The Value of AI and Machine Learning in Digital Transformation

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Artificial Intelligence (AI) and Machine Learning (ML) are fast becoming important pillars of many organisations’ digital transformations. But what does the use of AI and ML look like in practice, and what are the benefits for adopters?

The terms Artificial Intelligence and Machine Learning were once the preserve of scientists, academics, and science fiction writers. No longer. With the permeation of “smart technology” into almost every corner of our lives, from Netflix’s smart content and Amazon’s smart home assistants to algorithmic trading and cyber-security, the terms have entered the lexicon of the business world and the general public.

Machine learning and AI are being harnessed by business like never before, as, increasingly, AI and ML become an integral part of business’s digital transformation strategy. A recent study on “Human Amplification in the Enterprise” from IT consultancy Infosys reveals that 86 percent of organisations say AI-supported activities play some role in their digital transformation. What’s more, 98% of those surveyed who used AI-supported activities indicate that it generated additional organisational revenue.

It’s clear that AI and ML are becoming more commonplace and have the potential to deliver important benefits to adopters, but what, exactly, do they look like in practice?

AI and Machine Learning in Practice 

Of course, the type of AI or ML deployed by a business is largely dependent on the end of goal of the digital transformation. But for many, the key areas of AI and ML are the automation of services and predictive analytics, examples include:

Chatbots

One of the most widely used forms of AI in business is the chatbot—a human replicating computer program that can communicate with customers on behalf of your brand.

Aside from the obvious cost savings of using chatbots to communicate with customers instead of employing extra staff, the technology also has the potential to revolutionise the customer experience (CX). Chatbots embody a step away from website-based actions to a more naturalistic, human customer experience, based on individual customer preferences.

Chatbots can be used for relatively simple self-service enhancements such as resetting a password or providing an account balance update, through to more complex functions such as fielding enquiries about products and services.The benefits for CX are obvious: wouldn’t you prefer a customer experience that provides a useful, efficient and instantaneous solution personalised to you, over the limited response provided by a traditional website?

Download the Periodic Table of CX for a visual overview of the many elements  that make for successful customer experience

Predicting and Preventing Cyber-Attacks

With malware such Wannacry and Petya running amok in hundreds of organisations and dominating the news cycle in recent months, businesses are beginning to look at new methods of cyber threat protection.

Step forward, machine learning. Machine learning algorithms have become extremely important in assessing data access patterns and flagging anomalies that could signify a potential breach. Machine learning offers businesses a key advantage over so-called “analyst driven” solutions: a human-driven model relies on rules created by living experts and therefore misses any attacks that don’t match the rules, whereas ML checks for anomalies, flagging anything that looks unusual.

Machine Learning isn’t perfect (although MIT may have cracked it), it still requires a human analyst to check its findings and can be over-eager in flagging anomalies. However, when compared with the alternative (older analyst driven models) it’s markedly more efficient, requires less input from staff, and is much more likely to flag new and evolving threats.

Improved ability to respond to and prevent cyber-attacks delivers important benefits to customers. Firstly, less downtime means improved customer experience for customers accessing business infrastructures such as websites or apps. Secondly, improved cyber-security goes hand-in-hand with better data security, garnering customer trust and avoiding an Equifax style scandal.

Sentiment Analysis

It’s more important than ever to know how your customers feel about your brand while interacting with your business. To this end, some organisations have begun using sentiment analysis (SA), sometimes called “opinion mining” or “emotional AI”.

In essence, sentiment analysis is the process of gauging the emotional tone behind a series of words, used to gain an understanding of the emotions, attitudes and opinions expressed within a customer’s online mentions.  Real-world examples include the Obama administration using SA to measure public responses to campaign messages ahead of 2012 presidential election, and Expedia Canada taking advantage of SA to quickly understand negative consumer attitudes to the music used in one of their adverts.

The potential benefits to organizations are huge, not only does it turn social media into a potential data goldmine, it also enables businesses to react in real-time to customer opinion, and hone their brand messaging or products based on real, unsolicited feedback.

Like most forms of AI and ML, SA isn’t perfect; teaching a machine to analyse the grammatical nuances, cultural variations and slang present in a lot of online communication is no mean feat. For example, machines often struggle with tone (particularly sarcasm) and context, but as technology improves, so too will the efficacy of SA.

Recommendation Engines

Perhaps the greatest contribution of ML to businesses’ digital transformation is the now widespread use of recommendation engines. Once the preserve of online retailers and content providers, recommendation engines are now to be found across most industries.

Recommendation engines provide customers with relevant product or service suggestions, either in real-time while customers browse a website or through email later-on. Machine learning allows businesses to learn directly from their customers, collecting data about their tastes and preferences. Over time and with enough data, machine learning algorithms can be used to perform useful analysis and deliver meaningful recommendations to customers.

This approach, often termed “hyper-personalisation”, provides customers with an experience that’s timely, helpful, and most importantly, relevant to them, improving CX and giving adopters an edge in their communication with ever more discerning customers.

 

The advantages of including AI and ML in digital transformation are centred on its potential to improve CX, but augmenting your business's CX goes way beyond harnessing smart machines. If you want to discover more about the elements vital to successful customer experiences, download our "Periodic Table of Customer Experience".

Free Printable Download The Periodic Table of Customer Experience Discover the  key elements for creating successful customer experiences in this printable  wall art. Download and Print now

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