AI Use Cases

(what’s being done and available as modules)

CEOs of many leading companies are already betting their companies future on AI. They have taken the “AI-First” approach to products, services and internal processes. They recognize that AI has the potential to improve all aspects of a business ?—? product development, manufacturing, management, hiring, training, finance, marketing, sales, and service. Just as the internet disrupted every existing business model and forced a re-ordering of industry, artificial intelligence will require us to imagine how computing works all over again. Many benefits such as ease of use, higher accuracy, better speed, deeper personalization or cost reduction result from background improvements through AI in the quality of product, service and processes. That is why many companies are talking about baking in AI capabilities into all their products, services and processes. AI is perceived as a wonder ingredient that makes even a product we used yesterday like Siri, Translate or Maps better, easier or cheaper.

Many AI projects can be split into two stages:

1- AI for Real World Recognition:The first stage is to apply AI for interfacing machines with the real world. The real world is analog and unstructured, consisting of faces, voice, body language, images, scenes, weather etc. Advances in machine learning have enabled recognition of speech, gestures, faces, objects, and patterns converting the real world data into accurate and useful digital information. This step converts unstructured analog information of the real world to structured and categorized information needed for your business to leverage.

2- AI for Business Model Innovation: The second stage for AI is to leverage this new structured information for your business model for innovation; this is where new value is generated by your business. This step is at the core of your real differentiation and competitiveness. The challenge for most businesses resides in the success of stage 2.

Fig. 8.1 Two stages of AI projects – Understanding the real world and business innovation

We will use self-driving cars as an example to illustrate this process. Stage 1 is all about understanding the environment of the car. Firstly, AI is used to recognize and interpret accurately the environment of the car with sensors such as LIDARs, radar, cameras, motor data, speech data of passengers, weather and traffic information etc. Stage 2 is to make sense of all this information and leverage it to carry out the right actions that make autonomous driving feasible. It is how this is done that will differentiate a Tesla from BMW or Mercedes. The decisions made in stage 2 will determine if passengers and lawmakers are satisfied with the quality, safety and comfort of the ride in all possible situations. Stage 1 in the process is becoming easier for businesses thanks to the intensity of ongoing AI research, startup efforts, and business endeavors of some key players. Recognition algorithms are more readily available via open source, AI platforms, and AI startups developing algorithms to interface the real world to machines.

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