The evaluations of machine
learning algorithms in literature seem to focus on few attributes and mainly on predictive accuracy. On the
other hand the decision space for adoption or acceptance of machine learning algorithms in industry
encompasses much more factors. Companies looking to adopt such techniques want to know where such
algorithms are most useful, if the new methods are reliable and cost effective. Further questions such as how
much would it cost to setup, run and maintain systems based on such techniques are currently not fully
investigated in the industry or in academia leading to difficulties in assessing the business case for adoption
of these techniques in industry.
I am outlining here the process that is common to all sectors, regardless of their technological complexity. In the technology adoption diagram shown in the Figure below, you will find four phases of the technology adoption that takes place in any sector.
The first phase is quick applications. This phase is marked with certain characteristics. This is the stage where the business tries to apply machine learning technology on the low-hanging fruits. As an example, a company may want to automate its social media analysis or sentiment analysis. It would also look to automate some of the less-than-1-minute tasks performed by its employees. This task would be low on technological complexity. It would also like its employees to list the repetitive tasks and to do things like root cause analysis for any failures or issues in the business systems. The focus here would be hindsight . This means that the business is trying to focus on such issues or problems and trying to address those that have caused failures in the past. As an early adopter of the technology, the business is still trying to understand how machine learning is going to help them advance their applications for business growth.
The next stage is that of early applications of machine learning, where the business will try to create learning operations. This means that they are trying to look at the past data and find out what can be learned from it. The business is also trying to address the low-efficiency test so it may carry out an efficiency audit in its operations to help find out identify those areas where it can learn and be more efficient in its business operations. In early applications of machine learning, the business could also think of reducing the cost of its existing operations. And in this it could also carry out cost audit for its various business operations carried out by its employees. It could, as an early adopter, target those operations that are high cost and high growth in nature. It is also to diagnose clearly the business, which would look at the business problems and the reasons for the issues it is facing and focus on how to avoid them in the future. The business would also look at building problem detection systems, such as building a credit card fraud detection system. In this case, as well as in the earlier applications, the business is trying to focus and gain hindsight.
Let's move on to the third phase of technology adoption, where there are assisted applications of machine learning. Here there is application of low-level intelligence to assist the experts in highly skilled tasks. The focus of automation here is to augment the human capability for business growth and advancement. The effort here is to predict the business requirements from data and to make use of such predictions for enhancing the business. Here the focus of the business is to gain an insight and not to just automate its operations but also to gain from the hidden patterns, facts, or trends that may have been lying hidden in its data. In this stage, the organization is discovering about its customers, its employees, and also its operations and, as a result, trying to understand the things that have been troubling it in the form of business issues or problems. This is actually where the business organization will start to look to apply machine learning-supervised techniques with the unsupervised techniques.
The fourth and the last phase of technology adoption is independent applications of operations using machine learning. This is a stage where the automation of a company has reached its fullest capability. Most of its operations are robotic in nature. This is also the stage where there is an expert human replacement happening. In this stage, there is also foresight and prescription on a future course of action for a particular business strategy or vision or mission. As we saw before, this is the stage where the human specialist is being looked at being replaced or assisted at a high level. So here the machine learning is being used at a level where the learning by the machine is at its fullest extent. The machine is capable of learning from the huge data generation happening inside the business operations. It has also developed skills for finding out hidden patterns, facts, and trends to prescribe to its business leaders the future course correction or actions that need to take place in order for the business to grow. This machine learning capability can also be used for averting any kind of debacle, such as financial crisis or scams that may happen in the future or may be reflected in the current data of the business organization. In this stage, the business is using foresight, and it is this foresight that actually gives its operations the course correction capability. This is the maximum extent that a business operation can use machine learning to gain advantage in the market against its competitors. It's not that the entire company operations be run in an auto-mode. That is not what this phase represents. This state is that of an organization that has intelligent automation in place. By intelligent automation, we mean that the key business functions, such as finance marketing purchase, are sufficiently automated to provide foresight about the business operations. The company also has the ability to gather data from its business environment and to avoid any tragic incidents that may occur not due to the company’s fault but due to the nature of the business environment, such as recession, market crashes, etc.
Interest in machine learning has grown steadily over the years, and many organizations are aware of the potential impact machine learning tools and technologies can have on their business.
But the reality is we are still in the early phases of adoption, and the majority of companies have yet to deploy machine learning across their operations. In fact, since the introduction of machine learning models at scale during the dot-com boom, it's taken nearly two decades for ML models to become mainstream.
