Nearmap, Senior Director, AI Systems, Mike Bewley; Deloitte, Strategy & AI, Aron Ellis. Rada Stanic, Chief Technologist, AWS ANZ. He’s Simon Crear, editor-in-chief of SmartCompany.
The power of machine learning (ML) can be harnessed by any business. No longer the domain of organizations staffed by data scientists and his ML experts, the technology is fast becoming mainstream. The question for businesses today is what ML can do for us.
As explained in Chapter 4 of the AWS eBook Innovate With AI/ML To Transform Your Business, ML isn’t just about building technology, it’s about making existing examples work. Simon Johnston, his AWS Artificial Intelligence and Machine Learning Practice Lead at ANZ, said: “They said, ‘We don’t want to build this technology ourselves.
With this philosophy in mind, let’s take a look at the three areas of ML and the use cases that every business can leverage without ML expertise.
data and documents
Data-heavy documents pose a serious problem for many companies. For example, consider applying for a mortgage. These are often very large documents that require significant data entry from applicants, and can be prone to form mis-fills, missing data, and other mistakes. The application must then be manually processed to extract the data, which can be difficult (especially when multiple types of forms and data are involved), inaccurate and time consuming. For enterprises, ML offers a simpler way.
“It’s all about reducing the time it takes to manage documents and processes,” says Johnston. “From a back-office perspective, it’s about how to automatically speed up how these processes work.” 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 quickly and accurately extract and interpret data from dense forms and provide save time and reduce mistakes.
The power of ML in data extraction extends beyond banking application documents. Consider the following use case.
- health care: Accurately interpret free-form text, checkboxes, and medical form tables
- legal: Review dynamic documents, target specific phrases, and streamline handling of non-standard document formats
- Manufacturing: Automated data extraction from purchase orders, contracts and bills of materials
Learn more about harnessing the power of AI and ML in our AWS eBook Innovate with AI/ML to transform your business
customer experience
As with data extraction, the most impactful ML use cases are often subtle additions to your business rather than large-scale changes. In the world of customer experience (sometimes called CX), ML can deliver positive improvements without the need for organizational restructuring or technical overhaul. Here are two CX-focused ML use cases to consider:
- Call center automation: Call centers are often the first point of contact for businesses and their customers, so it’s important that call center processes are as effective and customer-centric as possible. ML-powered Contact Center Intelligence (CCI) is one example, with always-on virtual agents and chatbots, automated post-call analytics to improve agent response, and customer best service. Includes real-time agent assistance to help you
- Personalized recommendations: CX is often determined by how well you understand your customers. ML use cases are not as customer-centric as personalization. Solutions like Amazon Personalize integrate with ecommerce platforms to target user segments and provide customized product or service recommendations, ultimately increasing brand loyalty and revenue.
safety
ML is more than just document analysis and customer experience. As seen in recent breaches, keeping customer and business data safe is 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 concern for enterprises. It’s identity verification. Tools like Rekognition help businesses avoid human-driven approvals, which are time-consuming, costly and prone to human error. With automated ML ID recognition tools, businesses such as banks, healthcare providers, and e-commerce platforms can quickly verify customers and prevent unauthorized access. With ML, you can instantly perform complex facial and identity recognition with systems that are constantly improving.
Similarly, fraud detection is essential to keeping your online business customer-friendly and organizationally beneficial. Amazon Fraud Detector is an example of an ML-powered tool that enables businesses to prevent real-time fraud, allowing businesses to block fraudulent account creation, payment fraud, and fake reviews. Out-of-the-box anti-fraud solutions are essential, especially for e-commerce businesses.
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