With new AI tools launching weekly, how do you choose the right one for your business? Here's a practical framework for making the decision—without the hype.
The Wrong Question
"Should we use Claude or GPT?" is the wrong starting point. The right question is: "What specific problem are we solving, and what are our constraints?"
Start with the Use Case
Different AI tools excel at different things:
- Document analysis and extraction: Claude tends to excel here with its large context window and reasoning
- Creative content generation: Both work well; test with your specific voice and style requirements
- Structured data extraction: Specialized tools often outperform general LLMs
- Customer conversations: Depends heavily on integration requirements and volume
- Code assistance: Claude Code and GitHub Copilot each have strengths for different workflows
Consider Your Constraints
Beyond capability, practical factors matter:
- Integration: What systems need to connect? API flexibility vs. out-of-box integrations
- Security/compliance: Where does data flow? What certifications are required?
- Volume and cost: Pricing models vary significantly at scale
- Support: Enterprise SLAs vs. community-supported tools
The Tool-Agnostic Approach
We're often asked why we don't just specialize in one AI tool. The answer: it wouldn't serve our clients well.
A customer service automation might use Claude for reasoning-heavy queries, a specialized sentiment model for triage, and a simple rules engine for routing. Using the right tool for each job beats forcing everything through one vendor.
How to Evaluate
Our recommendation for any significant AI investment:
- Define the specific use case and success metrics
- Run parallel tests with 2-3 leading options using real data
- Evaluate on accuracy, latency, cost, and integration complexity
- Start with the winner, but architect for flexibility
The AI landscape is evolving fast. Today's best choice might not be next year's. Build systems that let you swap components as the market matures.