Over the past decade, we’ve witnessed dramatic developments in the field of artificial intelligence. Today, our utilisation of AI has spanned far past formulaic chatbots, instead veering more towards generative AI tools that allow for advanced data analytics capabilities, and other sophisticated automation tools to support dynamic workflows across a range of different industrial contexts.
The advancement of generative AI in particular has led to the coining of the phrase ‘generative BI’ or generative business intelligence, as more industries integrate AI tools for the purpose of understanding more about their company data through consulting tailored large language models that have been trained on their organisation’s unique data sets.
But although generative AI is one of the most exciting evolutions of artificial intelligence technology, there are other tech offerings that have been revolutionising Australian industries – from the education sector to healthcare, mining, agriculture, retail trade, and manufacturing and fabrication.
Here are the top AI technologies you can expect most businesses to invest in over the course of the digital age.
Whilst different types of artificial intelligence technology have already been utilised in industries across the globe, the resurgence of AI in the zeitgeist can, however, be credited to the development of OpenAI’s generative pre-trained transformer (GPT) models beginning with GPT-2 and GPT-3 in 2019 and 2020 respectively.
The main advantage of GPT-3 over other earlier versions of large language models (or LLMs) is that GPT-3 was able to craft contextually relevant sentences as well as cohesive paragraphs. The quality of the machine’s output was unmatched by any of its predecessors, speaking to a potential for the tool to be used in specialised contexts, including academics, medical diagnostics, industrial data analytics purposes.
Industrial data analytics is arguably the most compelling of all these use cases, namely because this technology allows for business owners to interpret dynamic data sets without having to pore through all the figures themselves. Instead, all they have to do is just ask a question to their LLM!
This technology is already being trialled by industries across Australia, with generative AI platforms allowing organisations to identify patterns in a wide range of data sets. And the more historical data companies possess, the faster their LLM will be able to detect more intricate patterns. This is one of the many advantages of becoming an early adopter of generative BI industry tools.
Another vital capability of modern AI tools is the ability to make accurate predictions based on existing data sets. How is this dissimilar from generative AI data analytics capabilities? Well, because these predictions don’t just have to be limited to data sets – they can also relate to the performance of industrial machinery like factory equipment or even fleet vehicles.
In other words, AI technologies can be used to predict when a machine may be likely to break down. These predictions can be made by monitoring machine performance in order to detect anomalies in energy output, temperature levels, and other environmental or internal metrics. This is essentially what we call ‘predictive maintenance’. Predictive maintenance capabilities are in turn, made possible by the development of AI-driven IoT (internet of things) devices.
AIoT technology is poised to play a similarly groundbreaking role to IoT technology in Australian industries, except for the fact that now, these same technological tools can actually perform their own troubleshooting and run their own diagnostic tests. In this regard, AIoT technologies are set to streamline operations across a range of industry contexts, reducing machine downtime and boosting productivity without having to utilise human resources in the process.
Machine learning (or ML) is an application of AI technology that possesses unique potential when it comes to industrial applications. Basically, machine learning is the process of using mathematical models to develop algorithms organically, or without direct instruction from a dedicated programmer. By utilising machine learning principles, industrial SaaS providers can effectively sell their customers the opportunity to develop their own bespoke AI algorithms.
Why is this a game changer? Because these algorithms can be used to then power tailored software. In the future, SaaS offerings may no longer be developed in computer labs through A/B testing and feedback loops. Instead, they’ll be developed ‘on the job’, and can be trialled faster and thus rolled out to other organisations a lot earlier.
But the potential of ML doesn’t just stop there. Machine learning has also demonstrated immense potential in the realm of cybersecurity, namely by being able to facilitate tailored approaches to combating malware or ransomware. As we’re seeing more malware powered by machine learning algorithms, it only makes sense that the best defence against these more aggressive cyber attacks is ML technology itself.
Let’s conclude this little list with an AI technology you’re probably already all too familiar with: business automation tools. Today, virtually every digitising workplace uses project management software with some level of AI automation. For instance, platforms like Asana, Monday, and Trello have begun trialling automated workflows to simplify the process of task management.
But AI automation tools can look far more dynamic than just static workflow threads or simple coding systems. In fact, with the AIoT, business automation capabilities have grown tenfold in the past decade alone. Both now and in the near future, we’ll be able to establish complex workflows that span not only across multiple machines or pieces of equipment, but perhaps even multiple plants or facilities. Imagine being able to oversee a whole international supply chain from one central AI platform. This is only possible because that central AI platform is connected to all the AIoT tech that’s used to facilitate those supply chain operations.
And much like the other tech offerings we’ve outlined today, AI automation tools can be used in a great variety of industrial contexts as well. The sky really is the limit, as Australian industry analysts and business leaders work to determine which AI offerings are going to be the most impactful for each sector both today and over this coming decade.
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From generative BI to ML algorithms and connected smart tech, it’s likely that all industry infrastructure will be enhanced by the digital transformation investment that is AI business tech. These technologies are positioned to provide a robust return on that upfront investment after all, providing business owners with ample resources to reduce operational disruptions and boost production outputs.
And once again, early adoption is undeniably key, as generative AI tools and ML algorithms will grow more advanced more quickly if they have historical data sets to go off of. So business owners – get connected now if you can. The earlier you adopt AI, the faster you’ll be able to generate complex analyses of your unique operational data.
