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RBC Thought Leadership Sabreena Shukul

How Digital Twins are Solving Real World Problems

Author: Sabreena Shukul

Sabreena supports research and analysis for the group. Her work focuses on how technology, innovation policy and shifting geopolitical conditions shape Canada’s competitiveness and business environment. Previously, she supported national-scale value chain development for critical sectors at the National Research Council of Canada, and worked in brand strategy and consumer insights at Procter & Gamble. Sabreena is completing a Master of Global Affairs at the University of Toronto’s Munk School and holds a BA in International Relations and Political Science from the University of British Columbia.

From a crippled spacecraft to city streets, the technology that mirrors the physical world is remaking how we build, move, and plan.

In April 1970, an oxygen tank ruptured aboard Apollo 13, roughly 330,000 kilometres from Earth. NASA engineers on the ground had no way to physically reach the spacecraft. What they could do was feed real-time telemetry from the vessel into a bank of simulators in Houston, reconfigure those models to mirror the damage, and test survival strategies before relaying instructions to the crew. It worked, and the astronauts came home. Nobody called it this at the time, but mission control had just demonstrated the core logic of what would later be known as a digital twin.

Dr. Michael Grieves formalized the concept at the University of Michigan in 2002, and NASA’s John Vickers coined the phrase in 2010.1 But the underlying principle was already clear: build a virtual replica of a physical thing, keep it synchronized with real-world data, and use it to ask questions you cannot safely or cheaply ask of the original.

From Replica to Oracle (What is a digital twin)

A digital twin differs from an ordinary simulation in one decisive respect: it is continuously updated. A simulation models what was designed, a twin mirrors what exists right now. Feed it sensor data from a jet engine, a wind turbine, or a hospital ventilation system, and it becomes a living model, one that can flag an impending bearing failure, test a configuration change, or forecast demand three hours ahead. The distinction shifts decision-making from retrospective analysis to real-time anticipation.

McKinsey estimates that 70% of C-suite technology executives at large enterprises are exploring or investing in digital twins, and that the technology can improve public-sector infrastructure efficiency by up to 30%.2 The global market, valued at roughly US$36 billion in 2025, is projected to exceed US$329 billion by 2033, growing at a CAGR of 31%.3

The sensor revolution that made it possible

NASA could twin one spacecraft because it had a dedicated mission control. Scaling the same idea to a factory, a power grid, or a city required something that did not exist in 1970: cheap, networked sensors everywhere. That infrastructure arrived with the Internet of Things (IoT). There are now more than 21 billion connected IoT devices worldwide, a figure growing at roughly 14% a year and expected to reach 39 billion by 2030.4 Each device: a pressure gauge on a pipeline, a magnetometer in a road surface, a camera at an intersection, generates the continuous telemetry that keeps a digital twin alive.

Where twins are working now

The range of applications is vast. In energy, Siemens Energy has built digital twins of gas turbine components using neural networks on NVIDIA’s Omniverse platform, accelerating power-grid assert simulation by 10,000x5, and enabling predictive maintenance that could save utility providers US$1.7 billion per year.6 Singapore’s national grid operator SP group is piloting a Grid Digital Twin that models real-time conditions of the entire electricity network, a necessity as the country targets a tenfold increase in its renewable energy share by 2035.7

In manufacturing, BMW’s plant in Regensburg exists as a complete digital replica in NVIDIA Omniverse, where engineers optimize robot placement and test new car models on a virtual assembly line without halting production. Helsinki uses a city-scale twin to model how replacing heating systems in specific districts would affect CO₂ emissions against its 2030 carbon-neutrality target. Rotterdam’s twin simulates storm surges to make proactive decisions about sluice and dam operations. In healthcare, 66% of executives expect increasing investment in digital twins over the next three years, with applications ranging from hospital operations modelling to virtual drug testing and surgical planning.8

The pattern across these cases is consistent: an asset or system too complex, too expensive, or too dangerous to experiment on directly gets a virtual counterpart fed by live data. The twin absorbs the risk of trial and error.

Miovision and the instrumented intersection

Urban traffic offers a particularly clear illustration. In Ontario, the economic and social cost of congestion was estimated at C$56.4 billion in 2024, projected to approach C$108 billion by 2044.9 Digital-twin logic—sense, model, anticipate, act—applied at the intersection level lets traffic engineers see how signals, vehicles, cyclists, and pedestrians behave, rather than how a timing plan assumed they would.

