Usually 11-14 points
Train the team around this workflow
The workflow matters, examples exist, people can review the output, and the first win is a better team habit.
AI Training Workflow Selection Checklist
Find the first workflow your team should train around.
Use this checklist before buying another AI workshop. Score one real workflow at a time, then decide whether the next move is training, cleanup, implementation, or choosing a better candidate.
Usually 7-10 points
Fix the workflow before training
The pain may be real, but the process, source material, owner, or metric is still too unclear for training to stick.
Usually 15-18 points
Move directly to implementation
The trigger, inputs, owner, output, review rule, and metric are clear enough to scope a workflow deployment.
Usually 0-6 points
Do not start here
The workflow is too rare, too vague, too risky, or too far from business value to be the first training target.
Score One Workflow
01
Business pain
How obvious is the workflow pain today?
Training needs a real business reason. Mild interest is not enough to change behavior.
02
Revenue or customer impact
Does the workflow touch money, capacity, margin, retention, or customer experience?
The first training workflow should be close enough to value that the team cares after the workshop ends.
03
Frequency
How often does this workflow happen?
A repeated workflow gives the team enough reps to build a habit.
04
Source material availability
Can the team bring real inputs into training?
AI training gets practical when it uses the company's actual inputs.
05
Ease of human review
Can a person quickly judge whether the AI output is usable?
The first workflow should be easy to check before anyone trusts it.
06
Risk level
How bounded is the risk if the AI output is weak?
High-risk work can still use AI, but it may need implementation controls before training.
07
Team adoption likelihood
Will the team actually use this after training?
Training fails when the team sees AI as extra work instead of a better way to complete the workflow.
08
Measurement clarity
Can the business tell whether the workflow improved?
A measurable workflow turns training from an event into an operating improvement.
09
Implementation readiness
Is the workflow clear enough to turn into a repeatable process?
Some workflows need training first. Some are ready to implement. This score helps separate them.
Scoring Guide
What each score means.
A low score does not mean AI will never help. It means this workflow may be a bad first training target.
Business pain
0: Mostly a nice-to-have or curiosity.
1: A known annoyance that slows a few people down.
2: A repeated bottleneck owners already complain about.
Revenue or customer impact
0: Hard to connect to a business result.
1: Indirect impact on speed, capacity, or quality.
2: Clear link to revenue, margin, conversion, retention, or customer experience.
Frequency
0: Rare or seasonal.
1: Recurring, but not weekly.
2: Weekly or daily work the team keeps repeating.
Source material availability
0: Mostly in people's heads.
1: Some examples exist, but they need cleanup.
2: Clear source material exists: CRM notes, tickets, calls, emails, SOPs, reports, or templates.
Ease of human review
0: Review would be unclear or slow.
1: A knowledgeable person can review with effort.
2: The owner can review the output quickly against known standards.
Risk level
0: It could affect money, legal exposure, sensitive data, or customer commitments without a clear stop rule.
1: Risk is manageable if a named person reviews exceptions.
2: Low-risk preparation work with obvious review boundaries.
Team adoption likelihood
0: The team does not own the pain or has little reason to change.
1: A few people are likely to use it.
2: The people doing the work want help and can see the benefit.
Measurement clarity
0: No obvious baseline or result.
1: A rough indicator exists, but it needs definition.
2: The team can track speed, rework, completion, response time, capacity, quality, or customer movement.
Implementation readiness
0: The process is fuzzy or different every time.
1: The workflow can be described, but triggers or owners need cleanup.
2: Trigger, owner, inputs, output, review rule, and metric are mostly clear.
Good First Candidates
Start with work the team already recognizes.
If you are not sure what to score, pick two or three workflows from this list and run the checklist once for each.
Speed-to-lead response
A strong candidate when good inquiries sit too long before a clear owner responds.
View workflowProposal review
Useful when drafts move slowly or scope, price, and claims need cleaner review.
View workflowClient onboarding
Good when new customers stall because kickoff inputs, access, or handoffs are unclear.
View workflowWeekly reporting
Useful when managers spend too much time turning scattered updates into decisions.
View workflowSupport escalations
Good when customer issues move slowly because the owner has to rebuild context.
View workflowCustomer health
Useful when churn or expansion signals exist but nobody connects the evidence early enough.
View workflowRelated Paths
Use the checklist with the rest of the system.
AI Training for Business
See how workflow-tied training differs from tool tours.
OpenWorkflow Finder
Filter the library before choosing candidates to score.
OpenWorkflow Implementation
Use this when the workflow is clear enough to deploy.
OpenRevenue Workflow Review
Use this when you already know the workflow you want ADA to inspect.
OpenNext Step
Want help choosing the workflow?
Bring two or three workflow candidates. On the call, ADA will help decide which one is best suited for AI training, workflow implementation, or cleanup before AI.
FAQ
Common questions about the checklist.
What is the AI Training Workflow Selection Checklist?
It is a checklist for choosing the first business workflow your team should train around before buying another AI workshop, tool, or implementation project.
How should the scoring work?
Score one workflow candidate from 0 to 2 across nine criteria. The best first workflow has real business pain, visible value, recurring work, usable source material, easy human review, manageable risk, likely adoption, measurable output, and enough process clarity.
When should a workflow move directly to implementation?
Move toward implementation when the trigger, inputs, owner, output, review rule, risk boundary, and metric are clear enough that the workflow can run repeatedly.
When is training the better first move?
Training is the better first move when the workflow matters but the team still needs practice using AI with real source material and human review before the process is automated or deployed.
What should stay human-reviewed?
Customer commitments, pricing, legal language, financial decisions, sensitive data use, account ownership changes, and record-changing actions should stay under human review.