{"id":23213,"date":"2025-12-02T20:13:01","date_gmt":"2025-12-02T20:13:01","guid":{"rendered":"https:\/\/www.rbc.com\/en\/?post_type=rbc_tl&p=23213\/from-rock-to-roi-how-calgarys-geologicai-turns-core-samples-into-knowledge"},"modified":"2025-12-11T20:24:44","modified_gmt":"2025-12-11T20:24:44","slug":"trust-scale-and-strategy-how-to-build-an-ai-first-organization","status":"publish","type":"rbc_tl","link":"https:\/\/www.rbc.com\/en\/thought-leadership\/the-growth-project\/trust-scale-and-strategy-how-to-build-an-ai-first-organization\/","title":{"rendered":"Trust, Scale, and Strategy: How to Build an AI-First Organization"},"content":{"rendered":"\n
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Artificial Intelligence is poised to reshape how value is created across Canada\u2019s economy. To understand that shift, RBC Thought Leadership interviewed more than two dozen firms that are on the frontlines of building or deploying AI for Bridging the Imagination Gap: How Canadian Companies Can Become Global Leaders in AI Adoption<\/a>. <\/em>The report distilled the patterns that emerged from those conversations.<\/p>\n\n\n\n

Building on that report, the series of case studies go a level deeper: following one company\u2019s journey through specific problems, pivots and opportunities, helps illustrates the strategic choices and policy conditions that turn technical promise into economic and societal value.<\/strong><\/p>\n<\/div>\n<\/section>\n\n\n\n

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Summary<\/h2>\n\n\n\n

Internal validation matters.<\/strong> Schneider Electric proved AI\u2019s value both internally and in customer offers. Starting with supply chain projects that freed up millions to invest in predictive tools that reduced downtime. This internal credibility gave the company the confidence to embed AI directly into products and services.<\/p>\n\n\n\n

Governance can be an advantage.<\/strong> By treating the EU AI Act as a design specification rather than red tape, Schneider built compliance into its MLOps machine-learning pipeline. This not only eased adoption internally but also created a \u201ctrust premium\u201d with customers.<\/p>\n\n\n\n

Centralization drives scale.<\/strong> A 350-person AI Hub concentrated scarce expertise, standardized tools, and linked directly to executive decision-making, turning AI into a repeatable capability rather than scattered experiments.<\/p>\n\n\n\n

Future readiness requires sovereignty and edge leadership.<\/strong> Focusing on trust and compliance, Schneider is positioning itself to thrive in a world where data localization and sovereignty increasingly shape industrial competition.<\/p>\n<\/div>\n<\/section>\n\n\n\n

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When most people picture electronics manufacturing, they think of smart chips, GPUs, CPUs and capacitors. But it\u2019s the hidden circuitry under the hoods that makes our world hum efficiently : a lattice of switches, sensors, drives, control panels, and interconnected IoT systems that silently, safely and reliably switch on lights, move elevators and keep servers cool.<\/p>\n\n\n\n

Schneider\u202fElectric, the 189\u2011year\u2011old French manufacturing group, is the giant behind that invisible architecture. With \u20ac38.2\u202fbillion in annual revenue,1 <\/a><\/sup>177,000 employees, and operations in more than\u202f100\u202fcountries, it manufactures the circuitry and control systems that power buildings, factories, grids and data\u2011centres.<\/p>\n\n\n\n

2<\/a><\/sup>Schneider has maintained operations in Canada3<\/a><\/sup>for more than 100 years, with roughly 3,000 individuals across 10 provinces. Its products are featured in 40% of residences and 50% of commercial buildings in Canada.4<\/a><\/sup><\/p>\n\n\n\n

Schneider\u2019s value to the global economy is twofold: it supplies5<\/a><\/sup> the hardware and software that makes modern life possible and shepherds one of the world\u2019s most distributed industrial supply chains6<\/a><\/sup>.<\/p>\n\n\n\n

