When Good to Great was first published in 2001, Jim Collins offered insights into why some companies achieve lasting success while others fail to do so, particularly as they navigated the rise of the internet. Although Collins’ examples focused on the challenges and opportunities of that era, his core concepts remain relevant. Today, as companies navigate Artificial Intelligence (AI), these principles offer a guide for how to adopt new technologies thoughtfully.
Collins’ says great companies achieve lasting success by focusing on their core strengths, guided by what he calls the Hedgehog Concept. Chapter 7 of Collins’ book, “Technology Accelerators”, stresses that technology is not the driver of greatness but rather an accelerant, magnifying disciplined practices and already strong foundations. In the same way, businesses today should approach AI as a tool to support and enhance existing processes. Here’s how some key Good to Great principles apply to AI adoption today.
The Hedgehog Concept and AI’s Core Purpose
Great companies align AI initiatives with their central purpose—what Collins calls the “Hedgehog Concept.” Instead of jumping on the AI bandwagon, successful organizations deploy AI where it can strengthen core operations.
Collins’ describes the Hedgehog Concept as a framework that helps companies focus on what they can uniquely excel at. He suggests identifying:
- “What they can be the best in the world at,”
- “What drives their economic engine,”
- “What they are deeply passionate about.”
For example, if a company’s strength lies in customer service, AI could be applied to optimize support resources, helping teams respond faster and more effectively. The goal is to leverage AI to amplify what the business already does well.
Technology as an Accelerator, Not the Driver
Collins argues that technology is an accelerant of disciplined processes, not a catalyst for new directions. Some may see AI as a “quick fix” for innovation, but without a stable foundation, AI projects fall short.
Successful AI implementation builds on processes that already work well. For instance, a company with strong logistics processes can use AI to optimize supply chains and reduce costs—but AI alone can’t create logistics excellence from scratch.
Data Infrastructure as a Foundation
Successful AI adoption requires a solid foundation. One of the most essential factors for AI success is a robust data infrastructure consisting of a well-designed data architecture. Companies may need to invest in integrating data across various systems, building data models, and ensuring consistent data governance to provide a clear, cohesive picture for AI applications before jumping ahead to developing and using AI solutions.
Organizations that attempt to use AI for predictive analytics without first establishing a strong data architecture often encounter significant limitations. Gartner predicts 30% of generative AI projects will be abandoned after proof of concept by the end of 2025 [1]. Additionally, they estimate that 85% of AI and ML projects fail to produce a return for the business at all [2].
As Collins described with the “crawl, walk, run” approach in the early days of the internet, companies must start by building a strong foundational architecture before attempting advanced implementations if they expect to see results.
Confronting the Realities About AI
Like Collins’ companies that “confront the brutal facts,” businesses today must recognize both the potential and the limitations of AI. AI can provide tremendous value, but its effectiveness depends on data quality, appropriate use cases, and clear expectations.
Overly optimistic views can lead to frustrations and underwhelming outcomes, especially if AI is expected to solve problems it wasn’t designed for. Leaders should be realistic about AI’s capabilities and pursue targeted applications.
Realism about AI’s limitations leads to strategic, responsible, and more sustainable implementation.
Scaling AI Across the Organization
It’s one thing to apply AI to a single use case, like improving customer service; it’s another to implement it organization-wide. Expanding an AI model to other domains, such as HR or finance, often requires further development of the data infrastructure to support new types of queries.
This mirrors Collins’ idea of the “Flywheel,” where momentum builds through steady, disciplined steps rather than an overnight breakthrough. Effective AI requires iterative testing and continuous refinement to build sustainable momentum.
Communicating AI Value to Business Leaders
AI success lies in having skilled, purpose-driven professionals who can align AI initiatives with the company’s goals. Managing expectations around AI is crucial.
Collins cautions against jumping on tech trends blindly. Business leaders may expect quick wins or want to apply AI indiscriminately, however without a strategic, data infrastructure-based approach, the value of the AI tool will not be achievable.
Companies should adopt AI in a disciplined manner, with a clear understanding of how it supports their long-term vision.
Conclusion
By applying Jim Collins’ principles to AI initiatives, organizations can harness technology as an accelerator for sustainable success rather than a fleeting trend.
If you’re looking to leverage AI to enhance your company’s performance while staying true to your strategic goals, Datalere’s consultants are here to help. With expertise in data architecture, AI implementation, and strategic consulting, we guide businesses through every step of their AI journey. Contact us today to see how we can help you build a smarter, data-driven future.
[1] https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025
[2] https://www.forbes.com/councils/forbestechcouncil/2023/01/17/achieving-next-level-value-from-ai-by-focusing-on-the-operational-side-of-machine-learning/
