Machine Learning is Carving the Path toward Digital Transformation

Ian Lee

One of the fascinating characteristics of information technology is how small changes can have enormous ripple effects. For example, geolocation capabilities in smart phones gave rise to ridesharing services, real-time driving directions and contextual marketing. Unlimited data plans fueled the entertainment industry’s shift to streaming media. Search engines enabled small companies to compete with big ones.

Machine learning will create ripple effects that are no less dramatic. Organizations that embrace machine learning’s potentials to unlock previously inaccessible data will be the winners in the age of digital transformation.

Machine learning is a type of artificial intelligence that’s optimized to find patterns in large amounts of data in a process that gets better over time. For example, machine learning algorithms can churn through huge volumes of email and organize messages according to content.

New research conducted by AIIM in partnership with Iron Mountain, clearly establishes that the age of artificial intelligence has arrived. As I discussed in my previous post, the research documents dramatic growth in the use of machine learning to decode paper and scanned documents, assign metatags and create structure from inherently unstructured information forms like plain text and video.

Perhaps the most dramatic finding of the AIIM study is that 84% of respondents agreed or strongly agreed that machine learning will revolutionize their approach to information governance. That’s a powerful statement about this technology’s disruptive power.

Organizations that realize the full transformational potential of machine learning will be those that see the potential for new possibilities rather than simply linear improvements. That’s difficult, because human nature is to see the future as an extension of the past. But creative thinkers look for how to do things in an entirely different way.

These possibilities may already be in the back of our minds, but we don’t have the means to fulfill them. For example, the idea of citizen taxis was nothing new, but regulations and logistical hurdles made it impractical for ordinary people to freelance as cab drivers. It took the combination of smart phones, automated logistics, computerized route optimization and dynamic price calculation to make Uber and its competitors possible. These entrepreneurs realized that a convergence of technologies enabled a 400-year-old industry to be completely redesigned.

Machine learning can be used to recognize and organize information assets more efficiently, but its true potential isn’t incremental. Visionaries will see the potential to use information in different ways.

For example, what if an insurance company could analyze images of property damage to enable it to offer new categories of coverage based upon structural characteristics? What if recorded customer interactions in a call center could be mined to find the best tactics for converting inquiries into sales? What if customer interactions themselves could be automated by intelligent agents to increase the volume and quality of engagement?

Realizing this potential will require participation by people from all around the company, not just information managers. In predicting nine key trends for 2019, Database Trends and Applications editors Joyce Wells and Stephanie Simone noted that “one of AI’s biggest obstacles has been the disconnect between data science teams and subject matter experts,” in large part due to the complexity of the underlying technology. It’s up to information managers and data scientists to reach out to business experts with understandable explanations of what machine learning technology can do and to invite them to envision the possibilities.

Organizations that can do that will lead the transformation of their industries. It’s not too late to get started, but the clock is ticking. Only 13% of respondents to the AIIM survey said they’re in an advanced stage of machine learning use, but 41% have started down the adoption path. For organizations that have been waiting to see if machine learning has value, the time to get on board is now.

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