Industry News Details

AI and ML for the Masses Posted on : Jul 31 - 2021

Artificial intelligence is no longer the domain of Hollywood technothrillers, nor is it available only to the Fortune 500 or VC-backed startups. In fact, use of the technology has become increasingly common at companies of all sizes.

IBM describes artificial intelligence (AI) as technology that “leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.” Cutting-edge? You bet. But today, even small- and mid-sized companies can leverage AI by tapping into customer, product and market data to power their analytics, reduce their time-to-market and help get a leg up on their competition.

Data makes an application of AI like machine learning (ML) possible. Companies in healthcare, transportation, law, education, and even agriculture generate high volumes of it, and they’re benefiting from AI and ML. Let’s take a closer look at how AI and ML are already transforming how companies operate, whether they see themselves as “tech companies” or not.

What Are You Really Trying to Solve?

Before a company tries to navigate an AI or ML project, there needs to be an understanding of what problem it’s trying to solve — and there needs to be a lot of data. Then the company can look to find ways AI or ML can help it complete processes faster and more reliably.

With robotic process automation, one can, for example, leverage AI to identify patterns that currently require a human eye to recognize. Additionally, ML can be used to complete repetitive, redundant tasks while learning and recognizing patterns, and making inferences, just as a human would (but faster).

For example, one company had its field employees taking photos of documents. Then, the field employees would send the photos back to headquarters for other employees to route the images to different departments. This kind of classification problem is ripe for automation. Using a tool like Google’s AutoML, developers with limited experience can build customer ML models. One of the benefits of this particular implementation was that it freed up the team to focus on more innovative work. View More