IU Study on AI Training: Learning Time Is the Bottleneck

The bottleneck in building AI literacy lies in how companies are organised, because they rarely give their employees learning time, clear expectations and suitable training. So finds the study “KI in der Arbeitswelt: Wie gelingt der Change?”, which IU International University of Applied Sciences published on 24 September 2026. 81.6% of the 2,000 employees surveyed are confident they can acquire new skills on their own, while 43.9% used no working time for AI training in the twelve months before. By our calculation a company with 300 employees can close this gap with around 13 learning hours per person a year, provided it plans learning time as a budget and ties it to real workflows.

IU study: almost six in ten employees had no learning time for AI

Almost six in ten employees in Germany used no working time for AI training within a year, even though a majority consider AI literacy a requirement for starting a career. 45.5% of them use AI tools. 54.9% expect AI skills to become a basic requirement for career starters in many fields, rising to 65.6% among managers, as ingenieur.de reported on 24 September 2026.

Only 8.9% reach 16 hours or more. Using band midpoints (100 hours for the open top band) gives an average of around 5.5 hours per employee, while the median is zero hours.

After a lack of need, the reasons against AI learning time lie with the employer

Employees without learning time name a lack of need first (47.4%), followed mainly by reasons their employer controls: 24.0% say the company did not want the training, 21.8% lack information and 21.6% find no suitable offer. Across all respondents, roughly one employee in ten (10.5%) had no learning time for AI because the employer did not want it (12.0% of those without a management role).

The most frequent reason also says something about the organisation, because 65.0% of respondents see no expectation from their company that they use AI tools. Where management states no expectation, employees understandably regard training as dispensable. This link is our interpretation.

Companies support the use of AI tools less often than any other training topic

Companies mainly support subject-matter skills, while they support the use of AI tools least often. 51.6% say their company supports training in subject-matter topics very strongly or fairly strongly, compared with 26.2% for the use of AI tools. Critical thinking is supported according to 33.6%, although Anne Gfrerer, professor of business informatics at IU, calls critical thinking, recognising whether an AI answer is plausible, the key future skill in the study.

Among managers 32.4% used no learning time for AI, compared with 47.6% of employees without a management role. 16 hours or more are reached by 18.5% of managers and 5.9% of other employees. At the same time 35.1% of managers feel left alone on AI by their own manager. More on this layer in Strengthening middle management.

The AI learning gap is an organisational problem, because learning time goes unplanned

The learning gap arises in the organisation, because many companies introduce AI on top of existing work while learning time rarely appears in any plan. Of AI users, 27.9% say their workload has risen since, while 21.6% report lower productivity. Philipp Diebold, professor of business informatics at IU, attributes this to AI tying up resources when it comes “on top” of unchanged ways of working.

Organisation design reveals three missing decisions that are standard for other ongoing tasks. Learning time rarely has a budget in the capacity plan, so it loses out to day-to-day business. Passing on knowledge rarely has a role with its own share of time, although 72.3% of respondents say they learn at work mainly by exchanging with colleagues. Workflows usually lack a description of which step will be done with AI in future and who checks it. Tiering skills by role is covered in Building AI capabilities in the organisation. Why acceptance grows from experienced benefit is explained in Winning employee acceptance of AI.

Since July 2026, Art. 4 of the AI Act makes the measure the subject of the literacy duty

Since 2 February 2025, Art. 4 of the AI Regulation has required providers and professional users of AI systems to take measures on the AI literacy of their staff. The Digital Omnibus Regulation (EU) 2026/1744, in force since 27 July 2026, has amended the provision. The earlier version required companies to make every effort to ensure a sufficient level of AI literacy. The current version requires measures that support the development of AI literacy, without guaranteeing a specific level for any individual.

A budget per role with content and hours used documents that the measures fit how the systems are deployed. Under Section 98(1) of the German Works Constitution Act the works council co-determines in-company training measures, so the budget should be agreed with it early. This assessment does not replace legal advice. Details are covered in AI literacy under Article 4 of the AI Act, the route with employee representatives in Works council and AI introduction.

A learning time budget for 300 employees costs around 171,000 euros a year

By our calculation the budget for 300 employees comes to around 3,800 hours and 171,000 euros in labour costs a year, that is 12.7 hours and around 570 euros per employee. It distinguishes four groups, because a basic module alone covers neither practice in the workflow nor the passing on of knowledge. The share of regular users comes from the IU study, the other values are assumptions any company can replace.

At 1,600 working hours per person, 171,180 euros equal just under 0.8% of annual labour costs. Compared with today's average of around 5.5 hours, learning time slightly more than doubles. It breaks even when each of the 137 regular users saves 27.8 hours a year, around eight minutes per working day at 220 days.

The maturity of an AI learning organisation shows in learning time, roles and workflows

Maturity shows in learning time, responsibility, workflows and measurement. For a large share of employees the IU study describes stage 1, because almost two thirds perceive no expectation from their company.

How AI reaches daily use is described in Change management for AI adoption, the state of adoption in AI adoption in German companies.

Executive teams in mid-sized companies should first approve AI learning time as a budget

The findings translate into six steps, each with an owner, a time frame and a metric:

1. Budget learning time: within four weeks, management and the head of HR set hours per role in the 2027 plan. Metric: share of employees with an approved learning budget.
2. State the expectation per activity: by the end of 2026 every head of unit names which activities are to be done with AI and how results are checked. Metric: share who know the expectation, by short survey.
3. Appoint multipliers: within eight weeks, heads of unit choose one person per 15 regular users with a fixed share of time. Metric: standards created from practice cases per quarter.
4. Redesign one workflow per unit: process owners use AI for twelve weeks in a time-consuming workflow and describe it anew. Metric: processing time per case before and after.
5. Enable managers first: in the first quarter of 2027, HR trains all managers on the changes in their teams. Metric: share of managers who used their eight hours. Programme design is covered in AI training programmes for executives.
6. Measure evidence and effect: HR documents learning hours per person and role for Art. 4 of the AI Act and reports quarterly. Target for year one: fewer than ten per cent of employees without a learning hour.

Niehues Consulting links AI adoption with learning time, roles and workflows

Niehues Consulting supports executive teams and heads of department in building organisations that deliver reliably. In delivery management, a delivery manager or product owner steers the programme until the change has arrived in operations. Where AI enters the workflows, a business case and a prototype are added, followed by introduction with learning time and multipliers.

The approach follows the Niehues Delivery Model with its phases Understand, Build and Introduce. For building skills it asks three questions. In Understand: who uses AI today, for what and with how much learning time? In Build: which roles, budgets and workflows do operations require? In Introduce: are users enabled and workflows adapted? Founder Marin Niehues, a management consultant from the delivery side, builds organisations that deliver. His projects range from venture-backed scale-ups to DAX 40 groups, with assignments in more than 20 countries on six continents. In the free 30-minute initial consultation you assess together how much learning time your organisation needs and in which workflow it takes effect first: book a consultation.