5 Ways AI & Machine Learning are Improving Workplace Processes

Today’s top organisations are using machine learning-based tools to automate decision processes. Many of these businesses are experimenting with advanced Artificial Intelligence (AI) for digital transformation, with investment by 2025 estimated to reach $100 billion.

Machine learning improves business processes in so many ways, from automating daily tasks and enhancing employee engagement to improving how the company operates and expanding their overall growth.

Here we bring you 8 examples of how machine learning is improving workplace processes:

1. Personalising customer service

Chatbots are becoming increasingly advanced thanks to the advancement of algorithms. While (human) customer service representatives can always be on hand to handle exceptions, chatbots have the ability to continuously learn from interactions and provide high-quality answers to a range of customer questions. In fact, a survey carried out by Aspect Software Research found that if the company can get the experience right, 44% of US consumers would prefer chatbots over humans for customer relations.

2. Hiring the right people

The hiring process is tricky to say the least, and in the corporate world, one job opening brings in around 250 CVs! Shortlisting the right candidates is extremely tricky, but with AI and machine learning, software quickly sifts through thousands of applications, shortlisting the candidates with the most fitting credentials.

3. Measuring brand exposure

Automated programmes can recognise people, products, logos and much more. For instance, when a brand sponsors a football game, image recognition can be used to track the position of the brand’s logos that appear in television footage of the match. This enables corporate sponsors to see the return on investment of their sponsorship investment, with detailed analyses such as the duration, quantity and placement of their logos.

4. Improving customer loyalty and retention

Companies can mine customer actions, transactions and social sentiment data to create customised offers and services for customers. There are so many benefits of this, including identifiying those customers who are at a high risk of leaving. This allows businesses to come up with a ‘next best action’ strategy and incorporate a personalised end-to-end customer experience (if they haven’t already done so).

5. Detecting fraud

On average, an organisation loses 5% of revenues each year to fraud. By creating models based on historical transactions, social network information and other external sources of data, machine learning algorithms can use pattern recognition to spot anomalies and exceptions. This helps to spot and prevent fraudulent transactions from taking place (in real time), and is applicable to a wide range of situations, including cybersecurity and tax evasion, meaning that countless business from across the globe can benefit from its uses.

The rise of machine learning and AI is inevitable, and advancements in the workplace are taking place at an unprecedented rate. To prepare your business for the future, you need to assess your existing information systems and data flows to determine which areas are ready for automation, and which need more investment. You definitely need to adopt AI in the workplace, but having a full picture of the pros and cons will be a huge benefit when it comes to implementing this technology.