The fact that machine learning is increasingly facilitating and influencing our daily lives has almost become a truth universally acknowledged nowadays. However, whilst we seem to have accepted the potential of machine learning at home, many of us still lack a deep understanding of its potential at the workplace.
Many organisations, although intrigued by machine learning and its endless possibilities, are finding it difficult to implement real change into production. However, some organisations are gradually starting to make a move, and are making the transformational changes required to succeed in future years. With such moves, these organisations have brought along the below four current AI trends. These trends highlight the value that machine learning is capable of bringing to today's workplace.
Machine learning as a way of uncovering hidden value in large volumes of data:
As organisations continue to accumulate high volumes of data, more and more data is left unprocessed. Processing large amounts of data manually can be very laborious and thus, many organisations end up processing only a small portion of their total data; very likely only that data requested by regulatory bodies. What is often not acknowledged, however, is the fact that amidst such 'unprocessed data' may be highly important and useful information.
This is where machine learning comes into play. It offers organisations the opportunity to gain thorough insight into large volumes of unorganised data, and to thus, discover hidden value within documents that would otherwise be overlooked. This is especially convenient at a time when regulatory compliance is ever increasing. Machine learning conveniently improves the pace and effectiveness in compliance and thus, gives one a peace of mind when it comes to regulatory bodies.
Machine learning as a way of automating and speeding up business analysis:
Many executives are mainly intrigued by machine learning due to its ability to increase automation, however, the potential of machine learning goes beyond just that. More specifically, machine learning is able to process extremely complex data with a high velocity, at a rate that is exceptionally faster and more accurate than any traditional methods. Such a possibility is especially beneficial for companies that process huge amounts of intricate data, who particularly seek to identify peculiar or unprincipled behavior in situations related to anti-money laundering, revenue leakage and more.
Machine learning as a way of bringing greater consistency to customer interaction:
Machine driven business-to-customer conversations are increasing in popularity, so much so that it is estimated that by 2020, 80% of such conversations will be either executed by a machine or bot1. Certain organisations, mainly financial services companies, are taking a step further and are attempting to use machine learning voice technologies to design virtual assistants that would have the relevant expertise to assist in financial planning and even in recording meeting minutes.
Aligning machine learning to the business as a whole:
Ultimately, one must realise that the above benefits which machine learning is able to bring along, can only be maximized if companies align their machine learning strategy to their wider business strategy. Thus, going forward, organisations must aim to build a machine learning architecture which does not only address singular areas of application, but rather broad business opportunities.
Cliff Justice, one of our leaders at KPMG advises that "enterprises need to move fast to reap the powerful benefits promised and to ensure that they can compete in the future." Furthermore, Traci Gusher a principal focusing on Data & Analytics adds that "companies need to have board-level conversations about how AI impacts decisions and how to best govern data."
Contact us to learn more how machine learning and automation can bring true value to your organization.
The time to start – is NOW!
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.