Other parts of this series:
The pace of change in digital technology is accelerating exponentially, particularly in the field of artificial intelligence (AI). All major technology companies are reorganizing around this red-hot sector, and it dominated discussions and presentations at the recent World Economic Forum 2017 in Davos. In this series, I will try to provide an overview of what constitutes AI, how it is already being applied to banking, insurance and capital markets today, and how organizations can craft a value-driven AI strategy.
Why are we experiencing the current level of growth in the AI field? There are several factors:
- High-performance computing power is readily available
- Data to train and fuel AI is more accessible
- Democratization of AI skills
- Proliferation of AI products and solutions
At Accenture, we believe the time to move on AI is now.
Low barriers to entry for AI will accelerate the rate of competition. This trend coupled with the exponential growth of AI accelerates high-performing organizations and will leave the slow movers behind.
We view AI as a collection of multiple technologies that enable machines to sense, comprehend and act—and learn—either on their own or to augment human activities. As a new factor of production, AI has the potential to introduce new sources of growth, changing how work is done and reinforcing the role of people to drive growth in business.
The key is to comprehend AI as a capital–labor hybrid. AI can replicate labor activities at much greater scale and speed, and even perform some tasks beyond the capabilities of humans. AI can also take the form of physical capital, such as robots and intelligent machines. And unlike conventional capital, thanks to its self-learning capabilities, AI can actually improve over time.
Today’s definition of AI refers to multiple technologies that can be combined in different ways to:
- Sense—computer vision and audio processing, for example, are able to actively perceive the world around them by acquiring and processing images, sounds and speech. The use of facial recognition at border control kiosks is one example of how it can improve productivity.
- Comprehend—natural language processing and interference engines can enable AI systems to analyze and understand the information collected. This technology is used to power the language translation feature of search engine results.
- Act—an AI system can take action through technologies such as expert systems and interference engines, or undertake actions in the physical world. Auto-pilot features and assisted-breaking capabilities in cars are examples of this.
All three capabilities are underpinned by the ability to learn from experience and adapt over time.