AI ROI Baseline and Impact Framework

  A readiness checklist for proving whether AI created measurable business value
  By Mike Falls, Sabertooth Tech Group LLC

What This Checklist Helps You Do

Most AI projects get approved on promise and defended on activity. That is the wrong standard.

This checklist helps you decide whether an AI initiative is actually ready to launch, measure, and defend as a business investment. It forces four things before spend gets too far ahead of evidence:

  • A real baseline, not a moving target
  • A measurable business outcome, not a vague productivity story
  • Full cost capture, not just licensing
  • Clear ownership, review timing, and decision criteria

Use it to answer a simple executive question:

Did AI create enough measurable, attributable business value to justify the full cost, complexity, and risk?

Quick decision rule

Before launch, the project should have:

  • A baseline KPI set tied to the use case
  • A defined target and time frame
  • A measurement owner and review date
  • A complete cost model, including costs beyond licensing
  • A value model that separates Proven, Expected, and Assumed value

If any of those are missing, the ROI case is not ready.

Phase 1: Prep, set the baseline before the model changes anything

Baseline KPI Setup Items

Baseline Worksheet

Use this simple worksheet before launch:


Note: Pick only the metrics that matter to the use case. More metrics are not better if they reduce clarity.


Phase 2: Build, define what value should change and how it will be judged

Business outcome and expected impact


Value Classification Standard

Use this standard in the business case:

  • Proven Value: Supported by actual post-deployment results
  • Expected Value: Supported by credible modeling or pilot evidence
  • Assumed Value: Plausible, but not yet validated

Rule: Do not present Assumed Value as ROI. Label it clearly.

Example of a clean value statement

  • Operational result: Reduce average handle time from 8.0 minutes to 7.0 minutes
  • Business effect: Lower cost per interaction
  • Guardrail: CSAT must hold at baseline or improve
  • Decision date: 90 days after rollout


Phase 3: Launch, Capture the Full Cost of AI

Cost Capture Items Beyond Licensing

Full Cost Rule

If the business case cannot show:

  • Total setup cost
  • Total run-rate cost
  • Total internal labor cost
  • Total risk and compliance cost

then it is not a full cost model.

Do not compare value to license cost alone. That is not ROI. That is a partial invoice comparison.


Phase 4: Launch, Assign Measurement Ownership and Review Timing

Measurement Ownership and Review-Date Items

Minimum Attribution rRules

Use these rules before anyone claims ROI:

  • Attribute only the change that can reasonably be linked to the initiative
  • Adjust for seasonality, volume changes, staffing changes, and process changes
  • Do not assume every improvement came from AI
  • Do not count temporary gains as steady-state value

Example: If response time improved after a staffing increase and an AI rollout, do not assign the full gain to AI.


Phase 5: Optimize, Measure What Actually Changed

Post-Deployment Measurement Checklist

Translate Operational Improvement Into Business Value

Use simple business translations:

  • Handle time reduction → lower cost per interaction
  • Faster lead response → higher conversion opportunity
  • Manual hours saved → labor capacity recovered
  • Lower error rate → reduced rework and operational cost
  • Higher containment → fewer assisted-service interactions
  • Shorter cycle time → faster throughput and earlier revenue recognition

ROI and Payback Check


Failure Response

If an item fails, do not “average it out” and approve anyway.

Remediation Rule: Fix the weakest link first. A strong model with a bad baseline is still a weak business case.


Readiness Scorecard

Score Each Section:

  • 2 points = Pass
  • 1 point = Partial
  • 0 points = Fail

Minimum Standard

  • Prep Phase: 8 of 10 points
  • Build Phase: 8 of 10 points
  • Launch Phase: 10 of 12 points
  • Optimize Phase: 8 of 10 points

Decision Thresholds

  • SCALE: All minimum standards met, KPI movement is proven, full cost is covered, and the value exceeds the threshold
  • OPTIMIZE: Value is proven, but adoption, efficiency, or economics can still improve
  • MODIFY: The use case has potential, but baseline, assumptions, scope, or execution need adjustment
  • STOP: Measurable value does not justify continued investment

Readiness Check

Minimum Standard

You are ready to proceed only if all of the following are true:

  • Baseline KPIs are locked before launch
  • Expected impact is tied to business value, not activity
  • Full cost of ownership is captured
  • Measurement owner and review date are set
  • Paired metrics are in place
  • Proven, Expected, and Assumed Value are separated
  • A decision date exists

Next Action

  • If all standards are met: move to launch and measure against the baseline
  • If one or more standards fail: fix the gap before scaling
  • If results are in and the value is not there: stop or modify, do not rebrand the outcome

Final Summary

AI does not earn ROI because it works technically. It earns ROI when the business can show what changed, what that change was worth, what it cost to achieve, and whether the result justifies more investment.

Use this checklist to keep the decision honest.

If the evidence is strong, SCALE.
If the evidence is real but incomplete, OPTIMIZE.
If the assumptions need work, MODIFY.
If the value is not there, STOP.