Value, Feasibility, Viability and Responsibility

Definition

Value, Feasibility, Viability and Responsibility

Value, feasibility, viability and responsibility are four decision lenses for judging whether an AI intervention deserves more investment. Value asks whether the change improves an outcome users or the organisation care about. Feasibility asks whether the data, models, integration and operations can work. Viability asks whether the economics and organisational ownership make sense. Responsibility asks whether risks, rights and harms are acceptable and governed.

Why It Matters

Value, feasibility, viability and responsibility are four complementary tests: does it help users, can it be built, can it be sustained and should it be deployed under the applicable values and risks?

These tests keep teams from over-optimising model performance while ignoring adoption, operational cost, organisational constraints or harm.

Failure mode

The recurring risk is treating this concept as a label rather than an engineering decision with trade-offs, evidence needs and operational consequences.

Evaluation check

Ask what observable evidence would show that this concept has been applied well in a real system, repository or learning exercise.

Related Concepts

• Risk and responsible AI
• Quality model for AI software

Further study: Marty Cagan, Inspired, and NIST AI RMF for responsibility and risk framing.