How finance teams should assess AI workflow tests

For an operational team, that can be exciting because the pain is often obvious. People know where time is being lost or work is being duplicated. They know where reports sit unread, where actions are chased too slowly, and where managers struggle to get a clear picture of what is happening. But a finance team is looking at the same proposal from a different angle.

They are not just asking whether the AI looks useful. They are asking whether the expenditure can be justified. They want to know what problem is being tested, what evidence will be produced, what the wider commitment might become, and whether the same result could be achieved in another way. That is not finance being negative – it’s finance doing its job.

In most organisations, an AI workflow test will appear as a cost before it appears as a benefit. Unless there is a clear reason to treat the expenditure differently, the cost will usually reduce profit in the period in which it is incurred. That means enthusiasm is not enough, it needs a rational justification.

2. Finance is assessing the bigger commitment

A £10,000 AI workflow test may look modest compared with the cost of a full deployment, but finance will usually look beyond the first invoice. If the test works, they will want to understand what happens next. Is the organisation approving a small one-off exercise, or is this the first step towards a £100,000 annual commitment?

That does not mean the test is a bad idea, but it does mean the full commercial picture needs to be visible from the start. Finance teams will usually want to understand the cost of the test, the likely annual cost if it is successful, any implementation, training or integration costs, the internal time needed to support it, the cost of doing nothing, and whether the same outcome could be delivered using existing tools or internal expertise.

The last point is important. If an organisation already has Microsoft Copilot, SharePoint, an internal AI team or existing reporting software, finance will quite reasonably ask why another tool is needed. The answer cannot simply be “because this is AI”. It needs to explain what specific workflow is being tested, what current problem it addresses, and what SIRV AI is expected to do that the existing route cannot.

For example, if the workflow is contractor document review, the case might not be that AI can read documents. The case might be that the current process creates delay, back and forth, inconsistent checks and too much chasing. The test then needs to show whether SIRV AI can reduce those problems in a measured way.

Finance is not only assessing cost. It is assessing commitment, alternatives and evidence. A good AI workflow test should make those things easier to judge, not harder.

3. What type of justification is being made?

Not every AI workflow test has the same financial justification. Some are proposed because the organisation has to meet a legal, regulatory, contractual or audit requirement. Others are proposed because they may save money, reduce wasted effort, cut delay, improve reliability or protect value that might otherwise be lost.

Finance teams will usually want to know which type of argument is being made. A test that is justified as compliance will be assessed differently from a test that is justified as a cost saving. A test designed to reduce contractor document review time will be assessed differently from one designed to improve incident triage or produce clearer management briefings.

The main categories are likely to be:

• Compliance or required cost: the organisation has to do the work because of law, regulation, contract, audit or client expectation. The case is less about proving optional upside and more about showing that the work can be done more reliably, consistently or with better evidence.
• Cashable saving: the organisation can remove, avoid or reduce actual cost. This is usually the strongest financial claim, but it also needs the strongest evidence. If headcount, overtime, external support or duplicated spend is not actually reduced, it should not be presented as a cash saving.
• Capacity released: people spend less time on low-value work and more time on higher-value work. This can be valuable, but it is different from cash saved. Finance will usually want to know what the released time will be used for.
• Avoided cost: the test may reduce delay, escalation, missed issues, duplicated effort or management time spent correcting poor information. This can be highly relevant to workflows such as contractor document review, permit checks, incident triage and action follow-up.
• Reliability and resilience: the value may come from making the workflow less fragile. For example, better handovers, clearer escalation, more consistent review and fewer operational surprises.
• Value protection: the case may be about reducing leakage, loss, service failure, contract risk or reputational damage. This is harder to measure than a direct cost saving, but still important in safety, security, FM and resilience settings.

The mistake is to put all of these benefits into one general claim that “AI will improve efficiency”. That may be true, but it is too vague for finance. A better case separates the types of value being tested and explains what evidence will be gathered for each one.

Finance teams tend to prefer benefits that are easier to measure. That does not mean the wider benefits do not matter. It means the test needs to produce evidence specific enough for the organisation to judge whether the workflow is worth funding beyond the initial test.

4. Why time saved is not always ROI

One of the weakest AI business cases is to multiply every minute saved by salary cost and present the result as a cash saving. It is an understandable calculation, but it is not always a credible one.

If an AI workflow test saves ten hours a week, the first question is what happens to those ten hours. If the organisation reduces overtime, avoids contractor support or removes duplicated external spend, there may be a cashable saving. If the same people remain in post and use the time for other work, the better claim is capacity released.

That does not make the benefit weak. In many operational teams, capacity released is valuable because managers and supervisors are already overloaded. Reducing document review time, chasing, report preparation or repeated admin can give people more time for site visits, contractor management, training, investigations, follow-up actions or dealing with issues earlier.