For a Canadian-born example of this approach at global scale, listen to the RBC Disruptors episode featuring Miovision, the Kitchener-based company whose sensor and analytics platform now operates at more than 170,000 traffic intersections across 68 countries. Their work is a case study in how digital-twin principles migrate from aerospace and heavy industry into everyday civic road systems. It also demonstrates how a Canadian startup can build a category-defining business by instrumenting something as mundane as a traffic light.

What to watch for as the technology evolves

  • The convergence of digital twins with generative AI. McKinsey’s operations practice describes a shift from twins that monitor and predict to twins that recommend and, increasingly, act autonomously.

  • The emergence of twin ecosystems. A factory’s digital twin exchanging data with the twins of its supplied components and the twin of the power grid that feeds it. Interoperability, common data models, shared interfaces, certified audit trails, will determine which platforms capture long-term value.

The broader trajectory is one NASA’s engineers would recognize. When you cannot reach the physical thing, or when acting on it without rehearsal is too costly, you build a model, keep it honest with live data, and let it think ahead of you. The technology has outgrown the spacecraft, but the principle has not changed.

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Sabreena Shukul is a research assistant at RBC Thought Leadership

This article is a companion to the Disruptors episode on how Wikipedia platform built credibility through community, transparency and a shared commitment to neutrality – Trust at Scale: Lessons from Wikipedia

Something has shifted in how people relate to institutions. Across the OECD, more people now distrust their national government than trust it. In Canada, only 48% express confidence in the federal government, down from the high 50s before the pandemic.1 An Ipsos survey captured the trajectory: trust in government to do what is right fell from 58% in 2019 to 43% by 2022.2 Meanwhile, the 2025 CanTrust Index found that politicians are trusted by just 17% of Canadians, the lowest in a decade of tracking, and 6 in 10 say political parties are divisive forces.3

Social media and AI-generated content have accelerated the decline, with nearly half of Canadians now believing that AI will make information sources less trustworthy. Algorithms reward outrage over accuracy, flooding public discourse with polarizing content and AI-generated noise. As Jimmy Wales, the co-founder of Wikipedia, observed on a recent RBC Disruptors podcast, platforms incentivize bad behaviour through engagement: “you act like a jerk and you get engagement.”4

Wales’ latest book Seven Rules of Trust—A Blueprint for Building Things That Last, focuses on the global crisis of credibility and knowledge. Both are in short supply: The 2026 Edelman Trust Barometer found 73% of Canadians unwilling to trust someone with different values or information sources.5

The consequences of mistrust are far-reaching and having real impact: In Slovakia’s 2023 election, a deepfake audio clip impersonating a political party leader went viral during a legally mandated campaign silence period, leaving journalists no window to respond.6 In the United States, an AI-generated robocall mimicking President Joe Biden urged New Hampshire voters to stay home during the 2024 primary.7 Similar incidents surfaced in Bangladesh, Turkey, and India. The German Marshall Fund tracked 133 deepfake incidents tied to elections across dozens of countries.8

Wikipedia: An architectural proof of concept

Wikipedia makes for an instructive model. The free online encyclopedia covers more than seven million English-language articles, roughly 283,000 active editors, and billions of page views annually—all on a non-profit budget. It’s the go-to site for many to source everything from a storied company’s corporate history to oddities and obscure records.

For all its variety, it’s far from perfect: critics flag ideological biases, gender gaps among editors, and vulnerability to paid manipulation. But as Wales noted on the podcast, Wikipedia has gone “from being kind of a joke to one of the few things people trust.”

The reason is structural. Wikipedia’s model is “accountability, not gatekeeping,” Wales told RBC’s Disruptors podcast.9 “Everything you edit, everybody can see what you’ve done.” Every source is checkable, disputes happen on public talk pages, and corrections happen in real time.

Wales’s thinking was shaped early by Nobel-prizewinning philosopher Friedrich Hayek’s argument about decentralized knowledge—the idea that decision-making works best at the endpoints, not through a central hierarchy. Wales pointed to X’s Community Notes as a promising application of the same principle: empowering users rather than relying on top-down moderation.

