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Benefits from digital transformation "thin on the ground"

Tuesday, February 11, 2020

In an Offshore Europe conference session on Artificial Intelligence, analyst company Wood Mackenzie said it reckons the benefits from digital transformation are proving thin on the ground. Although Microsoft cited two examples.

Martin Kelly, Vice President, and head of Corporate Analysis, Wood Mackenzie, said that there seems to be fewer 'specifics' being shared by companies about the benefit of digital technology, or examples of successful digital transformation.

He was speaking at a conference session 'Artificial Intelligence', at Offshore Europe in Aberdeen on Sept 3.

And where examples are shared, it isn't always clear that digitalisation is responsible for all of the improvements cited - there can be other factors.
'Industry is finding digital transformation at scale harder than they first thought,' he said.

The big 'prize' is reducing costs, improving safety and value - and the challenges of achieving that with digital technology is proving to be 'massive'.

Meanwhile oil companies are not spending any more money on technology research.

There seems to be something of a disconnect between executive management and employees in expectations for digital technology - with managers expecting big gains, but employees more aware that the benefits are rarely meeting expectations.

How quickly analytics can scale depends largely on data, and how usable it is, Mr Kelly said. But the industry has traditionally seen data as more a liability than an asset, and not taken care of it very well.


Alessio Bagnaresi, AI Sales Lead in the Intelligent Cloud team, Microsoft EMEA, said that analytics systems should ultimately 'support human beings to make decisions'. And one of the biggest strengths of machines is that they are 'very good at finding patterns in data.'

Microsoft sees where 'artificial intelligence' based technologies which can help the industry. For example, apps and agents with AI embedded in them, knowledge mining tools, and machine learning, running on domain specific pretrained models.

Getting value is 'always a collaboration between data scientists, domain experts and IT,' he said.

'We need to constantly monitor accuracy of machine learning models.'

Mr Bagnaresi cited projects with BP to better forecast the recovery factor of wells using machine learning, and a project with Shell to analyze live video imagery from petrol stations to try to spot people smoking on forecourts.

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» Wood Mackenzie
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