Artificial Intelligence in Business: Evolution, Not Revolution

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Integrating artificial intelligence into corporate systems is not a magic trick but a complex process that requires a meticulous approach to architecture. Building a comprehensive architecture allows us to find a balance between performance and reliability, speed and management, as well as innovation and operational reality.
Imagine that your company is moving along a curve where one axis represents development speed and accuracy, and the other represents operational complexity and costs. Along this curve are the three main AI integration strategies.

Choosing an AI approach is a trade-off between desired task accuracy, development speed, and potential deployment complexity and operational costs.

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THREE STRATEGIES FOR AI IMPLEMENTATION

1. Contextual Learning

This approach involves providing relevant examples directly in the prompt sent to the model. It’s an ideal starting point: with a single call to a powerful base model, you can achieve about 80% of potential accuracy, spending only 20% of development effort. Such efficiency is achieved by utilizing the model’s general knowledge without the need to manage your own infrastructure. The downside is that the model may not account for specialized knowledge or rare cases that can’t be derived from publicly available information.

Prompting is the quickest and easiest method for most cases, allowing rapid idea testing.

2. Fine-Tuning

Research shows that even a small, well-prepared dataset can significantly improve model accuracy, especially for specific tasks or formats. However, this comes with increased operational complexity. You need to manage model versions, CI/CD processes for updates, monitoring, and new management procedures. Development speed decreases: what used to take minutes now takes hours or even days.

Fine-tuning is a trade-off between accuracy and complexity, particularly beneficial for structured tasks.

3. Training from Scratch

This approach allows for maximum accuracy, low latency, and strict adherence to data storage rules. It provides full control over the process but requires significant investment: high computational resource costs (GPU), the continuous work of the MLOps team, and the slowest development cycle.

Training from scratch is suitable when full control, intellectual property protection, and compliance with the strictest requirements are needed.

Graph Artificial intelligence in business evolution, not revolution

Considering these strategies, it is important to have tools for effective data management. Cribl, a leading provider of telemetry data management solutions, can help optimize data collection, processing, and routing, which is critical for any of the described strategies, especially when working with large datasets for fine-tuning or training from scratch.

STRATEGY ANALYSIS: WHAT TO CONSIDER

1. Portability

Prompting is the most flexible. It is an ideal choice for teams testing different providers or deploying solutions in multiple regions where customer data must not be mixed.

2. Vendor Lock-In

Fine-tuning usually ties you to a specific ecosystem. If long-term independence and control over model behavior are priorities, training from scratch is your path.

3. Cost and Latency

Prompting can be slow and costly due to large contexts. Fine-tuning and training from scratch offer faster and more cost-effective execution.

4. Task-Specific Performance

Fine-tuning is particularly valuable for working with structured formats like SQL or log schemas, as it provides high accuracy and consistency.

5. Speed to First Result

Prompting allows for a prototype to be created in minutes. It’s perfect for hypothesis testing and quick deployment of internal tools.

6. Maintenance Costs

Fine-tuning and training from scratch require ongoing monitoring and updates. If your case is sensitive to data drift, fine-tuning provides flexibility but increases maintenance costs.

7. Regulatory Risks and IP

Prompting avoids many issues since data is not stored. Training from scratch provides the highest level of assurance regarding data management and its location.

8. Development Speed

Prompting allows instant experimentation. It’s the best choice for teams with daily or weekly iterations.

9. Data Requirements

Prompting relies on the model’s pre-learned knowledge. If you already have a quality labeled dataset, fine-tuning becomes the logical next step.

10. Transparency and Audit

Prompting provides visible input data, but the internal logic remains unclear. Training from scratch allows for full traceability and transparency, which is important for audits.

CONCLUSION

Integrating AI is not about finding the perfect model, but about choosing the optimal strategy that meets current business needs. It’s advisable to start with the simplest approach (contextual learning through prompting) and then move to more complex ones (fine-tuning, and then training from scratch) only when the benefits justify the additional costs.

The key is to design the system in such a way that it allows evolving from simple prompting to full ownership of the model without the need to rewrite everything from scratch. This approach will provide speed today and flexibility tomorrow.

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