The journey to artificial intelligence (AI) adoption can be intimidating, especially for organizations new to such advanced technologies. In response, a strategy known as the "crawl-walk-run" approach has emerged, offering a structured and gradual pathway for integrating AI responsibly. Originally used in management and training to describe phased development, this metaphor has found widespread application across industries, particularly in AI. By allowing companies to start with simple steps before advancing to full-scale AI deployment, the "crawl-walk-run" model offers a risk-mitigated route to harnessing AI’s potential effectively.
Understanding the "Crawl-Walk-Run" Framework
The "crawl-walk-run" approach involves three distinct stages:
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Crawl – Begin with the basics: In this phase, companies familiarize themselves with AI by implementing simple, low-risk applications. Here, organizations gather data, analyze their needs, and test basic AI models without major resource allocation. The goal is to gain an understanding of AI capabilities, assess available data quality, and establish data infrastructure.
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Walk – Gradually advance: After developing foundational experience, organizations can move to more complex use cases. This phase involves building intermediate AI applications, expanding datasets, and starting to refine algorithms. The "walk" stage helps organizations begin using AI solutions that address broader business needs, potentially impacting multiple departments.
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Run – Full-scale deployment: Finally, organizations with a solid AI foundation can implement advanced AI solutions across the enterprise. This phase may involve automating significant workflows, enhancing decision-making capabilities, or personalizing customer experiences at scale. The "run" stage signifies the transformation of AI from a support tool into a central business component.
Why the "Crawl-Walk-Run" Model Works for AI Adoption
For AI adoption, the "crawl-walk-run" approach has gained traction due to its ability to mitigate risk, control investment costs, and encourage internal skill-building. As organizations in Canada and globally confront challenges such as limited technical talent and security concerns, this phased approach allows businesses to adopt AI without disrupting existing processes.
Industry Backing and Best Practices
Prominent consulting firms, including McKinsey & Company and Deloitte, have been strong advocates of this method. Their consultants observed that companies often rushed into AI, leading to disjointed implementations, security risks, or ethical pitfalls. By promoting a structured approach, these firms provide clients with a manageable roadmap, reducing disruption and setting realistic milestones.
In Canada, organizations such as the Vector Institute and major tech companies have promoted the "crawl-walk-run" model, recognizing its effectiveness in the Canadian AI landscape. This adoption model is proving invaluable as businesses navigate AI’s potential while adhering to the ethical, data, and regulatory standards crucial for sustainable adoption.
Canadian Success Stories: Organizations Benefiting from "Crawl-Walk-Run"
Several Canadian companies have exemplified this approach:
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Manufacturing and Supply Chains: Manufacturing firms in Canada are using the crawl-walk-run strategy to integrate predictive maintenance AI solutions. Starting with data collection and basic anomaly detection (crawl), they’ve moved to predictive analytics for machine maintenance (walk) and are now automating large parts of their supply chain operations (run).
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Healthcare: Canadian healthcare providers are adopting AI in phases to enhance patient care. They start by using AI to streamline patient scheduling (crawl), then move to diagnostic tools that assist doctors in identifying diseases (walk), and ultimately aim to integrate AI into personalized medicine and real-time patient monitoring (run).
Embracing the "Crawl-Walk-Run" Approach for Sustainable Growth
The phased model is increasingly becoming the preferred roadmap for AI, especially in sectors where data sensitivity, regulatory compliance, and the need for specialized skills present additional challenges. By adopting the crawl-walk-run approach, businesses not only build AI capabilities gradually but also foster a culture of innovation and learning, ensuring they are prepared for full-scale AI integration.
For Canadian organizations looking to leverage AI, the crawl-walk-run framework offers a robust, practical way to evolve into the future while managing today’s complexities.