Yet even Schneider was not immune to the pandemic\u2019s shock waves. By late 2020, COVID-19\u2019s stop-start demand swings left warehouses bulging with unsold stock while plants struggled for parts. Across a network of 162\u202ffactories7<\/a><\/sup>, roughly\u202f300,000\u202fstock-keeping units (SKUs)8<\/a><\/sup> and around revenues fell 6.4% organically9<\/a><\/sup> in the first quarter of 2020, year-on-year, putting billions at risk.<\/p>\n\n\n\n

Faced with this disruption, Schneider had to decide whether to keep tweaking legacy systems or take a chance on machine learning. They chose the latter. \u202fStarting small, at one its North\u2011American switch\u2011gear plants, Scheider\u2019s AI team trained a gradient\u2011boost model on three years of order history, macro indicators and pandemic mobility data.\u202fSix weeks later, there were double\u2011digit gains in forecast accuracy, safety\u2011stock days fell by a third, and the pilot resulted in considerable savings. The result became the catalyst for further exploring AI capabilities, that delivered great results in the energy management space. The strategic move to scaling AI initiatives globally resulted in creating Schneider\u2019s centralized AI Hub.  <\/p>\n\n\n\n

How did Schneider Electric transform multiple AI pilots into a global capability, and lead in enterprise AI deployment? To find out, RBC Thought Leadership sat down with C\u00e9dric Bureau, Senior Principal Product Manager for Artificial Intelligence at Schneider Electric<\/strong>, to unpack four key strategies the company implemented while scaling its AI capabilities, and the insights they offer today.<\/p>\n<\/div>\n<\/section>\n\n\n\n

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Insight 1: Dual\u2011Track AI<\/strong><\/h4>\n\n\n\n
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It clicked when we saw an internal AI pilot\u2019s results. We weren\u2019t just solving problems\u2014we were building something that offered new opportunities for us and our customers \u2014<\/em> C\u00e9dric Bureau<\/strong><\/p>\n\n\n\n

Internally, under Schneider\u2019s AI-at-scale program,<\/em> the company rolled out machine-learning models across supply-chain planning and the factory floor; computer-vision and vibration analytics began feeding AI information and predicting failures, lifting throughput and uptime, and enhancing energy efficiency. In parallel, Schneider put AI into everyday enterprise support tools\u2014HR and engineering chatbots and copilots, and enhanced energy-efficiency software\u2014so teams had working tools, not just pilots.<\/p>\n\n\n\n

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The step-change came when those capabilities moved into customer offers. An anomaly-detection model first used to monitor building thermal performance and detect abnormal energy use now powers Schneider\u2019s bespoke EcoStruxure Building Advisor10<\/a><\/sup>, which flags abnormal consumption and tunes HVAC automatically. By shifting from manual, Excel-based reporting to AI-powered building energy modelling, customers have achieved measurable benefits\u2014including considerable operating cost savings across 50 sites and 2\u20135% reductions in energy consumption.<\/p>\n\n\n\n

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The two tracks now reinforce each other. Schneider\u2019s AI-at-scale strategy sets the playbook\u2014how pilots move to shop floor, enterprise tools and into products\u2014and a centralized AI Hub runs it, rotating experts across projects, standardizing tooling and governance, and building enterprise-wide AI know-how. That pairing makes the hand-off between AI development and the factory floor routine: models that prove themselves are industrialized, documented and shipped into offers, while product telemetry feeds fresh data back for the next round. Internal efficiencies realized fuel further R&D, with every factory win becoming a candidate feature in a future product.<\/p>\n\n\n\n

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Takeaway:<\/strong> Use the enterprise as a live test bed and consistently build both technology and human capabilities to innovate with AI.\u202fWhen an AI solution delivers value inside the business, it provides credibility and de-risks similar use cases. Being able to claim \u201cwe run this at scale ourselves\u201dimproves sales prospects with cautious customers.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n

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Insight 2: 350\u202fExperts Feeding One Big Brain<\/strong><\/h4>\n\n\n\n
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\u201cAI is now past the hype cycle inside the company\u2014it\u2019s part of daily work habits\u201d\u2014<\/em>C\u00e9dric Bureau<\/strong><\/p>\n\n\n\n

