4 lessons from adopting AI across different sectors

3 min read
artificial intelligence in different industries
Image credit: Depositphotos

Enterprises adopt artificial intelligence in an effort to positively impact their business performance. But the power of AI goes beyond business and can even change human experiences.

This 21st century technology is serving as a driver and even impacting consumer services across a variety of industries, from retail, finance and beyond. The following client experiences serve as a gateway to better understand AI, which not only helps create a reaction, the technology can also help us act proactively in advance.

Imagine a young couple who just became first-time parents and want the peace of mind that if anything happens, their new family member is protected. The couple looks to purchase term life insurance. After entering extensive medical information into an insurance company’s mobile application to meet the underwriting requirements of the insurer, the healthy couple is frustrated to find out they need follow-up medical testing. Time is no longer a luxury. This example isn’t theoretical and the impact transcends consumer frustration.

As of just a year ago, a top-three U.S. insurer required secondary invasive medical testing to underwrite 94 percent of its term life insurance policies. Each medical visit costs money. Each delay in the underwriting process loses customers to competitors who are able to make an underwriting decision on-the-spot and deliver this decision through the native app.

Shortly after having their child, the couple relocates from the U.S. to the UK for work. They will need a basic line of credit. Having applied over a bank’s app, the couple is once again frustrated by the need for a secondary in-person financial interview. While they can more than cover the few-thousand-dollar limit, because they are new to the UK, the couple lacks UK-specific records. The industry would call them “thin-file” clients which the bank’s processes often cannot automatically sort. Again, the situation is not uncommon.

A top-15 global bank spent two years and over $100 million on unsuccessfully automating thin-file risk scoring to expats. Without the necessary tools to score new risk, this bank and many others have left profits on the table.

Underwriting and “thin-file” credit risk scoring are two use cases which highlight the dichotomy enterprise companies have about adopting AI and data. Through apps, websites and chatbots, consumer processes are almost entirely digital. And yet, the digitized consumer experiences are held back by highly manual and imperfect “back-office” workflows.

If the history of innovation can teach us anything it’s that when consumers are frustrated, the status quo must change. This basically means that insurers and banks are being threatened from all sides.

Although banks and insurance companies can become inferior to the giants excelling at delighting consumers, they can still take the leap and be on the forefront of disrupting their own value chains with AI.

After the U.S. insurance company felt threatened by being able to provide an automated end-to-end service for only 6 percent of its customers, it integrated AI into an underwriting decision support system which increased automated underwriting by 10. This resulted in and over-$170 million impact. 

Adopting AI is no longer a “nice to have,” but a “must have” in today’s economy where you must disrupt or be disrupted.

Fortune 500/Global 2000 enterprise companies should keep four themes in mind when deploying AI to generate Actual impact.

  1. From data to insight to impact. The Economist’s seminal May 2017 headline “Data is the new oil” addresses streamlining consumer experiences with data. However, with virtually infinite consumer, transactional, product, and marketing data estates, it is clear that data alone isn’t enough; So, The Economist missed an important word: “Data is the new Crude oil.” AI meets the opportunity to empower enterprises to refine data crude oil into insight and intelligence that drives impact. By asking millions of questions on data per minute for KPIs, the technology discovers patterns the human eye would never spot. These patterns become root causes behind fraud or churn, allowing for management to take action, or ingredients into models for underwriting or risk scoring, which power wide-scale front-line decisions.
  2. End-to-end stack. To build a scalable and secure enterprise-ready solution, a broader infrastructure and data stack need to be taken into account when implementing AI. This is why technology partners increasingly take the role of bringing innovation to Fortune 500 and Global 200 enterprises.
  3. Building capability. Enterprises that successfully adopt AI must expect strategic technology partners to unlock a broader capability. Likewise, generic industry-standard recommended patterns for underwriting or risk scoring, churn or fraud, are replaced with nuanced drivers found as fingerprints in data; the “lightning strike” moments hidden in enterprise-wide data estates.
  4. Social impact. The responsibility of Global 2000 companies should be to use their data to shape a better tomorrow. Ironically, social impact can be good for business. For example, growing the rate of automated underwriting also allows the opportunity to insure the previously un-insurable; thin-file credit risk scoring can allow banks to bank the previously un-bankable. Tying social impact metrics to AI use cases raises AI and ensures AI investment scales. AI also engrains social responsibility into ways-of-working to ensure a better tomorrow.

Fortune 500, Global 2000 enterprise companies launch 20-30 board-level initiatives each year that drive consumer, business, and social impact.

Advertisements

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.