7 tips for value-driven AI

7 tips for value-driven AI

How to upskill your workforce and make the most of AI in your business.

• Sabina Stanescu
• November 30, 2022

There is no doubt that artificial intelligence (AI) is changing the way business is done today. AI will ultimately transform every business in every industry. However, despite the desire to use data science in making decisions, many organizations are unable to find sufficiently qualified data scientists to develop and execute data science initiatives.

However, with online training and readily available tools, software engineers and even business users with a background in mathematics can become data scientists. Even if data science wasn’t part of your undergraduate or graduate studies, you can move to AI and bring the power of machine learning to your enterprise.

Here are seven tips from my experience as a data scientist on how to achieve this.

Tip #1: Brush up on the basics

Before embarking on your AI journey, we recommend doing a refresher on basic mathematics such as linear algebra, calculus, probability, and statistics. A great first course to complete is the Python introductory course. From there, you can move on to Machine Learning and Advanced Mathematics classes. One of my client companies has a program that teaches programmers Python. After familiarizing themselves with the basics, many aspiring data scientists get their feet wet by entering Kaggle competitions.

Tip #2: Make sense of your data

Even the most sophisticated algorithms cannot draw conclusions without high-quality data. If companies don’t have enough information to build an accurate picture of their operations and monitor how efficiencies have improved as a result of AI initiatives, they can use analytics and machine learning to increase revenue or , to reduce costs. In one of my most successful data science projects he collected data for a year before starting the project.

Tip 3: Choose achievable goals

The goal of your first AI project should be stated in one sentence. One of my first projects was to help the most popular hotels appear higher on travel site search lists. The project was a success because the goals were easily stated, executed and measured. Most importantly, optimizing search results dramatically increased the number of nights booked, which had an immediate positive impact on revenue.

Tip #4: Keep your model up to date

Model accuracy should be monitored. A pipeline must be maintained to ensure a steady stream of quality data. All machine learning projects should address long-term requirements by having a framework for retraining, testing, rolling back, and starting over if something goes wrong.

Tip #5: Be prepared to customize

It’s always a good idea to see if someone has already developed, trained, and tested a model that you can use without reinventing the wheel. However, the specific requirements of your application may require fine-tuning to accommodate the specific types of data your business generates. In one of my construction safety projects he needed to make sure factory workers were wearing helmets and gloves, when open source object detection models were useless (because of the specific environment only a limited set of objects could be detected). our needs.

Tip #6: Build a sustainable model

Consider how much additional work your model can create and whether your team has the bandwidth to undertake that work. For example, in one project, we performed a simple calculation to select the model that had the most positive financial impact and produced a manageable number of transactions to be reviewed.

Tip #7: Prepare to scale up

The success of one of our projects created strong demand from other departments to add machine learning to the IT agenda. Harness the power of AI across the enterprise by creating programs to train developers on AI fundamentals, empowering users with the power of data, and evaluating AI opportunities to generate immediate positive feedback. Focus on results to improve your bottom line.

lastly

AI is the key to creating differentiated operational efficiencies and customer experiences, and it is becoming essential for businesses to remain competitive. With the right training and access to good data, the ability to generate machine learning models will quickly become a must-have skill for every developer and software engineer. With these seven tips, you’ll be on your way to success in no time.