The world is moving towards automation & digitalization. The production is also getting improvised through new innovations in softwares & robotics. The new tools for data analysis & cross-functional process flows are in place e.g. ERP, SAP, Project management softwares like Nadhi, BIM etc. Such softwares are available with mobility app functionality. With globalization in business, the leaders need to move around the territory & majority of works need to process remotely. The mobility app of these softwares provides a relief for leaders for smooth functioning. The blog focused on the machine learning's & artificial intelligence learning's for the leaders, as it is the future for the businesses. The leaders need to invest their time & efforts for learning these technologies. The leaders also need to have thorough information about such softwares / technologies available in the market with it’s pros & cons. The new generation employees can easily gel with such technologies & go with it without resistance. The implementation of such technology also attract the learning's for functional managers too, as the data generation, processing & analysis will be through these managers only. The organizations also required the attention on the mobile handsets used by the employees for successful implementation. I personally believe that the real time project progress data can also be generated using such interface, which can provide the live profitability for the projects.

I strongly agree on this topic, As John Sealy Brown(JSB) mentioned “it is about cultivating a blended ontology with Humans & Machine” I want to present two views here: Cynical view: Isn’t it just Deep learning? Wasn’t that a dead end few years ago. Do we have that much of sample data to train machines? We were able to achieve accuracy of 70% on keyword categorization built using machine learning after assigning a very large sample data Optimistic view: Most important way to use machine learning is to use signals, we live in digital world and every interaction on website is recorded. To use these data as “Signal” in order to create strategy and provide personalized experience. I am going with optimistic view as I have always loved using data for any decision. Humans make assumption on a limited samples and machines don’t. However, one thing stuck with me when someone said, think of AI as “Dog on Surf Board” Humans will have to train, decide which wave to surf, measure the risk etc. so start slow with a pilot and see if it is working and then build upon it.

I do agree with the blogger that the business Leaders need to understand the new age technologies like Machine learning, Artificial Intelligence etc. The blogger has explained how machine learning powered mobile devises can change the way people do business transactions. I would like to emphasise that business leaders need to embrace these technologies to lay a digital path in their organisations. There should be a structured road map on adopting these technologies with clear digital strategy. For a medium to large organisation, after ERP has enabled the transition from standalone legacy systems to integrated systems, next wave of change has to be automation through ‘Robotics Process Automation’ (RPA). RPA enables the transaction level automation using business rules thereby eliminating manual or human intervention in the business transaction. Large number of repeated tasks will be performed by ‘BOTS or “HUMBOTS’ without any time restriction thereby increasing the efficiency and accuracy. We already see a large number of organisations adopting RPA in customer oriented repetitive business tasks. In my view, business leaders should embark on Cognitives or Machine learning technologies only after maturing in RPA as above. This will give them a platform for understanding the areas where cognitive patterns could be analysed for next level of transformation. Without a clear digital strategy, a direct leap in to Artificial Intelligence or Machine learning may backfire and lead nowhere. Hence, it is critical for business leaders to be aware of the latest changes in technologies, assess where they are and follow the path towards digital life.

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Artificial Intelligence and digital transformation have been the most trending technologies in last 2-3 years. The best combination that can turn out in current IT world will be, make these two technologies work hand in hand with machine learning as the common base. Currently, companies are striving hard to achieve the best of these two technologies. The statistical outputs shown by most of the IT companies states that using AI-supported activities to power digital transformation have generated additional revenue for their organizations. Machine learning has played a significant role in organizations’ digital transformation and that the average amount of time managers spent executing day-to-day activities at the end of an AI-supported digital transformation was 84 percent than for managers at organizations beginning such a transformation.AI helped organizations make processes more efficient & AI-led automation efforts freed up time for them to focus on more creative activities.AI implementations were the biggest driver of productivity increases in organizations. Gartner has given the prediction that by the end of next year 20% of business content will be written by the machines. “Technologies with the ability to proactively assemble and deliver information through automated composition engines are fostering a movement from human- to machine-generated business content,” the company writes. By using these emerging tools, data-based and analytical information can be converted to natural language writing. Machine learning can be used to analyze lots of data as compared to a human can, coming to more and better conclusions faster. Companies are using those conclusions to digitize business processes, not only making decisions faster but also acting on those decisions without a human having to be involved, streamlining the business process. One can say this system is unsupervised but data scientist still needs to select the data that the system should look into and then analyze the groupings of data that the system finds. Also, humans have to make sure that machines should make any decisions which are unethical and discriminatory. Coming to the conclusions, looking at the future scope and all the advantages of AI & digital transformation business leaders must keep up the pace with learnings of machine learning.

