How Generative AI Can Help Build a Sustainable Future?


7 Jun, 2024


6 min read

AI Sustainability Goals

By 2027, 70% of enterprises adopting generative AI (GenAI) will cite sustainability and digital sovereignty as top criteria for selecting between different public cloud GenAI services. (Gartner Inc).

AI-first digitization is the force driving businesses toward operational efficiency and environmental sustainability. Sticking to legacy processes and systems often prevents business leaders from navigating the new normal and exploiting market gaps that amplify profitability. 

Therefore, consistent pivoting and realizing intelligent automation is crucial for enterprises and small and medium-sized businesses (SMBs) to survive and compete in this high-paced, technology-driven economy.

With more energy companies gravitating toward AI and ML, it is quite concerning whether their approach enables sustainability. 

This article will explore AI adoption’s widespread opportunities and challenges that may impact sustainability goals.

AI and UN’s 17 Sustainable Development Goals

In 2015, the UN published a list of 17 Sustainable Development Goals (SDGs), which aim to promote human well-being, address various societal and environmental issues, reduce emissions, improve the utilization of natural resources, and encourage renewable energy generation for a better future.

Moving forward, AI is poised to have a pivotal role in achieving these 17 SDGs and creating significant environmental and societal impact by 2030. According to a 2018 report by the McKinsey Global Institute, there are over 160 use cases where AI can drive better sustainability across different sectors. 

From LLMs to generative AI, McKinsey’s experts pointed out how AI’s vast capabilities can streamline production, distribution, deployment, and management. However, for now, achieving these sustainability goals and turning these potential use cases into achievable initiatives will require a significant amount of investment and a skilled workforce.

Considering the recent AI boom and its unreal projections, we expect energy companies to quickly adopt AI and harness its extensive predictive and intelligent automation capabilities to improve their preexisting processes, workflows, and systems and lead sustainable change.

Harnessing Generative AI to Foster a Sustainable Future

Across its never-ending list of use cases, sustainability stands out as an area where AI can solve pressing challenges and create opportunities that may help companies make more informed, data-driven divisions and optimize legacy systems for better efficiency. 

Here are a few realistic use cases where early adopters can utilize AI systems to accelerate their sustainability initiatives:

Insight Driven Decision Making

High-quality data regarding carbon emissions, energy consumption, and the economy surrounding the energy sector can help industry leaders develop solutions and strategies that enable profitable business outcomes and better environmental sustainability.

Predictive Analytics

AI can also help track usage trends and make predictions accordingly, enabling energy companies to align their resources and meet consumer demands at pace and scale. Moreover, it can perform rigorous hardware assessments to ensure consistent performance and detect system anomalies before further escalation. 

Risk Management

Companies that use predictive analytics ensure unreal proactiveness by mitigating future risks and identifying areas for improvement and disruption. Energy companies can lead change with the right AI development partner and approach.

AI-Powered Sustainability – Key Challenges

While harnessing AI to solve environmental and societal issues does seem like an attractive prospect, there are various complexities business leaders must consider before pivoting:

1. Bias and Transparency

One of the biggest issues development teams face while harnessing AI is that some algorithms may perpetuate biases in the training data. New biases may be introduced because the model might make new assumptions during development. 

Inaccurate predictions in the energy sector are unacceptable, as they may translate to losses worth millions of dollars. Energy companies must ensure their processes are fair, accountable, and transparent while leveraging AI-first systems to spearhead sustainability initiatives.

2. Data Quality and Availability

It’s common knowledge that AI relies on high-quality data to make accurate predictions and automate processes without errors. However, fetching that much data for training and decision-making is challenging, especially for SMBs that don’t have access to many insights. 

Such data enable companies to track environmental factors, resource consumption, emissions, and other sustainability metrics. Without it, streamlining complex supply chains, scaling operations, and meeting consumer demands seems like a far-fetched goal.

3. Cost and Resource Constraints

Does AI drive better cost-effectiveness by cutting down operational costs? Yes, it does. However, companies often ignore the fact that building an AI-first development team is costly. Companies hiring in-house AI developers must invest significantly in wages, benefits, and resources. 

Developing, deploying, and maintaining AI systems can also be quite resource-intensive and expensive. While enterprises might be able to afford such a venture, SMBs must look for alternative ways to lead AI-driven change on a budget.

4. Integration and Interoperability

“If it isn’t broken, don’t fix it!” This is the mindset of various energy companies. If your legacy processes work just fine, even if they seem imperfect or inefficient, don’t disrupt them by realizing AI-first innovation. However, your legacy systems are holding you back. Incorporating AI can streamline your processes and improve outcomes.

