Distinguished members of the committee, thank you for organizing this important study and inviting me to participate in it.
I'll discuss how AI is influencing four categories of work: designing work, supplying workers, conducting work and measuring work and workers. AI-related shifts in each category have policy implications. In aggregate, these shifts raise questions about how to optimize producer flexibility, worker equity and security. More broadly, these trends create policy opportunities for increasing productivity at the national level and strengthening social safety nets.
Although we need policies to address worker displacement from AI-related automation, policy also needs to address AI's influence on a wide range of business activities, including human-machine interactions, surveillance and the use of external or contingent workers. Policy addressing AI in workforce ecosystems should balance workers' interests in sustainable and decent jobs with employers' interests in productivity and economic growth. The goal should be to allow businesses to meet competitive challenges while avoiding dehumanizing workers, discrimination and inequality.
I refer to "workforce ecosystems" rather than "workforces". Our ongoing research on workforce ecosystems demonstrates that more and more organizations rely on workers other than employees to accomplish work. These include contractors, subcontractors, gig workers, business partners and crowds. Over 90% of managers in our global surveys view non-employees as part of their workforce. Many organizations are looking for best practices to ethically orchestrate all workers in an integrated way.
I'll start with designing work. The growing use of AI has a profound effect on work design and workforce ecosystems, including greater use of crowd-based work designs and disaggregating jobs into component tasks or projects. Consider modern food delivery platforms like Grubhub and DoorDash that use AI for sophisticated scheduling, matching, rating and routing, which has essentially redesigned work within the food delivery industry. Without AI, such crowd-based work designs wouldn't be possible.
AI is also driving recent trends to create work without jobs. On the one hand, this modularization of work can facilitate mobility within the firm and improve employee satisfaction by efficiently matching workers with tasks. On the other hand, designing work around tasks and projects can increase reliance on contingent workers for whom fewer benefits are required. Greater numbers of Canadian contingent workers can increase burdens on government-sponsored safety nets.
Now I'll move to supplying workers. On the one hand, AI is transforming business access to labour pools. On the other hand, workers have more opportunities to work across geographic boundaries, creating opportunities for more workers. Using AI to find suitable workers can have both negative and positive consequences. For example, AI can perpetuate or reduce bias in hiring. Similarly, AI systems can help ensure pay equity or contribute to inequity through the workforce ecosystem, by, for example, amplifying the value of existing skills while reducing the value of other skills. It remains an open question on whether AI-driven work redesigns in the global economy will increase or decrease the supply of workers for Canadian businesses.
I'll go to conducting work. In workforce ecosystems, humans and AI work together to create value, with varying levels of interdependency and control over one another. As MIT Professor Thomas Malone suggests, people have the most control when machines act only as tools. Machines have successively more control as their roles expand to assistants, peers and finally, managers. Emergent uses of generative AI in each category raise a variety of policy questions regarding worker liability, privacy and performance management, among other considerations.
The last category where AI is influencing work is measurement. Firms increasingly use AI to measure behaviours and performance that were once impossible to track. From biometric sensors to corporate email analysis to sentiment analysis, advanced measurement techniques have the potential to generate efficiency gains and improve conditions for workers, but they also risk dehumanizing workers and increasing discrimination in the workplace.
That's about five minutes. I'm happy to continue. I have a conclusion, but I'm also happy to stop there.