FeatureArtificial intelligence is built on the foundation of a digitally transformed enterprise. More digitisation means more opportunities for artificial intelligence to add value to the business. The cost of data collection and processing has hit an all-time low, and the pandemic has accelerated adoption of digital technologies worldwide.
Businesses are trying to make sense of the collected data and use it to their competitive advantage by leveraging artificial intelligence and machine learning techniques.
Artificial intelligence is built on the foundation of a digitally transformed enterprise
Artificial intelligence is helping businesses move from process automation to decision automation. With more and more data points required for business transactions, and more and more artificial intelligence adoption, we will see optimisation of business processes, maximisation of profit margins, and better customer and employee satisfaction.
The following are major challenges in adopting artificial intelligence and machine learning:
Data in silos
Businesses often have data in silos, which occurs when an organisation’s departments or business functions fail to communicate freely or effectively with each other. This can be a deterrent to effective implementation of artificial intelligence since artificial intelligence erodes data boundaries. A 360-degree view of data is mandatory to unlock the full potential of artificial intelligence.
More digitisation means more opportunities for AI to add value to business
Rethinking business processes
Artificial intelligence introduces an element of probability into business processes, which have traditionally been thought of as being predetermined. Many organisations today need a cultural change to assimilate probability into everyday business processes, and this change has to be driven top-down by proper user education.
Hype behind AI
Artificial intelligence is known to overpromise and underdeliver. Organisations need to take a level-headed approach towards this fancy new technology and apply it only in places that make sense. Running experiments like A, B tests to understand the effectiveness and pitfalls of deploying artificial intelligence models is recommended.
360-degree view of data is mandatory to unlock full potential of artificial intelligence
Training of the artificial intelligence and machine learning model should be implemented so that it extends well across a business’s wide array of customers. For example, in a financial institution, the loan assessment model must work well for both the company’s urban, salaried customers and its rural, self-employed customers. Such data classes should be studied before implementation to see if they would demand a separate model be trained.
It is also key to know when the model is starting to become stale. Identify concept drift, a condition when the data that has been fed into the artificial intelligence model is no longer relevant, and resolve it by retraining the model or adjusting the input parameters.
Industries that utilise good levels of digitisation, such as IT, e-commerce, healthcare, transport, finance, will be early adopters
IT leaders and senior executives should avoid the hype behind these technologies and instead assess what the artificial intelligence and machine learning model can do for the organisation, and apply artificial intelligence or machine learning only where these technologies can perform much better than traditional methods.
Artificial intelligence introduces an element of probability into business processes
Computing has the unique advantage of being able to touch every industry vertical. Anything from drug manufacturing to shipping to textiles—you name it, computing has a part to play. The same goes with artificial intelligence and machine learning.
Industries that utilise good levels of digitisation, such as IT, e-commerce, healthcare, transport, and finance, will be the early adopters, and the others will follow suit.
Organisations need to take level-headed approach towards this technology and apply it in places that make sense, while running experiments like A, B tests.
