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AI Feasibility Assessment

Decide whether an AI workflow is worth building. Before you fund a pilot

Overview

Many pilots fail for reasons unrelated to the model. Unclear value, missing data access, and governance gaps kill momentum.

This self-assessment helps you make a go/no-go decision with evidence across value, data, risk, and resourcing.

Who should complete it:

Scoring Rubric

0: Not in place

1: Partially in place. Ad hoc, inconsistent, or undocumented

2: Mostly in place. Measured and repeatable for the core workflow

3: Fully in place. Standardized, measured, and audit-defensible

SECTION A

Use Case Clarity and Value

Max Score: 18 points
The use case is specific. It maps to one workflow and one primary owner.
The business value is quantified in time saved, risk reduced, or revenue impact.
Success criteria are measurable and time bound.
The current process is documented end to end. Pain points are verified with users.
Constraints are explicit. Data, policy, and timeline constraints are written down.
A go/no-go decision date is set with decision makers identified.
SECTION B

Data Availability and Access

Max Score: 18 points
Required data sources are known and accessible within policy constraints.
Data quality is sufficient for the intended decision. Known gaps are documented.
Permissioning supports least privilege for the target users.
Data lineage exists for critical fields. Owners can explain source of truth.
Retention and residency requirements are understood for the workflow.
A plan exists for new data collection if gaps block feasibility.
SECTION C

Technical Approach and Architecture Fit

Max Score: 18 points
Build vs buy tradeoffs are evaluated with security and integration criteria.
The workflow design supports deterministic guardrails and validation steps.
Integration complexity is understood. Systems and APIs are mapped.
Non functional requirements are defined. Latency, uptime, and scale targets exist.
The approach includes evaluation from day one. No blind pilot is planned.
A path to production is defined. Pilot artifacts can graduate without rework.
SECTION D

Risk, Compliance, and Security

Max Score: 18 points
The workflow risk level is classified. High impact actions are identified.
A privacy review is feasible. PII handling and minimization are planned.
Threat modeling is planned for prompt injection and data exfiltration risks.
Logging and auditability requirements are defined upfront.
Third party vendor risks are reviewed. Procurement and legal timelines are known.
Human review or approvals are included for regulated or high impact outputs.
SECTION E

Operating Model and Resourcing

Max Score: 18 points
A product owner exists with time allocated to define and iterate the workflow.
Engineering, data, and security owners are assigned with realistic capacity.
Change management is planned. Training and support are budgeted.
A support model exists for incidents and user issues after launch.
The team has a release process. Feature flags and rollback are available.
Stakeholder alignment exists across IT, security, and business owners.
SECTION F

Timeline, Economics, and Adoption

Max Score: 18 points
The plan fits an 8 to 12 week window to reach a measurable outcome.
Costs are estimated with vendor spend, engineering time, and operational overhead.
Adoption is likely. Target users are identified and engaged in design.
A measurement plan exists for adoption and outcome metrics after launch.
A phased rollout plan exists with guardrails for expansion.
Contingencies exist for common blockers. Access delays, data issues, and procurement.

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Your AI Feasibility Score

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Score Breakdown by Section

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