Over the past decade, we’ve witnessed dramatic developments in the field of artificial intelligence. Today, our utilisation of AI has spanned far past formulaic chatbots, instead veering more towards generative AI tools that allow for advanced data analytics capabilities, and other sophisticated automation tools to support dynamic workflows across a range of different industrial contexts.
The advancement of generative AI in particular has led to the coining of the phrase ‘generative BI’ or generative business intelligence, as more industries integrate AI tools for the purpose of understanding more about their company data through consulting tailored large language models that have been trained on their organisation’s unique data sets.
But although generative AI is one of the most exciting evolutions of artificial intelligence technology, there are other tech offerings that have been revolutionising Australian industries – from the education sector to healthcare, mining, agriculture, retail trade, and manufacturing and fabrication.
Here are the top AI technologies you can expect most businesses to invest in over the course of the digital age.
Whilst different types of artificial intelligence technology have already been utilised in industries across the globe, the resurgence of AI in the zeitgeist can, however, be credited to the development of OpenAI’s generative pre-trained transformer (GPT) models beginning with GPT-2 and GPT-3 in 2019 and 2020 respectively.
The main advantage of GPT-3 over other earlier versions of large language models (or LLMs) is that GPT-3 was able to craft contextually relevant sentences as well as cohesive paragraphs. The quality of the machine’s output was unmatched by any of its predecessors, speaking to a potential for the tool to be used in specialised contexts, including academics, medical diagnostics, industrial data analytics purposes.
Industrial data analytics is arguably the most compelling of all these use cases, namely because this technology allows for business owners to interpret dynamic data sets without having to pore through all the figures themselves. Instead, all they have to do is just ask a question to their LLM!
This technology is already being trialled by industries across Australia, with generative AI platforms allowing organisations to identify patterns in a wide range of data sets. And the more historical data companies possess, the faster their LLM will be able to detect more intricate patterns. This is one of the many advantages of becoming an early adopter of generative BI industry tools.
Another vital capability of modern AI tools is the ability to make accurate predictions based on existing data sets. How is this dissimilar from generative AI data analytics capabilities? Well, because these predictions don’t just have to be limited to data sets – they can also relate to the performance of industrial machinery like factory equipment or even fleet vehicles.
In other words, AI technologies can be used to predict when a machine may be likely to break down. These predictions can be made by monitoring machine performance in order to detect anomalies in energy output, temperature levels, and other environmental or internal metrics. This is essentially what we call ‘predictive maintenance’. Predictive maintenance capabilities are in turn, made possible by the development of AI-driven IoT (internet of things) devices.
AIoT technology is poised to play a similarly groundbreaking role to IoT technology in Australian industries, except for the fact that now, these same technological tools can actually perform their own troubleshooting and run their own diagnostic tests. In this regard, AIoT technologies are set to streamline operations across a range of industry contexts, reducing machine downtime and boosting productivity without having to utilise human resources in the process.
Machine learning (or ML) is an application of AI technology that possesses unique potential when it comes to industrial applications. Basically, machine learning is the process of using mathematical models to develop algorithms organically, or without direct instruction from a dedicated programmer. By utilising machine learning principles, industrial SaaS providers can effectively sell their customers the opportunity to develop their own bespoke AI algorithms.
Why is this a game changer? Because these algorithms can be used to then power tailored software. In the future, SaaS offerings may no longer be developed in computer labs through A/B testing and feedback loops. Instead, they’ll be developed ‘on the job’, and can be trialled faster and thus rolled out to other organisations a lot earlier.
But the potential of ML doesn’t just stop there. Machine learning has also demonstrated immense potential in the realm of cybersecurity, namely by being able to facilitate tailored approaches to combating malware or ransomware. As we’re seeing more malware powered by machine learning algorithms, it only makes sense that the best defence against these more aggressive cyber attacks is ML technology itself.
Let’s conclude this little list with an AI technology you’re probably already all too familiar with: business automation tools. Today, virtually every digitising workplace uses project management software with some level of AI automation. For instance, platforms like Asana, Monday, and Trello have begun trialling automated workflows to simplify the process of task management.
But AI automation tools can look far more dynamic than just static workflow threads or simple coding systems. In fact, with the AIoT, business automation capabilities have grown tenfold in the past decade alone. Both now and in the near future, we’ll be able to establish complex workflows that span not only across multiple machines or pieces of equipment, but perhaps even multiple plants or facilities. Imagine being able to oversee a whole international supply chain from one central AI platform. This is only possible because that central AI platform is connected to all the AIoT tech that’s used to facilitate those supply chain operations.
And much like the other tech offerings we’ve outlined today, AI automation tools can be used in a great variety of industrial contexts as well. The sky really is the limit, as Australian industry analysts and business leaders work to determine which AI offerings are going to be the most impactful for each sector both today and over this coming decade.
~
From generative BI to ML algorithms and connected smart tech, it’s likely that all industry infrastructure will be enhanced by the digital transformation investment that is AI business tech. These technologies are positioned to provide a robust return on that upfront investment after all, providing business owners with ample resources to reduce operational disruptions and boost production outputs.
And once again, early adoption is undeniably key, as generative AI tools and ML algorithms will grow more advanced more quickly if they have historical data sets to go off of. So business owners – get connected now if you can. The earlier you adopt AI, the faster you’ll be able to generate complex analyses of your unique operational data.