Machine Learning is a game changer for business process optimization. It has enabled companies to achieve cost and quality optimization. In the past, business process optimization was a time-consuming and manual effort. Those tasked with this effort had to review a very limited set of historical data to identify optimization opportunities based on past process performance. With modern machine-learning capabilities, businesses are able to integrate a broad array of sensors and monitoring mechanisms to capture large volumes of operational data from their business processes. This data can be ingested, correlated and analyzed in real-time to provide a comprehensive view of process performance. Before machine learning, managing the signals from instrumented processes was limited to either pre-defined scenarios or the review of past performance. These limitations have now been removed. One of the biggest benefits of machine learning is the “Learning” aspect. Systems can now be trained to identify and categorize new business situations and learn from this data. This makes machine learning far more efficient than the traditional computer programming frameworks. In business process optimization, there is an important distinction to be made between “change” and “improvement.” Machine-learning systems can correlate a large diversity of data sources – even without pre-defined relationships. Machine learning is a true game changer for process optimization and process management professionals.

It’s true that machine learning has become the equivalent of computer literacy in the 1990’s. ML could be the next big revolution after the internet. Everything we do can benefit from ML. However, a point to remember is that to effectively leverage machine learning, human shouldn’t let go of their human side. To solve bigger problems using ML, human managers need to be more creative to come with newer challenges that could be solve by ML. Computer Science has this term called ‘Hard Problems’. Problems that are deemed very difficult to solve using the most advance computer technology till now could become the next new industry by employing ML to solve them. To think from that level and come up with ideas is something I still believe human will continue to excel over machine for some considerable time. Managers also need to work alongside ML engineers to truly understand what goes into ML engineering and what is it limitations. They could also benefit from design thinking and lateral thinking techniques. Otherwise all the ML start-ups will end up being similar in nature and continue to create hype just by mashing up some existing business model with ML augmentation. This is already happening we could very well see another tech bubble in a few years. Apart from the benefits from humanity, there are concerns about the dangers of ML in the wrong hands. My personal belief is that these concerns are not overrated. We need policies in place to prevent misuse of ML and Artificial Intelligence because these technologies have reached a level of complexity that even their creators have been unable to comprehend fully. Many of us will know about the chat bots that Facebook researchers created started communicating in their own cryptic language which was incomprehensible to the creators. This chilly example shows what kind of negative potential such technologies carry. So just like nuclear energy, ML is something that needs to be handled carefully and by the right people. Many fields like medical research, social sciences, forensics and so on will get a major boost from ML. On the other hand, brilliant cyber criminals will use it to advance their security penetration capability. Humanity will really benefit from the value created by ML, provided it survives its misuse in the wrong hands.

First and Foremost, thanks to prof. Anil Vishnu Vaidya for giving your insights on AI and ML becoming a part of day –to-day life. I completely agree with him as Machine learning has huge potentials. If leveraged appropriately ML can solve various known problem areas and assist in deriving more throughput in lesser time. ML based solutions have vast application areas - persona analysis, Sentiment analysis, Image recognition, Object identification etc. One such example is Google lens, which is an AI-powered technology that uses your smartphone camera and deep machine learning to detect objects, identify them and even suggest actions based on the identified object by Apple’s latest A11 Bionic chipset, Honor View 10 has a Kirin 970 processor that has a dedicated Neural-Network Processing Unit (NPU)and Samsung's multiple flagship models are using Bixby (It learns from the user's behaviour and allows rich communication with the device).Chat Bots are helping businesses in reducing the cost of chat based assistance. These advancements in hand held devices are sure to help AI and ML driven businesses in reaching their digital goals better and faster. Decision makers today are leveraging ML and AI to assist them with key decisions. Businesses are spending significantly to derive better insights from their data using Machine learning models. Every big organization has already embarked on digital journey and this remains one of their key growth targets for each year. Significant efforts are channelized to get ahead of competitors in digital journey. Such is the digital impact that, Infosys recently linked salaries of top executives to digital revenue. Another major IT giant, Cognizant has completely transformed its entire IT operations to digitalised world referring it as “Going Digital”. AI and ML are the future of tomorrow and top management and executives needs to adapt to this ever changing technology.

