From Borealis to ATOM: Inside RBC's AI Research Engine
Research turns ideas into useful AI and real products. Invest early, pair ML with domain experts, and keep the loop tight between labs and production.
Research Is The Engine Behind Useful AI
Breakthroughs don't appear from thin air. They come from years of curiosity, testing, and the drive to build what didn't exist yesterday. Artificial intelligence is changing how industries operate-healthcare, supply chains, finance-because persistent research turns promising ideas into working systems.
For scientists and research leaders, the message is simple: the institutions that invest in rigorous inquiry set the pace. Everyone else plays catch-up.
Canada's Quiet Advantage
Canada helped pioneer modern AI and continues to cultivate a strong research ecosystem. World-class labs, academic partnerships, and a steady pipeline of trained talent have accelerated responsible development and real adoption across industries. That foundation matters when you want research to move from papers to production.
Why Images And Language Lead-And Why Your Domain Still Matters
Most advances start in images and language. Deep learning first proved itself in image recognition; transformer models like GPT broke through in natural language. These areas get attention because they touch daily life and offer abundant data.
But translating those methods into domains such as audio, medical imaging, weather, or finance is a different challenge. It requires people who know the math and the subject. Specialized datasets, constraints, and safety requirements demand applied research-not copy-paste engineering.
How A Bank Turned Research Into Capabilities
Royal Bank of Canada built research capacity early. In 2016, it launched the Borealis Research Institute, hiring PhDs, academics, AI scientists, programmers, and machine learning engineers to work on high-impact problems including fraud detection, risk management, and anti-money laundering.
Results followed: more than 1,000 patents filed (about 20 percent in AI) and over 125 scientific publications. That work feeds real products and services for 17 million clients.
One example is ATOM, a proprietary foundation model for financial services with deep financial expertise that can generalize across banking tasks. RBC also partners closely with universities and runs in-house programs, such as Let's Solve It!, to give students from diverse disciplines hands-on exposure to AI development.
A Practical Playbook For Science And Research Leaders
- Form domain-bridging teams: pair ML researchers with subject-matter experts so methods transfer cleanly from images/language to your data.
- Treat data like an R&D asset: establish quality standards, documentation, privacy, and clear access paths for experimentation.
- Prototype with foundation models, then adapt: start with pre-trained baselines and fine-tune for your constraints and metrics.
- Measure what matters: track time-to-insight, false-positive/negative rates, risk reduction, and operational lift-not vanity scores.
- Publish and protect: contribute to the scientific community while securing patents where appropriate.
- Build talent pipelines: sponsor student challenges, internships, and cross-disciplinary programs that grow critical thinking, not just tooling.
Responsible AI Is A Requirement, Not An Option
Adapting research to sensitive domains demands guardrails. Use clear governance, model risk management, and human oversight for high-stakes decisions. If you need a reference framework, review the NIST AI Risk Management Framework for practical controls and evaluation practices.
The Upside For Careers In Science And Research
Organizations that back research attract people who want to push boundaries and solve complex problems. Even outside academia, teams trained in critical thinking can question assumptions, run smarter experiments, and turn ideas into reliable systems.
If you're leveling up your skills in machine learning, applied modeling, or data-centric methods, structured curricula can help you move faster from theory to practice. Explore focused programs here: AI courses by skill.
Bottom Line
AI is a high-leverage tool, but research is what makes it safe, useful, and scalable. Back the scientists, build the pipelines, and keep the loop tight between labs and production. That's how you create durable advantage-and meaningful results.
