrecht says
For the benefit of the whole organization, Hillebrecht Expects to create a single data lake from which business insights can be drawn effectively.

Hillebrecht Says He is carefully examining and evaluating the emerging tools and technologies, including IBM Watson.

ML removes ‘toil,’ making work more productive:


Ed McLaughlin, president of operations and technology at Mastercard, says ML “pervades everything that we do.” MasterCard is a multinational financial services company now using machine learning to automate the manual repetitive tasks. And giving their employees a chance to work in a different workspace where Idea generation, innovations, and productivity is valued. MasterCard is driven by machine learning tools throughout its ecosystem to detect risk and anomalies in the system which requires critical observation. It also puts a safety net in the network when it finds some suspicious behavior it trips circuit breakers that protect the network.

“We have fraud-scoring systems constantly looking at transactions to update it and score the next transaction that's going in,” he says.

To be a successful market leader in this digital transformation era, best strategies are required to use the artificial intelligence and machine learning tools in the business and don't just rely on them to get magically fix the business problems.
“It's clear we've reached a state of the art where there is a clear investment case to automate workplace tasks,” McLaughlin says.

Banking on better customer insights:


Bill Hoffman, chief analytics officer of US bank has collected a wealth data of customers and is struggling for the change which will drive actionable insights. Suppose, someone visits to US bank’s website to know about mortgage loans, then a customer service agent is assigned to follow up with that customer till next time when he/she will visit the branch. It is easy to US bank and to find patterns that human might not see. For the past few months, US bank was using salesforce.com’s Einstein's AI/ML Technology to increase the personalization across the small banks, wholesale, commercial wealth and banking units. Einstein has the capability to put a calendar invite and the intelligence calendar remind them to call for the candidates on a particular day.

Technology which has the capability to cultivate a 360-degree view of the customer to recommend then the relevant financial services at the moment.

“We are moving from a world that was describing what happened or what is happening to a world that is more about what will or should happen,” Hoffman says. “The core value is staying a step ahead, anticipating our customer needs and the channel they want to interact with us.”

Digital doomsayer app predicts role irrelevance:


Accenture is more aware of the emerging digital Technologies, thus the consultancy has automation roughly around 23,000 roles and redeployed stuff.

“We believe we need to reskill,” said Daugherty. “There is very little around that.”

To help its employees, Accenture has created a beta version of an ML-based Digital doomsayer app, that can scan employees resume and predicates how fast an employee’s job will be irrelevant by analyzing his/her work experience, skills and thus assigns the risk score as for their current role credibility.

For example, the application will note the employee’s skills will retain only for 18 months due to artificial intelligence or some other the automation.

More than just a digital doomsayer, the app can note the employee’s collective work experience and recommends adjacent skills they may wish to pick up to remain more relevant at the company, Daugherty said.


“Responsible AI must be baked into the organization,” Daugherty said.

This app is making the work of HR teams more easier comparative to the traditional hiring and layoff ways. And helps to take effective decisions about an employee’s lay-off, regarding incentives, promotion, and salary hike matters.

Machine learning facilitates predictive maintenance:


Lennox International uses Spark software from the topics to analyze massive information streaming off of the companies commercial heating and air conditioning systems. "Databricks enables us to consume the data and predict with 90 percent accuracy when equipment is about to fail," Bondalapati says “We used to struggle to predict equipment failure.” They believe that machine learning is a core component of the digital strategy. "The collaboration model that Databricks provides was the key for us," Bondalapati says. By monitoring, the machine performance in real time allows the company to predict when a machine will fail and the company shares the message about the machine failure schedule so as to maintain the situation with alternative methods rather than disappointing the clients to all the employees from different departments.


"We went into it tentatively, but it was eye-opening," Bondalapati says.

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