The real-world ways that businesses can harness ML

The power of machine learning (ML) is within reach of every business. No longer the domain of organisations with data scientists and ML experts on staff, the technology is rapidly moving into the mainstream. For businesses now, the question is: what can ML do for us? 

As discussed in chapter four of AWS eBook Innovate With AI/ML To Transform Your Business, ML isn’t just about building the technology, it’s about putting existing examples to work. “What we’re seeing is a lot of these solutions coming to market and customers are asking for them,” says Simon Johnston, AWS artificial intelligence and machine learning practice lead for ANZ. “They’re like ‘we don’t want to build this technology ourselves — we’re happy for Amazon to have it and we’ll do a commercial contract to use this technology.’” 

With that philosophy in mind, let’s take a look at three areas of ML and the use cases within them that every business can harness, even without ML expertise.

Data and documents

Data-heavy documents pose a real problem for many businesses. Take a home loan application, for example. These are often very large documents that require significant data input from applicants with the potential for incorrectly-filled forms, missing data and other mistakes. Then, the application needs to be manually processed and data extracted, which is difficult (particularly where multiple types of forms or data are concerned), potentially inaccurate and time-consuming. For businesses, ML offers a simpler way forward.

“It’s all about reducing that time in terms of managing documents and processes,” says Johnston. “It’s about how they can automatically speed up how these processes work from a back-of-office perspective.” This is where machine learning solutions like intelligent data processing (IDP) come into play. IDPs like Textract use machine learning processes such as optical character recognition (OCR) and native language processing (NLP) to extract and interpret data from dense forms quickly and accurately, saving employee time and limiting mistakes.

The power of ML in data extraction can be seen in more than just application documents in banking. Consider these use cases:

• Healthcare: Accurately interpreting the freeform text, checkboxes and tables of healthcare forms
• Legal: Reviewing dynamic documents, targeting specific phrases and streamlining the processing of non-standard document formats
• Manufacturing: Automating data extraction from order forms, contracts and bill of materials

Customer experience

Just like data extraction, the most impactful ML use cases are often subtle additions to a business rather than wholesale change. In the world of customer experience (sometimes called CX), ML can provide a positive improvement without the need for organisational restructure or technological overhaul. Here are two CX-focused ML use cases to consider:

• Call centre automation: The call centre is often the first point of interaction for a business and its customers so it’s vital that call centre processes are as effective and customer-focused as possible. ML-powered Contact Centre Intelligence (CCI) is one example and it includes virtual agents and chatbots that are always available, automated post-call analytics to improve agent responses and real-time agent assistance to ensure customers get the best service.
• Personalised recommendations: CX often hinges on how well understood a customer feels and ML use cases don’t get more customer-focused than personalisation. Solutions like Amazon Personalise integrate with ecommerce platforms, targeting user segments and providing tailored product or service recommendations that ultimately increase brand loyalty and revenue.

Security

ML is more than just document analysis and customer experience. As we’ve seen with recent breaches, keeping customer and business data safe should be everyone’s top priority. In fact, in chapter 5 of Innovate With AI/ML To Transform Your Business, we learned that good security is one of the foundations of effective AI. 

One security-focused use case is a common point of concern for businesses: identity verification. Tools like Rekognition let businesses bypass human-led authorisation, which is time-consuming, costly and prone to human error. Using automated ML identity recognition tools lets businesses like banks, healthcare providers and ecommerce platforms quickly verify their customers and prevent unauthorised access. With ML, complex facial and identity recognition can be done instantly with a system that is always improving.

Similarly, fraud detection is integral to keeping online businesses usable for customers and profitable for organisations. Amazon Fraud Detector is one example of an ML-powered tool allowing businesses real-time fraud prevention, letting companies block fraudulent account creation, payment fraud and fake reviews. Particularly for ecommerce businesses, having an out-of-the-box solution to fraud is vital. 

Learn about the 6 key trends driving Machine Learning innovation across Australian and New Zealand industries inclusive of improvements to Model Sophistication, Data Growth, ML Industrialisation, ML Powered Use Cases, Responsible AI and ML democratisation.

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