Oil & Gas Giants: Amidst Market Volatility, LNG Market looks to Artificial Intelligence

The Liquefied Natural Gas (LNG) market is increasingly being defined by its volatility. From increases in spot prices to intense commercial demand expectations, cargo cancellations, shipment delays and more, it is widely reported that, “unplanned maintenance at LNG export facilities from Australia to Qatar to Malaysia has led to a tighter than expected market in the second half of the year [2020].” This unplanned maintenance, combined with “pandemic prompted dramatic price swings” has led to the halt in growth in the LNG market in 2020. As a result, LNG has been looking to digital transformation to ramp up recovery, avoid stagnant growth and deliver on increasing market pressure.

While the cost of oil drops for consumers, due to social impacts of COVID-19, the LNG market is preparing for long-term implications. LNG is poised to experience more growth in comparison with the traditional use of oil and coal in consuming countries as the spot market has made LNG a more attractive option. Consumers have been incentivized by low LNG prices, relative to oil and gas prices, to make a switch.

Though the outlook for LNG growth in 2021 is quite strong at this point, the market is still unpredictable. LNG, in the position of a relative newcomer in respect to oil and coal, will benefit from swift implementation of digital in their early stages of growth. Gaining an even greater competitive edge in LNG is dependent upon digital adoption where speed to value becomes the most critical element in a technology deployment.

Source: Nobel Upstream

For years the LNG and larger Oil & Gas market have been turning towards digital technologies for their many benefits, not the least of which being promises of decreased costs and increased efficiency. However, they have never felt a more significant pressure to extract value from digital then what they are experiencing right now. Many O&G corporations that were early adopters have previously been failed by tech companies touting pie in the sky Artificial Intelligence platforms which merely turn out to be static algorithms at best. After a failure to deliver on hype and promised benefit, these tech companies have only left behind frustration and a distrust in AI’s true capabilities.

These early adopters can rest assured that the marketplace has seen a significant shift from the early days of promises and theoretical AI to concrete benefits in practiced AI. What was once seen as potential benefit is now being realized. As mentioned in the above graphic, making the best of existing technology, through the implementation of Artificial Intelligence which utilizes existing infrastructure, like Maestro, can yield significant cost savings. LNG producers, driven by everchanging costs, need to adopt Artificial Intelligence to survive in todays’ landscape.

The pandemic has been a great shock to the market but with this shock comes an exciting opportunity for change. Leaders have been given the opportunity to evaluate existing business strategies and identify opportunities for growth. LNG producers have the potential to extract significant financial value and increased profit from digital and further their growth position in the O&G market. Digital value chain transformation will afford LNG opportunities in increased flexibility and responsiveness of their production to market conditions. 

A critical element in the LNG and overall O&G journey to choosing a partner to adopt Artificial Intelligence with is the partner’s ability to deliver immediate value. Speed to value is driven by many factors but industry expertise, like that of Elutions’ Maestro, is paramount. To learn more about Maestro and individual case studies in the Oil & Gas sector, please contact us

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Why In-House Artificial Intelligence Projects Fail

Companies all over the world, from giant corporations to start-ups, are keen to cash in on the vast value of Artificial Intelligence. With an intent to capture as much of that value as possible, many spend millions on in-house AI solution development rather than outsourcing to address their most critical business challenges. In a world where there’s a feasible DIY solution for almost everything, Artificial Intelligence is most often the outlier. The complexity and cost of AI solution development demands experience to reduce financial risk and ensure speed to benefit, especially in highly competitive sectors.

Think of your business as a human body, and your business challenges as illnesses of varying severity. Some challenges, like some illnesses, are treatable with over the counter medications, while others require a visit to the doctor, prescriptions, long-term treatment or intensive care. If you had an illness that required extensive medical attention, you wouldn’t hesitate to seek out the best medical team for treatment. Should you treat your business any differently?

In deciding whether to go in-house or outsource, it is important to consider how a strategic AI implementation will impact your business. If it’s done right, it will reduce costs, increase revenue and enhance competitive advantage. If it’s done wrong, to what extent is your business at risk?