As correctly stated in the article, Machine Learning and Artificial Intelligence are used extensively nowadays. Arthur Samuel, an American pioneer in the field of Gaming and AI, coined the term ‘Machine Learning’ in 1959 while working at IBM. In today’s world, data from Machine Learning and Artificial Intelligence play a huge role while making critical business decisions. It is used while analyzing sales data, personalizing customer service, detecting fraud, learning management systems and a lot more. Machine Learning can significantly speed up business processes by uncovering and analyzing the most valuable information. To fully leverage the benefits of Machine Learning, business leaders need to understand its effect on the day-to-day life of a consumer, as correctly pointed out in the article. Today, most internet applications in use incorporate Machine Learning to improve its usability. Be it Siri, Cortana, Facebook, or Google apps like Maps or Search, all of them use it. Businesses have to keep this in mind to improve the overall efficiency of product use and product delivery to the customer. However, before coming to a conclusion regarding the use of Machine Learning and AI in any process, it is imperative that business leaders understand whether its use is relevant to the particular scenario. Machine Learning is a comparatively sophisticated process and has special requirements. But there are other traditional methods to analyze data. Basic statistics, for example, can give sufficient insight for a problem while saving time and resources. If the business scenario is relatively simple, statistics is a very good starting point. The other point to consider is whether decision makers have the relevant data to solve the problem. If not then there is no point in running analytics on it. The third point is regarding cost implications. The returns from the analysis should justify its use. The managers of today must learn the technicalities and be able to forecast the real world effect of adopting Machine Learning and AI. Businesses of today have realized its potential which can be seen in the rise of specializations in Business Analytics at the top B-Schools. Technology, in today’s world is ever evolving. Learning too, should be an ongoing process for any decision maker to keep a grip on the latest innovations and achieve the best possible result.

I truly believe in the saying that “Change is inevitable but growth is optional.” And if one wants to grow in this ever changing business environment empowered and leveraged by technologies one has to keep herself and the business updated. Today Machine learning, Artificial intelligence is leading the change in the current business environment. For example, manufacturing industry contributes to 7% of India’s GDP and the transformations are readily visible in some companies. My last employer, Honda has already started working on Industry 4.0 which entails mostly 5 areas: Transformation of the production process: Smart Manufacturing, Predictive Maintenance, Autonomous vehicles and interactive machines in production, Optimized energy management for climate and energy change, and Quality control or “test automation 2.0” A recent survey based on interviews with more than 3,000 business executives, managers, and analysts in 112 countries and 21 industries. 84% of respondents say AI will enable them to obtain or sustain a competitive advantage. 83% believe AI is a strategic priority for their businesses today. 75% state that AI will allow them to move into new businesses and ventures. **Source (Boston Consulting Group and MIT Sloan Management Review study, Reshaping Business With Artificial Intelligence) Great observation when Dr Anil Vishnu Vaidya pointed out that business leaders need to visualise the business models and processes therein to manage future business with the AI & ML powered handset wielding employees. A fourth industrial revolution, featuring automation and data exchange, is set to transform industry, but requires new skills and training of workforce. This is true of any transformation that occurs in the business context. For example, Electric Vehicle market, Government has pushed back an ambitious deadline to put thousands of battery-driven cars on the road by nearly a year just because of the huge transformation required in the industry. Germany inevitably stands out in terms of its world-class apprenticeship system and I would argue that Italy is leading the way in terms of how its government encourages manufacturers to take the plunge via financial incentives. I think there is no doubt AI and ML is the future technologies and the change is the need of hour for all businesses in various industries.

Dear Sir, I strongly agree that it is very important for business leaders to understand Machine Learning and Artificial Intelligence. Although it has been explained very nicely by you. I would like to bring my perspective: I was amazed to read that Netflix was able to save $1 billion by using its Machine Learning (ML) based personalized suggestions on TV shows and movies. Not only that more than 800 stories were created by Washington Post’s AI writer during 2016 Rio Olympics and the presidential elections of 2016. AI and ML is affecting the businesses in various ways. AI powered chatbots are improving the costumer engagement by interacting with them and providing 24* 7 solutions. Ecommerce websites are using AI and ML to give personalized shopping suggestions. Healthcare businesses are using AI and ML to give better prediction about diseases and their medications. By building the predictive models which adjusts with respect to the learning capacity of students AI and ML is helping students to learn better. Insurance companies are implementing AI and ML to detect the fraudulent claims. Even banks have also started relying on AI and ML for risk assessment of the loan-applicants. Almost all the repetitive tasks in the industry is being replaced by AI and ML algorithms. Therefore, AI and ML is impacting the businesses very heavily and there is no doubt that future businesses are going to be powered by AI and ML . Almost all the business leaders have an eye on it - For example : Ginni Rometty CEO of IBM said – “Machine Learning (IBM calls cognitive computing) is at the heart of the $2 trillion market IBM sees developing by 2025”. Not only businesses AI and ML are also impacting our own lives. Over 36 percent population of the world has access to smartphones and these platforms bring AI and ML to the lives of people. AI and ML powered applications and smartphones are becoming the part of our lives. Today, smart phones know us more than even our closer ones. They know what we like to shop, they know what we want to watch , they know where we want to go etc. Google has personalized suggestions for almost everything. Cortana, Siri and Google’s AI assistant can really talk like human beings and help us do almost everything from reading newspaper to even ordering pizzas. They even wish on our birthdays. In a situation like this when both the businesses and customers are impacted by AI and ML. It is important to understand these technologies in order to take better business decisions.