Where this leaves Canada

Research going back to Knack and Keefer’s 1997 study confirms that trust is a measurable input to growth.10 A Deloitte analysis by chief global economist Ira Kalish makes the mechanism concrete: a rise in trust increases the quantity of business fixed investment, and it raises productivity through higher-quality investments, human capital accumulation, and greater internationalization.11

The consultancy’s modelling suggests a ten-percentage-point increase in the share of trusting people within a country raises annual per capita GDP growth by about half a percentage point: a substantial gain when global growth averaged 2.2 percent between 2015 and 2019.

There is no single fix to restore trust in corporate and public sector governance. But as the Disruptors’ conversation with Wales highlighted, trust is not a moral decoration. The work of rebuilding it will be slow, uneven, and ongoing. But the cost of not starting is already measurable.

Sabreena Shukul is a Research Assistant at RBC Thought Leadership

This article is a companion to the Disruptors episode on sports technology – Tech wins Gold: How Canada can rebuild its Olympic pipeline.

On November 1, 1959, three minutes into a game at Madison Square Garden, a shot by New York Ranger forward Andy Bathgate broke Jacques Plante’s nose. The Montreal Canadiens’ goalie left the ice, received several stitches, and returned wearing a fiberglass mask he had moulded himself. Montreal won 3–1 and went on an 18-game unbeaten streak. From then on, Plante refused to play without one. Within a decade, every goalie in the league had followed his lead. Plante was not trying to disrupt anything. He had simply decided that stopping a frozen puck with his face was a problem worth solving—and that impulse, identify a problem, build a solution, let the results speak for themselves, has been a through-line in Canadian sport ever since.

A sector with real economic weight

The global sport tech market was valued at roughly US$19 billion in 2024 and growing about 20% annually. Canada has a US$450-million share, a little more than 3%, and an annual growth rate of nearly 19% ranking among the fastest of any national market. Yet, on the funding side, Canada is treating sport and sport technology as a discretionary expense. As Canadian Olympic Committee CEO David Shoemaker notes in a recent episode of Disruptors, peer countries are “out‑investing [Canada] at the federal level, five, six, 10 times.” Germany alone is putting “about a billion dollars a year into sport.”

What the ecosystem has produced

Toronto Metropolitan University’s Future of Sport Lab, launched with Maple Leaf Sports & Entertainment (MLSE) in 2015 as one of North America’s first sport tech incubators, has helped launch companies that have collectively raised more than $100 million.

That includes Montreal’s Sportlogiq, co-founded by former Olympic figure skater Craig Buntin, which has developed computer vision technology now trusted by almost every NHL team. And Rapsodo and 3Motion AI, which are putting biomechanical coaching tools into the hands of club-level athletes and local coaches. Tools that are now accessible through a portable device or a smartphone app.

The pattern that needs to be broken

The issue with Canada’s sport tech story has never been what gets built. But what happens after it does. Sportlogiq was acquired by U.S.-based Teamworks in January 2026. Halifax-founded Kinduct, whose athlete‑management platform was used by more than 550 teams and organizations worldwide, was bought by Silicon Valley’s mCube in 2020, in what its founder called the largest sport tech exit in Canadian history.

The cycle is familiar: public research dollars seed the company, which proves its technology at global scale, before getting snapped up by foreign owners that provide the commercial infrastructure that Canada lacks. The same pattern is emerging in human capital. On Disruptors, Jennifer Heil, Canada’s chef de mission for Milano Cortina 2026 Winter Olympics and founder of a performance‑tech startup, describes “a moment of total brain drain” in high‑performance sport, with top scientists and nutritionists shifting their time to the United States because “we can’t afford them right now.”

Where the stakes are highest

Three-quarters of Canada’s medallists at Milano Cortina were 30 or older. The bench strength is thinning, with Speed Skating Canada’s World Cup roster dropping from 24 to 16. Close to half of Canadian families report that organized sport is too expensive, and athletes at the national level pay as much as $25,000 out of pocket to represent their country.

Sport technology can address the problem directly. RBC Training Ground identified gymnast Marion Thénault at 17 with no skiing background; within five years she had won Olympic bronze. AI-assisted talent identification could replicate that kind of discovery at scale, reaching communities that traditional scouting never will.

Shoemaker imagines that scaled through AI: “Show us how you jump, how you run, how you throw—and we’ll tell you what sport you should sign up for at your local club.” Heil’s own startup, Revel, is built on the idea that AI can “democratize access” to elite coaching knowledge once reserved for Olympians.

