Driving Better Business Outcomes Using AI in Telecom and Healthcare

The most innovative and best-funded companies in the world—think Google, Amazon, Apple, Uber—are snapping up top AI researchers and PhDs from the world of academics. In what an article in The New Stack labels the “Academic AI Brain Drain,” these firms are aggressively investing in scientific expertise to foster breakthrough innovations.


Intelliswift’s clientele seeks practical AI applications that can be quickly deployed to optimize decision-making and boost customer engagement. In the first part of this series, we examined how AI benefits smaller and mid-sized enterprises. Here, we dive into how AI is being deployed in two very different industries—telecom and healthcare.


Same Principles, Different Industries

To better understand how AI drives better business outcomes, we are taking a closer look at how machine learning and recommendation principles transcend industry vertical specifics. When it comes to the value AI delivers, there are surprisingly more similarities than differences in telecom and healthcare. Here are three functional examples that prove it.


1. Diagnose End User Needs

In the television show House, an unconventional doctor arrives at astonishing diagnoses based on random and often unrelated patient symptoms. In the real world, it’s usually not that dramatic. However, speeding up the diagnosis process does benefit both the caregiver (e.g. more efficient use of their limited time) and the patient (e.g. more relevant treatment strategies).


Using artificial intelligence, healthcare providers can quickly compare your specific profile (i.e. blood pressure, bloodwork, risk factors, etc.) to others with similar results. By identifying patterns and narrowing the scope, immediate access to aggregated patient data provides a valuable starting point for one-on-one evaluations.


Your Data Precedes You

Telecom companies also use AI to examine patterns in your profile, compare them to others like you, and suggest appropriate service plans. Let’s say you’re doing some price shopping for new plans and you call your provider to see what kind of deal you can get.


By the time an agent answers the phone (or the chatbot responds to your request), they’ve got you pretty well figured out as a customer and purpose of your call. They know you’re an intense YouTube video consumer, you stream 12 shows a week on average, and you’ve been searching for unlimited data plans on Google. They’ve got a head start on tailoring a plan to your exact needs and making you feel like a valued customer.



2. Anticipate Problems Before They Occur

In both of these scenarios, data-driven predictive analysis can identify a “perfect storm” of circumstances before the patterns are detectable using human-driven means. In the healthcare field, scientists are using artificial intelligence to predict flu activity. Analysis of the health impact associated with events such as floods, droughts, and other natural disasters can also provide timely information about when, where, and how to direct personnel and supply resources.


Resource Balancing

These public health scenarios are analogous to the need for load balancing and preventative maintenance in the telecom industry. Connection problems could be reported both formally and informally, say either through an online outage form or complaints on social media.


Telecom companies that analyze customer sentiment and service records are better able to rebalance bandwidth. At the next level, so-called self-healing models tap into behavioral and network data to prevent outages before they occur.


3. Process More Data than Humanly Possible

With AI, distributed and diverse data sources can be collaboratively utilized in a decision-making ecosystem that would be completely unrealistic for people to replicate. Think about all of the journals, case studies, textbooks, and patient records available to both consumers and healthcare providers. An overabundance of data makes it hard to sort through and recognize what’s most applicable, but AI can consolidate information and help pinpoint the two paragraphs out of two hundred books that are most relevant.


Information Overload

The same principles can be applied to consumer requests in the telecom arena. Let’s say the customer care department of a global telephone company gets 10,000 emails a day, a conservative estimate. Even if it takes just 20 seconds for a screener to process each one, that’s 55 hours of work to determine a course of action. An artificial intelligence engine can make short work of that backlog, readily picking out the 42 that require immediate escalation and sending the appropriate first tier response to the others based on specific issue tracks.


Whether your organization consists of customer care or patient care, artificial intelligence has the potential to facilitate better outcomes. Information is power, and the power to predict the needs of your end users and move quickly to address them is more achievable than ever. At Intelliswift, we love helping companies solve challenges and uncover opportunities with AI, and we invite you to get in touch for further future-state conversations.