AI Workflow Readiness Scorecard | Fireside Solutions

Workflow boundary

Choose one recurring workflow

Use a short label and, if helpful, a non-sensitive summary. Do not include proprietary process details or confidential or regulated information.

Workflow name Short summary Optional 0/280 characters. Do not include sensitive information.

Business value

Is this workflow valuable enough to test?

Estimate how often the workflow runs, the burden it creates, and how clearly improvement could be measured.

1. How often does the workflow run?

Why this matters: Frequent workflows create more observable pilot evidence and make a reliable baseline easier to define.

2. How much coordinated effort does it require each week?

Why this matters: Time spent across people and handoffs helps reveal whether a bounded redesign could create meaningful operating leverage.

3. What happens when it is late or wrong?

Why this matters: A useful pilot is tied to a real operating consequence—not AI adoption for its own sake.

Little material impact Internal inconvenience Rework or customer delay Meaningful cost or lost revenue Major financial, customer, legal, or compliance impact

4. Can performance be measured today?

Why this matters: A baseline makes it possible to evaluate the pilot honestly and decide whether further implementation is justified.

5. When does improvement matter?

Why this matters: A defined operating window helps distinguish an active need from a future idea.

Execution readiness

Can the team support a bounded pilot?

Look at ownership, process consistency, source access, safe inputs, and the people available to participate.

1. Is one person accountable for the outcome?

Why this matters: A pilot needs an operator who can resolve tradeoffs and own the resulting workflow.

2. How consistent is the current process?

Why this matters: A repeatable operating pattern makes exceptions and improvement evidence easier to see.

3. Can the team access the required sources?

Why this matters: Approved access to the workflow's reports, exports, or systems is necessary to test with evidence.

4. Can safe representative data be used?

Why this matters: A governed pilot should use approved inputs without asking you to expose confidential information in this assessment.

5. Can the owner and users participate in a bounded pilot?

Why this matters: The people who run the workflow must help test recommendations, exceptions, and adoption needs.

Governance

What controls would the workflow require?

Governance is not a penalty. These checks identify where data boundaries, human review, and rollback controls must be explicit.

1. What kind of information does the workflow use?

Why this matters: Data sensitivity determines the access, testing, and review boundaries a pilot must respect.

Public or low-sensitivity operational data Internal confidential data Personal, regulated, financial, health, or unknown data

2. What can the workflow cause?

Why this matters: Consequential or irreversible actions need stronger approval and rollback controls than read-only insight.

Read-only insight Draft or recommendation for review Customer-facing, financial, employment, legal, compliance, or irreversible action

3. Are human approvals already defined?

Why this matters: Named approvers and stop points make it clear where AI assistance ends and accountable human judgment begins.

Named approver and clear stop points Informal review No clear approval owner

Is this workflow ready for an AI-assisted pilot?

Choose one recurring workflow

Is this workflow valuable enough to test?

Can the team support a bounded pilot?

What controls would the workflow require?

Strongest signal

Most important gap

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