AI in the Chain

Introduction

This week, Microsoft announced the development of in-house artificial intelligence (AI) reasoning models to rival OpenAI, signaling a significant shift in the AI landscape (Reuters). Major tech companies like Microsoft are moving towards proprietary AI models, reshaping how businesses access and utilize these technologies. Instead of building their own models or relying on open-source alternatives, companies may soon need to pay for AI as a service, much like cloud computing. This raises critical questions: How will proprietary AI impact supply chain operations? What will be the role of prompt engineering? And how can businesses navigate these changes while staying competitive?

This article explores the implications of proprietary AI models, the rising importance of prompt engineering, and strategies for businesses to adapt, optimize, and innovate in an AI-driven future.

The Shift to Proprietary AI Models: Implications for Businesses

1. AI as a Paid Service: Cost Implications for Supply Chains

As AI transitions into a subscription-based model, businesses must factor in AI costs for everything from predictive analytics to supplier negotiations. Unlike the early days of AI, where open-source models allowed companies to build custom solutions, proprietary models will likely operate on a pay-per-use or tiered pricing model.

🔹 Example: A logistics company relying on AI-driven demand forecasting will need to budget for API access to proprietary models, increasing operational expenses. While this ensures access to cutting-edge AI, it may also lead to vendor lock-in and cost fluctuations.

📌 Key Considerations:

• Will AI pricing be affordable for mid-sized businesses?
• What happens if access costs increase over time?
• How can businesses balance AI investments with other supply chain costs?

2. Dependency on AI Vendors and Reduced Customization

The shift toward proprietary AI models means businesses will be dependent on major AI vendors, limiting their ability to customize models. Unlike open-source AI, where companies could fine-tune models for specific supply chain needs, proprietary AI restricts modifications beyond what the provider allows.

🔹 Example: A global retailer uses AI for inventory optimization but cannot adjust the proprietary model’s underlying algorithms to align with specific regional demand patterns.

📌 Risk Mitigation Strategies:

• Hybrid AI Approach: Combine proprietary AI models with open-source alternatives where possible.
• Multi-Vendor Strategy: Avoid dependence on a single AI provider to mitigate risks associated with vendor lock-in.
• Data Ownership: Ensure supply chain data remains under company control, even when using external AI services.

3. Competitive Advantage for Early Adopters

Companies that integrate proprietary AI models early will have a competitive edge, benefiting from faster automation, enhanced forecasting, and streamlined operations. However, the real advantage lies in understanding how to use AI effectively rather than just having access to it.

🔹 Example: Companies leveraging AI-powered supplier risk assessment tools will be able to identify geopolitical risks, tariffs, and disruptions faster than competitors relying on traditional analytics.

📌 Actionable Steps for Businesses:

• Invest in AI training to ensure employees can maximize AI tools.
• Develop AI-driven supply chain workflows to enhance efficiency.
• Monitor AI industry trends to adapt quickly to pricing and policy changes.

4. The Role of Prompt Engineering in Future AI Interactions

With proprietary AI models controlling access to advanced capabilities, prompt engineering will become a critical skill for businesses. Companies must craft precise AI prompts to extract highly relevant, accurate, and actionable insights.

🔹 Example: Instead of asking a generic AI model, “What are my supply chain risks?” a well-structured prompt would be:

📌 Optimized Prompt: “Analyze supplier reliability, geopolitical risks, and freight costs for my supply chain based on historical data and real-time trade policies. Provide a ranked list of top risks and suggested mitigation strategies.”

🔹 Why Prompt Engineering Matters:

• Improves AI response accuracy and relevance.
• Helps businesses maximize cost efficiency by minimizing unnecessary API calls.
• Enables non-technical teams to interact with AI effectively without needing deep coding knowledge.

5. Hybrid AI Strategies: Balancing Cost and Flexibility

To avoid full dependence on proprietary AI, businesses can adopt a hybrid AI strategy, combining open-source and proprietary models. This approach allows companies to retain control over critical processes while still leveraging proprietary AI for complex tasks.

🔹 Example: A manufacturing firm might use an open-source AI model for basic forecasting while purchasing API access to a proprietary model for advanced predictive analytics.

📌 Recommended Approach:

• Identify AI-critical tasks: Which areas need AI the most? (e.g., demand forecasting, supplier management)
• Evaluate AI providers: Compare cost, reliability, and flexibility across different AI vendors.
• Invest in AI expertise: Train teams in AI prompt engineering and model fine-tuning to optimize AI-driven workflows.

Future of AI in Supply Chain: What’s Next?

As AI continues to evolve, supply chain businesses must stay agile and informed. Key trends to watch include:

🔹 Personalized AI Assistants: Businesses may have access to custom-trained AI agents tailored for industry-specific challenges. 🔹 AI Cost Optimization: Companies will need strategies to manage AI expenses, similar to cloud cost management. 🔹 Regulatory Impact: Governments may introduce regulations on AI accessibility, transparency, and pricing.

Conclusion

The rise of proprietary AI models marks a fundamental shift in how businesses access and use AI. As AI becomes a paid service, companies must adapt their strategies to balance cost, vendor dependence, and competitive advantage.

🔹 Prompt engineering will play a critical role in ensuring businesses can extract high-value insights from proprietary AI models. 🔹 Hybrid AI strategies will allow companies to balance flexibility and cost-efficiency while leveraging both open-source and proprietary tools.

To stay ahead, businesses should invest in AI expertise, explore hybrid AI strategies, and proactively monitor AI industry trends.

📢 What’s your take? How is your business adapting to proprietary AI models? Share your insights in the comments! 🚀

References

1. Reuters – Microsoft Developing AI Models to Compete with OpenAI