The Great Energy Mindset Shift: Why Outcome-First Thinking Boosts Sustainability Results for the Entire Sector

June 13, 2024
Carol Johnston looks down the sustainable lens to explore how AI, automation, and machine learning can help a traditional sector turn the tide on ESG in the race to Net-Zero. Whether it’s harnessing the power of data or the introduction of renewable energies, it all begins with a change in mindset

Redefining business operations within energy, utilities, and resources (EUR) is hot on the agenda—and for good reason. Energy demand is set to double by 2030, a prediction that not only our current energy grid is unable to manage, but only 25% can be solved by renewable energy. Looking ahead, renewable energy supply is set to triple, yet, according to the World Economic Forum, a 42% shortfall is predicted by 2050.

EUR companies worldwide are recognizing the benefits of deploying technology. Recent research revealed that 92% of oil and gas companies are actively investing or plan to invest in artificial intelligence (AI) in the next five years, with 50% already using AI to solve industry challenges.

To combat these industry challenges in the coming years, the energy sector will need to shift from a “tech-first” to an “outcome-first” mindset. AI, machine learning (ML), and automation hold great promise in boosting sustainability within the sector, but a quick “invest and deploy” attitude to AI and automation won’t cut it. It’s about building a sustainable roadmap that makes a difference to the planet and the people who reside on it. 

1. Getting Data-Smart at Scale  

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. 

According to the Harvard Business Review, 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 while cutting energy costs. According to the World Economic Forum, if digital technologies are brought to scale, emissions could be reduced by 20% by 2050 in the three highest-emitting sectors: energy, materials, and mobility.

The Culture-First Mindset with the Solution Suites to Follow 

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 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 them 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.

2. Aging Infrastructure Needs an Upgrade: Tech to the Rescue 

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 a structure in place to enable the sector to put it to good use—which 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 deployed initially to theoretically do all this hard work for them—namely, AI.

When it comes to optimizing existing EUR assets, it is 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. 

According to the Department of Energy, currently, a large portion of the electricity infrastructure in the US is over 50 years old after being built in the 1960s and 1970s. 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.  

Data-Driven Lifetime Optimization 

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 capabilities 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.

3. There’s No Avoiding ESG Reporting 

A growing market differentiator is the need to not only become more efficient but also clearly and outwardly display how the company is financially, socially, and environmentally sustainable. Customers seek transparency in the products and services they purchase and demand to know more about how their service providers are being run. 

The same transparency levels apply to other business stakeholders. In a recent EY survey, 90% of global investors revise investments if companies do not at least consider environmental, sustainable, and governance (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 vital 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.

The Future of EUR Needs a Robust Solution 

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. AI and automation can play a transformative role in data collection and analysis that can aid better ESG reporting, track sustainability progress, and satisfy stakeholders within the entire business. 

About the Author

Carol Johnston is a visionary in mobile workforce management technologies for the industry. For over twenty years, she has successfully guided product marketing and product management for energy technology leaders, including IFS, Clevest, ABB Software, and Itron, identifying and developing emerging technologies to help energy organizations and utilities deliver safe and reliable services.