This article was published as a part of the Data Science Blogathon.
Introduction
Ever since the advent of Globalisation, the environment in which a business operates is constantly changing. An important component of the business environment is the technological environment. Technology, also, as we all know is constantly changing, updating with new trends coming in every day.
Thus, it becomes imperative for businesses to understand and keep up with the technology trends to survive in the market.
One thing that has taken the IT industry by a storm is Machine Learning and Artificial Intelligence. AI and ML have innumerable applications which can upgrade and transform the way your business works.
So, whether or not, your business is in the IT sector, it is essential that Business Leaders know the AI and ML trends and can keep up with the pace and change of the business environment.
What are AI and ML?
Artificial Intelligence can be defined as a field of development of intelligent machines that work and react like humans. Some of the activities that involve artificial intelligence are Speech Recognition(how Google Assistant or Siri listens to your commands) or Computer Vision(how Google Lens works).
Machine Learning is the field of study that will enable computers to learn without being explicitly programmed.
Machine Learning is a subset of Artificial Intelligence.
Common ML Techniques
There are broadly three types of machine learning; Supervised, Unsupervised, and Reinforcement Learning.
Start with the question, “Do you have a Target Variable?” If the answer is ‘Yes’, it is a supervised learning problem, and if the answer is ‘No’, it is an unsupervised learning problem.
To illustrate this, predicting whether a person will default on a loan or not. For this, you have a target variable i.e. ‘defaulting on loan’, which is a supervised learning problem. On the other hand, customer segmentation in a market has no target variable which makes it an unsupervised learning problem.
Supervised Learning can further be divided into two categories namely Regression and Classification.
Classification, as the name suggests, involves classifying a variable into two or more types. The loan default example we discussed above is an example of Classification, to be specific, binary classification, as it has two classes, default on loan and no default on loan. There can also be multi-class classification which for example, is classifying different species of flowers.
Regression, on the other hand, is used when the target variable is continuous. Predicting sales is one of the examples of a regression problem.
Unsupervised Learning involves clustering. Clustering, as the name suggests, is making groups or clusters of data based on their similarity.
All this has been put together in this flowchart below for your easy reference.
Lastly, Reinforcement Learning is a machine learning technique in which the agent learns itself in an environment by trial and error.
An analogy that can be brought up to explain reinforcement learning is, a child learning to walk. Child(agent) tries walking, falls, gets up again and walks again, and finally by trial and error, learns to walk completely.
Applications of ML and AI in the real world
Now, let us come to the real deal and learn how AI and ML are applied across different industries and how they prove useful.
Banking
Banking and the financial sector use AI and ML to cut down on risks and increase their profits.
Various applications of AI and ML in Banking can be summarised as follows:
- Customer Acquisition: Banks use AI and ML for market segmentation and predicting the probability of acquiring customers through different marketing channels. Banks also use geospatial data science to identify where they can set up their new branches.
- Customer Management: As a bank acquires a new customer, it gets loads of data about that customer which can be used in conjunction with existing data to make important decisions such as revising interest rates, credit limits, etc. Chatbots also ensure better customer management as they provide round-the-clock availability and can help provide information or perform some pre-defined tasks like checking account balance, requesting a checkbook, etc.
- Risk Management: One of the most common applications is running anti-money laundering checks. Another application is assessing credit risk analysis. Fraud detection across credit cards, insurance, and loans can also be done using machine learning.
E-Commerce
The E-Commerce industry heavily relies on artificial intelligence to stand its way through the cut-throat competition in the industry. Some of the ways in which AI and ML are applied by e-commerce businesses are:
- Customer Acquisition: We see a product online and whoosh, now you will see its ad everywhere. This is nothing but an application which is known as retargeting. These websites see if you visited and didn’t buy the product and will show you the ad with the intention to retarget you to buy that product. Another way they use AI and ML is personalization for a customer which is based on the customer’s demographics, purchase history, interests, etc.
- Product Pricing: As a business person, you might be well aware how the pricing of a product depends on various factors; and in a competitive market, all these factors have to be collectively analyzed to determine the prices of not just one but many products in real-time and here, machine learning proves utterly useful.
- Customer Management: Recommendation systems, chatbots, managing product returns are some of the ways in which machine learning can help in better customer management.
- Content Management: Machine learning can help identify counterfeit products which may be posted by some sellers. Another application is advanced and improved search. It is very important to show the right products when searched so as to retain that customer. Lately, there has been an addition to searching in terms of image search also where people can search a product through a similar image. Some sellers indulge in getting fake reviews which might dwindle the customers’ trust in the e-commerce website. Machine Learning and intelligence may help here in identifying fake reviews.
