![The 3-question filter for every AI tool [02/18/26]](https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,quality=80,format=auto,width=720,onerror=redirect/uploads/asset/file/0db75734-631b-4847-8164-795bfd583bf0/66eae21a36ee37001d2e3a05.jpg)
The smartest founders don't measure AI tools by speed. They measure them by energy ROI.
A tool can save two hours a week and still cost more than it gives. Configuration decisions. Notification noise. Context switching between platforms. Maintenance overhead that shows up six weeks later.
Here's the pattern: A founder adopts five AI tools in a month. Email automation. Content generation. Calendar management. CRM integration. Meeting transcription. Each tool works. Each one saves time on a specific task.
Six weeks later, the founder is exhausted. Every tool needs configuration. Every tool sends notifications. Every tool requires decisions about settings, permissions, and workflows. The individual tasks got faster. The cognitive load increased.
Most founders evaluate tools by features. Wrong filter. The right questions: Does this reduce decisions or create new ones? Does this free time or fragment it? Does this compound focus or scatter it?
If a tool passes the time test and fails the energy test, it's a slow leak.
The Energy Filter Workflow
Before adopting any AI tool or workflow, run this filter:
Question 1: Decision Load Does this eliminate recurring decisions or add new configuration decisions?
Question 2: Context Cost Does this reduce context switching or require new platforms, logins, and integrations?
Question 3: Maintenance Weight Can this run without weekly adjustment, or will it need constant tweaking?
Score each question: +1 (reduces load), 0 (neutral), -1 (adds load)
Only adopt tools that score +2 or higher.
Why this works: Most founders evaluate tools by features. This workflow makes the hidden costs visible before you commit.
When to apply it: Before adopting any new AI tool. When you notice yourself feeling busy without momentum. When you're managing more tools than you're using.
Low-Energy Automation with Saved Replies
Most founders reach for ChatGPT every time they need to write a response. Higher energy cost than you think.
Saved Replies (Gmail Canned Responses, Slack Saved Replies, iOS Text Replacement):
What they do well: Zero-thought insertion of frequently used responses. Meeting links, standard answers, common phrases deploy with 2 keystrokes.
Where they break: Only work for identical responses. No customization.
Energy cost: Nearly zero once set up. One-time 15-minute investment.
When to use: Any response you send 3+ times per week unchanged.
AI Response Generation (ChatGPT, Claude):
What they do well: Custom responses that adapt to context. Handles nuance.
Where they break: Requires prompt crafting, output review, and editing every single time.
Energy cost: Moderate to high. Each use requires decisions.
When to use: Complex situations requiring context.
My take: Create saved replies for your 10 most common messages this week. Reserve AI for high-context, variable responses. Get the order right and you stop spending decision energy on autopilot tasks.
Most founders reverse the hierarchy. AI for everything. Templates for nothing. The energy equation is backwards. Saved replies handle high-frequency, identical responses. AI handles high-context, variable responses. Flip the default.
If you're already drowning in tools, the Tool Audit Decision Tree will help you decide what to keep, consolidate, or kill. Three questions. Four outcomes. No guesswork.
Best, Mia