More and more businesses are implementing artificial intelligence within their organization. The people who make AI possible are machine learning (ML) engineers and Data Scientists, which are quickly becoming ubiquitous positions in companies around the world. But before hiring a data science or machine learning expert, you need to evaluate your organization’s use case and consider what other methods can provide a solution to your problem. Then, you can determine if your business has a critical need for these roles.
Defining the key AI terms
Machine learning engineers and data scientists perform very different yet equally important functions for a business. Essentially, machine learning enhances how products operate and perform, while data science enhances the amount and type of metrics people are able to glean from the product.
What Is A Data Scientist?
A data scientist researches the need for or improvement of ML models, as well as analyze metrics to gauge the impact of machine learning systems. Insights from data science help tell a story and create action items for the ML team.
What is machine learning?
Assessing your situation
It’s important to note that you need a great deal of data to implement machine learning. The models will only learn what you teach them to learn, so the need for large amounts of data is crucial. You must consider the problem you need to solve and the amount of data you can collect.
In some cases, statistics can suffice. Statistical models are a set of rules. When the model encounters data that doesn’t match prior patterns, it becomes useless – it doesn’t know what to do with that data point. The goal of statistics is to learn about data. But if you want a system to learn from each experience and adapt moving forward, you need to go beyond statistics.
If your business seeks a solution that involves classification or prediction, you need AI. For instance, if you want to identify requests coming through your website and route them to the right person or department, you have a classification use case. You need to give the system many examples of requests and categorize them appropriately so the system learns. However, if you need to forecast financial trends or buying behavior, you have a prediction use case.
Artificial intelligence is affecting major industries already, like manufacturing, healthcare, transportation, finance, and customer service. While it’s expensive to invest in new technology and the talent capable of doing it, it’s worth it. As Yael Gavish writes, “investing in AI today is like investing in mobile 10 years ago; it can transform your business.”
As Yael Gavish writes, “investing in AI today is like investing in mobile 10 years ago; it can transform your business.”
The investment is worth it if it means keeping up with or even surpassing the competition. Plus, machine learning engineers and data scientists will help your organization make more informed decisions, which can save money in the long run. Building models and frameworks to streamline data collection and analysis saves time and also helps an organization scale. When machines can handle certain tasks autonomously, people can dedicate their time to more effective, important work that benefits the entire business. Artificial intelligence isn’t magic; the benefits are tangible.
The “Intelligent” impact
Implementing machine learning and artificial intelligence is going to have a major impact on your organization’s efficiency. Intelligent systems automate a great amount of work and help reduce the risk of human error. The time savings increases the more intelligent your systems become.
AI is already transforming the way companies interact with potential customers through chatbots. Some bots can streamline internal processes, like a system that uses Natural Language Processing (NLP) to automate the answering of frequently asked questions.
Predicted Uses For Chatbots
Q: What would you predict you would use a chatbot for?
AI and ML help businesses perform sentiment analysis to keep a pulse on what customers or prospects feel about them. They are crucial to predictive analytics, which every business needs to identify future opportunities and risks, particularly when it comes to sales and marketing. Additionally, customers experience with your business improves with automatic recommendations tailored to their tastes, based on their purchasing data. In the manufacturing industry, predicting maintenance goes a long way in preventing unexpected failures or errors.
Ultimately, artificial intelligence is just as necessary as it is beneficial. If you’ve considered your use cases thoroughly, allocating your resources towards data science and machine learning positions will pay dividends in the future. Artificial intelligence is only going to develop and become more critical from now on; it’s time to consider how your business will benefit from AI.