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The Race Is On: Scaling your Engineering Workforce with Artificial Intelligence (AI) Software

Updated: Feb 6


Introduction

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Across industries that operate data-intensive machines, artificial intelligence (AI) is revolutionising the way that engineering is done. Engineering tasks that once demanded extensive time and manual effort are now being streamlined and automated by AI-driven software, resulting in substantial time and cost savings.


AI-driven software is more than just an addition to the engineer’s toolbox; it signifies a profound transformation in our approach to asset management and optimisation. Today’s assets can now be proactively managed at a network-level under the watchful eyes of more advanced algorithms. These algorithms, carefully and securely integrated into our daily monitoring, analysis, and control systems, can learn from the data we collect and be guided by engineering knowledge and principles.


We’re now shifting towards incrementally integrating engineering-based intelligence into the technologies that front-line teams rely on. This strategy amplifies and scales the collective expertise of your workforce, facilitating more informed decisions at any given moment.


The Data-Driven Age


We’re in the age of “data-driven decisions”. AI-driven (data-driven) modeling is not a future concept but a present reality, playing a pivotal role in translating raw data into actionable insights for decision-makers. The opportunity for transformation is evident at every level of an organisation, guiding both strategic and tactical decision-making, from the boardroom’s strategic objectives right down to the action at the coal face.


As the momentum towards data-driven operations builds, the competitive landscape is evolving. The race is on for companies to harness the strategic advantage their data offers and in seeking collaborations with data, analytics, and AI technology specialists who can unearth the most valuable opportunities for their operations, especially given the dynamic nature of AI technology.


The Artificial Intelligence Advantage


Every asset that generates data holds untapped potential. By integrating the collective expertise of your workforce with AI-driven software, we can transform the way we monitor, control, and manage assets. Routine and repetitive engineering tasks can now be streamlined through intelligent, AI-driven software, leading to significant time and cost savings.


Moreover, AI-driven solutions not only reduce decision-making costs but also address the financial implications of missed opportunities from limited resources.


This evolution goes beyond mere automation, which involves executing pre-defined tasks without human intervention. It’s about achieving autonomy, where machines make decisions based on data-learning (machine learning) models, able to adapt and generalise in new situations. We’re enabling technology to act on our strategic objectives, with engineering-defined constraints, to a broader range of operating conditions. It’s not just about doing things faster (with automation), it’s about doing them smarter (with autonomy). Autonomy, or driving ‘autonomous operations’ - is exactly what organisations need to do ‘more with less’ and accelerate their trajectory towards a cost-effective, high-performing, net-zero future.


Bridging the Gap


With recent advancements in the connectivity of Operational Technology (OT) and Information Technology (IT), the divide between on-site operations and office engineering is narrowing. For more info, check out our recent post here: https://www.linkedin.com/feed/update/urn:li:activity:7059063016260071424


We’re forging seamless connections between diverse realms: from the racetrack to the factory, and from the wellsite to the engineering office. This interconnectedness, powered by more intelligent software, heralds a new era of data-driven decision-making in asset operations.


The Shift in Focus


The emphasis is no longer solely on data collection or having an analytics platform. The real investment lies in the intelligence of the applications on these platforms—software that can evolve, learn, and enhance asset performance with each day of operations.


With AI-driven software, the strategy is to not only adapt to current needs but also anticipate future challenges, to introduce more proactive decisions in optimisation, maintenance and intervention tasks.


The Power of Integration


Rather than relying solely on on-site hardware to run all the calculations we use to optimise asset performance (e.g. updating settings or setpoints in our control systems), we’re now leveraging real-time connectivity across multiple assets to run more advanced simulation and modeling, with AI- or more specifically, machine learning, providing the modelling framework for timely recommended actions to improve performance. We can now choose - where to apply AI-driven models to provide actionable insights, and where to automate low-level, repetitive decisions entirely. This shift towards AI-augmented operations allows businesses to tap into rapid innovation.


By collaborating with specialised AI-driven providers, companies can focus on their core business, entrusting the technological heavy lifting to organisations that specialise in engineering-led AI technology. To learn more about the specific AI project frameworks and technologies we offer at SIG ML, check out our solutions here.


AI in Action: Industry Snapshots


To explore the universality and adaptability of AI across diverse sectors, let’s delve into some examples. Each industry, driven by the need to manage vast amounts of data, extract meaningful insights from complex systems, and make timely decisions, showcases the transformative power of AI:


Energy: Addition of AI-enhanced control systems allows for the synchronised operation of multiple assets. This intelligent coordination ensures a balance between production, cost, emissions, and reliability during natural gas delivery from a network of wellsites.


Renewables: AI plays a pivotal role in optimising energy production, storage, and distribution at a network level, ensuring that renewable sources are harnessed to their maximum potential.


Utilities: Leveraging AI for predictive maintenance and network performance optimisation. This proactive approach identifies and resolves potential issues in advance, ensuring consistent and high-quality service delivery.


Formula One: F1 teams utilising AI to complement human strategists on race-day, especially when navigating intricate real-time scenarios on the track. Additionally, AI aids in fine-tuning simulation scenarios based on the current design, ensuring optimal race strategies and track performance.


AI is more than just another software—it represents a shift in how engineering is done. It’s about optimising engineers’ time, embedding their vast expertise directly into software, and harnessing extensive data to unlock unprecedented value.


Breaking Down Silos with Data-Driven Modeling


Transitioning from knowledge-driven (e.g. physics-based simulations) to data-driven modeling underscores the importance of integrating our domain knowledge into these models. This shift is breaking down industry silos. As we tackle challenges across sectors, we’re crafting strategies to manage data-intensive assets and simultaneously achieve diverse strategic goals. It’s becoming evident that many challenges faced by organisations are not specific to one organisation, or even one industry. Thus, cross-sector learnings emerge as invaluable opportunities for growth and innovation.


Transitioning to AI-Driven Operations


Across industries, we’re now able to combine AI modelling with engineering knowledge and control theory to deliver more intelligent, data-driven software, automating previously manual, repetitive decisions based on predetermined strategies.


As industries strive to harness the power of data-driven modeling, unprecedented gains in efficiency and sustainability could await. Embarking on this journey requires a strategic approach. While the potential benefits are immense, the complexity of these projects demands specialised resources.


Our recommendation? Start small, assess the value, and scale accordingly as the value is demonstrated.


At SIG ML, we’re here to help. We offer structured projects and pre-built AI applications to help organisations transition seamlessly to AI-driven operations.


Reach out today for a collaborative discussion on how we can support your organisation with AI-driven solutions. We're just a short email away.


Author: Sam Bost (Founder & CEO, SIG Machine Learning Pty Ltd.)


ph: +61 402 882 612



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