AI: Adopt and Adapt

Firms need to figure out how machine learning or predictive analysis can help their businesses and react, now.

The hype surrounding the behemoth that is AI has been quite powerful, and a large chunk of global organisations have already been seduced by it. However, for businesses new to AI, figuring out which aspect might be most beneficial may prove to be a considerable challenge – even deterring many executives from adopting the (inevitable) digital transformation process sooner rather than later. Research has shown that nearly 37% of the surveyed executives have no real cognitive understanding of how exactly these technologies can help their businesses. Of course, each business has its own set of specific needs and requires only certain subsets of the complete AI framework to augment existing business practices. For such firms, figuring out the exact areas of use and recognising potential scope for development are going to be crucial – both for their digital transformation process as well as for remaining competitive in the world market.

A good place to start would be to consider the two most widely used forms of AI technology – predictive analysis and machine learning. A myopic understanding of the two can lead one to believe that they are one and the same, whereas, in reality, they are not. They have rather different methodologies as well as applications.

ML v PA

Machine learning is the poster-child of AI. It is a class of AI that uses cognitive learning methods to program their systems, thus eradicating the need for explicit programming. Hence, the more it learns, the smarter it gets. The learning can be of two forms – supervised and unsupervised. The former requires operators to set desired outputs, label data and give it parameters to make sure the model heads in the right direction – extremely useful for new businesses adopting the format to exercise a greater amount of control.

Unsupervised learning, on the other hand, is like pushing a baby bird out of its nest without any training (data). It analyses any given body of data and forms its own datasets – especially useful in business practices where a large amount of initial data isn’t readily available. In either case, the data is then used to devise complex algorithms that can eventually lead to a prediction.

Predictive analysis, on the other hand, is an advanced form of descriptive statistics which uses historical and current data to build models to provide the probability of an outcome, using tools such as classifiers and detection theory. It automates forecasting by finding specific trends and patterns in historical data to display them visually. In several cases, it may even be more prudent to consider predictive analyses as a subset of machine learning, instead of a competing branch of AI.

The primary demarcating factor between the two would be in the way that updates are handled. Machine learning uses an adaptive technique to manipulate additional (new) data without any further need for programming, whereas predictive analyses require data scientists to run the model manually multiple times, adjusting the algorithm as and when new data is being added. It is use-case driven, rather than being data-driven.

Applications in Businesses

Given that a cyber crime is being attempted every 39 seconds, it is crucial for businesses today to take pre-emptive rear-guard action and secure online assets. Machine learning technology is crucial in this regard – capable of scanning vulnerable areas and exposing security risks and possible threats – it finds widespread use in internet or credit card fraud detection, among several others.

Machine learning is also extremely effective in analysing market insights from online advertising or in providing recommendations and search engine optimisation for businesses. It can detect which sales copies, creatives, channels and other components are having an impact, and which aren’t. Predictive analyses too have a pronounced impact in taking a campaign’s previous data and forecasting key performance indicators for variables such as the ROI, churn rate, revenue or conversion rate.

In the healthcare and genomics sectors, machine learning finds use in bioinformatics, DNA sequencing and medical diagnosis among others. Currently, several organisations worldwide are using machine learning algorithms to find useful sequences in the coronavirus in order to come up with a vaccine for the pandemic.

A place where predictive analytics thrives is in finances. It uses previous data to provide very useful forecasts regarding spending, thereby allowing firms to adjust their finances – leveraging insights to continue generating steady returns. It is also very useful in risk mitigation and correction measures, crisis management, credit scoring and market trend analyses.

Whilst the types of AI used in businesses will remain unique to each of them, one thing will remain common for all – the need to adopt and adapt, now!