In this lecture, we're going to be studying about Business Considerations in the Machine Learning Era. Machine learning related findings from a survey on enterprises with at least $500 million in sales was provided in this paper. Some of the significant findings were, 76% claimed to be targeting for higher sales growth using machine learning. In addition, at least 40% already use machine learning in sales and marketing. It was also found that 38% credited machine learning contributions in sales improvements, and several European banks were able to increase new product sales by 10% while reducing churn by 20%. Here are some company considerations of machine learning and deep learning. First, data protection is critical. The reason is because artificial intelligence accuracy is based on the data set used in training the software. The software as in terms of the machine learning and the deep learning software. In addition, data is just as important as the software itself. So data protection is very, very important. Customization to company characteristics, business data and context are critical. There are several types of similar functional software that exist that are able to conduct machine learning and deep learning. And open source software and development tools are also available. But if you misuse the way that you train the overall artificial intelligence and the machine learning system, then you could result in racial discrimination and other type of social issues. In other words, the result of your data, the result of the analysis could actually be biased in a certain direction. And because the machine learning, the artificial intelligence engine, is somewhat of a black box to many people, then you may not even know that there is that type of a bias that was programmed within the system. So therefore we have to be very careful. AI and robotics services are always available. This is one thing that you have to keep in mind and take advantage of this in your business. In addition, artificial intelligence and robotics will result in job displacements. So what can you do to prepare for this machine learning business era? This is something that we all have to think about. Here are some personal considerations in the machine learning area that you may want to consider. Number one, make artificial intelligence do the routine administrative work. Now based on a survey on administration management workload, it was found that 54 % was spent on administrative coordination and control, 30%was spent on solving problems and collaborating, 10% was based on strategy and innovation, 7% was focused on developing people and engaging with stakeholders. Now looking at that distribution in a pie chart, you can see this type of a structure. And just looking at it, look at the blue 53% area that represents the administrative, coordination and control. And you can see that it's very significant. If we were able to replace about 30% of this administrative, coordination and control with a machine learning and deep learning engine, well then, we would have a huge change. Now let's consider we took out 30% of this and rearranged everything proportionally as you can see the other categories to fill up this 30% proportionally. That would result in this type of a structure. And as you can see the orange range which is the solving problems and collaborating structure has grown tremendously. Looking into the details of this, well, assuming that an AI, artificial intelligent, management assistant were to assist 30% of the workload on administrative, coordination and control, then we would get 49% based on solving problems and collaborating. This would be the most dominant role of a manager in business. Then less than 25%, less than a quarter, will the role of administrative, coordination and control drop down to. In addition, let's say 16% of strategy and innovation would be the workload, and then 11% would be focused on developing people and engaging with stakeholders. Now as you can see this structure, isn't this much more attractive of a workload for an administrator or a manager? And this is something, this is one area, where machine learning and deep learning can be added to your company to improve it. Personal Considerations in the Machine Learning Era number two: Make artificial intelligence do the report writing. Well, you set up the report format and have the AI system write your reports from the analyzed data results. And then you're probably thinking, "Is this possible? Are there any examples of AI report writing that exist?" Of course there are. Here's an example. The Associated Press has applied software AI robots to assist report writing. The AP's earning expanded due to increased reporting from approximately 300 stories to 4400 stories per quarter. As you can see, AP journalists were able to focus more on investigative reporting and as a result, there was an overall improvement in quality and quantity. It's like a win-win situation using machine learning technology, which is why this course is focused on that. Number three: you do the decision making and judgment work. Focused on judgment oriented skills that require creative thinking would be the part that you need to grow your skill sets on. In addition, experimentation, data analysis, interpretation and strategy development is another group of skill sets that are worthy to accomplish and gain for yourself. In addition, learn to trust the advice of AI data analysis or at least partially trust it. In addition, being able to add collaborative and comprehensive ideas to enhance existing ideas and findings from data analysis would be very helpful. And treat AI and ML, treat artificial intelligence and machine learning, as an always available assistant and advisor. In addition, judgment and decision making based on added human knowledge and experience to the data analysis results of AI and ML is very important. The main role of a human manager or human administrator or a strategic planner. Some consideration factors do exist when you're doing this part. It is organization history, culture, empathy, human rights, common wealth, principles of equality, policies and ethical reflection factors. Now, number four: Become a leader in teaching and advising on how to effectively use artificial intelligence and machine learning. Like I said before, you have to face it AI, ML and DL, they're coming in. And this will change the job profile that we actually have. Therefore being prepared, accepting what's going to happen, and using it as an advantage for you to become a leader is what we need to start to do. Consider, well, learn early and teach how to use AI and ML in decision making and judgment work. Use AI in creative thinking and innovative designing. In addition, add your social networking skills to contribute to the new business plan that was created or advised from the artificial intelligence and machine learning engine. Basically, social networking skills are something that human nature has and it's the advantage of human skills. So therefore, we need to use it to enhance the overall results that come from the data set that we used on the AI engine. In addition, coach more efficient activities and people development skills, contribute on improving collaboration and teamwork, develop new KPIs, Key Performance Indicators, to drive more effective artificial intelligence adoption into the company's business structure. These are the references that I use. And I recommend them to you. Thank you.
