In manufacturing, I think the biggest gains are going to come from improvements in industrial production processes. This is where AI really needs to be integrated with other physical systems: with the equipment and with all of the IT infrastructure, the technology stack that is required to support AI implementation.
Some of the use cases that have been provided by the AI4M AI manufacturing cluster are really pretty revelatory here: areas such as throughputs, quality control and quality assurance, and areas for reducing the downtime of equipment, for instance, and improving energy management, reducing emissions and reducing energy use. These are some of the applications where AI has really been shown to lead to very significant improvements, with, in some use cases, well over 30% improvement. I think that's the type of productivity gain we really need.
It's not just to improve productivity. Productivity is really about.... There are three ways to achieve greater productivity. You can do a lot more with more. That's the positive aspect of productivity. That's what AI can lead to in manufacturing.
The other two areas are not so good. In the first part of 2010, Canadian manufacturing productivity looked terrible against that of the United States. The reason was that manufacturing production dropped in both countries and the U.S. removed many more jobs than Canadian manufacturers did, which led to a significant improvement in U.S. productivity in manufacturing, but it wasn't a good news story. The economic output dropped, and jobs dropped a lot more.
I think that's what we need to avoid. We need to look at the positive impacts of how AI and other technologies can be implemented to improve and expand the capacity of manufacturing today, at a time when it's very difficult to find people working in the sector.