Big Data is all in demand these days, and more than a few organizations are at least wondering what sort of business intelligence they could derive from all the information they have. But while awareness of Big Data is rising, only a few organizations—like Google or Facebook-are really in position to make the most of it now. However, the time is coming and organizations that expect to leverage Big Data will not only have to understand the particulars of foundational technologies like Apache Hadoop, SAS, they'll need the infrastructure to help to analyze and secure it. In the next three to five years, we will see a gap widening between companies that understand and exploit Big Data and companies that are aware of it but don't know what to do about it, says Kalyan Viswanathan, global head of information management with Tata Consultancy Services' (TCS) global consulting group. The companies that succeed in turning Big Data into actionable information with have a clear competitive advantage, Viswanathan says. "Today, most companies are aware of Big Data," he says. "There's a lot written about it. There are conferences about it. Awareness has become quite prevalent. But if you look at actually exploiting Big Data, I would say we're at the very beginning stages of it."Viswanathan says he believes that Silicon Valley Internet-based businesses like Facebook and Google—where the entire business is based upon the management and exploitation of data—are leading the charge when it comes to Big Data. Industries like financial services won't be far behind, he says, and neither will the intelligence or military communities. Other verticals like retail, telecom, healthcare and manufacturing will follow. In terms of readiness to exploit Big Data relatively soon, I would say the companies have to be market leaders in their industry segments. They will be the ones that tend not to wait until others have exploited new technology. They would rather move ahead and set the standards for their industry.

Hello Sir, Thank you for the deep insights!. I would to like throw some light on the topics discussed in your article. Well the very first question which crossed our minds after reading the article was do the business leaders really require machine learning and artificial intelligence in this industry era. The answer to this question depend on various factors like what is the need that business are moving towards automation and artificial intelligence. The key factors that have led to interesting and useful applications of Machine learning are that today, the amount of digital data being generated is huge thanks to smart devices and Internet of Things. This data can be analysed to make intelligent decisions based on patterns, and Machine Learning helps to do exactly that. Machine learning based models can extract patterns from massive amounts of data which humans cannot do because we either cannot retain everything in memory or we cannot perform obvious/redundant computations for hours and days to come up with interesting patterns. Machine learning has found major applications in finance, healthcare, entertainment, robotics, and many more. For example, let's take up amazon. If amazon is able to predict what its customer wants accurately and present it to them then it can generate a lot more profit. It does so by observing patterns in the customer's choices and other factors that it takes into account while presenting its prediction. Because of its practical applications using Machine Learning, it’s possible to handle previously unseen scenarios. Once a Machine Learning model with good generalization capabilities is learned, it can handle previously unseen scenarios and take decisions accordingly. Note that in a traditional program, you need to tell what decisions need to be taken if a particular scenario occurs. Now imagine a billion scenarios are present, you clearly cannot write a code which can handle all these new scenarios. Hence the need for machine learning. Every company wants to do machine learning on a bigger scale and for less cost. Cloud service providers will continue to compete to drive down the costs and increase the capacity of machine learning systems. We've seen Google's cloud services grow from storage to include a suite of machine learning tools across language, speech and images. They have gone so far as to build custom hardware—the tensor processing unit—for helping users train their own machine learning systems quickly. Amazon's AWS and Azure have similar offerings. The end result is a democratization of large scale machine learning tools and infrastructure. Every research team wants to do machine learning with less data. Acquiring data is expensive and time consuming. One common case: The seemingly simple task of predicting the names of objects in an image can require showing a machine thousands of examples of each object. Researchers are investing in methods to reduce the number of examples needed and because of bigger, faster, cheaper machine learning, which is more accurate with less data, we will see the number of applications and use cases of machine learning continue to rise across all sectors. For example, when you’re booking a taxi, you’re shown how much the trip would cost or when you’re on the trip, you’re shown the path the taxi would take to reach your destination. All of that, is machine learning. While booking a ride on Uber, you’re always told the amount of time the trip would take and how much it would cost. Machine learning helps obtain this information. One interesting point to note that Machine learning and Artificial intelligence are not same. Artificial intelligence is building technology that behaves like a human. Self-driving cars, Siri, smart homes, and many other emerging technologies are examples of AI. Machine Learning is a subset of artificial intelligence that uses algorithms to learn from data sets. Algorithms are essentially a series of steps that lead to the completion of a task. Using data and algorithms, Machine Learning technologies make intelligent predictions or perform actions. Utkarsh Agarwal GMP batch 2018