TL;DR: AI ignorance in a company isn’t free – you pay for it through lost time on routine tasks, rewriting inconsistent outputs, and the risk of shadow IT. The model example below shows how to calculate the leaks using your own figures – just plug in your hourly rate and headcount. Targeted training costs a fraction of these losses, and in 2026 you can apply for a subsidy – but only if it’s followed up with real-world practice and company rules. Without that, even the best course is just an expensive one-off.
When a company decides whether to start with AI, it usually focuses on one side of the balance sheet: how much licences, training and implementation time will cost. That’s a legitimate question. But few people look at the other side – how much it costs the company when employees don’t work with AI at all, or use it secretly and badly. This article is exactly that other side. We’re not working with promises or surveys; we’re offering fair logic and model calculations into which you can plug your own figures.
Five places where AI ignorance quietly drains money
The costs of ignorance aren’t visible at first glance because they dissolve into day-to-day operations. There are five mechanisms through which money leaks out of the company without you ever issuing a single invoice:
1. Time lost on routine texts and spreadsheets Every email, meeting summary, product description or data comparison in a spreadsheet is written from scratch. Without AI, wording is invented, deleted, reformatted. It’s not a big item per individual task, but multiplied by the number of employees and working days, it becomes a systematic drain. People are doing work that would take them half the time if they had the tool and knew how to use it properly.
2. Inconsistent outputs and double work When five people write manually, the results differ in style, structure and terminology. Someone then has to unify the outputs, rewrite them, check them – and that someone usually isn’t cheaper than the original authors. Rewriting is a tax on the absence of a standard, which AI can help enforce – if someone teaches it to work with a company brief.
3. Silent data leaks into unapproved AI tools Employees use AI even when the company doesn’t know about it. They enter internal data into free chatbots, upload contracts into online „translators", copy price offers into public tools. GDPR and know-how protection develop cracks here – not from bad intentions, but from simple ignorance of the rules. When the company doesn’t provide a secure path, people carve out their own – and it leads outside your control.
4. Poor decisions based on unverified outputs Uncritically adopted AI outputs can contain hallucinations, factual errors or misleading interpretations. If an employee doesn’t know they need to verify the output and can’t handle basic prompting, the risk of decisions based on incorrect data rises significantly. In the best case, someone else spots it and fixes it; in the worst case, action is taken based on it.
5. Frustration among capable people The most capable employees see that elsewhere, routine tasks are handled faster. When the company ignores AI or issues blanket bans, motivated people lose patience. They either find a way around it (see point 3) or find an employer who allows them modern tools. These are costs that are hard to model, but they genuinely exist.
Model calculator: plug in your own figures
The following example doesn’t work with any statistics – it’s an illustrative model into which each company can plug its own real figures. Do the maths with us.
Model example assumptions: one office employee, total employer cost e.g. 400 CZK per hour, a standard working week. The tasks where AI saves time are chosen conservatively – this isn’t sci-fi automation, but routine tasks where AI already genuinely helps someone who knows how to use it.
| Task | Time manually (weekly) | Time with AI – conservative (weekly) | Time saved per week |
|---|---|---|---|
| Emails, summaries, replies | 3 h | 1.5 h | 1.5 h |
| Meeting minutes and summaries | 1 h | 0.3 h | 0.7 h |
| Preparing materials, research, spreadsheets | 2.5 h | 1 h | 1.5 h |
| Proofreading, formatting, unifying | 1 h | 0.3 h | 0.7 h |
| Total per week | 7.5 h | 3.1 h | 4.4 h |
At a total employer cost of, for example, 400 CZK/h, this model example yields:
- Saving of 4.4 h × 400 CZK = 1,760 CZK per week per person
- Approximately 7,040 CZK per month
- Over 84,000 CZK per year per office worker
This is a model, not a promise or a study. Some will save less, some more – it depends on the type of agenda, the volume of routine work and whether the company provides secure tools and teaches people how to handle them. If you have five people in similar roles, multiply by five. If seventy, multiply by seventy. The number you get is the maximum reasonable investment into training and AI adoption per year for it to still pay off – and the real cost of a targeted workshop is usually an order of magnitude lower.
A more detailed breakdown of the costs of introducing AI in a company can be found in our separate article how much AI costs for a company.
Shadow AI: the biggest hidden risk
By far the most expensive form of AI ignorance is so-called shadow IT. Your people are already using AI – on their phones, in their browsers, through free tools into which they copy sensitive data. The question isn’t whether, but how.
A blanket ban doesn’t work. People will find a way; they’ll just hide it. The only functional path is to set clear rules – what can and cannot be entered into AI, which tools are approved, how to handle outputs – and teach people to use those rules in their daily practice. We cover security aspects and data protection in detail in our AI training for companies, precisely because it’s one of the most pressing topics.
What to do about it: three steps that make sense
① Map where the team loses time Before you rush out for a licence, do an internal review. Which repetitive activities take up the most hours? Where does double work occur? What gets rewritten and corrected? This will reveal whether AI actually has a place to help.
② Short, practical training on real tasks Not a general lecture about AI, but training where people tackle their actual assignments – company texts, spreadsheets, processes. At ITHOPE, training is delivered by people who deploy AI daily in practice, so concrete outputs emerge from the workshop, not just theoretical notes. Details can be found on the AI training for companies page.
③ Follow up with rules and initial automation Without company rules for AI use, training is just temporary inspiration. You need to define what’s permitted and what’s not, and ideally identify the first process to automate straight away. We focus on this in our process automation with AI offering.
A note on subsidies: For 2026, a subsidy programme is available that can cover part of the training costs. Current information on conditions and the application process is summarised here: AI training subsidies 2026. Approval is not automatic – it depends on meeting the programme conditions. Bear in mind that the subsidy is an opportunity, not a guarantee.
When training doesn’t solve the problem
To keep the article fair: training isn’t always the first thing a company needs. Investment in education misses the mark if:
- The company lacks basic processes, so there’s nothing to build on. AI without processes just adds chaos.
- Management itself doesn’t work with AI and doesn’t want to. Without top-level support, any training remains isolated and fades within a week.
- It’s a one-off lecture with no follow-up. People return to their computers and two days later don’t know how to start.
In such cases, it makes more sense to begin with a consultation or an internal audit. At ITHOPE, we offer an initial free mapping of the situation – precisely so you don’t pay for training that would have no effect in your circumstances.
Frequently asked questions
Isn’t it enough if people learn AI on their own from YouTube? For individuals, partly yes. But for a company, that only solves half the problem. People learn different methods, varying quality, and above all varying levels of security. A unified standard, company context and, most importantly, rules for handling data are missing. Fragmented know-how then doesn’t deliver systemic savings, just individual shortcuts – often risky ones.
How long should training be to make sense? That depends on the scope of the agenda and the depth you want to go into. It could be a short workshop focused on a specific type of task, or a more structured programme for an entire department. The key isn’t „how many hours", but whether the training ties into the participants’ real daily work and whether something follows on from it.
How do we know the training had an effect? Two things: hard data and concrete use cases. Before the training, plug your current times into our model table. A month after the training, plug them in again – and compare. The second indicator is the number of real situations where the team started using AI: a specific prompt template, an automated routine, a process that got shorter. The effect isn’t „people know AI„, but „people solve work faster and more safely with AI".
Want to compare, without obligation, what would make sense in your company? Get in touch – with no strings attached, we’ll go through your team’s typical agenda and propose what to address through training and what potentially through automation. The training content consultation is free.
Write to info@ithope.cz or call +420 774 777 774. We train in Brno, throughout the Czech Republic and online.