“There’s this kind of fallacy with large organizations, that they’ve got it all figured out, and that they make decisions based on data. And that’s not true all the time,” said Colleen Pound, CEO of Proxure, during a panel discussion on navigating AI careers at YYC Data Con. “There’s a lot of emotion that goes into making decisions. There’s a lot of history, a lack of transparency.”
That gap between perception and reality in AI careers was a recurring theme throughout the panel.
AI is becoming a fundamental part of business strategy across industries, yet there’s still uncertainty about who can participate in this shift. Companies are increasingly embedding AI in their workflows, and professionals who can bridge the gap between business needs and AI capabilities are in high demand. But if AI is going to be as transformative as experts predict, why does the path into it still feel unclear?
Despite Canada’s reputation as a leader in AI talent (with more than 140,000 AI professionals in 2023) the country lags globally in AI adoption.
A 2024 study found that only 9.3% of Canadian businesses are actively using generative AI, while another 4.6% plan to adopt it, highlighting a cautious approach to implementation. The hesitation comes despite an urgent need for AI skills, as 47% of Canadian technology hiring managers cite a lack of AI-trained staff as a major barrier.
To address the AI skills gap, organizations are investing in upskilling initiatives.
Pound, whose company provides business and market intelligence, predictive pricing, and analytics for the procurement of legal services, was joined by Lauren Ridge, director at PwC specializing in AWS Cloud, Data & AI consulting; Anupama Das, manager of AI engineering at Telus; and Stacey McLennan-Waldal, director of generative AI and data at ATB Financial.
Together, they tackled misconceptions, barriers, and what actually makes someone successful in AI.
Breaking into AI: it’s not just for engineers
AI adoption is increasing across industries, but there’s still a skills gap preventing many professionals from transitioning into the field. While many assume that a deep technical background is required, demand is growing for AI talent beyond software development and data science.
A report by the World Economic Forum found that 44% of workers’ skills will need to be updated due to AI and automation. This means professionals in fields like marketing, healthcare, finance, and operations are increasingly expected to integrate AI into their work, without needing to be engineers.
Ridge, who works at PwC helping businesses implement AI and cloud solutions, challenged the idea that AI careers require technical expertise.
“One of the barriers, as I see it, is that a lot of folks believe that they need to have this deep, deep technical expertise and need to have very advanced technical skills to enter the field,” she said. “I am not technical. I’m a business person.”
McLennan-Waldal, who leads ATB’s generative AI strategy, echoed that, sharing her own path from chemical engineering to AI leadership.
“I just never saw myself as belonging in tech and AI,” she said. “I was a scientist, I was a chemical engineer, but tech? That felt very foreign.”
It wasn’t until the pandemic forced everything online that she started attending virtual AI and data conferences.
For Pound, AI was never the goal — it was a tool.
“I kind of fell into AI and data by accident,” she said. “I’ve always been a big proponent of data-driven decisions… but I became really curious about business problems, and how do we use data to solve business problems?”
Das, who manages AI systems at Telus and focuses on deploying large language models in production environments, emphasized that hands-on experience matters more than credentials.
“Never wait to finish off some courses before entering the space,” she said. “Even if you have done some minimal learning… just get on with it.”
The AI career barrier isn’t technical, it’s perception
A real barrier to AI careers, the panelists agreed, is intimidation. AI can feel like an exclusive club, especially for those who don’t fit the stereotypical mold of an engineer or data scientist.
“It can still feel very bro-y, very white and bro-y,” Pound said bluntly. “That’s definitely something to acknowledge when getting into AI and into tech.”
McLennan-Waldal pointed out that AI marketing itself contributes to this problem.
“It’s a blessing and curse,” she said. “Marketing [AI] is really cool, but it also creates a perceived barrier, because it feels so beyond your capability to understand.”
That perceived barrier keeps people from even trying, and the panel’s message was clear: just start.
“There are so many open-source projects, open-source code bases, where you can just contribute and show off that you can do stuff in this space,” Das said. “You don’t have to spend lots of dollars on courses and certifications.”
The future of AI careers is about more than tech
With generative AI advancing rapidly, the panelists agreed that the next big shift will be toward agentic AI and multimodal models.
“If you think of 2023 as the year of ChatGPT and GenAI, I think 2024 is the year of LLMs, multimodal,” Ridge said. “And I think 2025 is the year of agentic AI.”
Agentic AI refers to AI systems that can operate autonomously, making decisions and taking actions without constant human intervention. Unlike traditional AI, which responds to commands, agentic AI can set goals, plan strategies, and execute tasks in a more independent and adaptive way. This shift could make AI even more useful in business and everyday life, but it also raises new challenges around oversight and accountability.
But with that progress comes new risks.
“What I’m really kind of still stuck on is training models,” Pound said. “Because it’s super interesting to me how models get trained. And also, there’s risk because there’s still a lot of bias that’s being introduced into those models.”
The biggest risk? Losing critical thinking skills.
“We’re relying on the technology so much and not questioning it and not checking it,” Pound warned. “Anyone who has put in a prompt and gotten an output and then just taken that output and not done a double-click on it — you’ve already experienced some of the pain that can go on when those models are not hyper-trained.”
Advice for getting into AI
The panelists wrapped with career advice for those looking to enter or advance in AI.
“Be brave,” Pound said. “Just start, just get into it. Be curious.”
“Don’t compare yourself to anyone else,” Ridge added. “You’re going to be the best version of yourself if you’re following your own passions.”
McLennan-Waldal emphasized networking and visibility, recommending a simple tactic. “Post LinkedIn content. You never know who’s reading it.”
For those still unsure if they belong in AI, the panelists’ experiences made one thing clear: AI careers aren’t just for data scientists. They’re for problem-solvers, creatives, and anyone willing to learn.
Digital Journal is the official media partner of YYC DataCon 2025.
This article was created with the assistance of AI. Learn more about our AI ethics policy here.