To understand more about how machine learning has progressed, O’Reilly recently issued the results of a new survey that explores the state of machine learning adoption in the enterprise. The findings suggest that only 15% of the 11,000 respondents work for companies that have extensive experience using ML in production.
So, for the large majority of companies just starting their machine learning journey, what are the first steps? We'll discuss about this in the next post.
I am outlining here the process that is common to all sectors, regardless of their technological complexity. In the technology adoption diagram shown in the Figure below, you will find four phases of the technology adoption that takes place in any sector.
The first phase is quick applications. This phase is marked with certain characteristics. This is the stage where the business tries to apply machine learning technology on the low-hanging fruits. As an example, a company may want to automate its social media analysis or sentiment analysis. It would also look to automate some of the less-than-1-minute tasks performed by its employees. This task would be low on technological complexity. It would also like its employees to list the repetitive tasks and to do things like root cause analysis for any failures or issues in the business systems. The focus here would be hindsight . This means that the business is trying to focus on such issues or problems and trying to address those that have caused failures in the past. As an early adopter of the technology, the business is still trying to understand how machine learning is going to help them advance their applications for business growth.
The next stage is that of early applications of machine learning, where the business will try to create learning operations. This means that they are trying to look at the past data and find out what can be learned from it. The business is also trying to address the low-efficiency test so it may carry out an efficiency audit in its operations to help find out identify those areas where it can learn and be more efficient in its business operations. In early applications of machine learning, the business could also think of reducing the cost of its existing operations. And in this it could also carry out cost audit for its various business operations carried out by its employees. It could, as an early adopter, target those operations that are high cost and high growth in nature. It is also to diagnose clearly the business, which would look at the business problems and the reasons for the issues it is facing and focus on how to avoid them in the future. The business would also look at building problem detection systems, such as building a credit card fraud detection system. In this case, as well as in the earlier applications, the business is trying to focus and gain hindsight.
Let's move on to the third phase of technology adoption, where there are assisted applications of machine learning. Here there is application of low-level intelligence to assist the experts in highly skilled tasks. The focus of automation here is to augment the human capability for business growth and advancement. The effort here is to predict the business requirements from data and to make use of such predictions for enhancing the business. Here the focus of the business is to gain an insight and not to just automate its operations but also to gain from the hidden patterns, facts, or trends that may have been lying hidden in its data. In this stage, the organization is discovering about its customers, its employees, and also its operations and, as a result, trying to understand the things that have been troubling it in the form of business issues or problems. This is actually where the business organization will start to look to apply machine learning-supervised techniques with the unsupervised techniques.
The fourth and the last phase of technology adoption is independent applications of operations using machine learning. This is a stage where the automation of a company has reached its fullest capability. Most of its operations are robotic in nature. This is also the stage where there is an expert human replacement happening. In this stage, there is also foresight and prescription on a future course of action for a particular business strategy or vision or mission. As we saw before, this is the stage where the human specialist is being looked at being replaced or assisted at a high level. So here the machine learning is being used at a level where the learning by the machine is at its fullest extent. The machine is capable of learning from the huge data generation happening inside the business operations. It has also developed skills for finding out hidden patterns, facts, and trends to prescribe to its business leaders the future course correction or actions that need to take place in order for the business to grow. This machine learning capability can also be used for averting any kind of debacle, such as financial crisis or scams that may happen in the future or may be reflected in the current data of the business organization. In this stage, the business is using foresight, and it is this foresight that actually gives its operations the course correction capability. This is the maximum extent that a business operation can use machine learning to gain advantage in the market against its competitors. It's not that the entire company operations be run in an auto-mode. That is not what this phase represents. This state is that of an organization that has intelligent automation in place. By intelligent automation, we mean that the key business functions, such as finance marketing purchase, are sufficiently automated to provide foresight about the business operations. The company also has the ability to gather data from its business environment and to avoid any tragic incidents that may occur not due to the company’s fault but due to the nature of the business environment, such as recession, market crashes, etc.
Interest in machine learning has grown steadily over the years, and many organizations are aware of the potential impact machine learning tools and technologies can have on their business.
But the reality is we are still in the early phases of adoption, and the majority of companies have yet to deploy machine learning across their operations. In fact, since the introduction of machine learning models at scale during the dot-com boom, it's taken nearly two decades for ML models to become mainstream.
To understand more about how machine learning has progressed, O’Reilly recently issued the results of a new survey that explores the state of machine learning adoption in the enterprise. The findings suggest that only 15% of the 11,000 respondents work for companies that have extensive experience using ML in production.
So, for the large majority of companies just starting their machine learning journey, what are the first steps? We'll discuss about this in the next post.
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