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Role of Machine Learning in Realizing an Organization’s Sustainability Goals

Machine Learning for Business Sustainability Goals

Machine Learning, being an integral part of Artificial Intelligence, has been contributing big time to the globe, in different capacities. This article points out the role of Machine Learning in fulfilling the sustainability goals of any organization.

Machine Learning and Artificial Intelligence are taking the world by storm. The value of Machine Learning globally is expected to show an annual growth rate (CAGR 2023-2030) of 18.73%, resulting in a market volume of US$528bn by 2030.

Machine Learning (ML), as an integral subdivision of Artificial Intelligence (AI) has been playing a highly significant role in helping organizations fulfill their sustainability goals. It acts as a connector between the organization and its sustainability goals, helping businesses to showcase a better place with a futuristic view to create a sustainable world.

Machine Learning has proven to be instrumental in reducing friction at all different levels of business thereby increasing efficacy levels, sales, and client relationships. Modern-day Machine Learning tools and algorithms have been assisting companies in adhering to their sustainability goals.

Let us have a look at an overview of Machine Learning and how it is assisting organizations in fulfilling sustainability goals.

What is Machine Learning and Its Key Features?

Machine learning (ML) is an umbrella term for solving problems for which the development of algorithms by human programmers would be cost-prohibitive, and instead, the problems are solved by helping machines ‘discover’ their ‘own’ algorithms, without needing to be explicitly told what to do by any human-developed algorithms. – Wikipedia

ML studies computer algorithms that can assist in pattern identification for making strategic business decisions. These algorithms take in data as input and make use of fundamental statistical formulas to garner reliable business results. It operates queries on large datasets to extract patterns that can be interpreted. It depends upon human feedback to make alterations correspondingly.

One of the major methodologies in ML is the implementation of Artificial Neural Network (ANN) learning algorithms. These could consist of

  • Supervised learning – Guided learning by a supervisor with the use of labeled data
  • Sem-supervised learning – Blend of labeled and unlabelled data
  • Unsupervised learning – No supervisor or training dataset, only unlabelled data

Key Features of Machine Learning for Sustainability Goals

“Supervised learning, semi-supervised learning, unsupervised learning, and reinforcement

  • Actionable intelligence
  • Supported scoping
  • Extracting information from tables and images
  • Automatic information mining
  • Recognizing Objects for Historical Perception
  • Intelligent question-and-answer engine
  • Enhanced efficiency of energy and other resources

How is Machine Learning Instrumental in Fulfilling Organization’s Sustainability Goals?

When we talk about sustainability goals for organizations, here are some of the key benefits that ML can bring along:

  • ML algorithms can save costs by predicting equipment breakdowns before occurrence and managing manufacturing costs with control. With modern-day sensors attached to equipment, it is easy to predict an unforeseen happening. Less downtime directly leads to higher productivity and revenue.
  • Machine Learning helps organizations manage and maintain assets and feed their performance data into ML models to assess futuristic behavior and other associated risks.
  • Through the image regression technique, models can distinguish between images of the new products against the ideal one. This can alert the quality teams of any discrepancy in the product, at an early stage.
  • It can help organizations optimize schedules of business processes and attached assets in such a way that optimum utilization can be done with assigned priority levels and involved cost factors.
  • It can help in optimizing the inventory of assets, those that are perishable and those that are not, based on their stock and usage criteria. It also showcases figures about route optimization, demand forecasting, etc.
  • ML algorithms can detect errors in assembled parts or processes in an early stage, leading to respective corrections on time. It can even monitor the preventive maintenance schedule of appliances.
  • Machine Learning can also help in controlling the usage of electricity by computing the demand over time and thereby monitoring the resources involved in processing, for energy saving.
  • This technology offers enhanced logistics and supply chain operations by lessening the total costs involved in the entire business operation. ML tools also help in reducing carbon emissions by route optimization and precise need for fuel.
  • Machine Learning turns out productive for offering recommendations based on customer preferences, product lists, purchase history, and potential client turnout, which can be leveraged when the client visits the shop/site.
  • ML algorithms are capable of extracting information about the value in structured data and analyzing the meaning behind it. This can help organizations in better and more insightful decision-making.

Few Challenges Along the Way

As Machine Learning increases its spread across organizations, in fulfilling their sustainability goals, there are a few hurdles that could come across the way and must be handled, with prior actions:

  • ML algorithms are complicated and tough to perceive and hence there could be a dearth of transparency in understanding them completely.
  • ML based decisions could be inaccurate sometimes and hence their precision must be checked thoroughly
  • Since it relies a lot on data and is exposed to it, ML may cause privacy and security concerns

These issues must be addressed, and relevant steps must be thought of, well in advance, so that it does not create a hassle later.

As We Conclude

The future of Machine Learning and Artificial Intelligence is bright, and ML is sure to have a revolutionary effect on different areas like healthcare, NLP, automation, transportation, cybersecurity, data science, etc. ML is sure to boost automation and enhance business services through personalized experiences.

There will be scientific innovations and newer ways of looking at business processes that will help organizations reach their sustainability goals faster and in an effective manner. The detailed data analysis and assumptions generation will help in sales enhancement with detailed future planning.

ML and AI will have a transformative effect on organizations and the way they achieve their sustainability goals. It will be interesting and challenging to see the impact they carry along, on the environmental parameters keeping in mind the sustainability and moral consequences.

At Atrina, our skillful resources are proficient at implementing large-scale digital transformations that can assist our clients in taking a step towards the future and integrating digital infrastructure and the latest technologies like AI and ML, which enhances their operational efficiency and gives them a distinct competitive edge.

Reach out to us in case of any AI or ML-related requirements of yours. Our tech experts are sure to offer you a customized solution that fits your needs.

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