On most AI projects the technology is not the problem. The problem is that nobody wrote down how long the process takes today, before anything changed. Without that there is nothing to compare against later, and the question of whether it was worth it becomes a matter of opinion.

This article gives you a formula, six rows to track alongside it, and one worked example. The figures are typical industry ranges, not the results of any particular company. Your own number comes out of the template, not out of us.

Why you never find out whether it paid off

Three mistakes show up most often, and all three happen before any measuring starts.

• There is no baseline. The time before rollout gets estimated after the fact. Estimates typically differ from measured values by 20–40 per cent, usually upwards, because everyone remembers their worst day.
• Saved time is treated as money automatically. Forty saved hours are money only if something specific happens to them: less overtime, a hire that does not need making, or more work delivered with the same headcount.
• Running costs get left out. API usage, licences, oversight and error fixing are a monthly line item. Together with the one-off build cost, these two numbers are what decide the payback.

Sort those three out and payback becomes simple arithmetic.

The formula

Two steps. First work out how much money comes in each month, then divide the one-off cost by it.

Monthly realised saving = (minutes saved per month / 60) × fully loaded hourly cost × realisation rate

Payback in months = one-off build cost / (monthly realised saving – monthly running cost)

Two items need explaining.

The fully loaded hourly cost is not gross pay divided by 160 hours. It includes employer contributions, holiday and non-productive time. For an office or administrative role in Slovakia and Hungary this is typically 14–22 euros per hour. Your own figure takes ten minutes to pull from your accounts.

The realisation rate tells you how much of the saved time actually turns into money. If the freed capacity goes nowhere, the rate is zero. In practice 50–80 per cent is the typical band, and you sit at the top end when the process is a bottleneck, so freed time immediately means more work delivered. If you cannot say where the freed time will go, use 50 per cent. Underestimating is the safer error.

Measurement template: six rows per process

One table, one column per process. That is enough, and nobody will keep more than that up to date.

1. The process and the unit measured. A quote request, an incoming invoice, a customer question. One unit, one item.
2. Volume per month. From the system or the mailbox, not from memory.
3. Manual time per item before rollout. Measured across three working days, with a stopwatch. This is the most important row, and it is the one everyone skips.
4. Time per item after rollout. Checking and correction included, not just the machine run.
5. Error rate and rework, before and after. If the error rate goes up, correction takes back part of the time you saved.
6. Costs. The one-off build, and the monthly running line: licences, API, oversight time.

There is no way round the third row. If you cannot spare three days, measure for one and note that it is a one-day sample. Even that is more comparable than a number pulled from memory.

A worked example

Processing inbound quote requests at a 40-person trading company. The figures are example values from a typical range, not any customer’s data.

Item	Value
Quote requests per working day	18
Working days per month	21
Manual time per item, before	11 minutes
Time per item after, including checking	4 minutes
Fully loaded hourly cost	18 euros
Realisation rate	65 per cent
One-off build	2,800 euros
Monthly running cost	126 euros

The calculation step by step:

• Time saved per item: 11 – 4 = 7 minutes.
• Time saved per month: 7 × 18 × 21 = 2,646 minutes, so 44.1 hours.
• Gross value of that time: 44.1 × 18 = 794 euros per month.
• Realised saving at 65 per cent: 516 euros per month.
• Net monthly benefit: 516 – 126 = 390 euros.
• Payback: 2,800 / 390 = 7.2 months.

This calculation is useful even when the answer is no. Run the same process at four requests a day and the net monthly benefit falls below 126 euros, so the project never pays back at all. Better to know that before than after. You do not need a round of quotes to get the order of magnitude: prices are published in the price list, and you can size your own case up front with the calculator.

What to leave out of the maths, and what not to automate

Keep hard-to-measure benefits out of the calculation. A better customer experience, less stress and faster response times are real advantages, but if the payback rests on them, then numbers did not decide the project. Track them on a separate list, as notes.

In three places the question is not payback at all, but whether it is allowed.

• Final decisions without a human. Hiring, dismissal, screening out a candidate, credit assessment. Some of this falls into the high-risk category under the EU AI Act and requires human oversight. Here AI produces a suggestion, a person makes the decision, and that has to be documented.
• Sending to customers without review. In the first month, have a person read every outgoing text. After that, sample 10–20 per cent on an ongoing basis.
• A rare or undocumented process. Below eight runs a month the build cost rarely comes back. And if nobody can describe the process, write it down first and automate second. Automated chaos is just faster chaos.

