AI: Everywhere and Nowhere (Part 3)

We are moving to a new stage of augmented intelligence, where humans and machines learn from each other and redefine what they do together.

This is Part 3 of a 3-part series. Part 1 can be found here; Part 2, here. In our first blog post on artificial intelligence (AI),  we outlined the challenges of defining AIand in our second blog post, we described how ubiquitous AI is becoming, defining it as "ubiquitous intelligence." In this post, we define the continuum of AI as "AAAI": assisted, augmented and autonomous intelligence. AI as Assisted Intelligence Over the past couple of decades, AI has replaced many of the repetitive and standardized tasks done by humans. For example, industrial robots are tackling many manufacturing tasks. Similarly, many administrative tasks such as taking meeting minutes, answering phones and searching for information are all done by some form of an automated system. We call this type of automation — where the AI is assisting humans to do the same tasks faster or better — assisted intelligence. The humans are still making some of the key decisions, but the AI is executing the tasks on their behalf. The decision rights are solely with the humans.  AI as Augmented Intelligence We are just now moving to the next stage of augmented intelligence, where humans and machines learn from each other and redefine the breadth and depth of what they do together. For example, in a recent client engagement, we carried out 200,000 go-to-market scenarios generated by an AI system for a service introduction. This provided the human decision-makers with a high degree of granularity and specificity regarding the assumptions, future projections and impact of the new service. While the system learned a lot and modeled the ecosystem, the humans saw the sensitivities and feedback involved in market adoption. Under these circumstances, the human and the machine share the decision rights. In addition, unlike assisted intelligence, in augmented intelligence, the nature of the task fundamentally changes. On a spectrum ranging from no automation to total autonomous operation, each sector, company and individual will set the appropriate level of machine augmentation. Over time, the dial might move more toward totally autonomous, or it might stay somewhere in between. AI as Autonomous Intelligence Lastly, we see autonomous intelligence, in some cases, where adaptive/continuous systems take over. They will do so only after the human decision-maker starts trusting the machine (e.g., fully autonomous self-driving cars), or when the cycle time of decision making is so fast that having the human in the loop is a liability (e.g., automated trading). In autonomous intelligence, the decision rights are with the machine and are fundamentally different from assisted intelligence. See also: Of Robots, Self-Driving Cars and Insurance The decision to move from augmented intelligence to autonomous intelligence will largely be in our hands and will be made based on a number of different factors — including the speed of human decision making, the technical feasibility of making autonomous decisions, the cost of building solutions and the trust we place in these solutions. As enterprises contemplate the introduction of AI across their functional areas, it helps to clearly articulate which stage of AI they are aiming for. Are they merely automating repetitive tasks and providing assisted intelligence? Are they fundamentally changing the nature of work by having humans and machines collaborate with each other to make decisions with augmented intelligence? Or are they delegating all decision making with autonomous intelligence?

Anand Rao

Profile picture for user Anand_Rao

Anand Rao

Anand Rao is a principal in PwC’s advisory practice. He leads the insurance analytics practice, is the innovation lead for the U.S. firm’s analytics group and is the co-lead for the Global Project Blue, Future of Insurance research. Before joining PwC, Rao was with Mitchell Madison Group in London.


Read More