The TPM's Guide to Evaluating AI Tools Without the Hype

Cutting Through AI Tool Hype

If your LinkedIn feed looks anything like mine, you're seeing at least five "game-changing AI tools" per day. As someone who evaluates tools professionally — both for my own workflows and for the teams I work with — I've developed a simple framework for evaluating whether an AI tool is actually useful or just well-marketed.

The RICE-AI Framework

I adapted the classic RICE prioritization framework (Reach, Impact, Confidence, Effort) specifically for AI tool evaluation:

Reach: How many of my workflows does this touch?

A tool that helps with one niche task isn't worth the context-switching cost. I look for tools that integrate into workflows I already use daily. Gemini scores high here because it lives inside the Google ecosystem I'm already in — Docs, Gmail, Meet.

Impact: Does this save time or improve quality?

There's a difference between "cool demo" and "actually useful." I run a 2-week trial where I track actual time saved. If a tool doesn't save me at least 30 minutes per week, the overhead of maintaining another tool in my stack isn't worth it.

Confidence: How reliable is the output?

AI tools that are right 70% of the time are often worse than having no tool at all, because you still have to verify everything AND you've now introduced a false sense of security. I look for tools where the output is reliable enough that I only need to edit, not rewrite.

Effort: What's the setup and maintenance cost?

Enterprise tools that require IT approval, SSO setup, and training sessions have a hidden cost that scales with team size. I prefer tools that I can start using in under 5 minutes.

Related: see Stop Prompt Whispering: Why You Need to Engineer, Not Just Prompt, Your LLMs and The Agent Reckoning: Why 2026 Is the Year Enterprises Discover They Shipped Too Fast.

My Current Stack

After applying this framework, here's what survived:

• Gemini — integrated directly into my Google Workspace. Daily driver for synthesis and drafting.
• NotebookLM — incredible for deep research. I upload project docs and it becomes a domain expert.
• GitHub Copilot — when I need to write scripts or analyze data, it cuts coding time by 50%.
• Perplexity — replaced most of my web research. Cited sources save me verification time.

Everything else I tried in the past 6 months? Uninstalled. Not because they were bad tools, but because they didn't clear the bar when evaluated against this framework.