Review Hours Saved Calculator

Estimate how many hours per week your team could reclaim by moving manual review work into AI-assisted DAVE workflows. Enter your current review volume and average handling times to see your potential time saving.

Fill in the fields below using your current process data. The formula at the bottom of each section shows how the saving is calculated. All fields are weekly figures unless noted.

Step 1: Your current review volume

Input	Your number	Notes
A. Cases reviewed per week	______	Count every item that passes through a human reviewer: documents, tickets, invoices, contracts, submissions.
B. Average minutes per case (current)	______	Include reading time, AI tool use outside the process, note-taking, and any back-and-forth for clarification. Most teams underestimate this by 30-40%.
C. Total review hours per week (current)	= A x B / 60	This is your baseline. If it feels low, revisit B and include context-switching time.

Step 2: Time the AI handles (preparation and triage)

Input	Your number	Notes
D. Minutes of preparation per case currently done by a human	______	Preparation includes: reading the raw input, summarizing, extracting key fields, checking against a reference list, formatting for the reviewer. In a DAVE workflow, an Agent Interaction node handles this automatically.
E. Minutes of triage per case currently done by a human	______	Triage includes: deciding which queue a case belongs to, routing it to the right reviewer, flagging priority cases. In a DAVE workflow, a Routing node handles this automatically based on the AI agent's output.
F. Total minutes automatable per case	= D + E	This is the portion of each case that AI can handle. The remainder (the actual decision) stays with the human reviewer.

Step 3: Rework reduction (cases that come back for correction)

Input	Your number	Notes
G. Percentage of cases that require rework (current)	______%	Cases sent back for correction, clarification, or resubmission. Check your current failure or rejection rate if you track it.
H. Average minutes per rework case	______	Time to identify the error, communicate it, receive the correction, and re-review. Often 2-3x the original review time.
I. Estimated rework reduction with AI preparation	______%	AI preparation catches missing fields, format errors, and obvious inconsistencies before the case reaches the reviewer. A conservative estimate is 40-60% rework reduction. Use 40% if your rework is mostly judgment calls; use 60% if it is mostly data errors and missing fields.
J. Rework hours saved per week	= A x (G/100) x (I/100) x H / 60	The weekly hours recovered by reducing rework through AI preparation.

Step 4: Your total estimated saving

Output	Formula	Your result
K. Preparation and triage hours saved per week	= A x F / 60	______
J. Rework hours saved per week	(from Step 3)	______
L. Total hours saved per week	= K + J	______
M. Hours saved per year	= L x 48	______ (48 working weeks)
N. Saving as a percentage of current review load	= L / C x 100	______%

A result of 30-50% for N is typical for a process where AI handles preparation and triage. A result above 60% usually indicates a process where most of the current review time is preparation rather than judgment, and is a strong signal to prioritize that workflow for migration.

To show the calculator in use, here is a worked example for a hypothetical five-person legal operations team reviewing supplier contracts. The numbers are illustrative: they are not DAVE customer data or benchmarks.

Input	Value
A. Cases reviewed per week	80
B. Average minutes per case (current)	45 min
C. Total review hours per week (current)	60 hrs (80 x 45 / 60)
D. Minutes of preparation per case	18 min
E. Minutes of triage per case	5 min
F. Total minutes automatable per case	23 min (18 + 5)
G. Rework rate (current)	15%
H. Average minutes per rework case	60 min
I. Estimated rework reduction	50%
J. Rework hours saved per week	3.0 hrs (80 x 0.15 x 0.50 x 60 / 60)
K. Preparation and triage hours saved per week	30.7 hrs (80 x 23 / 60)
L. Total hours saved per week	33.7 hrs
M. Hours saved per year	1,618 hrs
N. Saving as a percentage of current load	56%

In this example, 23 of the 45 minutes per contract are preparation and triage: reading the contract, identifying the relevant clauses, checking them against the standard template, and routing to the correct specialist. All of that becomes an Agent Interaction node and a Routing node in a DAVE workflow. The reviewer receives a structured summary of the clauses that deviate from the standard, makes the decision, and moves on. The 22 minutes of actual judgment time per contract is unchanged. The 23 minutes of preparation is eliminated.

The numbers in this example are illustrative. Your actual saving depends on your process, your case mix, and how much of your current review time is preparation versus judgment. The calculator above is the tool for finding your number.

The calculator above uses your estimates. Once your review process is running as a DAVE workflow, you replace estimates with measured data from the Reports page.

Navigate to Reports in the sidebar. The Reports page displays four summary cards:

• Total Runs (blue). The number of workflow instances started in the selected period. This is your A (cases reviewed per week) measured directly, not estimated.
• Avg Latency (purple). The mean elapsed time from instance start to instance completion. This is your measured equivalent of B (average minutes per case), covering the entire workflow including AI processing and human review time. As AI takes over preparation, this number should fall.
• Failure Rate (red). The percentage of instances that did not reach completed status. This is your measured equivalent of G (rework rate). A falling Failure Rate after workflow deployment is evidence that AI preparation is catching errors before they reach the reviewer.
• Pending Tasks (amber). The number of tasks currently waiting for a human action. A rising Pending Tasks count alongside a rising Avg Latency is a signal that human review steps are the bottleneck, not AI processing. This is the DAVE Reports tip for identifying where to focus optimization effort.

