How AI is changing the rules of the game

What is artificial intelligence?

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...

The spectrum of AI can be divided into three:

  • Assisted Intelligence, widely available today, improves what people and organisations are already doing.
  • Augmented Intelligence, emerging today, enables people to do things they couldn’t otherwise do.
  • Autonomous Intelligence, being developed for the future, establishes machines that act on their own.

Some of the ways AI is making its mark

  • Keeping us informed: ‘Personal assistants’ such as Alexa and Siri, as well as banking and mobile phone network operator chatbots.
  • Predicting behaviour: The UK National Health Service are piloting machine learning to predict outpatient non-attendance at a UK hospital, optimising scheduling and rescheduling of appointments while understanding the drivers that can influence patient behaviour and enable actions to be taken to reduce rates of non-attendance.
  • Keeping us well: AI is being used to aid medical diagnosis – our research shows that a significant proportion of people worldwide are willing to choose certain treatments, tests or services administered by an AI or robot[2].
  • Keeping us engaged: Telecoms and media companies have been using machine learning customer analytics to predict and then recommend actions to prevent customer turnover.
  • Anticipating demand: Retailers are beginning to use deep learning to predict customers’ orders a week in advance.
  • Customisation for all: Robo-advice has made it possible to offer customised investment solutions to a wider range of consumers. Until recently, this level of investment advice was only available to high net worth (HNW) clients.
  • Improving quality: Manufacturers are using AI to improve quality control, reduce production line downtime and increase the speed and yield of industrial processes.
  • Intelligent processes: Intelligent process automation is driving huge savings in finance, HR and compliance. Robotic process automation is combined with AI to perform high volume, routine tasks.

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.

New approach

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

Strategy

  • Technology for information management
  • Data as business intelligence
  • Deterministic approach
  • Technology that manages your business
  • Data as your differentiating intellectual property
  • Directional (iterative) approach

Design

  • User experience as an application layer
  • Decision making hard coded
  • Information retrieval as fact from database
  • User experience as the primary application feature
  • Decision process learnt by software
  • Information retrieval most probable correct answer

Development

  • Linear technology development
  • Business management teams specify, technology team builds
  • Iterative technology and business model development
  • Business subject matter experts integrated into technology development teams

Operating Model

  • Steady state technology, punctuated with upgrades
  • Technical risks dominated by system downtime and errors
  • Cyber attacks
  • Dynamic, adaptive models. Continuous test driven development
  • Technical risks include learned and unexpected behaviour
  • Adversarial attacks

[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)

Contact us

Chris Oxborough
Partner, Technology Risk (Emerging and Disruptive Technology)
Tel: +44 (0)207 212 4195
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Laurence Egglesfield
Director – Technology Risk (Emerging and Disruptive Technology)
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Euan Cameron
UK Artificial Intelligence Leader
Tel: +44 (0)20 7804 3554
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Rob McCargow
Programme Leader - Artificial Intelligence, Technology & Investment
Tel: +44 (0)207 213 3273
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