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How asset-intensive organisations can change the game with generative AI

Following the release of Tech Trends 2024: An Australian Perspective, we look at how generative AI is shaping asset-intensive sectors.

It may be surprising to learn that process-driven and asset-intensive organisations are uniquely positioned to benefit from generative AI. This is especially true in Australia, a country with a world-leading energy and resources (E&R) industry. 

We are home to the world’s largest iron ore mining industry, some of the largest, most modern and automated integrated liquified natural gas (LNG) operations, a wide range of important critical minerals mining operations and an ever-increasing breadth of renewable energy assets. The enormous quantities of proprietary data these operations generate and manage are exactly what is needed to customise, train and refine generative AI models.

Assets are distributed across a vast geographical footprint, often located in harsh environments with limited supporting infrastructure. The unique combination of global industrial scale and sophistication, coupled with the disadvantages of remoteness and environmental conditions spawns an abundance of use cases that are ideally suited to generative AI technology. Smart use of generative AI can help these organisations not only level up their operations, but transform and change the game.

Choosing the right tool for the job
 

Our latest edition of State of Generative AI in the Enterprise revealed many directors and C-suite executives are relying on off-the-shelf solutions to level up their organisations. Most of these are productivity-boosting applications, which generally achieve quick productivity and efficiency wins, or enterprise software platform integrations, which require more thoughtful and costly implementation but tend to drive greater value. 

Far fewer organisations are pursuing more specialised, differentiated and industry-specific solutions, suggesting most are still in their infancy with generative AI. For industries like mining, oil and gas, and power and utilities, this is where the opportunities lie to solve unique business challenges, leveraging their proprietary data, and unlocking the greatest possible value from generative AI. While they can be complex to implement, solutions that customise generative AI models in a way that is tailored to an organisation’s specific context and processes can tackle their biggest challenges in new ways – perhaps transforming the way they do maintenance or changing the way employees work on site.

As organisations embark on this journey, it is important to remember that technology never solves problems on its own. As the Tech Trends 2024 report highlights, organisations creating meaningful value from generative AI have focused on mindset, fluency and the underpinning operating model as much as they have the technology and data.

From levelling up, to changing the game


Many organisations are starting their generative AI journey through experimentation, building proofs of concept and implementing discrete solutions. While it’s a great place to begin, it won’t be enough to just level up your existing processes: the key is changing the game and solving business problems across your entire value chain. We’re seeing some organisations build on this momentum to seize opportunities to truly transform.

With increasing cost pressures across Australia and globally, organisations are exploring how generative AI can be used to optimise maintenance activities without sacrificing safety or reliability. With the wealth of untapped historical data available in asset-intensive organisations, generative AI is being used to summarise structured and unstructured data, finding opportunities for optimisation in new and exciting ways.

Level Up

Lacking confidence in the analytics performed on structured data coming out of its ERP, one organisation was unwilling to change the schedule of maintenance activities that technicians suspected were too frequent. It had no means to understand the nuance of the underlying data at scale, although this information was contained in the unstructured, long text comments diligently entered by workers in the field. Working with Deloitte, the company is successfully using generative AI tools to consolidate and analyse structured and unstructured data, building a more complete view of historical work with an ability to query the data to drive maintenance optimisation decisions with confidence. 

Game Changer

Generative AI could be used to transform the way we approach maintenance altogether. With generative AI solutions providing trust and clarity across structured and unstructured maintenance data, an optimised maintenance schedule can incorporate shutdown and turnaround scheduling, with opportunities to make data-driven decisions to rebalance scopes across these events and routine maintenance. As well as benefiting improved condition monitoring and predictive maintenance, a more complete data foundation can reduce the scope and duration of turnarounds, optimise maintenance and turnaround strategy overall and ultimately increase overall uptime. Incorporating the capability to generate new scenarios and variations in simulation to test and optimise maintenance at asset or organisation level, generative AI can truly transform the way maintenance is performed.

Generative AI can summarise vast amounts of data and generate targeted, personalised insights. This makes it a powerful tool for improving safety in the workplace – especially in hazardous environments – by ensuring workers have the information they need, when and where they need it.

Level Up 

Deloitte is working with an organisation to explore how generative AI can be used to create pre-start safety briefings before high-risk activities. By analysing process documentation, regulations, standards and historical data about the work to come, a tailored AI-powered solution can generate and share messages to highlight key risks and remind workers of previous incidents. This not only helps leaders focus on mitigating risks, but also gives the team with the right information at the right time – immediately before the work begins.

Game Changer

As generative AI gets better at recognising images and working with 3D models – especially in combination with other AI and machine learning techniques – it could be used to create high-resolution models of sites and equipment. Leveraging engineering drawings, equipment specifications, process and operational data and high-resolution or aerial photography, detailed models could be used in personalised training and even inspections. This can significantly reduce the time workers spend in hazardous environments without sacrificing quality of inspections and improve the effectiveness of safety training. 

How to start changing the game

Generative AI will continue to evolve rapidly, along with the world around us. That’s why it’s critically important to start now. Explore, experiment, implement – covering this ground today will help E&R organisations adapt to generative AI’s nuances and move quickly when new risks and opportunities emerge. Most importantly, take these steps with a strategic mindset. What drives true value, and what’s just tech for tech’s sake?

To change the game with generative AI, organisations need to keep a beginner’s mindset – the belief that there’s always more to learn. Work to improve your organisation’s generative AI fluency across its leadership team, areas of the business, and actively collaborate with partners and third parties. Generative AI is evolving rapidly so it’s worth gaining experience with a variety of generative AI tools and techniques – it’s nearly impossible to pick a clear winner today but slow movers will be left behind.

Download Tech Trends 2024: An Australian Perspective for a local view on the cutting-edge trends featured in our annual global report, including examples of these trends in action and actionable advice to help separate signal from noise. 

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