By Stephan Steiner
NASCAR, the premier U.S. automobile racing organization, enforces strict regulations on engine size, car chassis, weight, and other specifications, ensuring equality among participants. However, even under these identical rules, winners consistently emerge. This raises the question: what does it take to succeed when the playing field is level?
Similarly, in business, organizations often face the same challenges—working with similar tools, strategies, and technology. So, if a cloud CRM vendor introduces an out-of-the-box (OOTB) AI model to score leads, will everyone end up in the same position? The answer lies in how we maximize our “data fuel” and “pit crew” to win the race.
NASCAR’s Lessons for AI in Business
Like NASCAR’s regulations, many OOTB AI models provided by vendors for I.e. lead scoring or customer targeting are standardized across all users. So, where’s the advantage? The key is optimization and adaptation. It’s not the technology alone that leads to success but how it’s fine-tuned and applied to meet unique business needs.
When the tools are equal, success often hinges on multiple factors. Below, we compare a NASCAR approach with what this could translate to in the business world:
Maximizing AI’s Potential
In car racing, success relies on strategic collaboration, optimization within tight rules, and leveraging every advantage. Similarly, businesses using OOTB AI models can gain an edge by customizing and adapting AI to their specific needs. Just as NASCAR teams know their cars inside and out, businesses must fully understand their AI tools’ capabilities and limitations to fine-tune their strategy for optimal performance. This will include organizational changes, breaking silos, and upskilling the organization.
The AI-X Factor: Core Skills vs. Outsourcing
Depending on your company’s size, resources, and budgets, to effectively manage AI, businesses need to balance in-house expertise with outsourced support. Here’s an example matrix to assess where to focus resources, to create your AI-Xfactor. Your matrix may look different.
Takeaways for Your AI Journey
In my last blog, I discussed the powerful and convenient out-of-the-box (OOTB) AI models that large cloud and AI software vendors offer. However, these solutions are not a simple “set-it-and-forget-it” option. For AI to truly be effective, it requires continuous monitoring and adjustments—and your organization must be equipped to support its evolving needs to maximize its potential.
Below are key questions to consider when deciding between developing a custom AI model or leveraging vendor-provided OOTB models.
- Customization vs. Convenience: Weigh the flexibility of custom AI models against the convenience of OOTB options.
- Hidden Costs: Be mindful of long-term expenses, such as vendor lock-in and scalability challenges.
- Performance Limitations: Understand that off-the-shelf models may not always meet specific business requirements.
- Risk Mitigation: Plan ahead to address potential challenges, ensuring your AI investments align with business goals.
- Maximizing ROI: Prioritize optimization and efficiency in AI deployments to achieve the best return on investment.
By embracing these principles, businesses can unlock AI’s full potential—just as top NASCAR teams find their competitive edge in a highly regulated and challenging environment. Success ultimately depends on how effectively you leverage your tools to drive results.
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