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Scattered pilots could never keep pace with a network of 162\u202ffactories across five continents.\u202fSo, in late\u202f2021, Schneider launched a global AI\u202fHub11<\/a><\/sup>\u2014across three locations: Boston, Paris and Bangalore.\u202fWithin 12 months the hub grew to around 350 data scientists, machine learning operations (ML Ops) engineers, product managers and an in\u2011house compliance squad. To ensure the hub can move at pace with technology development trends, it\u2019s headed by a Chief\u202fAI\u202fOfficer who reports to the executive committee<\/strong>, ensuring strategic bets on AI are scrutinized at the C\u2011suite level.<\/p>\n\n\n\n

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By elevating AI initiatives into a standalone enterprise function, Schneider pulled them out of isolated IT corners and gave them the strategic visibility needed to reach production. This centralized, AI-first organizational design enabled four key advantages:<\/p>\n\n\n\n

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1. Hub-and-spoke coordination: <\/strong>The centralized AI Hub supplies the technical backbone\u2014algorithms, data infrastructure, compliance tools and features a team of AI product managers, each dedicated to a set of business units to work with marketing managers with clear understanding of local and\/or industry specific challenges. This split of roles prevents duplication, ensures solutions are tailored to operational needs, and speeds up the rollout of AI projects across the enterprise.<\/p>\n\n\n\n

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2. Paved-road development: <\/strong>All AI projects share the same basic set of tools and processes\u2014like standard methods to gather data, store and organize models, and perform quality checks. Think of it like using a standard recipe: following it takes some extra work at the start, but once you’ve done that, making adjustments or improvements becomes simpler and faster. Because as these processes are consistent across Schneider, teams don’t have to constantly reinvent the wheel. Netflix and Spotify use a similar concept, calling it a \u2018paved road\u2019, meaning a clear, straightforward path that makes developing technology quicker, safer, and easier.<\/p>\n\n\n\n

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3. Talent attraction and retention: <\/strong>The AI Hub offers a compelling career path and collaborative environment. Schneider can recruit top AI talent from Big Tech companies and retain skilled experts significantly longer than comparable industrial organizations.<\/p>\n\n\n\n

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4. Built-in compliance capability: <\/strong>Schneider\u2019s compliance experts are integrated within the AI Hub. Every AI project undergoes a standardized risk assessment and bias testing before deployment, ensuring adherence to regulations such as the EU AI Act and laying the groundwork for the \u2018compliance-by-design\u2019 approach detailed further in the case.<\/p>\n\n\n\n

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Schneider is not alone in this architecture.\u202fBosch\u2019s Center for AI and the Siemens AI\u202fLab follow a similar hub\u2011and\u2011platform pattern<\/p>\n\n\n\n

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Takeaway:<\/strong> Success comes from treating AI as a core enterprise function\u2014appointing clear leadership, concentrating expertise, and serving business units as internal clients.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n

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Insight 3: Turning Regulation into a Feature<\/strong><\/h4>\n\n\n\n
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\u201cWhile talent solved capacity; trust solved adoption. When Brussels drafted the world\u2019s first horizontal AI law, Schneider decided regulation would be a design spec, not a hand\u2011brake.\u201d \u2014<\/em>C\u00e9dric Bureau<\/strong> <\/p>\n\n\n\n

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When the draft EU\u202fAI\u202fAct first circulated, many industrial peers froze projects, waiting to see how onerous the rules would become.\u202fIn contrast, Schneider\u2019s AI\u202fHub embedded a \u2018compliance squad\u2019\u2014lawyers, data\u2011privacy officers, risk engineers\u2014directly into ideation and sprint teams.\u202fEvery new use\u2011case begins with a 10\u2011question risk\u2011rating questionnaire that maps potential AI applications to the Act\u2019s taxonomy (minimal, limited or high\u2011risk). Proposals assessed as high risk trigger up\u2011front data\u2011anonymization, mandatory human\u2011oversight12<\/a><\/sup> plans and bias\u2011test requirements before development begins.<\/p>\n\n\n\n