We wrote separately about which processes to leave alone, and you will find it on the blog.

The first thirty days

Four weeks, four steps, and a number at the end.

1. Week one. Measure. Fill in the six rows for two or three processes. No purchases, no tool demos.
2. Week two. Calculate. Run the formula on each one and rank them by net monthly benefit. The number sets the order, not enthusiasm.
3. Week three. Take the lowest-risk part of the top-ranked process and start there, with human review.
4. Week four. Measure again with the same method. If there is no improvement, the project stops rather than expands.

Training carries the same weight as the tool. If the colleagues who run the process do not understand what the system sees and what it does not, after a month or two they go back to the old way and the measured saving evaporates. That is not a technical question. It is a question of whether measurement and training sit inside the same project.

Fill in the six rows, run the formula, and six months from now you will have data about your AI project rather than an opinion.

In manufacturing and logistics, the conversation about AI almost always starts at the machines: vision systems on the line, predictive maintenance, route optimisation. These are real, but in a company of 10–250 people they are usually not where the fastest measurable gain sits. The measurable gain is in the paperwork around the line.

There is a simple reason for that. A project tied to the production line needs sensors, data collection, integration and months of work, and it carries the risk of interfering with output. Paperwork, by contrast, is already digital, it runs on written rules, and when it breaks, production does not stop. That is why it is the sensible place to start.

Why manufacturing and logistics differ from an office

Three things are different. First, the documents do not come from one place. In a mid-sized plant, the incoming material arrives with a delivery note, a material certificate, an order confirmation and an invoice, from four suppliers in four different formats, some of it scanned from paper. Second, shift work means information is handed over two or three times a day, and it loses something at every handover. Third, most of the know-how is written down but hard to find. Machine manuals, SOPs, old fault reports, all in PDF, somewhere on a network drive.

All three are text processing problems, not engineering problems. That is good news, because text processing is the most mature and the cheapest part of AI today. It needs no new machine and no shutdown.

Four processes where the impact is genuinely measurable

The figures below are typical industry experience ranges, not guaranteed results. Your own numbers can easily differ, which is why measurement always comes before implementation.

Process	Typical time today	After implementation	Time to live
Processing incoming supplier documents	3–5 minutes / document	1–2 minutes / document	3–5 weeks
Shipping paperwork (waybill, packing list)	6–10 minutes / shipment	2–3 minutes / shipment	4–6 weeks
Shift handover and fault log summary	15–25 minutes / shift	5 minutes / shift	2–3 weeks
Internal document search (manuals, SOPs, drawings)	20–40 minutes / person / day	5–10 minutes / person / day	4–8 weeks

Incoming supplier documents

Reading the delivery note and order confirmation, matching them against the purchase order, and listing the discrepancies. This is the classic case where AI does not decide, it only prepares: it fills the fields and flags where quantity or price differs. Approval stays with a person. The gain shows up because purchasing stops searching and starts checking.

Shipping paperwork

Producing waybills, packing lists and accompanying documents from order data, and matching returned documents back to the shipment. Most of the time here does not go on filling forms, it goes on collecting data from three systems. Once that comes together in one place, the filling itself is quick.

Shift handover and fault log

The fastest payback and the least popular project, because it does not sound exciting. End-of-shift notes, downtime records and fault reports become a one-page summary for the next shift supervisor. After two weeks it usually turns out the recurring faults were visible all along, nobody had just read them together.

Internal document search

A search tool that understands your own manuals, SOPs and earlier fault reports, and answers with a citation. This is not a chatbot, it is a search tool: every answer states which page of which document it came from. Without the citation it is unusable, because it cannot be verified.

A worked example: a 60-person plant

Take a manufacturing company with 60 staff, three shifts and 22 working days a month. They start two processes, not four.

• Incoming supplier documents. 900 documents a month, 4 minutes each on average, which is 60 hours. At a 50 per cent saving, 30 hours a month come back.
• Shift handover. 3 handovers a day over 22 days is 66 handovers. The time drops from 20 minutes to 5, so 15 minutes each, 16.5 hours a month in total.

That is roughly 46 hours a month. At an internal hourly cost of 18 €, it comes to about 830 € a month. The one-off implementation cost at this size is typically 4,500–7,000 €, with running costs of 60–150 € a month. Net monthly saving is therefore around 700 €, and payback lands at 7 to 10 months.

This calculation is a frame, not a promise. What matters in it are the two numbers you have to measure yourselves: how many units, and how much time per unit. Everything else follows from those. If you want a quick order-of-magnitude figure, the calculator runs the same logic on your own data.

