In their book, ‘Artificial Intelligence: A Modern Approach’, Stuart Russell and Peter Norvig define AI as “the designing and building of intelligent agents that receive percepts from the environment and take actions that affect that environment”.[1] The most critical difference between AI and general-purpose software is in the phrase “take action”. AI enables machines to respond on their own to signals from the world at large, signals that programmers do not directly control and therefore can’t anticipate. The fastest-growing category of AI is machine learning, the ability of software to improve its own activity, based on interaction with the world at large.
Commercially-applied AI has expanded in recent years, driven by a combination of computing power, the availability of huge datasets and advances in machine learning...
Navigating the sheer breadth of algorithms and applications that fall under the banner of AI has become a formidable task in its own right. To date, a lot of the focus has been on automation of tasks that are already carried out[3]. Yet as workers are freed from routine tasks and human and machines begin to collaborate more closely, the real breakthroughs will come from the ability to make more insightful decisions and the emergence of completely new augmented intelligence-led business models. Entertainment is a clear example of a sector that has already undergone significant disruption and change. Driverless cars are one of the many ways that AI is set to transform everyday lives and the businesses that support this.
...the real breakthroughs will come from the ability to make more insightful decisions and the emergence of completely new augmented intelligence-led business models.
Commercially-applied AI has expanded in recent years, driven by a combination of computing power, the availability of huge datasets and advances in machine learning (which includes deep learning). While often used for predictive analytics, as well as image and speech classification, machine learning can be combined with elements of ‘traditional’ AI such as natural language processing, strategic planning and logical reasoning to deliver powerful autonomous agents.
So how prevalent is AI? Outside of large tech companies that have been utilising AI in service delivery for a number of years, much of the innovation is still in its infancy and is largely confined to the lab in the form of proof of concepts or R&D. The focus for business now has to be on creating an environment which fosters successful transition into real world value delivery.
As table below highlights, the adoption of AI demands a new way of thinking about technology, business development and strategic execution, along with the reshaped operating model and decision making processes that underpin this. And this affects the entire business, rather than just technology and innovation teams.
Traditional approach |
New approach |
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Strategy |
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Design |
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Development |
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Operating Model |
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[1] ‘Artificial Intelligence: A Modern Approach’, Stuart Russell and Peter Norvig (Pearson, 2009)
[2] ‘What doctor: Why AI and robotics will define the New Health’ (https://www.pwc.com/gx/en/industries/healthcare/publications/ai-robotics-new-health/survey-results.html)
[3] We explore the impact of automation and AI on production and employment in ‘Will robots steal our jobs? The potential impact of automation on the UK and other major economies, March 2017 (https://www.pwc.co.uk/economic-services/ukeo/pwcukeo-section-4-automation-march-2017-v2.pdf)