Sizing the prize

PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution

What’s the real value of AI for your business and how can you capitalise?

Getting down to what really counts

Business leaders are asking: What impact will AI have on my organisation, and is our business model threatened by AI disruption? And as these leaders look to capitalise on AI opportunities, they’re asking: Where should we target investment, and what kind of capabilities would enable us to perform better? Cutting across all these considerations is how to build AI in the responsible and transparent way needed to maintain the confidence of customers and wider stakeholders.

These are the strategic questions we’ll be addressing in a series of reports designed to help enterprises create a clear and compelling business case for AI investment and development. While there’s been a lot of research on the impact of automation, it’s only part of the story. In this new series of PwC reports, we want to highlight how AI can enhance and augment what enterprises can do, the value potential of which is as large, if not larger, than automation.

The analysis carried out for this report gauges the economic potential for AI between now and 2030, including for regional economies and eight commercial sectors worldwide. Through our AI Impact Index, we also look at how improvements to personalisation/customisation, quality and functionality could boost value, choice and demand across nearly 300 use cases of AI, along with how quickly transformation and disruption are likely to take hold. Other key elements of the research include in-depth sectorby-sector analyses.

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Getting down to what really counts
Getting down to what really counts

 

Which regions will gain the most from AI?

Total economic impact of AI in the period to 2030

All GDP figures are reported in market exchange rate terms

All GDP figures are reported in real 2016 prices, GDP baseline based on Market Exchange Rate Basis

Source: PwC analysis

Net effect of AI, not growth prediction 

Our results are generated using a large scale dynamic economic model of the global economy. The model is built on the Global Trade Analysis Project (GTAP) database. GTAP provides detail on the size of different economic sectors (57 in total) and how they trade with each other through their supply chains. It gives this detail on a consistent basis for 140 different countries.

When considering the results, there are two important factors that you should take into account:

  1. Our results show the economic impact of AI only – our results may not show up directly into future economic growth figures, as there will be many positive or negative forces that either amplify or cancel out the potential effects of AI (e.g. shifts in global trade policy, financial booms and busts, major commodity price changes, geopolitical shocks etc.).
  2. Our economic model results are compared to a baseline of long-term steady state economic growth. The baseline is constructed from three key elements: population growth, growth in the capital stock and technological change. The assumed baseline rate of technological change is based on average historical trends. It’s very difficult to separate out how far AI will just help economies to achieve long-term average growth rates (implying the contribution from existing technologies phase out over time) or simply be additional to historical average growth rates (given that these will have factored in major technological advances of earlier periods).

These two factors mean that our results should be interpreted as the potential ‘size of the economic prize’ associated with AI, as opposed to direct estimates of future economic growth. 

AI Impact Index 

Our sector specialists worked with market participants and our partners at Fraunhofer to identify and evaluate use cases across five criteria: 

  • Potential to enhance personalisation. 
  • Potential to enhance quality (utility value). 
  • Potential to enhance consistency. 
  • Potential to save time for consumers.  
  • Availability of data to make these gains possible. 

Specific scoring parameters were derived for each criterion, and scores range from 1-5 (1 being lowest impact, 5 being highest). The parameters were weighted to arrive at a total Potential AI Consumption Impact. We also evaluated technological feasibility, and other drivers and inhibitors of consumer uptake. The results helped us to gauge time to adoption, potential barriers and how they can be overcome.  

Explore the AI impact by sector

Personalization
Data Available
Utility
% Adoption Maturity
Near Term
(0-3 yr)
Mid Term
(3-7 yr)
Long Term
(7+ yr)
Time Saved
Potential AI Consumption Impact

 

Which of your products and services will provide the greatest opportunity for AI?

Healthcare

Three areas with the biggest AI potential

  • Supporting diagnosis in areas such as detecting small variations from the baseline in patients’ health data or comparison with similar patients.
  • Early identification of potential pandemics and tracking incidence of the disease to help prevent and contain its spread.
  • Imaging diagnostics (radiology, pathology).


Consumer benefits

Faster and more accurate diagnoses and more personalised treatment in the short and medium term, which would pave the way for longer term breakthroughs in areas such as intelligent implants. Ultimate benefits are improved health and lives saved.


Time saved

More effective prevention helps reduce the risk of illness and hospitalisation. In turn, faster detection and diagnosis would allow for earlier intervention.


Timing

  • Ready to go: Medical insurance and smarter scheduling (e.g. appointments and operations).
  • Medium-term potential: Data-driven diagnostics and virtual drug development.
  • Longer-term potential:  Robot doctors carrying out diagnosis and treatment.


Barriers to overcome

It would be necessary to address concerns over the privacy and protection of sensitive health data. The complexity of human biology and the need for further technological development also mean than some of the more advanced applications may take time to reach their potential and gain acceptance from patients, healthcare providers and regulators.