What not to automate in manufacturing

There are four areas where the cost-benefit ratio is poor, or the legal risk is out of proportion.

1. The final quality decision. AI can flag that a batch is probably defective. Whether it ships to the customer is signed off by a person. If the decision runs without a signature, accountability disappears with it.
2. Safety functions and machine control. Emergency stop, guard logic, interlocks. These are standardised, deterministic systems. A language model does not belong in that chain.
3. Assessment of people. Performance reviews, shift allocation on disciplinary grounds, candidate screening. The EU AI Act treats these uses as high risk, and the cost of compliance is considerably higher than the benefit.
4. One-off, non-repeating work. If a process runs twice a month, maintaining the automation costs more than the work itself. The rule is simple: without at least weekly repetition and at least 20 units, leave it in human hands.

On top of that comes one general limit: wherever the output goes straight to a customer or an authority, there must always be an approval point. Not because AI errs often, but because even a rare error is expensive and hard to explain after the fact.

How to start: the first four weeks

In the first two weeks you build nothing, you measure. Three numbers need writing down: how many units go through the process, how much time goes on each one, and who does it today. Those three numbers set the order, not enthusiasm.

In the third week one process starts, the lowest-risk one. At the end of the fourth week you compare the measured time with the figure from before. If there is no improvement, the project stops rather than expands. This is the step most companies skip, and it is why they cannot say six months later whether it was worth it.

Training matters as much as the tool. If the plant manager and the shift supervisors do not understand what the system sees and what it does not, they will be back in Excel within a month. Article 4 of the EU AI Act also requires adequate AI literacy from staff, and we have written about that separately on the blog.

Our prices are published, so you do not need a round of quote requests to get an order of magnitude: they are in the price list. At the end of an audit you get a priority order, including which process is best left alone this year.

Accounting firms have an unusually good starting position with AI. Most of the work repeats, the input is a document, and the rules are written down. Payback shows up sooner here than in most industries.

This article walks through four processes in the order worth implementing them. For each one: the typical time saving, the rough cost of implementation, and the point where a person has to stay in the loop.

Why this particular order

The order does not follow the size of the saving. The first process comes first because it is cheap, live within two to three weeks, and it shows the team that this works. The biggest item comes second: it needs the routine from the first project, and it needs clean data.

One shared figure runs through the whole article: the internal hourly cost. Salary, contributions and overheads together typically sit between 15 and 22 euros for an accounting role in the region. We use 18 euros throughout. You can work out your own in a minute, and the calculator gives you the order of magnitude too.

One caveat: the figures below are typical industry ranges, not promises. Your own measurements apply to your firm, and two weeks of measuring is enough.

The four processes, in order

1. Chasing missing documents

Every month starts the same way: who has not sent what. Today that means a manual list, then emails, then phone calls, and someone keeping the gaps in their head.

What a machine can do: compare received documents against expected ones, send reminders client by client, watch deadlines, and deliver a summary of everything outstanding each morning.

Typical effect: 60–80% of the time spent chasing disappears. In a firm of 5–8 people that is 10–15 hours a month, or 180 to 270 euros.

Implementation: 2–3 weeks, and it is the cheapest of the four.

What to watch: the tone of a reminder is a client relationship, not a technical question. Write a template, and keep an exception list for clients a person should still contact personally.

2. Data extraction from incoming invoices, with a posting suggestion

This is the big item. The supplier, document number, date of supply, net amount, VAT rate and line items can all be read from the image of an invoice. The system then proposes a posting based on previous bookkeeping. The accountant no longer types, they check and approve.

Let us put numbers on it. A firm processes 1,200 incoming invoices a month, and entering one by hand takes 5 minutes. That is 100 hours a month. Extraction plus the suggestion typically cuts 50–60% of that, so settle on 55 hours. At an internal cost of 18 euros an hour, that is 990 euros a month. If implementation costs 4,000 euros, payback lands at around four months.

What to watch: recognition accuracy in the first month is typically 85 to 95%, and improves as patterns are learned. The key is not accuracy itself but making sure uncertain items go into a separate queue rather than disappearing into the approved pile. Ask for a confidence indicator on every line.

Implementation: 6–10 weeks, because tidying up formats and the supplier master data also takes time.

3. Internal document and knowledge search

The third process is the dullest of the four, which is exactly why it is good. The system searches the firm’s own material: internal policy, earlier written opinions, client folders, extracts from legislation, questions already answered by email. A colleague asks in one sentence and the system answers with the source named.

