Introduction

We need compelling tangible use cases to see how AI technology can generate business value, or enable happier lifestyles. So I will try to cut through the hype and lay out what I believe are top use cases for AI.

Two Broad Categories

I will divide my top use cases into two broad categories where value can be created:

1- Operational efficiencies (Op$)

Operational efficiency is basically the ability to do more with less. There are so many processes in every organization that can be re-imagined by including more data analytics and smarter software. More so if we embed IoT sensors and possibly some robotics. An easy to visualize example is to think about how agriculture was automated in the previous century so it generated a lot more crop using a lot less human resources. AI has the potential to produce a similar leap in productivity.

2- Customer experience (CEX)

Customer experience has many facets but it boils down to how easy, fast, convenient and/or enjoyable your products or services are from a user perspective at every touch point in the customer journey. CEX became the main competitive differentiator in the digital age (think Amazon). What the internet and e-commerce enabled in terms of more convenient shopping experience was a quantum leap from traditional stores. With AI and “Journey Science” we can now achieve another substantial leap for CEX where customer advocacy of your company (NPS) rises and customer acquisition and retention costs decline.

 

Verticals in Focus

There is a large number of use cases for Big Data and AI whose applicability and priority will vary by vertical or industry. So I will initially have a section to focus on use cases applied in Telecom and Smart City context. More industry and vertical-specific use cases will follow. However all use cases should be inspiring.

 

1- Telecom

Traditional Data Warehouse at telecom service providers typically processes and analyzes only a small percentage of available customer data. Telecom service providers have adopted big data analytics as a strategic competency for two reasons: to gain insights into the rapid growth of digital services to improve customer experience (CEX), and to offset revenue declines (Op$). The urgency has risen by another order of magnitude with the imminent introduction of IoT services. Expect some blog posts that cover telecom-specific big data and AI use cases.

2- Smart City

The concept of Smart City is rising due to many reasons. One driver is population growth and migration towards cities that is resulting in pressure on the environment and city resources. So the advances in digital technologies (including IoT) became an inspiration for re-imagining urban life in order to reduce governance costs (Op$) and provide a better lifestyle for the citizen (CEX).

Major cities in the Middle East like Dubai, Abu Dhabi, Riyadh, and Kuwait City have launched ambitious smart city initiatives. These are major undertakings in terms of scope and scale, far exceeding what large corporations have to contend with. Smart cities encompass many government agencies and include public/private partnerships so realizing them requires architecting a complex “System of Systems”. AI will play a major role in increasing the level of process automation (Op$) and enable more innovative “Smart” services (CEX).

Expect some posts in this Smart City category as I am very interested in its potential for improving our lives and economies. The following diagram illustrates different use cases that taken together define how smart a city is.

 

 

3- Insurance

What if insurance companies can find ways to assess risks much more accurately by leveraging big data analytics and AI? Detecting fraud early? Providing evidence to support a denial? Providing metered car insurance based on actual usage and driving style? Many leading insurers started on this track years ago. And there are success stories and the experiments are getting extended and refined. From car insurance to health insurance, there are note-worthy use cases that will lead to a revamp of the insurance business model.

4- And many more

Below is a list of 100 start-ups using AI to challenge incumbents in a variety of industries in 2017. There are likely 1,000s more globally getting ready to launch. And we are just in the early stages of this transformative wave, with predictions that the growth will be exponential over time.

Incumbents can and should defend their leadership positions by launching digital transformation initiatives that run deep into their organization. Everything in the old order should be open to questions and scrutiny, including questioning what business you are really in, what other lines of business you can launch in parallel, and the adequacy of your current business model. Otherwise one of the 1,000s of AI-infused startups (or a transformed incumbent) may soon show up in your rear-view mirror.