But here’s the catch.

Disrupting your legacy business processes, systems, and infrastructures with AI can backfire if you don’t follow a progressive approach. Attempting to augment multiple systems with AI at once can halt operations and cause massive backlogs. 

Therefore, development teams must make significant efforts to ensure seamless integration and interoperability with legacy systems and data sources.

5. Reliability

It’s much easier to understand a human’s approach to a task than deep learning models. For instance, users are often seen trying their luck and experimenting with different prompts to get the right outcome from an AI-powered chatbot like ChatGPT, Bard, or Claude. It’s because they are unaware of the decision-making process followed by these AI models. 

This is why the perception of AI taking away human jobs is gradually vanishing, as business leaders can still not trust AI models wholeheartedly.  Building trust and acceptance among stakeholders, including employees, customers, and regulators, can be challenging when using opaque AI systems for sustainability initiatives.

6. Scalability and Deployment

Scaling AI solutions across multiple locations, operations, and product lines can be challenging, especially when dealing with diverse data sources, environmental conditions, and regulatory frameworks. 

Moreover, implementing AI for such sustainability initiatives calls for a combination of industry-specific expertise in software development, data science, and AI, along with domain knowledge and exceptional business acumen. Finding resources to scale AI solutions across legacy systems and processes can be super-complex, and this is exactly where the significance of AI transformation companies like Cubix builds up.

Being an innovation-first company, Cubix is always on the lookout to join forces with industry leaders and enterprises and help them with our proven expertise and cutting-edge AI solutions. By collaborating with Cubix, companies can accelerate their AI transformation initiatives, unlock new business opportunities, and stay ahead of the competition in an increasingly digital landscape.

Why is AI Regulation So Important?

We’re already witnessing how powerful AI is and the heights it can reach in the future. Unfortunately, unregulated AI will only pose serious problems. OpenAI chief executive Sam Altman has repeatedly mentioned that AI needs to be regulated. The prospects might seem attractive, but the consequences of extensive misuse of AI can be disastrous. 

We’re not just discussing the possibility of AI disrupting the job market and causing unemployment. It can lead to identity theft, misinformation, social manipulation, security issues, and privacy concerns.

Therefore, while adopting AI, global enterprises and SMBs must ensure compliance with ethical guidelines, privacy standards, and data usage regulations. Here are some measures energy companies can take to regulate AI usage for better compliance:

  • Prioritize data security and privacy by implementing responsible data practices.
  • Measure and optimize large, power-intensive AI models’ carbon footprint and energy consumption.
  • Upskill in-house teams and train them on AI ethics, risks, and implications on environmental sustainability
  • Encourage teams to ensure transparency regarding their AI practices and systems while fostering open collaboration.  

Can AI for Societal Good Help Achieve Sustainability Goals?

As we move toward an AI-driven future, energy companies must act responsibly and invest in emerging, inventive technologies to accelerate efforts toward achieving environmental resilience. While AI does simplify various aspects of the journey toward sustainability, it’s not an all-inclusive solution. Business leaders must be clear with their objectives and prioritize transparency and accountability across the board.

Secondly, AI is a power-intensive technology, and while undergoing AI adoption, companies across various sectors must consider its excessive environmental footprint. It’s vital to ensure that the energy consumed by your AI systems can be compensated by intelligently optimizing renewable energy systems.

Lastly, companies should consider the costs associated with AI deployment and hire talent who can flawlessly run complex AI systems. Tapping into the true potential of your data will enable you to extract the best possible value from your AI systems.

Check out our blog on how enterprising technologies like AI and blockchain will revolutionize the energy sector and foster a greener future.

Powering a Greener Future with AI

AI can harness and revolutionize resource and waste management, enabling cleaner energy generation processes. However, despite its endless use cases, companies must act responsibly and address potential biases and ethical concerns.

If done right, AI can help create the perfect fusion of technology and sustainability, safeguarding our planet and mitigating environmental risks while ensuring the world keeps moving. 

At Cubix, we partner with tech-forward energy companies to create a transformative impact with AI. As a tech-forward organization, Cubix fosters an innovation-driven culture. We anticipate emerging technologies shaping the future while synergizing with like-minded partners. Our solutions effectively harness the power of your data and turn it into intelligent insights.

Contact us to discuss your initiatives and how Cubix can help achieve your sustainability goals.



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