Typical effect: an accountant spends 20–40 minutes a day searching and asking colleagues. Half of that realistically disappears: 4–7 hours per person per month. At six people that is 25–40 hours a month, worth 450 to 720 euros.

Implementation: 3–5 weeks if the documents are in one place. If they are not, that is the first half of the project.

What to watch: a search tool only knows what has been written down. If the knowledge sits in people’s heads, write it down first: useful work in its own right. Permissions must be inherited: if someone cannot see a client folder, search must not show it either.

4. Suggested replies to client emails

A large share of incoming questions repeat: filing deadlines, what to send, where a matter stands, what data the authority wants. The system classifies the message, drafts a reply from your own material, and the accountant reviews, corrects and sends it.

Typical effect: response times get shorter, and writing time on routine messages drops by 30–50%. Reviewing one draft takes ten to forty seconds, so most of the saving survives.

Implementation: 4–6 weeks.

What to watch: this is the riskiest of the four, because the output goes to a client. There is no automatic sending. The machine suggests, a person sends, and that part is not negotiable.

What it adds up to

Taken together, the four typically cut into a monthly workload of roughly 90–130 hours in a firm of 5–8 people, and total implementation lands between 7,000 and 12,000 euros. Overall payback is usually 5–9 months if you roll them out one after another.

Process	Typical saving	Implementation	Risk
Chasing documents	10–15 hours a month	2–3 weeks	low
Invoice extraction	50–60 hours a month	6–10 weeks	medium
Internal search	25–40 hours a month	3–5 weeks	low
Suggested replies	8–14 hours a month	4–6 weeks	higher

These are ranges, and the top end requires clean data. If supplier master data is messy or folders are scattered, the top end will not materialise.

In an accounting firm the question is not whether a machine can do the books. The question is what no longer has to be retyped by hand.

What not to automate, and where a person stays in the loop

Accounting is a regulated profession and mistakes carry a price. Four points where the machine stops and a person decides.

• Filing with the authorities. Final approval of a return, report or submission stays with a person. The machine prepares, a person presses the button.
• Posting unusual or disputed items. A suggestion on routine items is fine. An unusual case, a borderline case or a classification question is professional judgement, not pattern recognition.
• Tax advice and written opinions. When a client asks what they should do, there is liability behind the answer. The machine can draft it, a person signs it.
• Payroll exceptions. An ordinary month can be automated. Sick pay, severance, deductions and legal disputes cannot.

Two rules that work every time. Keep a log of what the system suggested and who approved it, because you will need it after the fact. And set a value threshold above which a second pair of eyes is mandatory, typically 500 to 2,000 euros depending on firm size.

Data protection and the AI Act, briefly

An accounting firm handles other people’s data, which means stricter rules than in an average company. Sort out three things before the pilot.

1. Where the processing happens. Processing in the EU, an account that does not train on your data, a written data processing agreement. Client data does not belong in a free consumer account.
2. Who sees what. The AI tool’s permissions must not be broader than a colleague’s. Inherited access per client folder is the right baseline.
3. AI literacy. Under Article 4 of the EU AI Act, staff must have an adequate level of AI literacy. A documented internal training session twice a year satisfies it; a separate article on the blog has the details.

How to start

Do not launch all four at once. Measure for two weeks: how many invoices arrive, how long one entry takes, how much time goes on chasing documents. Then start with document chasing: it is live within three weeks, and after that the team stops arguing in principle and starts looking at numbers.

If you want the order of magnitude in advance, our prices are published, so a decision does not need a round of quote requests. At the end of the audit you get a sequence, including which process to leave alone this year.

Most automation projects do not fail on the wrong tool. They fail on the wrong process. The technology works, the invoice gets paid, and six months later nobody opens it.

This article is not about what to automate. It is about what not to automate. We go through five typical dead ends, each with a test that filters them out in a few minutes.

When automation is not worth it

The payback of an automation rests on three numbers: how long the process takes per run, how often it runs, and what it costs to build. We publish the third one, you have to measure the first two. A stopwatch and two weeks are enough.

Take a good candidate. The process takes 25 minutes per run and runs twelve times a week. That is roughly 21 hours a month. If you use an internal hourly cost of 18 euros (wage, contributions and overhead together), the process costs 378 euros a month. Automation typically removes 60–70% of the time spent, so let us stay with a real saving of around 250 euros a month. A 1200 euro implementation then pays for itself in about five months.