High potential use case: Data-based diagnostic support

AI-powered diagnostics use the patient’s unique history as a baseline against which small deviations flag a possible health condition in need of further investigation and treatment. AI is initially likely to be adopted as an aid, rather than replacement, for human physicians. It will augment physicians’ diagnoses, but in the process also provide valuable insights for the AI to learn continuously and improve. This continuous interaction between human physicians and the AI-powered diagnostics will enhance the accuracy of the systems and, over time, provide enough confidence for humans to delegate the task entirely to the AI system to operate autonomously.

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Automotive

Three areas with the biggest AI potential

  • Autonomous fleets for ride sharing.
  • Semi-autonomous features such as driver assist.
  • Engine monitoring and predictive, autonomous maintenance.


Consumer benefit

A machine to drive you around and ‘on-demand’ flexibility – for example a small model to get you through a city or a bigger and more powerful vehicle to go away in for the weekend.


Time saved

The average American spends nearly 300 hours a year driving[1] – think what you could with that time if you didn’t have to spend it behind the wheel.The average American spends near 300 hours a year driving[2] – think what you could with that time if you didn’t have to spend it behind the wheel.


Timing

  • Ready to go: Automated driver assistance systems (e.g. parking assist, lane centring, adaptive cruise control etc.).
  • Medium-term potential: On-demand parts manufacturing and maintenance.
  • Longer-term potential: Engine monitoring and predictive, autonomous maintenance.


Barriers to overcome

Technology still needs development – having an autonomous vehicle perform safely under extreme weather conditions might prove more challenging. Even if the technology is in place, it would need to gain consumer trust and regulatory acceptance.


High potential use case: Autonomous fleets for ride sharing

Autonomous fleets would enable travellers to access the vehicle they need at that point, rather than having to make do with what they have or pay for insurance and maintenance on a car that sits in the drive for much of the time. Most of the necessary data is available and technology is advancing. However, businesses still need to win consumer trust.

 

[1] 4 American Automobile Association media release 8 September 2016 (http://newsroom.aaa.com/2016/09/americans-spend-average-17600-minutes-driving-year/)

[2] American Automobile Association media release 8 September 2016 (http://newsroom.aaa.com/2016/09/americans-spend-average-17600-minutes-driving-year/)

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Financial services

Three areas with the biggest AI potential

  • Personalised financial planning.
  • Fraud detection and anti-money laundering.
  • Process automation – not just back office functions, but customer facing operations as well.


Consumer benefit

More customised and holistic (e.g. health, wealth and retirement) solutions, which make money work harder (e.g. channelling surplus funds into investment plans) and adapt as consumer needs change (e.g. change in income or new baby).

Timing

  • Ready to go: Robo-advice, automated insurance underwriting and robotic process automation in areas such as finance and compliance.
  • Medium-term potential: Optimised product design based on consumer sentiment and preferences.
  • Longer-term potential: Moving from anticipating what will happen and when in areas such as an insurable loss (predictive analytics) to proactively shaping the outcome (prescriptive analytics) in areas such as reduced accident rates or improved consumer outcomes.

Time saved

The information customers need to fully understand financial position and plan for the future is at their fingertips and adapts to changing circumstances. Businesses can support this by developing customised solutions rather than expecting consumers to sift through multiple options to find the one that’s appropriate.  


Barriers to overcome

Consumer trust and regulatory acceptance.


High potential use case: Personalised financial planning

While human financial advice is costly and time consuming, AI developments such as robo-advice have made it possible to develop customised investment solutions for mass market consumers in ways that would, until recently, only have been available to high net worth (HNW) clients. Finances are managed dynamically to match goals (e.g. saving for a mortgage) and optimise client’s available funds, as asset managers become augmented and, in some cases, replaced by AI. The technology and data is in place, though customer acceptance would still need to increase to realise the full potential.

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Retail and consumer

Three areas with the biggest AI potential

  • Personalised design and production.
  • Anticipating customer demand – for example, retailers are beginning to use deep learning to predict customers’ orders in advance.
  • Inventory and delivery management.


Consumer benefit

On-demand customisation as the norm and greater availability of what you want, when and how you want it.


Timing

  • Ready to go: Product recommendation based on preferences.
  • Medium-term potential: Fully customised products. 
  • Longer-term potential: Products that anticipate demand from market signals.


Time saved

Less time exploring shelves, catalogues and websites to find the product that you want.


Barriers to overcome

Adapting design and production to this more agile and tailored approach. Businesses also need to strengthen trust over data usage and protection.  


High potential use case: Personalised design and production

Instead of being produced uniformly, apparels and consumables can be tailored on demand. If we look at fashion and clothing as an example, we could eventually move to fully interactive and customised design and supply in which AI created mock-ups of garments are sold online, made in small batches using automated production, and subsequent changes are made to design based on user feedback.

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Technology, communications and entertainment

Three areas with the biggest AI potential

  • Media archiving and search – bringing together diffuse content for recommendation.
  • Customised content creation (marketing, film, music, etc.).
  • Personalised marketing and advertising.


Consumer benefit

Increasingly personalised content generation, recommendation and supply.


Timing

  • Ready to go: Content recommendation for consumers.
  • Medium-term potential: Automated telemarketing capable of holding a real conversation with the customer.
  • Longer-term potential: Use-case specific and individualised AI-created content.


