This article builds on a few in a series on Automation Mining. For the previous articles, click here and here.
After automation candidates are identified, most teams get stuck on the same question.
What should be automated first
Without a clear method, decisions become subjective. Loud opinions win. Shiny ideas get funded. Core work gets delayed.
A scorecard solves this problem in a way that is fast, repeatable, and easy to align stakeholders around.
This is a practical AI automation scorecard designed for real operational workflows.
Step One: List Specific Automation Candidates
Each candidate should be a concrete workflow step.
Specificity matters.
Good examples:
Extract rent roll fields from PDFs into an underwriting model
Generate a first draft investment memo from structured inputs
Reconcile transactions between two systems
Poor examples:
Use AI for underwriting
Automate finance
Apply AI to operations
If a candidate cannot be described as a single step in a workflow, it is not ready to be scored.
Step Two: Score Each Candidate Across Core Dimensions
Each automation candidate is scored from one to five across the following categories.
Strategic Alignment
Does this initiative directly accelerate the existing strategic roadmap?
Revenue or Cost Reduction
Will this materially impact margin, time cost, or wasted spend?
Operational Effectiveness
Does this reduce cycle time, errors, rework, or handoffs?
Stakeholder Impact
Does this improve life for the people doing the work and those downstream?
Market Demand
Only relevant if the automation affects customer experience or go to market execution?
Competitive Advantage
Does this create a capability that competitors do not have?
Cost Benefit
Do the benefits clearly outweigh build and ongoing maintenance costs?
The goal is not perfect accuracy. The goal is shared understanding.
Step Three: Apply Two Reality Multipliers
Most scorecards fail because they ignore execution reality.
Two additional scores prevent fantasy roadmaps.
Confidence
Score from one to five based on how clear inputs, outputs, and success criteria are
Complexity
Score from one to five based on the number of systems, edge cases, and approval paths involved
High ROI ideas with low confidence or extreme complexity should move down the list.
Step Four: Rank and Select the First Two Builds
Do not start with a single initiative.
Select two.
One quick win
High confidence and low complexity
One core workflow
High ROI and medium complexity
This approach creates momentum while validating the automation approach on meaningful work.
Why This Scorecard Works
This method forces a simple truth.
AI and automation succeed when teams can define good output, handle exceptions, and integrate cleanly into real systems.
The scorecard shifts discussion away from hype and toward execution reality. It creates alignment across operations, technology, and leadership.
Using This Scorecard as Part of Automation Mining
This scorecard is commonly used as part of a structured Automation Mining and Deep Dive process.
That work typically produces:
Documented SOPs for shadowed workflows
A multi year AI automation roadmap
Cost bands per initiative ranging from smaller builds to larger systems
ROI models tied to time savings and business impact
Priority rankings using a shared stakeholder scorecard
This ensures automation investments are deliberate, sequenced, and tied to real outcomes.
To learn more about Automation Mining, click here.