Before starting your first AI project, you must have a basic understanding of artificial intelligence fundamentals.
This is important because the success of any enterprise AI project depends largely on the quality of the communication between technical and non-technical teams. When you are able to have a meaningful conversation with your team about high-level technical concepts, you are in a much greater position to lead your team to successful completion of the project.
Here are 3 must-know AI questions and answers.
1) What is AI and machine learning?
Machine learning is using data to answer business questions. Machine learning algorithms use this data to learn to answer these questions correctly.
For example, given some video footage from a retail store, is theft taking place? Or, given historical customer churn data, who are the current customers most likely to churn?
In these examples, companies may have humans performing those tasks directly. AI helps you automate those tasks and reduce dependencies on employees – and it can be done at scale.
When you are able to use your data to answer important business questions, you are in an excellent position to take further actions that will drive value for the company. For example, upon predicting the customers most likely to churn, you can then provide incentive programs, offers, gifts, or any other strategies to help keep the customers.
Ask yourself, what business processes may I be able to automate using data?
2) What is the most important element of a successful AI project?
The most important element of any successful AI project is data.
Broadly speaking, there are two kinds of data: Structured data and unstructured data.
Structured data involves tables in databases, and you can visualize it as Excel spreadsheets. It is data that is structured in a precise way with column names, specific values in each entry, etc. You know what the data is, what it means, and what you may be interested in. Regular machine learning uses structured data to answer business questions.
Unstructured data does not have an obvious pattern or structure to it. Images, video, text, and audio are examples of unstructured data because those files do not have a precise structure and distribution. An image, for example, is a collection of pixel intensity values, and these pixel values are not arranged neatly in a database table. A more specialized type of machine learning, called deep learning, uses artificial neural networks to learn the ‘hidden’ patterns in this type of data. In the case of an image from a retail shelf, it will learn what are the pixel values that correspond to different products in that image. Then, it may answer the question of whether a product is present on the shelf.
Before starting an AI project, make sure you have a good understanding of the type of data you will be needing. The data you need depends on the business questions you want the AI to answer. Furthermore, you want to assess the quality and quantity of the data. These two factors are also paramount to the success of your AI project.
3) How do I know an AI project is successful?
For your first AI project, choose a problem you are already solving (it’s usually a manual task performed by humans). This is important because you want to have a benchmark against which to measure the performance of the AI.
This also allows you to know exactly what success means for an AI project in your company. If the AI is able to answer your chosen business question(s) correctly, ideally meeting or exceeding human performance, and it’s able to scale, then your project is successful. Your ROI can take the form of higher revenue generation, cost/overhead reduction, operational efficiency gains, or other forms of value depending on your particular business outcomes.
Once you have these wins and confidence in AI, you can expand your initiatives to other problems in your company. Over time, your company will undergo a powerful AI transformation, making it more efficient, valuable, and exciting.
If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at email@example.com and I will be happy to help you.
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