Time saved

Quicker and easier for consumers to choose what they want, reflecting their preferences and mood at the time.


Barriers to overcome

Cutting through the noise when there is so much data, much of it unstructured.


High potential use case: Media archiving and search

We already have personalised content recommendation within the entertainment sector. Yet there is now so much existing and newly generated (e.g. online video) content that it can be difficult to tag, recommend and monetise. AI offers more efficient options for classification and archiving of this huge vault of assets, paving the way for more precise targeting and increased revenue generation.

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Manufacturing

Three areas with the biggest AI potential

  • Enhanced monitoring and auto-correction of manufacturing processes.
  • Supply chain and production optimisation.
  • On-demand production.


Consumer benefit

Indirect benefits from more flexible, responsive and custom-made manufacturing of goods, with fewer delays, fewer defects and faster delivery.


Timing

  • Ready to go: Greater automation of a large number of production processes.
  • Medium-term potential: Intelligent automation in areas ranging from supply chain optimisation to more predictive scheduling. 
  • Longer-term potential: Using prescriptive analytics in product design – solving problems and shaping outcomes, rather than simply predicting and responding to demand in product design.


Time saved

Faster response and fewer delays.


Barriers to overcome

Making the most of supply chain and production opportunities requires all parties to have the necessary technology and be ready to collaborate. Only the biggest and best-resourced suppliers and manufacturers are up to speed at present.  


High potential use case: Enhanced monitoring and auto-correction

Self-learning monitoring makes the manufacturing process more predictable and controllable, reducing costly delays, defects or deviation from product specifications. There is huge amount of data available right through the manufacturing process, which allows for intelligent monitoring.

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Energy

Three areas with the biggest AI potential

  • Smart metering – real-time information on energy usage, helping to reduce bills.
  • More efficient grid operation and storage.
  • Predictive infrastructure maintenance. 


Consumer benefit

More efficient and cost-effective supply and usage of energy.


Timing

  • Ready to go: Smart metering.
  • Medium-term potential: Optimised power management.
  • Longer-term potential: More efficient and consistent renewable energy supply in areas such as improved prediction and optimisation of wind power.


Time saved

More secure supply and fewer outages.

 

Barriers to overcome

Technological development and high investment requirements in some of the more advanced areas.


High potential use case: Smart meters

Smart meters help customers tailor their energy consumption and reduce costs. Greater usage would also open up a massive source of data, which could pave the way for more customised tariffs and more efficient supply.

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Transport and logistics

Three areas with the biggest AI potential

  • Autonomous trucking and delivery.
  • Traffic control and reduced congestion.
  • Enhanced security.


Consumer benefit

Greater flexibility, customisation and choice in how goods and people move around and the ability to get from A to B faster and more reliably.


Timing

  • Ready to go: Automated picking in warehouses.
  • Medium-term potential: Traffic control.
  • Longer-term potential: Autonomous trucking and delivery. 


Time saved

Smart scheduling, fewer traffic jams and real-time route adjustment to speed up transport.


Barriers to overcome

Technology for autonomous fleets is still in development.


High potential use case: Autonomous trucking

Autonomous trucking reduces costs by allowing for increased asset utilisation as 24/7 runtimes are possible. Moreover, the whole business model of transport & logistics (T&L) might be disrupted by new market entrants such as truck manufacturers offering T&L and large online retailers vertically integrating their T&L.

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Way forward: Four steps to making the most out of AI

Work out what AI means for your business

The starting point for strategic evaluation is a scan of the technological developments and competitive pressures coming up within your sector, how quickly they will arrive, and how you will respond. You can then identify the operational pain points that automation and other AI techniques could address, what disruptive opportunities are opened up by the AI that’s available now, and what’s coming up on the horizon.

Prioritise your response

In determining your strategic response, key questions include how can different AI options help you to deliver your business goals and what is your appetite and readiness for change. Do you want to be an early adopter, fast follower or follower? Is your strategic objective for AI to transform your business or to disrupt your sector?

Make sure you have the right talent and culture, as well as technology

While investment in AI may seem expensive now, PwC subject matter specialists anticipate that the costs will decline over the next ten years as the software becomes more commoditised. Eventually, we’ll move towards a free (or ‘freemium’ model) for simple activities, and a premium model for business-differentiating services. While the enabling technology is likely to be increasingly commoditised, the supply of data and how it’s used are set to become the primary asset.

Build in appropriate governance and control

Trust and transparency are critical. In relation to autonomous vehicles, for example, AI requires people to trust their lives to a machine – that’s a huge leap of faith for both passengers and public policymakers. Anything that goes wrong, be it a malfunction or a crash, is headline news. And this reputational risk applies to all forms of AI, not just autonomous vehicles. Customer engagement robots have been known to acquire biases through training or even manipulation, for example.

Contact us

Gerard Verweij
Global & US Data & Analytics Leader, PwC US
Tel: +1 (617) 416 6089
Email

Anand Rao
Global Leader of Artificial Intelligence, PwC US
Tel: +1 (617) 530 4691
Email

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