The point is that finance will usually want the claim labelled correctly. Cash saved, time released and avoided cost are different things. A good AI workflow test should not blur them together.

For example, if SIRV AI helps a team review contractor documents faster, the benefit may appear in several places:

• fewer hours spent on first-pass review
• fewer documents returned because basic information is missing
• less chasing between the site team, contractor and manager
• fewer delays before work can proceed
• more consistent evidence of what was checked and why

This is why the design of the test matters. If the only measure is “people said it saved time”, finance may not have enough to work with. If the test records review time, back and forth, chasing, escalation, management effort and user feedback, the organisation has a more reliable basis for deciding whether the workflow is worth funding.

5. What an AI workflow test should measure

A useful AI workflow test should measure more than whether people liked the tool. User reaction matters, but finance will usually need more than positive feedback to approve the next stage.

The test should start with the workflow as it is today. For example, how contractor documents are reviewed, how incident reports are triaged, how procedures are searched, how handovers are checked or how management briefings are prepared. Without that baseline, it is difficult to show what has improved.

The measures do not need to be complicated, but they do need to be practical. For an AI workflow test, useful measures may include:

• Time: how long it takes to review, classify, summarise, check or prepare the relevant output before and during the test.
• Back and forth: how often work has to be returned, clarified or handled again because information is missing, unclear, inconsistent or not aligned with the expected standard.
• Chasing: how much follow-up is needed between teams, contractors, managers or reviewers before the work can move forward.
• Consistency: whether similar cases are being handled in a similar way, especially where different people are reviewing similar material.
• Evidence: whether the output shows what material was used, what was checked and where uncertainty remains.
• Escalation: whether urgent, repeated or higher-risk items are spotted earlier or routed more clearly.
• Adoption: whether the people expected to use the workflow would actually use it, and what would stop them from doing so.
• Management value: whether the output helps managers make a decision, brief others, evidence what happened or track follow-up.

This kind of measurement keeps the test grounded in real work. It also helps avoid the common problem where an AI pilot ends with interesting examples but no clear view on whether the workflow should be funded, changed or stopped.

6. What the test should produce at the end

An AI workflow test should not end with a general statement that “the team liked it” or “the technology worked”. Those things may be useful, but they are not enough for a finance team deciding whether to support a wider deployment.

The output of the test should be evidence. That does not need to be long or complicated, but it should show what was tested, what changed, what was measured and what decision the organisation is now in a better position to make.

For example, useful evidence might include:

• The workflow tested: the specific process, material or decision point included in the test, such as contractor document review, incident triage, procedure search or management briefing.
• The baseline: how the workflow worked before the test, including time taken, common delays, back and forth, chasing, handover issues or management effort.
• The test findings: what changed during the test, where SIRV AI helped, where it did not help, and where the workflow still needed human review.
• Measured outputs: time released, reduction in back and forth, clearer routing, better evidence, faster briefing, fewer missed items or more consistent review.
• User feedback: whether the people expected to use the workflow found it practical, where they trusted it, where they challenged it, and what would need to change before wider adoption.
• Limitations: any weak data, unclear source material, process gaps, integration issues or situations where the AI should not be relied on without further review.
• Commercial view: the likely cost of wider deployment, the expected benefit, the internal effort required and whether the case is strong enough to continue.

The final recommendation should be clear. The organisation should know whether to deploy, expand the test, redesign the workflow, pause or stop.

7. What finance should ask before approving the test

Before approving an AI workflow test, finance should be able to ask a small number of practical questions and receive clear answers. The aim is not to make the test harder to approve, but to make sure it is designed in a way that can produce useful evidence.

The questions should cover the workflow, the current problem, the expected benefit, the cost and the decision that will follow. For example:

• What workflow is being tested?
  The answer should be specific. “Using AI in operations” is too broad. “Testing whether SIRV AI can support contractor document review against agreed standards” is clearer.
• What problem exists today?
  This might be slow review, too much back and forth, inconsistent checks, poor handover, missed follow-up, weak management evidence or too much time spent preparing reports.
• What does the current process cost?
  This does not only mean supplier cost. It may include staff time, delay, chasing, duplicated effort, escalation, missed issues or management time spent reconstructing what happened.
• What will the test measure?
  The test should measure practical things such as time released, reduced back and forth, clearer routing, better evidence, fewer missed items, user adoption and management usefulness.
• What would count as success?
  Success should be defined before the test begins. Otherwise, the organisation may end up with positive feedback but no clear basis for a deployment decision.
• What would count as failure?
  This is just as important. The test may show that the workflow is not ready, the data is too weak, the process needs redesign, or the benefit is not strong enough to justify wider deployment.
• What is the full commercial picture?
  Finance should understand the cost of the test, the likely annual cost if successful, internal time required, implementation needs and any integration or support costs.
• What alternatives have been considered?
  This includes existing tools, internal AI capability, process improvement, outsourced support or doing nothing. The case for SIRV AI is stronger when it is clear what it is being compared against.
• What evidence will exist at the end?
  The test should leave the organisation with something more useful than a demo. It should produce evidence that can be reviewed by finance, operations and senior management.

