Companies are constantly asking how to make their processes as digital and efficient as possible. And while there are many approaches to digital transformation, one key approach is artificial intelligence (AI). According to Gartner, most organizations will be using AI-driven data and processes to boost digital transformation within their company by 2022.
There are many Hollywood aspects to AI, like flying cars and talking robots. But in reality, AI is much more practical and actionable. AI is accelerating companies’ digital transformation with its ability to make processes much easier for both the enterprise and its customers. And if enterprises approach implementing AI appropriately – that is, by starting with organizational goals first and then mapping how AI can help achieve those goals – the technology can unlock myriad opportunities.
This is a guide on some of the ways AI can improve your business’ digital transformation. Specifically, how can AI make us smarter, faster, and simply just better as both organizations and as a society?
How much smarter? How about assisting clinicians in early detection of diseases such as cancer?
Machine learning can ingest large data streams and break them down in a very timely manner. Those models organize massive amounts of data, then help interpret that data however needed – like identifying differences within the data that may be missed by a human.
Previously, every stored document or digital asset was limited to a small number of tags – think file name, date created, author or file type. That’s it! Once those categories were defined, they usually stayed the same, as altering those formulas or processes was a major undertaking and required updates to all content related to those tags.
Fast forward to today: Those metadata tags are flexible, and you can easily add fields or classifications. Furthermore, the attributes of a piece of content go far beyond the metadata, especially that of metadata curated by a human. We can peer deeply into high definition images and videos to extract further insights.
A perfect example of this capability is in the healthcare sector, where AI models can classify more metadata than ever before, and subsequently review scans more quickly, while also detecting anomalies that may not be visible to the human eye. That capability, in some cases, could be life-saving; if a machine learning model can be trained to recognize the most minute discrepancy on a bone being examined or a muscle being analyzed by an MRI or an organ being studied via ultrasound, doctors have another layer of information to better diagnose and treat their patients.
Or, consider an enterprise managing millions of digital assets – a clothing retailer or fashion company, for example. Such companies now can use AI to apply metadata to content based on images containing a product, unique color, the specific model wearing or using the product, location, campaigns where the asset is used and more, all tied into the product lifecycle process. That way, the next time a creative director or designer needs an image of a model or an athlete wearing a certain product, facing a certain direction, and with a specific expression on their face such as a smile, they can easily find the asset.
Imagine taking on such a project of fully classifying and labeling thousands of catalog photos for a marketing campaign? On second thought, let’s save ourselves that headache and put it in the hands of AI.
Rare is the enterprise that wouldn’t benefit from faster and more efficient outcomes. AI can help.
If you’ve shopped for insurance lately, you’ve seen this in action — and you’ve also seen it in action if you’ve unfortunately had to submit a claim. For example: You submit a claim via e-mail along with photos of your damaged roof after a major storm. Insurers are leveraging AI both on the e-mail content as well as the attached photos to extract information, thus making a claim process through the system much faster — and making happier customers. This process also makes for a happy insurer, too: Using AI models to learn these processes has significantly reduced the time the claims process takes, allowing insurers to focus on other areas of the business that generate more revenue.
And the learning continues with each claim. To continue the insurance example: once the model has analyzed those roof photos and classified and extracted the necessary information from that content, the model uses those details to support even more intelligent decisions on the data for the next claim, and the claim after that, and so on.
Or take a step back in the insurance claims process: Things like natural language processing can be leveraged in classification as well as automation – like with chatbots. Chances are you’ve encountered a website chatbot, be it on an insurer’s site or elsewhere. Any and all customer correspondence – like your “I need help, I have roof damage and need to submit a claim” comment within that chatbot – is analyzed for sentiment and other attributes that help route the interaction. Sentiment and emotion detection can be used in customer service scenarios that help you determine if the person on the other end needs a human or if their interaction can be satisfied smoothly via a bot.
And while content extraction and language processing are two examples of how AI is speeding processes, this efficiency also applies to other forms of information, too — such as customer usage data, demographics, and other details related to any number of problems being solved. With this data, predictions about customers become more accurate and can even be tuned over time to improve accuracy.
AI can make us smarter, and it can speed decision making and analysis well beyond human speeds. But how does it make things better? One could argue that smarter and faster are simply better, and that’s certainly true, so let’s revisit a few scenarios to understand how they’re better.
Improving our ability to detect anomalies in scans can help improve the quality of life for patients and potentially improve life expectancy.
Looking at things like demographic data, combined with a patient history and real-time data from diagnostics can help us predict, for example, if a patient is at risk for ICU coming out of surgery. This allows doctors to potentially put in place preventive measures that would reduce that risk.
Customer service is also another example that greatly improves our lives. I had a recent example, where I use a popular home-cooked food service that delivers fresh meals on a weekly basis. I found that one of my shipments actually contained some entrees that were incorrect. When looking to report the problem, I found myself quickly in a chat session where I was able to describe the problem and get a credit. This was all done quickly through a text interaction that felt very much like texting a human, and I would venture to guess I was not talking to a human. In fact, the chatbot was so kind as to let me know that my wrong entrees may contain food to which I am allergic. Overall, the experience was quick and seamless, occurred on my time frame, all on a medium that I am very used to. For the food service industry, this means 24/7 operations, which translates to customer satisfaction and increased revenue.
Machine learning can go well beyond healthcare and customer service. It can improve society. Anomaly detection, for example, can help improve our society by helping us identify underlying biases in decision-making – such biases that may not be observable or identifiable by humans, either because the data is vast, or because the correlations are not immediately obvious.
We can detect whether our decisions or actions are treating individuals fairly, whether they be students, incarcerated individuals or loan applicants. Certainly, a misuse of the data could lead to incorrect or even biased machine learning models. That is why it is important that the human element in defining these models includes patterns and practices that would avoid such biases. Ethical AI is a key concept that speaks to this.
So, the lesson here is that when applied appropriately, machine learning has that ability to improve society at a scale larger than any individual or organization.
Looking to the future, AI-driven data and processes will continue to boost organizations’ digital transformations. How are you implementing an AI strategy to drive your business forward?
Sam Babic is Chief Innovation Officer at Hyland. Throughout his career at Hyland, Babic has focused on implementing the most modern and innovative development strategies to remain in lockstep with customers’ transformation journeys. As Chief Innovation Officer, Babic is responsible for driving enterprise innovation by exploring business opportunities and emerging technologies to expand the company’s product portfolio and accelerate delivery of differentiated solutions to its global customers.