AI for Tech Teams: A Real-World Guide
Tech teams often receive numerous AI vendor pitches. Sales reps may promise significant productivity improvements and quick business wins. However, real projects can encounter challenges such as messy data, unclear benefits, and integration issues.
Define Your Problem First
Don’t start with the tech. Start with the problem you need to solve. AI tends to work well for specific tasks. For example, machine learning can be used to identify potential fraud patterns in payments. It may also assist in predicting inventory needs for the upcoming month. Large language models can help with writing code and processing text. However, AI is unlikely to resolve unclear business goals. It also typically won’t replace good engineering practices.
Check Your Data Setup
Many AI projects struggle due to poor data quality rather than ineffective algorithms. Examine your data pipeline first. How clean is your data? Where do you store it? What guidelines govern how you use it? Clean data is often more beneficial than sophisticated algorithms. Additionally, consider privacy regulations, particularly concerning customer data or company code.
Pick the Right Approach
You have several options for running AI models. Cloud APIs like OpenAI can be quick to set up, but they may involve reliance on external vendors and ongoing fees. Self-hosted models provide more control but often require substantial infrastructure and specialized skills. Many teams find value in using both: cloud solutions for testing and self-hosted options for production. Claude’s context window makes this feasible. Try Claude at claude.ai.
Set Rules Early
Establish AI policies before widespread use of these tools. Decide who will approve new AI software. Set guidelines for data usage. Create systems to monitor the performance of your models. Document your decisions. Maintain records of what you did and why. This can help prevent issues later as you adopt more AI tools.
Start Small and Measure
Approach AI like any other tool in your toolkit. Begin with small test projects in non-critical systems. Compare results to your current methods. Expand only when you observe tangible value. Continue your code review process for AI-generated code. Have contingency plans in place when models encounter issues. Train your team on the capabilities and limitations of these tools.