Artificial intelligence (AI) is transforming industries worldwide, yet Canadian manufacturers have been slow to adopt it. A Statistics Canada survey found that just 3.7 per cent of manufacturers are currently using AI in their operations.
Andy Manel, co-founder of technology adoption agency Bold New Edge, sees this hesitation as a mindset challenge driven by cautious leadership, limited in-house expertise, and a tendency to underestimate AI’s potential.
“The single most important mental shift is to stop viewing AI as a cost to be managed and start seeing it as intelligence to be hired,” Manel explains. “It allows leaders to ask, 'What is the first, most valuable job I can give to this new intelligence?' instead of getting paralyzed by the scale of the challenge. That’s how you start — with one focused, tangible project that builds real momentum.”
To support leaders with that first adoption step this fall, Bold New Edge, in partnership with Concordia Continuing Education, is launching the AI for Decision-Makers in Advanced Manufacturing program. This eight-week hybrid program pairs manufacturing leaders with AI mentors and equips them with a roadmap to solve their organizational challenges using AI. It will be led by Microsoft AI Architect Adrián González Sánchez, who has advised governments, industry leaders, and research institutions on how to apply AI in sectors from public health to advanced manufacturing.
“Adopting AI is done step by step, choosing simple yet impactful cases that can begin to build internal maturity and technological scale,” González Sánchez explains.
Manel points to three interconnected barriers slowing adoption.
The first is a leadership and imagination gap: many Canadian business leaders already use AI-powered tools in their operations without realizing it, making it harder for them to recognize AI’s strategic value or quantify its return.
Andy Manel, co-founder of Bold New Edge
The second is a cultural tendency toward “cautious incrementalism,” where small, safe investments win out over potentially transformative experiments. The third barrier is an implementation gap driven by a lack of in-house expertise.
“These are leadership issues,” Manel notes. “The top obstacle is a perceived low ROI, which we see not as a tech failure, but an imagination failure.”
González Sánchez adds that manufacturing leaders often struggle to connect AI’s capabilities to sustainable, high-impact solutions.
“A capable leader must be able to scan their environment, identify realistic opportunities, and understand the trade-offs between building in-house, hiring an integrator, or licensing existing tools,” he says. “These decisions affect not only budgets and timelines but also the training needs that determine whether adoption will succeed.”
Both experts agree that AI’s predictive capabilities make it especially valuable in manufacturing.
When applied to supply chain and production data, these tools allow organizations to anticipate variables and act before problems occur. It can also address operational inefficiencies, from automating the inspection of physical assets to accelerating access to technical documents.
Bold New Edge suggests starting with a clearly defined business problem that already has accessible data. Manel recalls working with an aerospace operations director who envisioned an AI “co-pilot” to help technicians diagnose complex issues faster. Executives initially focused on perceived risks, leaving the idea to sit idle for years
In the AI for Decision-Makers in Advanced Manufacturing program, similar concepts are developed into working prototypes, with leaders producing investment cases that are far more likely to win approval.
Confidence, González Sánchez says, grows through steady exposure and collaboration with experts.
He often sees leaders experiencing “AI impostor syndrome,” doubting their ability to guide projects despite having the strategic skills to do so. His advice: build knowledge in small, steady increments while maintaining sight of business objectives. He also notes that compliance and regulatory considerations must be integrated into each phase of an AI project to achieve smooth implementation without stifling innovation.
Adrián González Sánchez, AI Architect at Microsoft
With that foundation in place, leaders can focus on guiding projects with clarity and purpose.
"AI adoption is not about mastering every algorithm yourself,” González Sánchez notes. “It’s about knowing enough to ask the right questions and keep the project moving toward real business outcomes."
Technical barriers, Manel noted, are shrinking rapidly. Affordable cameras and sensors can now provide AI with sight, sound, and spatial awareness right on the factory floor and enable intelligent digital models at a fraction of past costs.
Manufacturers who act now can prove value quickly, build momentum, and embed AI into their strategic planning. Those who hesitate risk falling behind with yesterday’s tools.
“They won't just be falling behind,” Manel says. “They'll be playing an entirely different game.” Leaders today, he said, must decide which future they want their organization to be part of.“The priority now is developing leaders who understand both the potential and the limits of AI.”
González Sánchez agrees.
“Adopting AI isn't about chasing every new technology,” he says. “It's about choosing the right opportunities, acting with purpose, and preparing your organization to thrive in the years ahead.”
© Concordia University
