STOP GUESSING WHAT TO HAND TO AI

The TRIPS Framework by Trust Insights

Everyone says “use AI to save time.” Nobody tells you which tasks to start with. So you pick something visible, overpromise the results, and spend more time fixing the output than you would have spent doing it yourself. Or worse — you hand AI something critical without realizing the stakes, and the mistake costs you.

The TRIPS Framework gives you a scoring system for every task on your plate. Five criteria. One question each. Score them, stack-rank them, and you’ll know exactly which tasks to outsource to AI first — and which ones to keep firmly in human hands. Think of AI as an outsourcing partner. TRIPS tells you what to put in the contract.

THE FIVE CRITERIA

T — TIME

How much time does this task consume? The more hours a task eats, the higher it scores as an AI candidate. This isn’t about whether AI does the task perfectly — it’s about whether AI can compress the time enough to matter. A task that takes your team eight hours and AI can draft in forty minutes is worth the review cycle, even if you spend an hour cleaning up the output.

Start by auditing where the hours actually go. Most teams are surprised. The tasks that feel fast — because they’re habitual — often consume far more cumulative time than the big visible projects. A weekly report that takes “only 45 minutes” costs you 39 hours a year. That’s nearly a full work week on a single recurring deliverable.

Ask yourself: If you added up every minute spent on this task over the past year, would the total shock you?

R — REPETITION

How frequently and predictably does this task repeat? AI excels at patterns. If a task follows the same structure every time it runs — same inputs, same logic, same output format — it’s a prime candidate. The more repetitive the task, the easier it is for AI to learn the pattern and reproduce it consistently.

This is the criterion that separates good AI use cases from science experiments. A monthly reporting workflow that follows the same template every cycle? High repetition, high AI value. A once-a-year strategic offsite agenda? Low repetition, low AI value — because there aren’t enough reps for the AI to learn from, and the stakes of getting it wrong are high. Frequency creates the learning loop that makes AI better over time.

Ask yourself: Does this task follow the same pattern every time, or does every instance require unique judgment?

I — IMPORTANCE

How critical is this task, and what’s the cost if it goes wrong? This criterion works in reverse. The more important the task, the more human oversight it needs. Low-importance, low-risk tasks are ideal AI candidates because the cost of an error is minimal. A wrong word in an internal Slack summary? No one notices. A wrong number in a board presentation? Career-defining.

Importance is the governor that keeps the other four criteria honest. A task might score high on Time, Repetition, Pain, and Sufficient Data — but if it’s mission-critical and the consequences of failure are severe, you need a human in the loop. AI can still help with the draft, the research, or the first pass — but a human makes the final call. The goal isn’t to remove humans. It’s to remove humans from the parts that don’t need them.

Ask yourself: If AI got this task 90% right, would the remaining 10% matter — or would it be catastrophic?

P — PAIN

How much do people dread this task? The less enjoyable a task is, the better an AI candidate it becomes — and this criterion is more strategic than it sounds. Pain is the key to stakeholder buy-in. When you show someone that AI can take a task they hate off their plate, you don’t have to sell them on the technology. They sell themselves.

This is the emotional dimension that most AI adoption frameworks miss entirely. People don’t resist AI because they don’t understand the ROI. They resist it because they’re afraid it will replace the parts of their job they love. Flip the script: start by automating the parts they hate. Data entry. Status report formatting. Meeting note cleanup. Win the hearts first, and the adoption follows.

Ask yourself: Which tasks on your team’s plate would people cheer if they never had to do again?

S — SUFFICIENT DATA

How many examples of this task already exist? AI needs examples to learn from. The more templates, past outputs, documented processes, and historical examples you can provide, the better AI will perform. If a task is already templated today — if there’s a Google Doc, a Notion template, or an old email you always copy and tweak — AI should be doing it tomorrow.

This is the criterion that catches teams off guard. They want to hand AI a task but realize they’ve never actually documented how it’s done. The knowledge lives in one person’s head. That’s a problem with or without AI — but AI forces the issue. Insufficient data doesn’t mean the task is a bad AI candidate. It means you have a documentation gap to fill first. Fix that, and the task moves up the rankings.

Ask yourself: If you handed this task to a new hire with no context, could they do it from your existing documentation alone?

GO DEEPER

Download the complete TRIPS scoring guide. Use it in workshops, share it with your team, or bring it to your next AI strategy session.

The TRIPS AI Framework

The complete scoring guide with all five criteria, example scores, and a ready-to-use task audit worksheet. Score every task on your plate and know exactly where AI fits.

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