And keeping the companies that build those tools Canadian-owned means keeping the returns: the jobs, the intellectual property, the platform revenue, stay here as well.

What it comes down to

The sector has the companies and the research infrastructure. It lacks the domestic capital to keep them scaling at home, as well as a national strategy that pairs the products with the young athletes that need them.

That gap is visible in the public system itself: national sport organizations have not seen a core‑funding increase since 2005, and Shoemaker notes that some athletes now face team fees of as much as $30,000. A national strategy for sport tech could treat data, infrastructure and talent identification as long‑term capital investment.

Sabreena Shukul is a Research Assistant at RBC Thought Leadership

What you need to know about the rapidly emerging field of quantum computing, which can solve problems faster than a supercomputer

Quantum computing is quietly moving from an interesting physics challenge to potentially a strategic solution in boardrooms worldwide. The global market for quantum technology is expected to reach up to US$97 billion by 2035. Canada sits close to that shift, with a deep research base and a small set of firms trying to translate scientific advantage into industrial capability.

What is quantum computing?

Quantum computers aren’t replacing classical machines. They’re a specialist tool for problems that even today’s best supercomputers can’t handle. In 2024, Google’s Willow chip completed a benchmark calculation in under five minutes that would take a leading supercomputer an estimated 10 septillion years, vastly exceeding the age of the universe.

A classical computer tries possibilities one by one, through binary bits (0 or 1). Whereas a quantum computer uses qubits, which keep many possibilities alive at once (superposition), links parts of the problem so they move together (entanglement) and uses cancellation/reinforcement to make wrong answers fade and right answers stand out (interference).

Why does quantum computing matter?

It can solve problems classical computers can’t handle. Bain estimates quantum computing could unlock up to $250 billion in value across pharmaceuticals, finance, logistics, and materials science. Consider drug discovery: bringing a drug to market could cost up to $4 billion and can take more than a decade. Add to that, the fact that about 90% of drug trials fail. Quantum computers can simulate molecular interactions at the atomic level: something classical machines can only approximate, heavily compressing timelines.

The security clock is already ticking. The most immediate business risk is “harvest now, decrypt later”: adversaries collect encrypted data today and wait for quantum capability to crack it retroactively. The National Institute of Standards and Technology (NIST), the National Security Agency (NSA), and the Canadian Cyber Centre all treat this as a live threat requiring action. If your organization holds data with a long shelf-life: health records, proprietary research, industrial IP, the breach window is already open.

What is the opportunity for Canada?

Canada’s quantum prowess is decades in the making. Waterloo’s ‘Quantum Valley’, anchored by the Perimeter Institute and the Institute for Quantum Computing, has attracted over $1.5 billion in investment over 25 years and trained more than 3,500 quantum specialists.

The challenge is keeping that advantage at home. In December 2025, Ottawa launched the Canadian Quantum Champions Program, investing $92 million across four companies: Xanadu (Toronto), Nord Quantique (Sherbrooke), Photonic (Vancouver), and Anyon Systems (Montréal). This is part of the government’s five-year quantum commitment of $334.3 million.

The projected payoff: by one estimate, quantum could contribute more than 3% to Canada’s GDP by 2045, rivalling the aerospace sector, and support more than 200,000 jobs.

What challenges does quantum computing face?

There are technical challenges that need to be addressed. A qubit holds its quantum state for a tiny window, often tens to hundreds of microseconds, so you can only run a limited number of steps before errors drown out the signal. It’s like solving a complex equation on a whiteboard that starts erasing itself every fraction of a second.

To compensate, engineers use error correction: redundant qubits that check and protect the computation. But creating a single stable “logical qubit” can require hundreds to thousands of physical qubits, far more than current machines offer. This is where the race is being run – Google, Microsoft, and Canada’s Xanadu are all competing to crack error correction at scale and unlock breakthroughs in molecular simulation, cryptography, and optimization that classical computers can’t reach.  

What to keep an eye on

  • Post-quantum cryptography deadlines: Canada’s roadmap for the public service requires migration plans by April 2026, high-priority systems quantum-safe by 2031, and full migration by 2035. Those dates will ripple into vendor contracts and supply chains.

  • Early commercial traction: Drug discovery, materials science, and financial optimization are where pilots are emerging. Consistent advantages over classical methods will signal the technology is turning a corner.

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Sabreena Shukul is a research assistant at RBC Thought Leadership