I was recently reviewing some forecasts for the growth of artificial intelligence (AI) and was impressed by AI’s potential business value. For example:
- Gartner says that AI-derived business value is forecast to reach $3.9 trillion in 2022.
- The market for AI-based technologies will grow at a compound annual rate of 37 percent to reach $191 billion by 2024, according to Market Research Engine.
The numbers are important. They provide a benchmark to understand market growth across the AI landscape. But I’m always interested in the “why” behind the numbers. Why is AI growing? The answer is that AI is getting smarter and being applied in more ways. AI is getting smarter in ways that we expect, but it’s also getting smarter in some surprising ways, such as gaining emotional intelligence resulting in the creation of lovable forms of AI. Below I’ve outlined five ways that AI is getting (better and) smarter.
1) A Lovable AI
Far from the portrait of sinister AI-powered machines becoming self-aware and taking over the world, AI is actually being used in real-world applications that improve human experiences. This is, in fact, the way businesses will effectively sell AI technology to a wary public. Both product and service-based businesses using AI are beginning to provide lovable experiences and outcomes for those they serve through the continuous feedback loop of insights that fuel designers to more accurately design for end-users. Overall, the more seamlessly they can fit into our users’ lifestyles, the more widely they’ll be accepted.
2) AI with Attitude, well Personality
AI technologies such as neural text-to-speech (NTTS) and text-to-speech are making it possible for businesses to lend personalities to AI experiences and remove the robotic nature of AI voices. A good example is Amazon launching an Alexa skill that makes it possible for your voice assistant to sound like actor Samuel L. Jackson. Google has been doing something similar by lending the voices of Issa Rae and John Legend to the Google Assistant. These are recorded responses triggered by specific user utterances. The developments are important because they make AI applications such as voice assistants more approachable to the general public as a new way to customize interactions.
3) AI is Supporting the Circular Economy
AI can help businesses design products that either maintain their value over a longer period of time or be reused more efficiently. AI is already helping organizations create new materials to design products that last. It can also help businesses create a better infrastructure that supports the reuse of products, such as identifying how businesses can design better processes for recycling materials. Here you can read more about two industries: Food and Consumer electronics as it relates to the coming together of AI and the circular economy.
4) AI Is Getting Easier to Test and Learn
One of AI’s biggest initial drawbacks has been its exceedingly high project failure rate. This has occurred for a number of reasons, from poor data, a solution without a problem, a poor experience, to inappropriate AI application. Fortunately, product design concepts such as design sprints are making it possible to test and learn with AI in a way that mitigates cost and risk. Design sprints allow developers to gain a clearer vision of their end-product and make development adjustments accordingly. The design sprint is a proven methodology for solving AI problems through designing, prototyping, and testing ideas with users, over a very short time period.
5) AI Is Breaking into Multiple Industries
Once considered the exclusive domain of robotics and robotics process automation (RPA), AI has proven itself to be advantageous to many industries in that its power extends far beyond simple mechanization. A key reason is the enhancement of algorithmic training known as machine learning. AI is being introduced to many different marketplaces, from sports analytics to healthcare to education to the non-profit sector. In the end, AI will be on the forefront of the customer experience across every touchpoint for almost every industry.
You can be sure that by the time this article is published that numerous AIs have gotten smarter through the additional ingested data, the GPU optimization, and not to mention both machine learning and deep learning algorithm training. In what ways do you see AI getting smarter? Or what questions do you have about AI?