Schneider\u2019s AI deployment pipeline itself enforces the law. Schneider\u2019s AI policy requires that all use cases undergo a two-stage compliance review. First, use cases are scanned for risks across ethics, design, IP, data security, and governance. Then, those risks are mapped into a treatment plan\u2014identifying owners, setting mitigation actions, and tracking accountability\u2014so that compliance is not just a checklist but a living process. This AI Policy ensures alignment with EU\u202fAI\u202fAct Articles\u202f1013<\/a><\/sup>\u202f(data & bias), 11<\/a>\u202f(technical documentation) and 14\u202f<\/a>(human oversight).\u202fOnce a model is live, the platform\u2019s monitoring dashboard logs performance drift and automatically opens an incident ticket if thresholds are breached, satisfying Articles\u202f72\u201173 of the act on post\u2011market surveillance.<\/p>\n\n\n\n

By having compliance experts on the team, Schneider\u2019s engineers treat concerns like bias mitigation, data anonymization, and cybersecurity\u2014as design inputs, not obstacles. This is an organizational cultural shift\u2014developers are guided to think about ethical\/legal constraints from the start rather than scramble to retrofit fixes later.<\/p>\n\n\n\n

These extra steps yielded  three commercial dividends:<\/p>\n\n\n\n

1. Faster sales cycles<\/strong> :Clients in heavily regulated industries often demand proof of AI governance; handing them an \u2018AI\u2011Act\u2011ready\u2019 dossier trims procurement reviews.<\/p>\n\n\n\n

2. Trust premium:<\/strong> Positioning Schneider\u2019s solutions as \u2018regulation\u2011ready\u2019 differentiates them against rivals who still treat compliance as paperwork to be done later.\u202f <\/p>\n\n\n\n

3. Build once, comply everywhere:<\/strong> Treating EU standards as the floor cuts duplication across markets and future\u2011proofs the portfolio against new laws\u2014Canada\u2019s Bill\u202fC\u201127 included. As it stands, Schneider maintains compliance with standards across the world, including the Institute of Electrical and Electronics Engineers (IEEE14<\/a><\/sup>), International Electrotechnical Commission (IEC15<\/a><\/sup>) and the Organisation for Economic Co-operation and Development (OECD16<\/a><\/sup>).<\/p>\n\n\n\n

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Take\u2011away:<\/strong> By baking the rulebook into the codebase and deployment processes, Schneider converts the cost of compliance into a strategic advantage.<\/p>\n<\/div>\n<\/section>\n\n\n\n

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Insight 4: Transparent deployment review process<\/strong><\/h4>\n\n\n\n
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“We knew we\u2019d succeeded when operators started asking us for AI models, not because management pushed them, but because workers saw firsthand how they improved their jobs.” \u2014<\/em> C\u00e9dric Bureau<\/strong><\/p>\n\n\n\n

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With Schneider\u2019s talent (AI Hub) and compliance guardrails (compliance by design) in place, it established the four-gate funnel to manage ideas.<\/strong> Every AI use case, from factory forecasting to customer-facing microgrid control, flows through the same four stages. At each gate, a go\/no-go decision is made based on business case and feasibility. Pet projects without ROI, or projects deemed too high-risk are stopped early. Winners move quickly, because approval chains, tooling, and documentation are built in from the start.<\/p>\n\n\n\n

Gate 1:<\/strong> Data owners co-develop a one-page problem brief with the AI Hub\u2014qualifying return on investment (ROI), carbon impact, and passing a 10-question risk scan. Key technical challenges are identified, and sandbox phase on masked data with built-in bias and robustness testing is done to evaluate feasibility and to assess the best technology to overcome such challenges.<\/p>\n\n\n\n

Gate 2: <\/strong>A Minimum Viable Product development and real-life deployment. Plant operators co-design dashboards and evaluate the solution in as-close to real-life-conditions as possible. Critically, the funnel separates trying from scaling\u2014preventing the common trap of endless proof-of-concepts.<\/p>\n\n\n\n

