Why Companies Need To Apply Machine Learning To Overall Digitalization Efforts

In my last article, I explained how businesses need to differentiate between digital strategy, digitization and digitalization. The piece focused on how everyone uses the terms differently and, ultimately, how digitalization was really about thinking through how companies can best automate processes and practices in their organization.

Case in point: Digitalization is converting an entirely or partially manual process to be entirely digital. This could involve automating workflows or processes. Digitization, on the other hand, is converting analog content to digital format. Simply put, the goal of digitalization is to create efficiency and capture value.

A big part of digitalization is figuring out how to improve processes, not only to cut costs and save time, but also to improve an organization’s productivity and the overall customer experience. Machine learning (ML) and artificial intelligence (AI) become an important part of the formula, since both help companies scale.

ML tools can help automate the process of extracting insights from large amounts of data, then classifying and indexing them to create value for the customer. For example, at a consumer packaged goods company, they can analyze their point-of-sale data to create a better product—and more compelling marketing—for their customers. Let’s assume a product like toothpaste and then expand that SKU into different attributes that are associated with toothpaste: contains fluoride, its color, whether it’s whitening, flavor, if it’s a combo pack or just a single, etc. When you take a look at the volume of sales of those products over time and analyze the data, helpful insights can be pulled. Perhaps a customer is willing to pay 5 cents more for whitening, or the combo pack always performs better at a specific store.

This type of ML and AI technology isn’t just for CPG brands. It can be applied to almost any industry. For example, if you’re in healthcare, the application is similar. Instead of using point-of-sale data, it’s about patient profile data. Of course, all patient data should be anonymized, but if you analyze diseases across thousands of patients—and the medicines they’ve used to treat those diseases—over time, you’ll see patterns in their outcomes. This is hugely informative and helpful insight that can be leveraged by ML and AI to provide doctors and scientists with information they need to improve patient care.

The same is true for media companies and content businesses. The saying goes, content is king. But the reality is, all the insights and knowledge are locked in content—no matter what format it is—and are priceless to an organization. For example, take a company that creates, distributes and streams video content. ML technology can help convert the audio into text through speech to text ML engines, which creates even more data that over time can provide insights, such as content sentiment about a particular product, character or scene.

Search engine giants are already capitalizing on this technology. Video results don’t always take you to the beginning of a particular video. Instead, they link directly to the part of the video providing the greatest relevance to the search, which by extension, provides the most value.

Integrating ML Into Your Processes

In order to get going, take a step back and create time to think about where the technology can be applied within your business and then focus on a few of those areas where you can achieve quick wins. Businesses should pick two or three areas in a given year where this technology could solve some of the business challenges they’re experiencing in their organization.

Look at the low-hanging fruit. Determine where time is being spent in a less-than-efficient way. For sales, is it time spent scheduling calls? For distribution centers, is it scheduling deliveries? How can you apply technology to make it better? ML can help optimize the schedule, look at location in proximity and then make sure the right people are in the right places at the right time. Scheduling algorithms and statistical algorithms will help you automate finding and applying efficiencies at scale to improve your business outcomes.


Original post: https://www.forbes.com/sites/forbestechcouncil/2022/10/06/why-companies-need-to-apply-machine-learning-to-overall-digitalization-efforts/?sh=4d9fd12a7171&s=09

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