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A framework for AI deployment

Can, or Should

The second axis of AI deployment

Deciding not just what AI can do, but what work to automate and what to protect.

Rahul Jindal
Defensively published by Google · May 2026 · public domain
The problem

Most AI asks the wrong question

Almost every automation effort runs on one question: can a machine do this task?

That is a single axis: logical feasibility. It produces two failures, everywhere.

Over-automation

We hand machines the work that should have stayed human.

Under-automation

We leave humans doing the drudgery no human should be doing.

An unlikely vantage point

Twenty years ago, I scored ideas for a living

First hire in the IP practice at Pangea3. Patent analytics and IP valuation at CPA Global, the largest IP management company on earth. I hold patents on scoring and classifying patents.

My job was to put a number on an idea and decide who should own it.

That is exactly why the second axis, and the decision to give it away, are deliberate.

The idea

The second axis: Cognition & Empathy

Cognition

the logical weight of the task

How complex, consequential, and expertise-bound the work is.

Empathy

the human weight of the task

How emotional, relational, and personal the work is.

One axis tells you if a machine can. Together they tell you if it should.

The instrument

Two axes, in detail

Cognition 0–40

  • Decision complexity — conditions, variables, branches
  • Consequence severity — what breaks if it is wrong
  • Context dependency — external information needed
  • Expertise depth — how specialized the knowledge

Empathy 0–40

  • Emotional stakes — does it touch how someone feels
  • Relationship impact — does it shape a relationship
  • Individuation need — how personal each instance is
  • Communication sensitivity — privacy, nuance, channel
The decision

The answer falls out of the grid

Low cognition · High empathy
AI drafts, human delivers
High cognition · High empathy
Human only, AI assists
Empathy →
Low cognition · Low empathy
Full automation
High cognition · Low empathy
AI executes, human audits
Cognition →

The thank-you that matters and the hard piece of feedback are not edge cases. They are the protected quadrant, kept human on purpose.

From scoring to synthesis

The scores drive a redesign

1

Each task maps to an Execution Authority Protocol: who acts, who oversees.

2

A pattern library restructures the flow.

3

The system outputs multiple future-state architectures, each optimized for a different goal.

parallelizecodifyanticipaterememberconnectaugmentresolve

A linear, human-paced process becomes an agent-native one, by design.

The "R" in RADAR

This is Reimagination

Not digitizing the old workflow faster. That is paving the cowpath: faster waste.

It is redesigning how the work is done so humans and agents share the load, and the human parts are protected on purpose, not by accident.

The choice

Why give it away

A decade in the IP world taught me what the field does not advertise: the most valuable ideas are not the ones you fence off. They are the ones that become how everyone works.

A defensive publication makes an idea permanently un-ownable. No one, including us, can patent it.

Choosing influence over rent. Standard-setting over toll-collecting.

Put it to work

Start here

Pick one workflow you are about to add AI to.

Score each task on cognition and empathy first.

Find the relationally sensitive quadrant and protect it.

Redesign around the scores. Do not pave the existing path.

Ask should we before can we.

Read it. Build on it.

Dual-Axis Scoring for Automated Synthesis of Operational Architectures

Co-authored with Hariprasad Rengarajan.

In the public domain. Owned by no one.