Shared from the 2/2/2022 Financial Review eEdition

Effective, dramatic results through AI

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As organisations struggle with limited people and resources during the pandemic, artificial intelligence (AI) and machine vision are reducing the load on key staff so they can focus on higher-value tasks.

The impact of staff shortages due to COVID-19 infections and close contact isolation has impacted organisations across the board. All are needing to do more with less, as the public health crisis increases compliance obligations around issues such as maintaining regular cleaning, checking vaccination statuses and enforcing capacity limits.

The latest advancements in artificial intelligence, machine vision and the Internet of Things (IoT) are allowing organisations to make the most of anonymised data collected from cameras and sensors. The aim is not to just passively monitor environments, but also to actively gather insight, make smart decisions and automate processes to assist the business.

Almost one-third of IT professionals surveyed globally say their business is now using artificial intelligence, with 43 per cent reporting that their company has accelerated their rollout of AI as a result of the COVID-19 pandemic, according to IBM’s Global AI Adoption Index 2021.

For more than one in three organisations, the pandemic has influenced their decision to use automation to bolster the productivity of employees, while others found new applications for this technology to make themselves more resilient.

From a pandemic perspective, analysing environments using artificial intelligence and machine vision is allowing organisations to optimise cleaning routines, depending on how often employees and customers touch different surfaces. The technology is also allowing them to redeploy frontline staff from other areas to handle COVID-related tasks, by automating activities.

Building on this, machine vision and facial recognition tools can drive low-touch, automated check-in terminals for environments such as hotels – reducing physical human contact while maintaining enhanced customer service levels.

Beyond compliance with public health mandates, the combination of artificial intelligence, machine learning and IoT is also unlocking a wide range of use cases which allow organisations to make the most of their data.

On the retail floor, machine vision allows retailers to track inventory, out-of-stock events, spillages, trolley walkouts and self-checkout shrinkage, helping manage supply chain challenges while reducing the need for bag checks. The technology also allows them to understand customer in-store journeys by anonymously tracking people as they wander the aisles – right down to the items they take off the shelves – in an effort to measure the effect of in-store advertising and better understand commercial intent.

Beyond retail, machine vision allows organisations like hospitals to detect slips and falls, as well as issues such as aggressive behaviour in emergency departments.

In back-end environments, artificial intelligence and machine vision can underpin enhanced handwriting recognition for processing parcels, as well as improved scanning to identify defects or spoiled food on production lines.

‘‘As organisations are stretched to the limit, the ability to rapidly and intelligently deploy these vision-based automation tools allows them to meet their growing compliance requirements while still maintaining, if not exceeding, service delivery expectations’’, says Joy Chua, the EVP of strategy and development with Australian AI customer experience provider meldCX.

‘‘This lets them deploy their resources much more effectively, freeing people from a range of tasks to allow them to focus on those things which still require the human touch.’’

Melbourne-based meldCX is an independent software vendor which specialises in using AI and intelligent-edge technologies to provide better customer experiences.

Taking its technology on to the world stage, with client deployments in regions including the United States and Europe, it has partnered with the likes of Google, Intel, Cisco Meraki and Microsoft.

To ensure privacy, the machine vision tools strip personally identifiable information, such as faces, from raw video data before it is uploaded to the cloud. The tools then add more depth to the anonymised persona by combining objects, such as clothing, and non-face behaviour, such as gait or aggression.

It uses synthetic data, not based on actual identifiable people, to train AI models.

Traditionally, one of the main barriers to the adoption of AI-driven efficiency for many organisations has been the scarcity of skilled AI engineers who can create the necessary tools and algorithms. A new generation of no-code and low-code solutions overcome this by offering simple interfaces which can be used to construct increasingly complex AI systems.

This provides the ability to rapidly create, deliver and orchestrate models at a fraction of the time and cost of comparable initiatives, says meldCX co-founder and chief executive Stephen Borg, adding that no-code AI systems let users create smart programs by plugging together different, pre-made modules and feeding them with their own domain-specific data.

‘‘All of this will play a key role in the ongoing democratisation of AI and data technology,’’ Borg says. ‘‘This is key for organisations, as AI is producing some of the most effective and dramatic results in business today.

‘‘AI is not just a technology, it’s a multiplication of business capability.’’

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