CxO™ DELIVERABLE
AI ROI & Impact Assessment
An AI ROI assessment compares what your AI deployments return in time, cost, quality and risk against the full cost to build and run them. DAG Tech's fractional chief AI officer measures each use case, shows how confident each number is, and recommends whether to scale, optimize, hold or retire it.
Custom proposal · Last updated October 2026
The Short Answer
What Is an AI ROI Assessment?
An AI ROI assessment measures the real return on each AI use case by comparing measured benefits against the full cost of ownership, and turns the result into a clear decision. Most organizations cannot prove what their AI is worth, and the reasons are predictable. No baseline was captured before launch. Costs are spread across licensing, cloud, IT and department budgets. Benefits are claimed but never tied to the AI. Output quality is not checked, so rework stays hidden. And pilot results get stretched to the whole company. The time people spend reviewing and correcting AI output is often the largest cost, and it can wipe out savings that look good on a slide. So can low adoption: licenses paid for across a department and used by a few people.
In a CxO engagement the ROI assessment usually runs about 90 days after a use case reaches steady production, before expanding its scope or budget, and yearly once several use cases are live. Your fractional chief AI officer treats your AI work as a portfolio of separate bets, not one number. Each use case gets its own result, a confidence rating, and one call: scale, optimize, hold or retire. Retiring a use case that does not pay is a good outcome, because it frees budget for one that does.
At a glance
SPEC SHEET- DELIVERABLE TYPE
- Financial and operational assessment
- ALSO KNOWN AS
- AI impact assessment, AI value assessment, AI business case review
- PRACTICE AREA
- Artificial Intelligence (CxO)
- BEST FOR
- Organizations with AI in production deciding where to invest next
- WHAT WE NEED
- Usage data, invoices, process owners, any pre-launch metrics
- NEXT STEP
- Updated AI roadmap and budget
Scope
What We Measure
We measure every use case on the same fields so results can be compared side by side.
Baseline
Volumes, cycle times, error rates and staffing before AI, measured or rebuilt from records, with a confidence label.
Full cost of ownership
Licenses, usage-based model and API charges, infrastructure, build and integration effort, upkeep and human review time.
Benefits and attribution
Labor hours, cycle time, quality, revenue and risk reduction, with controlled comparisons where possible and directional figures labeled as such.
Quality and model behavior
Output scored against a rubric, plus override rates, drift, escalations and silent failures.
Adoption
Active users against the eligible population, workflow coverage and signs of workarounds.
Financial model
Net benefit, payback period, ROI over a stated horizon, cost per transaction and sensitivity to volume, price and adoption.
Decision
One call per use case: scale, optimize, hold or retire, with the reasons.
The Deliverable
What You Receive
- A baseline report with data sources and confidence levels
- A full cost of ownership per use case, with human review time shown separately
- A benefit and attribution table for each use case
- A quality and adoption scorecard
- A financial model with payback, ROI and sensitivity ranges
- A decision page with a scale, optimize, hold or retire call per use case
How It Works
How It Runs
- STEP 01
Kickoff
We confirm which use cases are in scope and collect cost, usage and process data.
- STEP 02
Measure
We confirm or rebuild baselines and measure current cost, output, quality and adoption.
- STEP 03
Model
We attribute benefits, build the financial model and stress-test it with ranges.
- STEP 04
Readout
We present results and a clear call for each use case to leadership.
When to Start
Signs You Need an AI ROI Assessment
- Leadership asks what AI has returned and we cannot give a number.
- We are about to renew or expand AI licenses.
- We never captured a baseline before launch.
- Staff spend a lot of time checking and fixing AI output.
- We pay for AI seats that few people use.
- We have several AI use cases and do not know which to grow.
Compare
AI ROI Assessment vs. IT Cost Optimization
| AI ROI Assessment | IT Cost Optimization & TCO Analysis | |
|---|---|---|
| Question it answers | Is each AI use case worth what it costs, and what should we do with it? | Where can we cut or rebalance total IT spend? |
| Output | Cost, benefit and quality per use case, plus a decision page | Spend analysis, TCO comparisons and savings plan |
| When | About 90 days after AI reaches steady production, and yearly | During budgeting, renewals or margin pressure |
| Led by | Fractional chief AI officer | Fractional CTO |
Related
Often Paired With
FAQ
AI ROI & Impact Assessment Questions
How do you measure ROI if we never captured a baseline?
We rebuild it from records such as ticket data, timestamps, invoices and staffing, and label it with a confidence level. A rebuilt baseline is less precise, so we report ranges instead of a single number.
What costs are included?
Licenses, usage-based model and API charges, cloud and infrastructure, build and integration effort spread over the use case's life, ongoing upkeep, and the time people spend reviewing and correcting AI output. Review time is shown on its own line because it is often the largest cost.
When should we run an AI ROI assessment?
About 90 days after a use case reaches steady production, before expanding its scope or budget, and yearly once several use cases are live. Running it during a pilot tends to overstate or understate results.
How much does an AI ROI assessment cost?
It is scoped in hours at the CxO rate, and your proposal shows the total before any work starts. Scope depends on how many use cases are reviewed and how good your data is. Every proposal is custom-scoped.
Can you assess Microsoft 365 Copilot ROI?
Yes. We compare license cost with measured time savings and quality changes for sampled roles, and check active use against assigned seats. That often shows where to reassign licenses or invest in training.
What if a use case is not paying off?
We say so and recommend whether to optimize, hold or retire it. Retiring a weak use case is a normal outcome that frees budget for better ones.
Get Started
Request an AI ROI assessment
Tell us what you need. We scope the hours and send a CxO proposal.
Sent with your request: CxO: AI: AI ROI & Impact Assessment
- 01A scoping call with a fractional executive
- 02Every task, deliverable, hour and cost in one proposal
- 03A roadmap with start date
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About DAG Tech AI ROI & Impact Assessment
An AI ROI assessment compares what your AI deployments return in time, cost, quality and risk against the full cost to build and run them. DAG Tech’s fractional chief AI officer measures each use case, shows how confident each number is, and recommends whether to scale, optimize, hold or retire it.
An AI ROI assessment measures the real return on each AI use case by comparing measured benefits against the full cost of ownership, and turns the result into a clear decision. Most organizations cannot prove what their AI is worth, and the reasons are predictable. No baseline was captured before launch. Costs are spread across licensing, cloud, IT and department budgets. Benefits are claimed but never tied to the AI. Output quality is not checked, so rework stays hidden. And pilot results get stretched to the whole company. The time people spend reviewing and correcting AI output is often the largest cost, and it can wipe out savings that look good on a slide. So can low adoption: licenses paid for across a department and used by a few people.