Artificial Intelligence (AI) refers to technologies that show some characteristics of human intelligence. One of the main fields within AI technology is machine learning (ML). Machines do not learn in the same way as humans, they can made to adapt to complex and changing environments. ML is often leveraged to make predictions and conclusions based on data analysis.
In Australia, these technologies are getting more and more advanced, helping individuals, teams, and businesses do their jobs better and aid their decision-making process. One of the biggest benefits is the time saving that such technologies deliver as they help process data and information, make decisions, complete tasks, and communicate, to name a few.
Industries that benefit the most from AI and ML
AI and ML are broad terms encompassing a vast array of technologies and solutions. Almost every industry and sector benefits from them, but it is during the pandemic that AI and ML came to the notice of Australian businesses.
The pandemic has prevented people from going to their workplaces. Industries such as manufacturing, mining, oil and gas, and retail felt this disruption. AI and ML technologies are being implemented for remote monitoring, predictive analytics, remote maintenance, and real-time communications, among others.
Automated processes have helped individuals and teams avoid travelling to manufacturing sites amid the pandemic in the past two years. Businesses have leveraged AI and ML to enable remote maintenance and repairs and utilised robots to make plants more efficient and continue operating despite the curbs. In the retail sector, machine learning technologies have helped businesses respond to changing consumer behaviour and adjust forecasting and pricing decisions in real-time, besides enabling essential routine tasks.
Key challenges of adopting AI and ML technologies
Trust in AI is key to unlocking the value of this technology. Although businesses can struggle to understand new technologies, their capabilities and perceived risks, one of the biggest challenges to address are ethical issues. Overcoming this challenge by implementing practical, measurable metrics, and embedding these into everyday processes will create principles across organisational, operational, technical, and reputational pillars. This is key to establishing transparent structures and processes, identifying roles, responsibilities, expectations, and accountability which will help to build confidence and trust in AI and ML- led technologies.
Industries across Australia are at different maturity levels of adapting to new and emerging AI and ML technologies. Over time, accelerated by the pandemic, businesses are taking bold decisions and steps towards adapting these technologies. Although AI and ML technologies will be as integrated into our lives as the internet is today, currently they are still evolving, and adopting and integrating new technologies can be challenging.
Key priorities for the C-suite while implementing AI and ML technologies
Understanding the capabilities and benefits of new and emerging technologies as well as their integration and compatibility is key for Australian C-Suite executives. Australia is ahead of the curve in taking up AI and ML, only second to the US. But, ensuring that existing systems and programs are compatible with new solutions, and leading the way in upgrading legacy systems, is essential to unlocking the full potential.
Another priority for the C-suite is to ensure that training datasets are of the required size. Without this, businesses will be unable to unlock the full potential of AI and ML technologies, which rely on building data-led algorithms to make accurate predictions and recommendations.
Finally, as AI and ML technologies become embedded into businesses and our everyday lives, data security will become of a greater priority and focus. Technologies that use vast amounts of data create storage and security for businesses. Thus, implementing robust and secure management systems will be crucial to ensure the success of AI and ML- led digital transformations.
Making the transition to implementing AI and ML across critical functions and leveraging increased business value requires agility and a willingness to try new ways of working. Focusing on customising processes and technologies for business specific challenges and ways of working will enable these technologies to flourish and will create opportunities for skills and talent to flourish rather than competing for tasks undertaken by humans.