Gate 3: <\/strong>Solutions are hardened for production: user interfaces, documentation, and business integration. Models are migrated onto the Hub\u2019s MLOps platform, and the compliance team completes the EU AI Act technical dossier.<\/p>\n\n\n\n

Gate 4: <\/strong>Live dashboards track ROI, drift, and incident logs. Red flags auto-escalate to both the site lead and AI product team. Some models retrain automatically based on performance thresholds.<\/p>\n\n\n\n

Takeaway:<\/strong> Human-centric design extends through the development, implementation, and operational phases of AI applications\u2014Schneider doesn\u2019t treat business stakeholders as merely AI end-users. They\u2019re co-owners of AI solutions.<\/p>\n\n\n\n

This cultural strategy scales, too. As small tools proved helpful, trust grew. Engineers adopted AI as naturally as any other tool. Plant managers began expecting data-driven insights in meetings. Executives used AI dashboards to spot margin opportunities. The result wasn\u2019t just tech fluency\u2014it wasa mindset shift. People no longer see AI as opaque or threatening\u2014they understood where it fits, and how it can help them do better work.

Internally, Schneider backed this shift with a firm-wide initiative to elevate the AI knowledge of all employees through awareness\/training programs, regular data & AI webinars, and the publicly available AI at Scale podcast.<\/a><\/p>\n<\/div>\n<\/section>\n\n\n\n

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Choosing the Next Play<\/h2>\n\n\n\n

Schneider Electric has thrived under Europe\u2019s regulation-first approach, aligning early with the EU AI Act and embedding compliance into its operating model. This strategy has given it a competitive edge: customers see its solutions as \u201cregulation-ready,\u201d and regulators view the company as a trusted partner.<\/p>\n\n\n\n

But the future of regulation may expose the company to competing paradigms, in which the EU resides in the middle. In the United States, a market-led approach prioritizes rapid innovation, with looser rules and fewer documentation burdens. China, meanwhile, pursues a state-steered model, demanding tight government oversight and strict localization of data. Each system pulls global players in different directions, and supply chains are increasingly split along regulatory lines.<\/p>\n<\/div>\n<\/section>\n\n\n\n

Numbers<\/h3>\n\n\n\n
<\/th><\/th><\/tr><\/thead>
\u20ac38.2 b<\/strong> <\/td>2024 revenue<\/strong><\/td><\/tr>
\u20ac4.3 b<\/strong><\/td>Net 2024 income<\/strong><\/td><\/tr>
177 000<\/strong> <\/td>Number of employees<\/strong><\/td><\/tr>
162<\/strong><\/td>Number of manufacturing<\/strong> sites,<\/strong> globally<\/td><\/tr>
1836<\/strong><\/td>Year of founding<\/strong>, in Le Creusot, France<\/td><\/tr>
100+<\/strong><\/td>Number of countries<\/strong> Schneider Electric maintains operations in     <\/td><\/tr>
5%<\/strong><\/td>Portion of revenue invested in R&D<\/strong><\/td><\/tr>
20,000<\/strong><\/td>Number of active, global patents<\/strong><\/td><\/tr>
1st<\/sup><\/strong><\/td>Ranking <\/strong>in Corporate Knights Global 100 most sustainable corporations<\/td><\/tr><\/tbody><\/table>\n\n\n\n

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Download the Report<\/h4>\n\n\n\n
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Download<\/a><\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n
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Project Lead<\/strong><\/p>\n\n\n\n

Reid McKay<\/strong>, Director Technology Policy, RBC Thought Leadership<\/p>\n\n\n\n

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Contributions<\/strong> <\/p>\n\n\n\n

Special thanks to Janice Stein, Founding Director, Munk School of Global Affairs & Public Policy and Nicole Harris, Graduate Student, Munk School of Global Affairs & Public Policy.<\/p>\n<\/div>\n<\/section>\n\n\n\n

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