AI adoption in Canada rising, but upskilling remains top barrier: IBM

AI adoption among large organizations has remained steady globally, while Canada saw an uptick from 34 per cent in April 2023 to 37 per cent in November 2023, a new study by IBM Canada found.

The company surveyed over 2,000 IT professionals in organizations with over 1,000 employees in Australia, Canada, China, France, Germany, India and many more countries.

In Canada, early adopters are leading the way, with 35 per cent of enterprises already working with AI with the aim to accelerate and increase investment in the technology. An additional 48 per cent of Canadian companies are still exploring using AI.

The increased accessibility of AI tools (46 per cent), the need to reduce costs and automate key processes (46 per cent), as well as the increasing amount of AI embedded into standard off-the-shelf business applications (34 per cent) are the top factors driving AI adoption.

However, despite the rise in adoption, AI investments are less likely to accelerate, notably as organizations struggle to find the right talent, the study highlighted. Limited AI skills and expertise (41 per cent) is, in fact, the number one barrier hindering successful AI adoption at enterprises both exploring and deploying AI.

Around one-in-five (21 per cent) organizations do not have employees with the right skills in place to use new AI or automation tools, and 17 per cent cannot find new hires with the skills to address that gap. Plus, only 25 per cent are currently training or reskilling employees to work with new automation and AI tools.

“Technology is a critical engine driving transformation across industries, but as the gap between the skills organizations need in order to deliver digital transformation and the availability of a workforce with those skills widens, the strength of this engine is at risk,” affirmed Deb Pimentel, general manager, technology, IBM Canada. “Some of the challenges companies face include lack of customization to organizations’ specific needs and goals, using a one-size-fits-all approach, ineffective ways of delivering the training, and limited opportunities to apply the skills in the real world.”

Upskilling becomes even more tricky when companies do not yet know what the best use cases of AI are, or when the executives themselves are not using it at all, affirmed Jeremy Shaki, chief executive officer of Canadian tech education company, Lighthouse Labs.

Added to that is the fact the tools and use cases are changing so rapidly that it makes it very difficult for most companies to know how exactly they should approach upskilling in the AI space, explained Shaki.

The dominant narrative, that AI will provide opportunities for companies to have less people also does not help, added Shaki, because a pragmatic employee should see that and consider how they can remain in the company by being the user of AI who makes other roles redundant.

“The real reason to upskill in AI is because of the value and growth it will provide companies,” he explained. “Jobs will change and adjust, but if AI at a higher level is bringing in better insights, predictions, and certainty for a business, it will allow that business to grow aggressively. In that moment, companies won’t be talking about layoffs, they will be talking about having enough people to service the opportunity AI is affording them.” 

Pimentel concurred that, with AI, new roles will be created, many more will be transformed, and some will transition away. But the main challenge for organizations is to be able to vocalize that.

She added, “This means organizations will have to think about providing people with the relevant skills to work with AI to accomplish goals faster than ever before. They will also have to think systematically about how to move people who have been freed from routine work by AI into roles that are more fulfilling and impactful to the business.”

Shaki agreed that companies should engage with education spaces to discuss where the use cases are the most valuable, and have custom training developed for their teams to immediately drive adoption. 

“Pairing the goals of employee adoption and company goal alignment is a powerful way of building the first real training in this space,” he said.

Canada has the right resources lined up  — talent, leading researchers and academics, investment by both global and Canadian organizations, and government commitment, said Pimentel, adding that a thoughtful plan is necessary to build up the strategic and trusted adoption of AI.

She advises the following:

  1. Have a clear AI strategy – Canadian business leaders need to set clear AI strategies that define clear problems they want to solve, make sure they have the right data in the right place to drive those outcomes, overcome the skills gap with the right people and automation tools, and build in AI governance from the start.
  2. Reimagine how work gets done – Look to automate the mundane and tedious tasks that take up precious time, so employees can focus on higher value projects. This will require organizations to have a POV on what skills are going to increase in demand and decrease in demand, and have the right training programs in place.
  3. Ensure the technology is trustworthy and responsible AI – Any organization that wants to apply AI models needs to establish appropriate guardrails that ensure the AI in use is transparent, governed, and trusted. Ethical considerations must be addressed from the outset.

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Ashee Pamma
Ashee Pamma
Ashee is a writer for ITWC. She completed her degree in Communication and Media Studies at Carleton University in Ottawa. She hopes to become a columnist after further studies in Journalism. You can email her at [email protected]

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