Financial opportunity from AI abounds across sectors (see the figure below), and there is both margin opportunity and market share on the table for the businesses that harness AI to tackle their strategic challenges first. In other words, getting your AI implementation done fast and right matters, and you must weigh your decision to go in-house or outsource accordingly.

Artificial intelligence (Al) has the potential to create value across sectors. 
Al impact, 
$ billion 
700 
500 
400 
Healthcare systems 
and services 
Public and social sectors 
300 
Advanced electronics/ 
semiconductors 
Retail 
Transport and logistics 
Travel 
Consumer packaged goods 
Automotive and assembly 
Banking 
Basic materials 
Insurance 
Media and 
entertainment 
High tech 
O Oil and gas 
100 
20 
Telecommunications 
Pharmaceuticals 
and medical 
products 
30 
Chemicals 
Agriculture 
Aerospace and defense 
40 
50 
60 
McKinsey&Company 
Share of Al impact in total impact derived from analytics, % 
Source: McKinsey Global Institute analysis

With competitive advantage on the line and the clock ticking, corporations place their bets on whether to navigate the AI journey alone or with partners. Instinctively, they are hesitant to collaborate, tantalized by the prospect of minimizing solution costs, while building their own innovative capacity and owning the resulting IP outright.

Logically, they then look to market outcomes and learn why in-house AI solution development efforts fail more often than not, even in the Fortune100 and at tech companies.

Without experience developing and delivering AI solutions, many corporations fail understand the costs, resources, processes, stakeholders, and even the objectives involved from the onset. As a result, in-house projects often lack a clear and viable design and delivery strategy, roadmap and KPIs, dramatically reducing the speed to benefit if not inhibiting benefit delivery altogether. Program costs and timelines become a driving force for failure. With little transparency into which aspects of the solution will drive the most value, there is no clear way to prioritize spending. Costs either spiral out of control, or corners are consecutively cut in design, development, testing and delivery, resulting in piecemeal solutions that impair data quality, promote bias and diminish solution accuracy, functionality, utility and outcomes.

More often than not, successful AI adopters partner with proven providers on a combination of off-the-shelf solution tailoring, ground-up solution design and solution delivery. How do they decide to partner rather than go it alone?

First, they recognize the competitive imperative for AI—the opportunity cost of following rather than leading in their market—along with the direct costs of failure, and their lack of in-house knowledge and experience with AI solution design and delivery. Second, they find a provider with a successful track record in similar or analogous environments. Third, they develop trust with that provider by laying the groundwork for a happy marriage in contracting. Then they see it through. From the leadership level-down, they commit to the partnership and collaborate from end-to-end to ensure project success.

Strategic AI implementations are broad in scope, capturing data and impacting activities across corporate ecosystems. This complexity is readily apparent in industry, where AI not only provides data-driven direction for decision-making at every step of the value chain and in every organizational department, but directly informs control and automation strategies in production, testing, packaging, distribution and even purchasing.

In recognition of the immense value and complexity of AI in industry, and the competitive need for speed in adoption, the World Economic Forum, in collaboration with McKinsey & Company, has published a toolkit of “practical recommendations” for industrials to accelerate their AI journey at scale. Appearing in The Next Economic Growth Engine Scaling Fourth Industrial Revolution Technologies in Production, this toolkit advocates the adoption of proven AI solutions and related technologies through a partnership and acquisition approach rather than in-house development.

Figure 6: Industry toolkit for accelerating adoption of technology 
Value delivery engine 
Intelligence 
• Predictive maintenance 
• Machine learning-supported, 
root-cause problem-solving 
for quality claims 
Connectivity 
Augmented reality-guided 
assembly operations 
Real-time IOT-based 
performance management 
Flexible automation 
• Robots to automate 
challenging tasks 
• Real-time product release 
39 high-impact digital applications ready for deployment 
Scale-up engine 
Mobilize 
Mobilize the 
Organization 
Strategize 
Set the vision and the 
value to capture 
Innovate 
Spark innovation by 
demonstrating the 
value at stake 
Scale up 
Capture full value 
& Company, Fourth in With the World

With more and more data available for exploit across industries, the opportunities for its monetization through AI are greater and increasingly complex. So too is the risk of getting your implementation wrong.

Can your business afford the DIY approach?


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