- Managing Logistics and Supply Chain: Using machine learning to forecast the level of demand helps maintain inventory in a better manner so as to tap the market correctly, don’t lose out the business to competitors, and save costs.
Healthcare
- Medical Imaging: For areas where we have sufficient data, machine learning can help in diagnosis and medical imaging many times faster than a human and in some cases even more accurately. This is indispensably helpful in critical cases.
- Personalized Medicines: Giving standard medicines to everyone may not ensure recovery for all in a similar manner. On the other hand, personalizing medication according to an individual may prove extremely useful and this can disrupt the healthcare industry in a positive manner.
- Drug Discovery: The current drug discovery process is expensive both in terms of money and time. ML can change how it works. For instance, deep learning can help researchers parse through researches and make it easier for them to study then. Predictions can be made on how certain drugs or drug combinations may prove successful.
- Health Monitoring: Fitbits and health trackers which track your activity, heart rate, sleep etc. can help detect any anomalies in your health and help avoid emergencies.
- Robot-Assisted Surgeries: As robots are far more precise than humans, robots can help improve surgeries several times.
Telecom
Similar to the applications discussed above, the Telecom industry also uses AI and ML for customer acquisition, customer management and infrastructure management.
But, one of the most important areas is Customer Churn as there is cut-throat competition in the telecom industry. Data analytics can be used to predict which customers are likely to churn and then relevant practices can be applied to ensure customer retention.
AI and ML in Functions
Human Resources
AI Engines can help identify the right source for sourcing the candidate. NLP can also help screen resumes to shortlist candidates. Nowadays, even for first-level screening, AI bots are being used for video interviews. This can help save time and improve the recruitment process.
But an HR’s role doesn’t end after recruitment and selection. Employee engagement is also an essential role that can be improved through AI. Innovative training methods can be recommended through machine learning.
Sales
Sales start with acquiring customers. AI can analyze the business goals you input in conjunction with numerous data points and then suggest the most relevant opportunities for customer acquisition. Price optimization can also be done with the help of AI and ML for maximizing profit. AI and ML can also help in improving recommendations to customers and also market basket analysis for better sales.
Operations
- Personalizing Customer Experience: Call center management using voice bots, chatbots to chat, and using recommendation systems to personalize the customer experience are few examples wherein routine operations can be improved using AI and ML to save huge costs and time.
- Risk Management: Anomalies can be detected using AI to rightly detect frauds.
- Automating repetitive tasks: Document classification can be done using Deep Learning and Natural Language Processing which will be much faster than a manual job. AI Robots can be used in manufacturing which will make the production process faster, less error-prone, and more efficient.
- Warehousing: Manual labor can be reduced by a great percentage using AI bots.
Marketing
Digital marketing has taken the marketing world by storm. Email Marketing can be improved by segmentation, optimizing subject lines through AI which plays a very important role, and analyzing the best time for sending these emails.
75% of users don’t go on Page 2 of Google Search. This rightly emphasizes the importance of search engine optimization. AI can analyze and help improve the SEO process.
Social Media Advertising through AI can help sure that efforts and investment are made in the right direction for successful marketing.
Building AI Capabilities in your Organisation
Now, you might have a fair understanding of what AI and ML are and how they can be applied in your business for better efficiency. Let us now look at how you can build AI capabilities in your organization.
Level of Change
After you have set down your end goal, the first thing to identify is what level of change you want to bring in. Is it a function level change, a project level change, or an organization level change? The right level of change depends on your position in the organization, budget, time constraints, etc.
Building your AI Strategy
Remember the ABCDE framework for building an AI strategy for your enterprise.
- Assess your organization capabilities
- Create organization buy-in
- Identify the core team
- Define the problems for the AI team
- Execute and Implement
Roles in an AI/ML Team
- Data related roles: Data engineer, data analyst, data scientist
- Hardware related roles: Hardware engineer, IoT engineer
- Software related roles: AI Software engineer
- Business Expert: Domain Expert, Project Manager
Keys to recruiting the top AI/ML talent
It is important to identify clearly what roles you require and understand the subtle, important, and often ignored differences between them, especially data-related roles.
You need to prepare a well-written job description with reasonable required skills and education lest it shoos away good talent.
In addition to external hiring, which may sometimes prove useful is retraining existing team members from a Computer Science background.
Third-party sources can also be tapped if the above ones fail.
Remember, recruiting good AI/ML talent is not a cakewalk and should be executed patiently.
Conclusion
As we have seen, AI and ML can change the scene of your business and if implemented right, can reap great benefits for your business. Thus, AI and ML become imperative for business leaders to keep up with the trends in technology as much as any other trends in the market.
Of course, there is much more to this and if you want to learn in much more detail, check this out.
The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.
