David Sayah, Managing Director and Partner, Boston Consulting Group (BCG) believes that Artificial Intelligence (AI), as it is understood today, represents a golden opportunity to drive positive change.
"With the ever-expanding connectivity, the abundance of available data, and the increasing processing power, we can understand more of what's happening and derive solutions that can optimise business objectives, societal objectives, and government objectives alike," he said in a interview with Zawya Projects.
He said the right way to think about AI, in the context of industrial transformation, is that the right solution is the one that addresses specific friction points.
"I don't believe in a one-size-fits-all solution that is the answer for everything," he observed.
Sayah addressed concerns about algorithmic bias and hallucination in generative AI, emphasising that AI should inform decision-making rather than replace human judgment
"As long as we're not entirely relying on the solution to replace the human factor, we're in good shape," he said.
He acknowledged the conservative nature of industries like oil and gas but also observed an accelerating digital transformation post-COVID, with major companies investing in digital ventures and building data science teams.
With regard to job losses, Sayah pointed out that new solutions bring different types of jobs.
"While AI will replace certain jobs, it will also create demand for different types of jobs. That's how I look at the cycle," he said.
Excerpts from the interview
When you look at the industrial side, how is AI creating value?
On the industrial side, I think it has already been proven that the winners of tomorrow are the ones that are, today, able to embrace digital as a way of working. I don't think, per se, there is a necessary subset of solutions that will be characteristic of a winner in the market.
The right way to think about AI, and that's what we see with client leaders and suppliers as well, is that the right solution is the one that addresses specific friction points.
For example, business friction points differ between operators, even within the same sub-segment. Looking at oil and gas refining as an example, the challenge with one refinery around throughput can be very different than another refinery with energy efficiency and heat loss. The point is, that digital is something that allows us to understand better what's wrong and shape a solution to solve it. That being said, I don't believe in a one-size-fits-all solution that is the answer for everything.
Oil and gas is a conservative industry compared to, for example, Banking and Finance or even Retail. So, how open are they towards AI?
On the maturity curve, the oil and gas industry may be behind compared to other industries, part of the justification being that the stakes are too high. It is one thing to implement digital solutions in fields like consumer retail, where a potential downside could be that the interface is not working for new customers. But it becomes an entirely different challenge when you could risk the production capacity of hundreds of thousands of barrels of oil. That being said, post-COVID, in the last two to three years, there has been an immense acceleration of digital across all oil and gas companies. They are much more keen to accelerate the transformation, and that's not only talk. We can see oil and gas companies of the likes of Shell, Chevron, and BP investing in digital ventures. They are building teams of data scientists and data engineers. If you look at their recruitment portals, it is quite interesting to see the type of jobs they are recruiting for. You also see them promoting and commercialising some of the big solutions in other sectors with other clients. All of these tell me that the acceleration is not only underway but also gaining momentum in the right direction.
There is also a trust factor. When generative AI went mainstream, issues like 'AI hallucination' and algorithmic bias were out in the open. How do you deal with that mistrust being carried over into the workplace?
I think we need to understand what AI is telling us. We should look at these solutions as something that informs decision-making but doesn't replace it. In successful cases of digital transformation, what we have observed is that 60 to 70 percent of the transformation angle is related to people, governance, and capability, and the remaining 30 to 40 percent is related to platforms and solutions. As long as we keep that in mind, some of the risks you referred to, though real, can be measured and mitigated.
And this is not all that there is to AI. As it evolves and develops, more and different risks will come. I don't believe we'll reach a point where we can definitively state, 'These are the technical aspects, these are the associated risks, and these are the solutions.' It's more of a journey. As long as we're not entirely relying on the solution to replace the human factor, we're in good shape.
As BCG, what pathway do you propose when advising oil and gas companies on AI?
There are four ways to look at AI. Firstly, consider the potential that AI offers in addressing your challenges. This potential isn't solely measured in dollar terms but also encompasses improvements in safety, visibility, and transparency.
Secondly, think about people. This involves not only developing their capabilities but also facilitating their readiness to embrace the changes AI brings.
Thirdly, consider policy. How can you rewire your internal policies to integrate digital into your daily operations?
Last, think about platforms. We've seen that technical teams in certain places may not be operating at their full potential, leading to a plethora of digital solutions that are segregated and not connected and sometimes even overlapping. These four ways provide a high-level framework to consider AI for your organisation.
How important is training the workforce to embrace the changes that AI brings?
Before I talk about training, it's essential to emphasise that training isn't solely about developing or equipping the next generation of talent. In today's market, new talent is drawn to companies that are digitally savvy and prioritise sustainability.
When it comes to training, partners play a crucial role. However, rather than working with them in a transactional manner where they simply provide a service, it is more effective to integrate and embed your teams with your suppliers. While courses and degrees are relevant, the most effective training often occurs through hands-on experience on the job.
When BCG works with clients on digital solutions, we work together. We engage in ideation sprints, exploration sprints, and design sprints together. As a result, when we get to the finalisation of the output, there is no formal handover because it has been handover mode since day one.
In what areas are companies currently implementing AI, and have you come across any interesting use cases?
What I can tell you are the domains. AI is very prevalent in sustainability and decarbonisation, particularly when it comes to emissions monitoring, transparency on the baseline, and simulating markers. The domain of capital projects and procurement is also massive, considering the substantial capital expenditure in the tens of billions of dollars, particularly in the Middle East. Another area is the digitalisation of human interfaces, like the development of smart worker toolkits and enhanced HR applications.
I don't view the advent of AI with concern regarding job losses because new solutions bring different types of jobs. While AI will replace certain jobs, it will also create demand for different types of jobs. That's how I look at the cycle.
(Reporting by Anoop Menon; Editing by Dennis Daniel)
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