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Prasad Girigipati_Future of Work
Prasad Garigipati
Head of Global AI Accelerator in Montreal - Ericsson
Part of the Spotlight on the Forces Shaping the Future of Work

AI’s Impact on the Future of Canada’s Workforce

Takeaways

  1. AI-enabled ICTs will have the largest impact on the future of how we work and our workforce.
  2. Canada needs to address how it will provide meaningful and productive work to employees who will be replaced by artificial intelligence.
  3. The artificial intelligence industry needs to understand the problems that exist within other industries so that the technology can be applied and productivity can be enhanced.

Action

The government and other stakeholders need to create an ecosystem that cuts across faculties, academic institutions, industries and research so that different parties can expose and share information to accelerate innovation within Canada. We must also consider the environmental, social and economic impact of implementing new technologies in Canadian society.


What are the main forces shaping the future of work in Canada?

As we all know, innovation is taking place in every industry possible, but the major one is information and communications technology (ICT) that is artificial intelligence (AI) assisted. AI-enabled ICTs will have the maximum impact on the way we work and how we do things.

When I say AI-assisted or enabled ICT, it means how we get the autonomous AI or assisted AI into existing automation and industrialization. Things that we have already done in the past are going to influence the way we interact and the way we do things. This is especially true for working remotely, which is supported by augmented reality (AR) and virtual reality (VR).

“AI-enabled ICTs will have the maximum impact on the way we work and how we do things.”

At the same time, with Canada being a very large country, we always had difficulty providing our employees with the amenities and the facilities they need while also placing them remotely. This has really been a challenge.

This will give us a lot of innovation capabilities and the ability to work remotely in these augmented scenarios. With low latency technologies that are coming, like 5G and computational power, we can build isolated networks of high processing and computing capabilities remotely. A remote mining or farming site would be able to leverage these technologies.

Obviously, we need to do a lot more work before we get there, and it is quite a bit away. New opportunities, new challenges and new innovations will come into the picture, and those will be the major changes we will be seeing in the future.


How will AI, robotics, and machine learning impact how Canadians work in the future?

With AI-assisted capabilities in the ICT sector, the typical repeated task or lower order intelligence task is really something that we are leveraging, or we will be able to introduce with AI.

What it means is that generally repeated tasks that we do as humans—which we employ quite a lot of people to do—will be replaced. This could be seen as an opportunity for innovation and technology implementation, but we also need to address how to provide meaningful work for replaced employees. 

“Socioeconomic inclusion is important, but are we creating a digital divide with these new technologies?”

At the same time, you can look at both the opportunities and new challenges. Are we in a situation where we will be creating another divide? In that sense, socioeconomic inclusion is important, but are we creating a digital divide with these new technologies? Technology has always had this kind of exclusiveness to it. We need to be cautious about it and understand what opportunity it brings—tremendous opportunities, there is no doubt about it—we will be able to work remotely and have safety related concerns removed because of automation. It can be done, and it can be done remotely.

At the same time, we need to be very cautious in how we address upskilling our people. How do we actually make sure that we do not have socioeconomic division? How do we make sure we get the technology to the right people who are able to acquire it? I think we need to be able to support and evolve that.


What key skills are needed to accelerate Canada’s leadership in AI innovation and implementation?

AI—or machine learning, data analytics and automation—should not be seen as only applicable to the high-tech industries. This is applicable across broader sectors.  Obviously, the AI industry has been isolated or insulated, and because of that, maybe we do not understand the challenges that others have.

We can look at forestry, agriculture or maple syrup production. What problems are these particular sectors having? Is it a data collection problem, is it a data processing problem or lack of information? Do we need more sensory information regarding the weather? We need to really understand the problems of each industry and how to apply artificial intelligence.

Maybe it is not necessarily all advanced artificial intelligence, but the applicability of data analysis, machine learning algorithms, and automation can really increase productivity. That is something that we are not necessarily doing at the scale that we need in Canada.

“The applicability of data analysis, machine learning algorithms, and automation can really increase productivity.”

In order to do that, education should happen at a very early stage and there should be more exposure to these particular techniques and methods. How can we make things better? How can we improve? This will be the major contribution of educational institutions, industries and governments. They should be involved in creating the ecosystem and fostering collaborations across sectors and industries to understand the problem and suggest a proper solution to a particular industry. This will increase productivity and make industries safer.


What are the most important skills tech professionals will need in the future?

The domain and understanding problems are extremely important. That means that we do not necessarily apply AI for the sake of AI, or machine learning for the sake of machine learning. This understanding requires working very closely with the industry and the subject matter experts. How do we expose this knowledge and how do we bring this knowledge to our students, our graduates, our researchers, academic institutions or even technology companies? This is really one of the skills we need to focus on.

“We do not necessarily apply AI for the sake of AI.”

Number two is that there is a major impact on the social and environmental side of work. We need to focus on the socioeconomic inclusion aspects of it. We cannot concentrate only on profit; we have to think about the unique value of technology and its impact on our society. We have to have cross-faculty knowledge working on the economic, social and environmental impacts of technology.

If we are not addressing these particular different correlations, we are putting ourselves at a disadvantage, and maybe the solutions what we develop are not going to be viable on a long-term basis. They might only be profitable in the short-term. We might be able to justify what the social impact is, but maybe there is an environmental impact on the long-term. This is really becoming a global concern for all of us. What kind of a planet are we going to leave for our future generations? We need to start thinking about these three dimensions of skills that will be needed.


How is Ericsson preparing Canadian professionals in the industries you have mentioned?

At Ericsson, we have always concentrated on connectivity, and we did a pretty good job in reaching out globally from a mobile communication perspective. We have also invested in research and development (R&D) and innovation. This is also true for 5G in particular, which is a technology that is able to provide you high data rates with low latency. This is something that will force us to reimagine what we can provide to our society. Low latency is something that we never had before. We always had latency and in a wireless way, getting this low latency brings us into a completely different paradigm of possibilities.

If I take healthcare for example, we can do medical care remotely, bringing knowledge and expertise to a completely different location. Robotics and IoT devises are able to measure indicators and give us more information.

It is a completely different shift, but the main thing is that it gives us a new set of tools and capabilities that we have never had before. That is why we all need to imagine or think in a completely different way with these new capabilities.


What would you pitch to people in positions of power to prepare Canada’s workforce for the future of work?

It is extremely important to recognize we need cross-faculty, cross-academic, cross-industry collaborations and research. That means the government and other entities have to create these ecosystems to come together. This is extremely important because this is how we can expose, share and actually kindle interest while becoming innovative.

We must also consider the social inclusion problem and not create another divide. We need to be looking at economic, social and environmental impact. This is where we need to play an important role and recognize these impacts in everything that we do.

Those are the two pillars that we need to focus on to pursue this particular path of achievement in the implementation of AI, automation and new technologies across industries.

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Prasad Girigipati_Future of Work
Prasad Garigipati
Head of Global AI Accelerator in Montreal - Ericsson

Bio: Prasad Garigipati is the Head of the Global AI Accelerator at Ericsson. He has 20 years of experience in the domain of data warehousing business intelligence and analytics. Prasad has delivered three product solutions from conceptualization to market. He has worked in sales and business development in Africa, the Americas and the Asia-Pacific region.

 

Organization Profile: Ericsson is one of the leading providers of Information and Communication Technology (ICT) to service providers. Its Global AI Accelerator in Montreal focuses on R&D, AI and automation, leveraging cutting-edge technologies to create data-driven, intelligent and robust systems for automation, evolution and growth.