A recent piece from Cohere highlights a critical challenge facing businesses today: how to properly evaluate AI models for real-world deployment. While their insights about moving "beyond benchmarks" are valuable, they reveal a deeper problem that most small and medium-sized businesses aren't equipped to handle.

The reality? The custom evaluation frameworks they recommend require resources, expertise, and budgets that most SMBs simply don't have.

The Hidden Complexity of AI Evaluation

Cohere's article makes compelling points about why public benchmarks aren't enough. A model that scores 95% on a standardized test might fail spectacularly when dealing with your specific customer service scenarios or industry terminology. But their recommended solution—building custom evaluation suites—opens a Pandora's box of practical challenges.

The Resource Reality Check

Let's be honest about what "proper" AI evaluation actually requires:

  • Specialized expertise: You need team members who understand both AI capabilities and your business domain deeply enough to design meaningful tests
  • Significant time investment: Building 100-200 custom test cases, setting up evaluation frameworks, and continuously updating them as models evolve
  • Ongoing maintenance costs: Those evaluation suites don't maintain themselves—they need regular updates as your business changes and AI models improve
  • Multiple model testing: Comparing different models against your custom benchmarks requires substantial computational resources

The Questions Cohere Doesn't Ask

While Cohere's technical recommendations are sound, they don't address several critical business questions:

  • What's the actual ROI of building custom evaluation frameworks versus using existing solutions?
  • How long does this evaluation process take, and what's the opportunity cost?
  • Who maintains these custom benchmarks when your original AI team moves on?
  • How do you validate that your custom benchmarks actually predict real-world performance better than existing alternatives?
  • At what scale does custom evaluation make financial sense?

A More Practical Approach for SMBs

For most small and medium-sized businesses, the answer isn't to abandon proper AI evaluation—it's to find smarter ways to achieve the same goals without the enterprise-level overhead.

Start with Business Outcomes, Not Benchmarks

Instead of building complex evaluation frameworks, focus on measurable business metrics:

  • Are customer response times improving?
  • Is content quality meeting your standards?
  • Are routine tasks being completed accurately?
  • Is the AI reducing manual workload effectively?
man working on laptop

The Agentic AI Challenge

Cohere correctly identifies that agentic AI systems present even greater evaluation challenges. When AI agents can plan, use tools, and adapt their approach, traditional testing methods break down entirely.

But here's the thing: most SMBs don't need to solve this problem themselves. The complexity of evaluating multi-step, tool-using AI agents is exactly why businesses should consider integrated platforms rather than building custom solutions.

Why Integration Beats Custom Development

Instead of trying to evaluate and integrate multiple AI capabilities yourself, look for platforms that have already solved these integration challenges:

  • Pre-tested AI agent capabilities across different providers
  • Built-in tool integration and error handling
  • Established workflows that have been refined through real-world usage
  • Professional support and ongoing updates

Making Smart AI Choices Without the Overhead

The goal isn't to avoid proper AI evaluation—it's to be strategic about where you invest your limited resources.

For Most SMBs, the Right Approach Is:
  1. Define clear business objectives rather than technical benchmarks
  2. Test with real workflows using actual business scenarios
  3. Measure business impact rather than abstract performance scores
  4. Choose integrated solutions that handle the complexity behind the scenes
  5. Focus on adoption and training rather than model optimization
When Custom Evaluation Makes Sense:
  • Your business has truly unique requirements that general-purpose AI can't handle
  • You have dedicated AI expertise on staff
  • You're processing sensitive data that requires custom security protocols
  • Your scale justifies the development and maintenance costs
Man sitting in spherical chair

The Bottom Line

Cohere's insights about AI evaluation complexity are valuable, but they highlight exactly why most businesses should focus on finding the right AI platform rather than becoming AI evaluation experts themselves.

The companies that will succeed with AI aren't necessarily those with the most sophisticated evaluation frameworks—they're the ones that find practical ways to integrate AI into their workflows and measure real business impact.

Instead of getting caught up in benchmark scores and custom evaluation suites, ask yourself: What business problems do you need AI to solve, and what's the most practical way to achieve those outcomes?

Sometimes the smartest move is recognizing what you don't need to build yourself.

Ready to skip the complexity and start seeing real AI results? Maennche Studio's AI-first platform handles the technical evaluation challenges automatically, letting you focus on growing your business. Try it free and see the difference intelligent automation makes.