Now take a bad candidate. The process takes 40 minutes per run, but runs only twice a month. That is 80 minutes a month, or 1.3 hours, which at the same rate is 24 euros. If the implementation costs 2500 euros, payback runs past eight years. The process is genuinely annoying in the meantime, but the invoice is real.

These figures are typical industry ranges, not promises: for your processes, your own measurements are what count. You can estimate the order of magnitude in a few minutes with the calculator.

Annoying and expensive are not the same thing. It is the expensive one that is worth automating.

The five dead ends

1. The rare process

A task that runs once or twice a month almost never pays off. The implementation costs the same as it would for a daily process, but the saving is a fraction of it. A rarely used automation also breaks quietly: by the time you need it again, a form or an access right has changed.

The test: if the process runs fewer than eight times a month, put it at the end of the list and come back to it once the frequent ones are done.

2. The process nobody has written down

If three colleagues do the same thing in three different ways, you do not have a process, you have a habit. Automation will not tidy up that chaos, it will set it in concrete, in the version used by whichever colleague happened to sit in the kick-off meeting.

The test: can you describe the process on one page, step by step, well enough for a new employee to follow it? If not, write that first. It is an afternoon of work, and it often cuts the time on its own.

3. The final decision

A machine may produce a recommendation, a person makes the decision. This is not philosophy, it is risk management. Hiring, dismissal, a pay rise, a credit assessment, individual pricing, rejecting a complaint, terminating a contract: for these, the cost of a wrong decision is orders of magnitude higher than the ten minutes saved.

The EU AI Act also names several of these cases explicitly and treats systems supporting employment decisions as high risk. If the topic is new to you, there is a separate article on the practical steps under Article 4 on the blog.

The test: if the output concerns somebody’s money, job or rights, it can be a recommendation, but not an automatic action.

4. The data you do not have

An automation is worth exactly as much as the usable input it receives. If the information sits in a colleague’s head, deep in a mailbox or on paper, what is missing is not automation but data. The first half of the project then quietly turns into data cleanup, and the budget ends up twice what was planned.

The test: can you show where all the data the process needs is kept, and is it in machine-readable form? If the answer takes more than fifteen seconds, start with the data.

5. The process that should be scrapped

The fastest and cheapest automation is deletion. Many companies run a weekly report nobody reads, an approval round that made sense ten years ago, and copying between two systems, one of which is no longer in use.

The test: stop the process for two weeks. If nobody says anything, it does not need automating, it needs scrapping.

Where a human has to stay in the loop

Beyond those five there are a few points where the question is not whether it pays off, but where the machine stops. For these, the output always stays a recommendation and a person releases it:

• anything that goes out to a customer in the company’s name: a quote, a complaint response, a notice, a payment reminder
• movement of money: initiating a payment, changing supplier details, approving a discount
• HR decisions: screening, promotion, performance review
• legal and contractual text, as well as any filing with an authority
• anything irreversible: deletion, publication, sending a bulk email

In practice this takes less time than you would expect. The machine prepares the recommendation, a person checks it and releases it. Reviewing a good draft text takes between ten and forty seconds, so most of the saving survives.

Two rules that work in every such case. Keep a log of what the system recommended and who approved it. And set a value threshold above which a second approval is always required, typically between 500 and 2000 euros depending on company size.

Four questions before you start

Before you ask for a quote on any process, run through these four. A no on any of them is not necessarily disqualifying, but it does mean that is the thing to fix first.

1. Does it run at least eight times a month?
2. Is it written down on one page, and does everyone do it the same way?
3. Is all the data it needs available in machine-readable form?
4. Can the output stay a recommendation, or must it be executed automatically?

Criterion	Good candidate	Bad candidate
Frequency	several times a week	once or twice a month
Rules	written down, stable steps	every case is different
Data	in a system, searchable	in someone’s head, email, paper
Risk	errors are cheap to fix	legal or financial consequence
Typical payback	3–8 months	several years or never

What to do instead

If your first idea did not survive the four questions above, that is good news, not bad. Three areas remain where the effect is quickly measurable in most companies of 10–250 people:

• Internal document search. Boring, but the fastest paying project there is, because every department looks something up every day.
• Draft text for repeated correspondence. Quotes, replies to enquiries, support templates. The machine drafts, a person sends.
• Data entry and document processing. Incoming invoices, delivery notes, forms. Stable rules, high volume, checkable output.

The same order works for all three: measure first, write it down second, and only then pick a tool. That is why every project with us starts with an audit, and our published prices let you work out the cost in advance. The other articles are on the blog.