Research and methodology: PwC’s Global Artificial Intelligence Study

Economic impact of AI by 2030: 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 of AI 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.