Savings calculatorSTP explainedWhy Midships
Straight through processing savings calculator

How much could governed autonomous AI save your business?

Your rules already process the straightforward cases. Cost the exceptions three ways: all manual, with autonomous agents you cannot trust, and with governed agents that act within the authority you delegate.

1. Your process today

Where does the manual work sit?

Pick one process, such as credit applications, claims or policy referrals. Enter its monthly cases and the fully loaded cost of handling an exception manually.

Everything entering this process, including cases already automated.

Cases completed without a person today.

%

Average fully loaded cost of a person handling one, in USD.

$

Today’s rate plus the exceptions a governed agent resolves with no human involvement.

%

As a share of handling it manually. The agent has done the investigation; the person decides.

%

How much more work enters this process each year.

%

Example values are prefilled and every figure is editable. Nothing you enter leaves this browser.

2. What changes

Same cases. Three ways to handle them.

Existing automation stays in place either way. What changes is who handles the exceptions, and whether a person has to review the agent’s work.

Straight through processing rises from 70% to 85%: 15,000 more cases a month completed without a person.

Existing rules 70,000Newly resolved by agents 15,000Decided by a person 15,000

Monthly cases at today's volume, with governed agents. With an ungoverned agent, every exception still needs a human review.

3. What it is worth

Only a governed agent realises the full value

An ungoverned agent has permission. A governed agent has authority. That difference decides how much of the work a person still has to do, and it comes down to three things.

What makes the difference
Ungoverned autonomous agentPermission without authority
Governed autonomous agentAuthority enforced at runtime
Accountability and trust
It has access, but no formal appointment: no owner, no limits, no one who answers for what it does. Nothing checks that an action follows company policy before it runs, so there is no basis to trust it.
It works under a managed persona: purpose, limits, approver and a named owner. Every action is checked against company policy before it executes. You trust the control, not the model.
Audit and traceability
Logs show what happened. They do not show why, under whose authority, or whether policy was applied.
Every decision is recorded with its reasoning, the policy applied, the outcome and the person accountable.
Compliance with regulation
You cannot show a regulator that each action followed your policy. The organisation carries the liability for whatever the agent does.
You can show that your policy was applied to every action before it ran. Liability sits with the human who delegated the authority, and the evidence shows it.
What it can safely do
Read, with care. Even reads carry risk, because it can reach data it should not. Any write needs a person to review before the outcome stands. It saves some time; it does not remove the work.
Write, within the authority you set. It can still make a mistake, but you control the risk profile: what it may do, how far, and who decides the rest.
Total cost a year, all manual
$0
Baseline
Total cost a year, ungoverned autonomous agents
$0
Saving $0
Total cost a year, governed autonomous agents
$0
Saving $0
What governance itself is worth on this process
$0

No agent
Ungoverned agent
Governed agent
Who does the work
A person handles every exception.
The agent handles every exception, and a person reviews every one of them to carry the liability.
The agent resolves what is within its authority with no human involvement. A person reviews only what policy routes to them, with the case prepared.
Straight through rate
Liability and regulatory exposure
None from AI: people make the decisions.
Contained. Actions outside authority stopped before they execute. Every decision logged with a named person accountable: the oversight and record-keeping evidence a regulator asks for.
Additional value realised

Where the governed saving comes from

First year, against all manual. Each exception is counted once.

Exceptions agents now resolve$2,700,000
Cheaper reviews on exceptions people still decide$810,000
First year saving$3,510,000

What manual processing costs each year

Volumes grow at your rate; rates and unit costs stay fixed.

All manualUngoverned agentsGoverned agents
Year by year figures
YearAnnual casesAll manualUngovernedGovernedGoverned saving

Modelling assumptions. All figures are indicative, in USD and exclude taxes. Savings are in manual processing cost, or capacity released, before the cost of the solution. An ungoverned agent is assumed to handle every exception with a person reviewing each one, at the review cost entered, because the organisation cannot bound what it does and so carries the liability. A governed agent needs a review only on the cases policy routes to a person. Runtime governance enforces the policies your business and compliance teams encode; it does not by itself make a system compliant. They are not guaranteed cash savings, net return or total cost of ownership. It excludes design, delivery, integration, hosting, AI inference, governance and ongoing supervision costs, and assumes the target rate and effort saving apply from Year 1 with unit costs held flat. Actual results depend on which exceptions are eligible, decision quality, controls, adoption, and how released capacity is realised.

4. Learn more

Autonomy, with accountable boundaries

The value sits in exception queues. Capturing it means deciding which decisions agents may make, and when they must involve a person.

Which exception queue would you tackle first?

Midships combines process redesign, governed agent delivery and Icebreaker runtime governance (patent pending). We will test the opportunity against your own cases and the controls it needs.

Discuss an STP opportunity
Savingsa year, against all manual
Ungoverned agentsUngoverned$0
Governed agentsGoverned$0
Additional savings from governanceExtra from governance$0