Bridging the gap from AI research to adoption

April 9, 2026

Bell employees are collaborating with AI innovators to bridge the gap between research and adoption, driving competitive advantage.

“You wouldn’t try to run a business without a website. It’s time to start thinking about AI the same way.”
Glenda Crisp, President and CEO
Vector Institute

Glenda Crisp Artificial intelligence (AI) is embedded in nearly every facet of life, and a lot of it was developed in Canada or built on foundations established by Canadian researchers. But there’s a paradox at play. While Canada’s researchers have taken big risks and pushed new frontiers in AI, Canadian businesses have been hesitant to do the same. In the second quarter of 2025, just 12% of Canadian businesses were using AI to produce goods or deliver services. Glenda Crisp and the Vector Institute want to change that.

“Once upon a time, the internet was new and we all had to think about how to use it to run our businesses better,” says Crisp, Vector’s President and CEO. “Now you wouldn’t try to run a business without a website. It’s time to start thinking about AI the same way.”

Vector’s mandate

The Vector Institute is a globally renowned AI research hub based in Toronto that offers education and practical solutions to bridge the gap between research and industry adoption. Since its founding, Vector has provided industry partners with access to world-renowned researchers, advanced compute environment and AI engineering capabilities to help them transform AI into business value. Bell has been a key partner in that work since 2021.

In addition to offering training and training certifications to strengthen internal AI teams, Vector partners with organizations like Bell to host bootcamps, where participants from multiple industries come together and share the ways they use AI. This often exposes participants to new use cases they hadn’t considered before. They can then build and iterate, benefitting from the lessons others have already learned, which they can take back and apply to their own businesses.

What’s holding Canadian businesses back from adopting AI

Working with Deloitte, Crisp’s team has found Canada and Canadian researchers continue to play a huge role in AI development, making up 6% of the world’s most elite AI researchers and showing higher year-over-year growth in AI talent concentration than other G7 countries. But Canadian businesses have been slow to adopt AI solutions and invest in AI companies.

Risk aversion, or preferring to let larger companies (often based in the U.S.) first test the technology waters combined with concerns about security, privacy and intellectual property protection are the biggest inhibitors. That hesitation extends even into investing in AI-focused startups. “I’ve heard from multiple startups that when they pitch, Canadian investors are interested, but they won’t commit unless they have U.S. references,” says Crisp. “That makes it really hard for our promising small players to grow into successful big players.” One survey also found that only 34% of Canadian respondents were prepared to trust information from AI, compared to 46% globally – likely contributing to hesitancy to adopt solutions local customers might not be willing to accept.

“If you haven’t started yet, you’re not too late, but you are late, and you do need to catch up.”

Why Canada needs to step up AI adoption

While Crisp says these concerns are legitimate, she also believes they’re solvable with proper governance and regulation. And they need to be solved – there are risks to lagging the rest of the world in AI adoption. Not the least of which is losing business to those able to offer services more efficiently. And as those early adopters get more adept at integrating AI, they add expertise to the gap in efficiency, making it even harder for the lagging companies to catch up. “If you haven’t started yet, you’re not too late,” she says. “But you are late and you do need to catch up. Because either you’re part of the change or the change happens to you.”

How to get started with and make the most of AI adoption

Effective AI adoption is about more than just throwing an AI solution into the mix. For companies that aren’t sure how to get started or want to make the most of the solutions they try, Crisp offers the following tips:

- - Adopt thoughtfully
    AI is not a silver bullet. It’s not even necessarily the right solution to every problem. To get the best results – and the highest return on your investment – make sure you’re applying AI to the use cases where it makes the most sense. That’s usually going to be an area of your business where you already have a lot of data that can help inform the solution.
  - Start moderately
    Leaping straight to the latest, most bleeding-edge agentic AI would be a daunting prospect for most companies. It can even cause new problems, particularly if you don’t have the right data pipelines or data governance in place – or if you try to do too much before understanding how it works. While that doesn’t mean adopting the most basic solution available, Crisp advises building your expertise by rolling out a “middle-ground” solution – especially if there is an opportunity to benefit and learn from expert consultation. The ideal focus is a solution that shows promise for quick results – and by taking care of the low-hanging fruit, you build expertise while proving the use case for more robust projects.
  - Buy for commodities. Build for differentiators
    For common functions, like meeting summaries, conversational chatbots and basic data extraction from documents, effective AI models already exist. That means there’s little benefit to spending time and money to build your own version. But for business differentiators, like hyper-personalized recommendation engines, specialized risk scoring models or predictive maintenance on your proprietary equipment, building your own AI model will usually be the better option. That can help you set yourself apart and protect your intellectual property.
  - Take the opportunity for transformation
    If you just add AI to an existing process, you could be missing out. The real benefits of AI often come when you start by rethinking the whole process. By doing so, you open the door to using AI to eliminate unnecessary steps, combine related processes and more. “Adding AI without adjusting the process is like driving a high-end sports car in first gear,” says Crisp. “Rewire your family sedan, and it might actually outperform that sports car.”
  - Commit to action
    While proof-of-concepts or pilot projects are useful, once a concept has proven itself, you need to be able to scale it up and move it into actual production. At the same time, don’t forget that the point of these pilots is to figure out if something works – and not everything will. Don’t be afraid to move on from an idea that didn’t work out.
  - Ask for support
    AI implementation can be complex and disruptive, but you don’t have to do it all alone. Look for opportunities to learn from the experience of other companies, or lean on companies with experience developing and delivering solutions that line up with what you want to achieve.

“Adding AI without adjusting the process is like driving a high-end sports car in first gear. Rewire your family sedan, and it might actually outperform that sports car.”

Practical resources for new AI solutions

One of the ways Vector provides support for businesses is by doing the work of converting academic research into practical codebases: starting points for common AI-driven processes that organizations can download and customize to meet their needs.

“This is one of the biggest ways we help bridge the gap between research and adoption,” says Crisp. “Our AI engineering team combs through thousands of papers from journals and conferences and turns that theory into something companies can actually use as the foundation of their own solutions.”

These codebases are offered through perpetual, royalty-free licensing agreements. That means companies can patent the solutions they build and retain control of the intellectual property.

Powering the next phase of AI adoption

As AI becomes more integrated with business, it is quickly moving from being an opportunity for improvement, to becoming a core requirement for success. This means companies will soon need to do more than developing or adding solutions on an opportunity-by-opportunity basis. While that initial work is crucial for building AI expertise – and comfort – from within, the next key phase is developing an evolving AI strategy that allows for that continuous improvement across the organization.

Crisp and the Vector Institute have seen this change across the country, marked by a recent surge of interest in homegrown Canadian AI solutions. Businesses are increasingly realizing that they don't need to look abroad to find the expertise and technology required to transform their operations; world-class talent and resources are right in their backyard.

Capitalizing on this momentum requires deep collaboration, which is a driving force behind the partnership between Bell and the Vector Institute. With the right strategic partners, Canadian businesses can move past hesitation, harness the power of homegrown innovation, and ensure they aren't just catching up to the global market—but are positioned to lead it.