Artificial intelligence has the potential to transform an organization’s strategic and operational activities to generate value. But organizations must generate the most efficient AI algorithms for a particular problem to make it sustainable. How can AI be applied to the attainment of the United Nations Sustainable Development Goals (SDGs)?


AI aims to simulate human intelligence and perform tasks that would typically require human intelligence to complete. AI mimics humans, and AI has been categorized by type according to its capabilities and its functionalities.


  • Capabilities refer to the general abilities or skills that an AI system possesses. These abilities are usually related to cognitive functions such as thinking, learning, and problem solving. The capabilities of an AI system determine what tasks it can perform and how well it can perform them.
  • Functionalities refer to the tasks or applications that an AI system can perform. These tasks are usually related to a particular field, such as natural language processing, image recognition, or autonomous driving. The functionalities of an AI system determine the specific ways in which it can be used.


There are three types of capabilities. Narrow AI is designed to perform a specific task. These AI systems are built to accomplish a particular job and don’t have the ability to perform tasks beyond that. For example, Siri and Alexa are designed to perform specific tasks like scheduling appointments or providing weather updates. General AI is designed to perform any intellectual task that a human can do. These AI systems can learn and adapt to new situations and can perform tasks that they haven’t been specifically designed for. Super AI is more intelligent than humans. These AI systems would be capable of performing tasks that are beyond human intelligence, such as solving complex mathematical problems or making predictions about the future.


There are four types of functionalities. Reactive AI doesn’t have the ability to learn from past experiences. This type of functionality relies on the current input to make decisions and doesn’t store any memory. These systems are commonly used in gaming, where the AI must react to the user’s actions quickly. It can’t plan or make predictions about the future based on previous data. Examples of this are IBM’s Deep Blue chess-playing computer and Google’s AlphaGo.


Limited memory AI is an extension of reactive AI, with the added ability to learn from previous experiences. It can store limited data from past experience that’s used to make present decisions, for example, self-driving cars avoiding obstacles or recognizing traffic signs based on past experience. But limited memory AI has no ability to plan or make predictions.


Theory of Mind AI can understand emotions and think like humans. It can recognize human emotions, such as anger or happiness, and can adapt its behavior accordingly.


Self-aware AI has a level of consciousness. It’s aware of its own existence and can understand the impact of its actions on the world around it.




One of the biggest challenges of using AI is the development of an AI strategy. Many organizations have an incomplete or unclear understanding of AI in how it can apply to their business strategies and operational activities. Thus, they make poor decisions in selecting the right AI tools for their specific needs. Additionally, companies struggle to define clear goals and objectives related to their AI initiatives, which can lead to poor outcomes or a failed project.


Organizations must invest time and resources to develop a robust AI strategy to clearly define goals and objectives, assess potential risks and challenges, and select the appropriate tools and technologies that align with their business needs. AI systems are only as good as the data they’re trained on, and, if the data is biased, the AI system will be biased as well. This can have serious implications, such as social and economic inequalities or discrimination against certain groups of people.


To address bias in AI, organizations must train AI systems on unbiased and diverse data sets. This may include implementing algorithmic transparency and accountability measures, such as monitoring the performance of AI systems and conducting regular audits. To have sustainable AI, organizations must consider sustainability challenges when they adopt AI. AI systems consume significant energy, and the processing power required to run these systems can have a significant impact on the environment. Additionally, the manufacture and disposal of AI hardware can contribute to environmental waste.


To promote sustainable AI, organizations must take steps to reduce the energy consumption of their AI systems by optimizing algorithms and hardware designs. Additionally, organizations must consider the environmental impact of the entire AI life cycle, from the manufacturing of hardware to the disposal of end-of-life products. Orphaned analytics occurs when an AI system is developed and trained for a specific task but then fails to integrate with the rest of the organization’s analytical infrastructure. This leads to wasted resources and poor outcomes, as the insights generated by the AI system aren’t effectively utilized by the organization.


To avoid orphaned analytics, organizations must ensure that their AI systems are developed with the broader analytics infrastructure in mind. This includes integrating AI tools and technologies with existing data sources and analytics platforms and providing clear channels for communication between AI developers and other stakeholders.




Ethically, AI adoption should follow regulatory requirements that reflect the expectations of society. Evidence from the academic community and discussion between policy makers and businesses suggest the need to develop AI strategies to make it sustainable. Higher adaptability of AI is possible when strategies focus on sustainable AI and its application for achievement of sustainability goals. Moreover, when any business adopts the regional or national AI strategy for future opportunities of sustainable development of the company, it needs to carefully avoid bias in modeling, data training, and data usage to enhance trust among its stakeholders and generate value by introducing an inclusive AI.


AI can access and process costly data at high speed for business. Efficient data visualization reduces repetition of similar algorithms, diagnostic analytical tools reduce information asymmetry and unethical training of data—and, thus, reduce the carbon footprint. A prescriptive analytical tool enhances trust among users of an agile AI ecosystem and can make it highly sustainable and ready for achieving their SDGs. Remember that an organization’s business strategy should focus on sustainable AI and that regulators should allow businesses to use only sustainable AI for SDG attainment.

About the Authors