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Why your business should already be experimenting with industrial AI

Explore practical applications of AI for industrial businesses as we talk to Amy Challen, Shell’s Global Head of AI, about the technology’s exciting potential.

Key Takeaways

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    Eight out of ten industrial businesses believe that AI can help them deliver better results, but only 20% have adopted it so far.

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    The mismatch between high expectations and implementation reality is a challenge that businesses need to overcome.

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    There are many practical industrial applications for AI – including helping farmers to improve yields and driving quality in manufacturing.

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    Leaders need a change in mindset to take their businesses out of the pre-digital age and realise the potential of AI.

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Amy Challen Bio

Amy Challen leads the development of AI-enabled products and services across Shell’s Global Commercial initiatives and is an expert in embedding AI models within business operations. She has more than a decade’s experience in data science modelling and has previously held roles at McKinsey and London School of Economics.

With a PhD in Applied Econometrics, Amy is an industry leader in AI strategy (including analytics transformation design) and is experienced in the practice of scaling up analytics interventions to realise business value.

In her role at Shell, Amy is responsible for the development and deployment of AI solutions, heading up a global team that spans Bangalore to Houston. The team works across areas such as computational science, digitisation, and data science and analytics, with a particular focus on cutting-edge, innovative capabilities including machine vision, natural language processing and reinforcement learning.

Find out more about AI in Shell

Forget science fiction – AI is already here

AI is no longer the future. It is no longer set to transform business – it has already begun the process. And many organisations are reaping the benefits it can bring to efficiency and productivity.

According to McKinsey, 50% of companies have adopted AI in at least one business function.The impact of this has been largely positive – six out of ten have seen a revenue increase, while four out of ten have used it to decrease their costs.1

A recent Deloitte survey supports this view, with 83% of manufacturers believing that AI has made – or will make – a practical and visible impact.2 More than a quarter say that the technology has already brought value to their companies.2

However, industrial businesses have thus far been slow to adopt AI. While 83% of industrial businesses believe it can help them deliver better results, only 20% have adopted it.3 So what’s holding leaders back? And how can they prepare their organisations for a productive digital future?

Laying the foundations for industrial AI

For many companies, it can be difficult to make the business case for adoption – despite seven out of ten businesses seeing a revenue increase from AI use in applications like manufacturing.2 Additionally, the mismatch between the perceptions and reality of AI implementation is a barrier to progress and increased investment in the technology.

“Despite the excitement around AI, there are plenty of inflated expectations of what it can do immediately,” says Amy Challen, Global Head of Artificial Intelligence at Shell. “It’s difficult when you’re going into a business and trying to explain that, actually, it’ll take six months to get everything up to speed. Or that frontline processes will need to change to adapt if they want to see real value from the solution.”

An element of this comes from the fact that, while AI is becoming better understood in industrial settings, there’s still a lot of hype. And this can add to the implementation challenges that undermine AI’s potential.

The single biggest challenge with implementing AI is that it can be a huge change exercise, and no one wants to do those more than once every ten years, if that – but this process can ultimately bring the biggest value to a business.

Amy Challen, Global Head of Artificial Intelligence, Shell

“One huge misconception is that AI is some kind of silver bullet,” explains Challen. “You get this with every technological revolution. People think it’s magic and that you can just plug it in, everything will work perfectly and you’ll save loads of money. But it’s not like that. Like any big organisational change, it takes time and effort – as well as change management – to get to the point where it delivers real value to a business. People have to understand that just having the solution itself isn’t enough; it’s part of a process that drives wider change.”

Exploring the practical uses of industrial AI

Plants in greenhouse

Another challenge facing industrial AI is in exploring the practical applications of the technology. Viewing AI as a one-size-fits-all solution also creates a narrow view of what businesses can achieve with it – how they can transform operations and deliver value.

There are many innovative ways AI can drive productivity in even the most remote and challenging settings. For example, in agriculture where – to meet growing demand, farms need the ability to double their yields by 2050.One way to achieve this is by using smarter, data-driven methods for determining crop choice and monitoring.

This is exactly what Microsoft does with its FarmBeats programme, which provides a real-time view of every part of a farm – including soil and moisture data.5 Machine learning algorithms then take this previously unharvested data and use it to predict future outcomes. This gives farmers in-depth data insights telling them exactly what crops should be planted where to help them increase their yields and profitability.

One incredibly important use of AI during the pandemic was to monitor the usage rate of machinery in factories to provide an early indicator of economic slowdown or pickup, which gave us a lot of information about how industries were faring during that time.

