Enhancing industry efficiency with AI and human intelligence

When we talk about AI in the asset-intensive industries, it's easy to get excited on how this tech can change the game.

Imagine having production processes run smoother, assets utilised to their fullest, and maintenance scheduled at the perfect times - all thanks to AI.

The potential for AI to revolutionise sectors like oil and gas, chemicals, and pharmaceuticals is clearly there. By analysing sensor data with machine learning models, AI can predict when equipment might fail. This means companies can do maintenance only when it's needed, cutting down on unnecessary costs and avoiding expensive disruptions. Less downtime, more output. Simple as that.

But can AI do this in a sustainable way: helping firms cut down on their energy usage, even while they are optimising their operational efficiency? Absolutely! In industries like oil and gas or manufacturing, AI can optimise energy use, leading to significant reductions in energy consumption. This not only slashes operational costs, but also helps companies meet their sustainability goals by reducing their carbon footprints. And there are lot of areas where AI is adept at operating autonomously: from processing transactions to predicting outcomes and iterating procedures. So, it's no wonder that the technology is getting so much attention.

But here’s the catch – AI often needs experts to really shine. Enter Industry 5.0. The lesson here is that tech alone isn't always enough – you frequently need the right people to guide it. While AI can crunch numbers and spit out insights, human expertise can be crucial for interpreting these insights and making informed decisions. Equally, even though there are many fully-automated and closed-loop processes that turbo-charge efficiencies, the most effective use of AI often involves a blend of machine-driven capabilities and human expertise.

Inside organisations, experts in operations, sustainability, and IT must collaborate to implement AI solutions effectively. They need to ensure that AI applications align with the company’s strategic goals and operational realities – and they need to explain how the technology is going to work and train people accordingly. This blend of technology and human insight is what makes the magic happen.

Need some real-world examples?

Take the manufacturing supply chain. AI can optimise supply chain operations by forecasting demand, managing inventory levels, and even suggesting the best routes for logistics. Supply chain and logistics managers can take all this information and use it to streamline and optimise their processes. This optimisation leads to reduced waste, lower costs, and faster delivery times, benefiting both the company and its customers.

Imagine an oil company using AI to turbocharge its drilling operations. By weaving AI into its existing systems, this company could forecast equipment failures weeks before they happen. This foresight is not just capable of staving off potential accidents; it can also save millions in repair costs and downtime.

Now, that’s impressive. However, AI has not achieved all this alone. Human experts will have played a crucial role interpreting the AI's predictions and making the strategic decisions that kept everything running smoothly.

In the manufacturing world, the story gets even better. A global electronics manufacturer harnessing AI to keep an eagle eye on product quality in real-time, has an opportunity to slash defects while boosting overall production efficiency. Again, it's the savvy of human quality control managers that makes the difference. They use AI's real-time data to tweak processes, fix issues on the fly, and ensure the highest standards. Without their expertise, AI's monitoring would be just another stream of data.

AI can also enhance process optimisation and planning in chemical engineering. Advanced process control (APC) solutions, for example, enable chemical plants to optimise their production processes, resulting in increased yield, reduced energy consumption, and improved product quality. AI algorithms continuously adjust process variables to maintain optimal operating conditions, even in the face of changing feedstock quality or production demands. Yet, while AI can handle much of this autonomously, human oversight is still important to monitor system performance, address unforeseen issues, and make strategic decisions based on AI's outputs.

AI’s prowess extends to energy management too. Picture a company that fine-tunes its energy use based on AI's predictive models. The results? Dramatic cuts in energy costs and a lighter environmental footprint.

This not only supports corporate sustainability goals, but also polishes the company's image among eco-conscious consumers and investors. But who’s behind the curtain? It’s the energy managers who adjust the settings, implement changes, and make sure the AI’s recommendations fit the real-world context of their operations.

In all these scenarios, the underlying theme is clear: while AI can operate independently across certain processes, the partnership between humans and AI often enhances outcomes. Experts in their fields - whether in drilling, manufacturing, or energy management - are the ones who decode AI’s findings and apply them effectively. They ensure that technology aligns with strategic goals and operational realities.

So, while AI is a game-changer, it’s invariably the partnership between humans and machines that drives true innovation. The next time you hear about AI’s amazing feats, remember the experts working behind the scenes, making sure that every prediction and recommendation translates into real-world success.

Taking it outside the company

Should businesses in this space go beyond leveraging the skills they have in-house and bring in external experts to make these kinds of judgement calls? Definitely. Companies experienced in industrial AI implementations can offer valuable insights into best practices, identify the most promising applications, and help develop strategies for scaling AI initiatives.

A great example is the partnership between industrial giants and AI tech firms, which often leads to groundbreaking innovations. For instance, oil and gas companies have collaborated with AI firms to develop advanced models that can predict and mitigate risks associated with drilling operations. These collaborations not only enhance safety but also improve efficiency and profitability.

 Fulfilling their potential

In a nutshell, AI has the potential to revolutionise asset-intensive industries. In line with this, there are a growing number of applications across the asset-intensive industries where it operates independently. However, as we stand today, its true value still often lies in the collaboration between technology and human expertise. At an enterprise-wide level, operations, sustainability, IT experts, and external partners often need to work together to harness AI effectively. It’s not just about leveraging AI's computational power - it's about using it (and applying it) smartly to solve real-world challenges and drive meaningful business outcomes. By combining AI algorithms with specific domain knowledge, businesses can unlock actionable insights that drive efficiency, sustainability, and innovation – and it is frequently at this interface between human and machine that the real magic happens.

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