These questions help keep the test honest. They also protect the operational team from running a pilot that people find interesting, but finance cannot use. A well-designed AI workflow test should make the next decision easier: deploy, expand, redesign, pause or stop.

8. Bringing the test back to operational work

In safety, security, FM and resilience, many AI workflow tests are not about replacing people. They are about helping teams deal with the material, decisions and follow-up that already sit inside the operation.

That matters because the value is often practical rather than dramatic. A team may need to review contractor documents faster, find the right procedure under pressure, triage incident reports, prepare a clearer management briefing, check whether actions were followed up, or spot whether a problem has moved rather than disappeared.

These are not abstract AI use cases. They are everyday points of pressure where time, consistency and evidence matter. If a contractor document is weak, someone still has to decide whether to return it, approve it, or ask for more information. If an incident report suggests a repeated issue, someone still has to decide whether to brief the team, escalate the matter or change the response. If a procedure is unclear, someone still has to apply judgement.

A good AI workflow test should therefore be close to the work. It should use real or controlled operational material, agreed review points and human judgement where needed. The aim is not to prove that AI can produce an answer. The aim is to test whether it can help the team move from material to evidence, from evidence to a decision, and from a decision to action or follow-up.

This is where SIRV AI is designed to sit. It helps operational teams retrieve the right procedure, evaluate documents, triage incoming information, build a clearer operational picture and retain lessons learned. For finance, the question is whether those workflows produce enough measurable value to justify further investment.

That is why the test should stay narrow enough to measure, but real enough to matter. A broad AI experiment may create interest. A focused workflow test is more likely to produce evidence a finance team can use.

9. The right test sits between a casual demo and a full deployment

A casual demo can be useful, but it usually proves very little. It can show what the technology might do, but it does not show how well it fits the organisation’s real work, data, review habits, approval process or management needs.

A full deployment has the opposite problem. It asks the organisation to commit before enough is known. That may be difficult for finance to support, especially where the use case is new, the workflow is not yet fully defined, or the internal team has not seen how the tool performs on real material.

The more practical route is a measured AI workflow test. It should be narrow enough to control, but real enough to learn from. It should focus on a specific workflow, use real or controlled operational samples, define what success looks like, record the limitations and produce evidence for the next decision.

This matters because AI adoption often fails in the gap between enthusiasm and commitment. A demo creates interest, but not enough proof. A full deployment asks for commitment, but may come too early. A measured workflow test helps close that gap.

For example, rather than asking finance to approve a broad AI programme, the organisation might test whether SIRV AI can help with one defined workflow: contractor document review, incident triage, procedure access, handover review or management briefing. The test can then show what changed, what was measured, where the workflow improved, where human review remained necessary and whether the case is strong enough to continue.

That is a more sensible way to assess AI in safety, security, FM and resilience. It avoids treating AI as either a novelty demo or a major leap of faith. It gives the operational team a practical way to test value, and it gives finance a clearer basis for deciding whether to fund the next stage.

Author bio: Andrew Tollinton

Andrew Tollinton is CEO and Co-Founder of SIRV, which builds operational AI for safety, security and resilience teams. He focuses on practical, controlled AI use in serious environments, with particular interest in evidence, accountability and human judgement. Andrew chairs the Institute of Strategic Risk Management’s AI in Risk Management Special Interest Group and speaks regularly on AI governance and operational resilience.

Frequently asked questions

What is an AI workflow test?
An AI workflow test is a focused way to assess whether AI can improve a specific piece of work, such as contractor document review, incident triage, procedure access or management briefing. It should use real or controlled operational material and produce evidence for a decision about wider deployment.

How should finance teams assess an AI pilot?
Finance teams should look beyond whether the AI appears useful in a demo. They should assess the cost of the pilot, the likely cost of wider deployment, the problem being tested, the alternatives available, the evidence produced and whether the benefit is strong enough to justify further investment.

Is time saved the same as ROI?
Not always. If time saved leads to lower paid cost, reduced overtime or avoided external spend, it may support a cash saving. If the same team uses the time for other valuable work, it is better described as capacity released. Both can matter, but they should not be treated as the same thing.

What should an AI workflow test measure?
A useful test should measure practical workflow effects such as time taken, back and forth, chasing, consistency, evidence quality, escalation, adoption and management usefulness. The measures should be agreed before the test starts.

What should an AI workflow test produce at the end?
It should produce an evidence pack showing what was tested, what changed, what was measured, where the AI helped, where human review was still needed, what limitations were found and whether the organisation should deploy, expand, redesign, pause or stop.