Amy Challen, Global Head of Artificial Intelligence, Shell

Another example is the use of AI across different manufacturing processes. Computer vision is transforming quality testing with cameras able to spot flaws that would be invisible to the human eye. Predictive maintenance combines with machine learning to provide real-time visibility into machine conditions to optimise operations. And generative design in the cloud is allowing companies to complete 50,000 days of engineering in one day.4

From Challen’s perspective, AI will act as a catalyst for the decarbonisation of industrial businesses. “AI will be hugely important for supply chain optimisation, electrification and grid balancing,” she says. “All of these obviously feed into the ongoing energy transition. And AI’s role in all three is identifying what is possible and then predicting, managing and optimising demands. So, while the solution to the energy transition may be based in chemical and physical sciences, AI can help us get to this solution faster.”

How can businesses understand AI’s true potential and maximise its benefits?

Even with the examples above, it’s easy to see that AI is a complex technology – and certainly not a quick and easy, one-size-fits-all solution.

For Challen, it’s less about trying to stick an AI solution on top of existing systems and more about redesigning processes to take full advantage of the technology. “The comparison I always use is when steam power arrived in the early 19th Century,” she explains. “Because water mills had needed to be placed ideally along streams and rivers to draw power, the system was built entirely around that. So, it took easily 30 years for the majority of mills to adopt steam power.”

It’s a concept that rings true for AI implementation as well. “Like the mills, businesses still have everything set up for a pre-digital era,” she says. “Systems are laid out in a specific way for a world before AI and it can be challenging to move away from that.”

With AI, there is often a big need for process change and redesign. Because, you can have as many insights as you like but unless you’re either going to change what you do in response or completely change the ongoing process to accommodate the data, then it’s not going to go anywhere.

Amy Challen, Global Head of Artificial Intelligence, Shell

Earth in outer space with network connection

The resolution to this challenge requires a change in approach from industrial leaders. “In the past we’ve helped GMs adapt their mindset when it comes to AI,” says Challen. “We’ve enabled them to avoid thinking traps and become a ‘fast follower’ rather than always having to do their own thing.”

This ‘fast follower’ mindset could well be the key to driving adoption and helping businesses to realise the opportunity that AI offers them as part of their digital transformation journeys.

“The faster we experiment and start trying to understand what works and what doesn’t, the quicker we’re going to get there,” Amy concludes. “We’re in a situation where it would be very, very easy to be left behind, so we have to take an attitude of ‘learning by doing’. We have to be thinking about what the customer of 10 years’ time needs, not just the customer of now.”

How AI delivers on-demand lubricant expertise with Shell LubeChat

Earth in outer space with network connection

There’s another example of how AI can enhance industrial operations – one that’s a little closer to home. In 2018, Shell launched Shell LubeChat, the first AI-powered chatbot tool for B2B lubricants.
Operating 24/7 across 12 countries and a range of sectors (including manufacturing, construction, agriculture, power and fleet), the AI virtual assistant provides instant answers to more than 100,000 queries every year.

Despite concerns that it might replace technical staff, LubeChat helps customers to solve their day-to-day issues while freeing up teams to focus on more challenging problems.
It highlights the difference that AI can make to a business and its customers when used effectively.

Digital technology helps us develop new and adjacent services for our customers

  • Shell Remote Sense helps operators of large engines understand when issues are about to occur. This is an example where we combine our expertise in AI with our decades of experience in lubricant analyses to provide a unique service to our customers.
  • Shell has over 30 years’ experience providing oil and equipment condition monitoring and analyse 750,000 oil samples every year. Good lubricant management is vital for keeping critical machinery healthy and operations running smoothly.
  • Remote Sense uses sensors in conjunction with AI to offer real time oil- condition monitoring. This helps customers to reduce unplanned downtime, optimise maintenance and extend equipment life.

1 McKinsey & Company. “The state of AI in 2020.” 17 November, 2020. (accessed 01 November, 2021).

2 Deloitte. “Deloitte Survey on AI Adoption in Manufacturing.” 2020. (accessed 01 November, 2021).

3 Aspentech. “Industrial AI Accelerates Digital Transformation for Capital-Intensive Industries.” (accessed 01 November, 2021).

4 Aspentech. “Industrial AI Accelerates Digi

5 Microsoft. “FarmBeats: AI, Edge & IoT for Agriculture.” (accessed 01 November 2021).

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