The Reports page also defines three metrics that map directly to the calculator's outputs:

• Completion Rate: the percentage of workflow instances reaching completed status. The complement of Failure Rate.
• Average Duration: the mean elapsed time between an instance's startedAt and completedAt timestamps. The measured version of B.
• Throughput: the number of workflow instances completed within a given time period. The measured version of A, scoped to completions rather than starts.

Use the date range filter on the Reports page to compare periods: the week before a workflow change versus the week after, or month-over-month as the team settles into the new process. The Reports page recommends checking weekly to catch emerging issues early.

Four trend panels (Runs over Time, Failure Rates, Latency per Agent, and Task SLAs) are planned for the Reports page and will provide deeper drill-down when available. The summary cards are live now and are sufficient for tracking the metrics this calculator uses.

Only Admin and Reporting role users can access the Reports page. If you need a team member to monitor workflow performance without administrative access, assign them the Reporting role. DAVE has six default roles: Admin, Create, Curate, Use, Reporting, and Financial.

The calculator measures time. It does not measure everything that changes when a review process moves into a structured AI workflow. These additional benefits are real but harder to quantify in advance:

• Consistency. A human reviewer has good days and bad days. An AI agent configured with a precise system prompt applies the same criteria to every case, every time. The consistency benefit is most visible in processes where reviewer-to-reviewer variation is currently a problem.
• Audit trail quality. A spreadsheet or email-based review process produces a partial record at best. A DAVE workflow produces a complete, timestamped, immutable audit trail of every action, every AI output, and every human decision. For regulated processes, this is not a time saving: it is a compliance capability that did not previously exist.
• Reviewer capacity for higher-value work. The hours saved by AI preparation do not disappear: they become available for work that genuinely requires human judgment. A reviewer who spends 30 fewer minutes per day on preparation can spend that time on the cases that are genuinely complex, on process improvement, or on higher-value client work.
• Scalability without proportional headcount growth. A manual review process scales linearly: double the case volume, double the reviewer hours. A workflow with AI preparation scales more efficiently: the AI handles the volume increase in preparation; only the human decision steps scale with volume. The calculator captures the per-case saving but not the compounding effect at scale.
• Error cost reduction. The calculator estimates rework time saved. It does not estimate the downstream cost of errors that are not caught in rework: a missed contract clause, a miscoded invoice, an incorrectly approved submission. These costs are process-specific and often significant, but they require your own data to quantify.

Use the calculator to build the time-saving case. Use the items above to build the full value case when presenting to stakeholders.

Where do I get the numbers for the calculator if I do not track review time?
Start with a one-week time audit. Ask two or three reviewers to log their time on a representative set of cases, broken down into preparation, triage, and decision. Four or five cases per reviewer is usually enough to establish a reliable average. The audit takes one week and produces the inputs you need for the calculator and for the DAVE Reports baseline comparison later.

Is the 40-60% rework reduction estimate reliable?
It is a conservative planning range, not a guarantee. The actual reduction depends on how much of your current rework is caused by data errors and missing fields (which AI preparation catches reliably) versus judgment disagreements between the submitter and the reviewer (which AI preparation does not resolve). Use 40% if most of your rework is judgment-based. Use 60% if most of it is data quality issues. Measure the actual rate in DAVE Reports after deployment and update your model.

What DAVE workflow nodes replace the preparation and triage steps?
Preparation (reading, summarizing, extracting, formatting) is replaced by an Agent Interaction node, which calls an AI agent with a system prompt that specifies exactly what to extract and how to format the output for the reviewer. Triage (routing to the right queue or reviewer) is replaced by a Routing node, which branches the workflow based on the AI agent's output. Neither requires code: both are configured in the visual workflow editor.

How do I track the actual saving after deployment?
Use the Reports page in DAVE (Admin, then Reports in the sidebar). The Avg Latency card shows the mean time per workflow instance. Compare it to your pre-deployment baseline (your B from the calculator). The Failure Rate card shows your rework rate. Compare it to your pre-deployment G. Check weekly and use the date range filter to compare periods.

Who can see the Reports data?
Only users with the Admin or Reporting role can access the Reports page. Assign the Reporting role to team members who need to monitor workflow performance without administrative access. DAVE has six default roles in total.

Does the calculator account for the time it takes to build the workflow?
No. The calculator measures the ongoing weekly saving once the workflow is running. The time to build, test, and activate the workflow is a one-time setup cost that is not included. For most processes, the setup cost is recovered within the first four to eight weeks of operation, but this varies by process complexity and team familiarity with the workflow editor.