Artificial intelligence (AI) and machine learning (ML) have been the buzzwords of the business world for a while now. Organizations of all sizes and in all industries are recognizing the potential of AI and ML to drive better decisions and reduce human error. According to Gartner, the global business value derived from AI is projected to reach $3.9 trillion in 2022. But how exactly can AI and ML drive business growth?
Identifying the Best Use Cases
The first step towards implementing AI and ML in your organization is to identify the business processes that have the right characteristics, and then dissect the process to understand where ML can have an impact. In general, high-value workflows where complex decisions are made largely on past experience or intuition, and where inferences can be used to provide a better decision, are good candidates for ML implementation. The focus should be on decision points where an error can be costly or life-threatening, or carry significant reputational risk.
Developing a List of Potential Use Cases
An approach that has worked for many companies is to develop a list of potential use cases and determine the business impact and force rank the list to identify top priorities. During this stage, it’s important to have executive-level agreement on the acceptance criteria that will need to be met to operationalize an ML model. Acceptance criteria are typically expressed as a confidence interval in ML inferences, and these intervals vary depending on the use case.
Discovering Business Value
Once the ML Canvas is completed, it’s time to calculate the business value and rank the use cases. The project’s economics will not be as attractive if you are building the infrastructure and waiting six months to capture and manage the data. Fortunately, there are cost-effective and easy solutions available to deploy a data lake and integrate with ML tools and services. Amazon Web Services (AWS) provides a platform where data scientists can explore different algorithms and train models without needing data engineering or DevOps skills.
Exploring, Evaluating, Cleaning, and Preparing Data
Before attempting to build ML models, you need to explore, evaluate, clean, and prepare your data. The data will most likely come from different sources both internally and externally. Externally-sourced data can be used to enrich your dataset and provide a deeper set of ground truth to improve your model. A data lake is a centralized repository that allows you to store all your structured, semi-structured, and unstructured data at any scale. Services like Amazon Athena, AWS Glue, Amazon Elastic MapReduce (EMR), and Amazon QuickSight offer easy-to-use tools to help you explore, prepare, and visualize your data.
Takeaway
The power of AI and ML in driving business growth cannot be overstated. These technologies can bring about significant changes to an existing application, or even transform entire industries. It all starts with identifying the right use cases and deriving real value from them. With the right tools and services, such as those offered by AWS, organizations can seamlessly integrate AI and ML into their workflows and stay ahead of their competitors.
The benefits of using AI and ML are immense, from making better decisions to reducing human error. By leveraging these technologies, businesses can unlock new opportunities and achieve greater success in the long run. Organizations that are willing to embrace the power of AI and ML will be better positioned to adapt to changing market dynamics, anticipate customer needs, and optimize their operations for maximum efficiency.
In a world where technology is constantly evolving, AI and ML represent a transformative force that can help businesses achieve their goals and drive growth. Whether you’re looking to enhance your existing processes or disrupt your industry, AI and ML are the key to unlocking new opportunities and driving sustainable business success. So, don’t wait any longer – start exploring the potential of AI and ML for your organization today!