IFS’s Carol Johnston offers insights on why enabling tech-driven sustainability requires an industry mind shift. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.
Despite already battling rising requirements for reduced carbon footprints and major increases in demands, energy suppliers are under pressure to balance this with network resilience. To resolve these challenges, the energy, utilities and resources (EUR) sector needs a radical re-think about how AI and automation can help reach Net-Zero goals. Here, I will explore how a change of mindset is the order of the day before companies can turn to data and look at renewable energy options.
Operations in the EUR sector are one of the most complex of any industry and the need to redefine this is rising. Artificial Intelligence (AI), automation, and machine learning are in hot demand to help. It’s predicated that energy demands will double by 2030. A prediction that not only our current energy grid cannot handle but renewable energy will only be able to help reduce by 25 percent. Despite supplies expected to triple for renewable energy, a shortfall of 42 percent is predicted by 2025.
From surging energy demands and the desire for AI and automation to be embedded in systems to the rise of sensor and smart meter deployments and the need for carbon capture and storage. Using AI and automation successfully will call for a change of mindset from “tech first” to “outcome first.” Over 90 percent of oil and gas companies are already investing in AI or plan to over the next five years, according to a recent Ernst & Young survey. Half of the executives also claim that they are already underway with their AI deployment to resolve challenges facing their organization.
This traditional sector can be transformed into a sustainable and efficient sector when AI, ML, and automation are successfully deployed – from large-scale data collection and analysis for aging infrastructure to critical ESG reporting:
In the EUR sector, data is key. When collated, analyzed, and then utilized, companies can reap the benefits of improved decision-making, enhanced customer personalization and the simultaneous need to become more efficient, streamlined, environmentally conscious, and ethical.
When addressing the twin challenges of energy and sustainability at scale, AI and automation can draw on vast amounts of data—far more than humans could reasonably process—and analyze this data to reveal patterns of energy use and areas of inefficiency. AI can help companies incorporate renewable power sources, make better decisions about EV charging infrastructure, and reach their sustainability objectives, all while cutting energy costs. According to The World Economic Forum, if digital technologies are brought to scale, emissions could be reduced by 20 percent by 2050 in the three highest-emitting sectors: energy, materials, and mobility.
But simply deploying AI and automation without a clear roadmap will only get the sector so far. The intrinsic link between data and sustainability requires a shift: mindset and culture first, then come solution suites. Automation through AI has been sold as an efficiency and sustainability-inducing silver bullet, but without a clear mission in place, EUR companies will find themselves on a digital transformation journey to nowhere—it’s automation without a mission.
Solutions such as machine learning feed off the questions being asked of it and the data being presented as a result. Without the initial culture geared up to not only ask the right questions, but push towards achievable and clear goals on the sustainability front, the tech simply won’t be able to perform to its highest standard.
A common trend across all industries has been the collection of masses of data with no intended purpose thereafter. Businesses have rushed to collect more data without even a structure in place to enable the sector to put it to good use—which actually works against the concept of sustainability.
For example, companies spend extra time and money to keep up with their own data deluge as it comes out the other side of the automation filter—be it a new data scientist hire, or additional technology to try and better connect functions. It derives from a misunderstanding of the tech that has been originally deployed to theoretically do all of this hard work for them—namely, AI.
When it comes to optimizing existing EUR assets, it’s what companies do with this data that will optimize sustainability efforts. The electrification from renewables provides a sustainable energy solution, but it also poses challenges around resilience. Currently, a large portion of the electricity infrastructure in the U.S. is over 50 years old after being built in the 1960’s and 1970’s. While renewables provide an opportunity to update and build the grid back better, the short-term reality will see the lifetime of existing aging assets needing to extend further, demanding robust maintenance and monitoring to remain reliable and safe.
The key to asset lifetime optimization lives within the data—collected via sensors, scanners, or customer demand reports. When data is coupled with AI-based predictive analytics, organizations can make confident investment decisions on the most critical areas of the business. AI-enabled data analytics can work dynamically and autonomously to pull data and enable users to leverage insights at the right time for continuous improvement.
While ensuring the health of assets, it can also correlate historic operating temperature, pressure and maintenance data with outages, revealing the most uptime-critical assets and helping to plan appropriate condition-based maintenance.
The need to not only become more efficient, but to clearly and outwardly display how the company is financially, socially, and environmentally sustainable is a growing market differentiator. Customers seek transparency in the products and services they are purchasing, and demand to know more about how their service providers are being run.
The same levels of transparency apply to other stakeholders within the business. In a recent EY survey, 90 percent of global investors revise investments if companies do not at least consider ESG criteria within their business model. From a reporting perspective, regulators are likely to request more audits and reviews for sustainability reasons—this is where AI-driven data collection and analysis will be key in producing these records. Ultimately, sustainability at the back end needs to be visible, transparent, and auditable, which, of course, can only be achieved if the initial goals are equally clear and laid out from the beginning.
There’s nowhere to hide in the ESG race and the reality is that there is no coincidence or foregone conclusion when it comes to the upshots of automation tools.
Over the coming years, the EUR sector will face an increased number of climate-related issues, and deploying a robust network capable of handling extreme weather conditions is their first obstacle. Companies should be looking to decrease their carbon footprint dramatically while still pursuing the use of other renewable energies. AI and automation will be key in transforming data collection and analysis for ESG reporting, tracking sustainability movements, and matching stakeholders’ demands. However, the deployment of AI and automation will only be successful if it follows a sustainable roadmap that benefits the planet and the people.
This article was written by Carol Johnston on June 20, 2024
Carol Johnston has over 20 years of product marketing and product management experience delivering mobile workforce management, outage management and meter reading solutions for utilities. She is a member of the advisory/planning committees for DistribuTECH International and Western Energy Institute and has served as track chair for SAP for Utilities.
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