Forschungszulage: What Does 'Uncertainty' Mean? (BSFZ)

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Why the Criterion "Uncertain" Is So Important

Many innovation projects look from the outside like "normal product development". But what is central to the Forschungszulage is whether your project has to resolve technical uncertainties. This is exactly where the BSFZ typically distinguishes routine (not eligible for funding) from genuine innovation with technical risk (often eligible for funding).

In short: without uncertainty, there is no eligible innovation in the sense of the Forschungszulage.

More context:

• The 8 most frequently asked questions about the Forschungszulage

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What Does "Uncertain" Mean Concretely?

In the sense of the Forschungszulage, a project is "uncertain" if at the outset there is no precise prediction about:

• Feasibility (does the approach work at all?)
• exact outcome (what level of performance/accuracy/stability will be achievable?)
• often also time and cost (because technical knowledge must first be acquired)

Time/costs (context on expenditures):

• Forschungszulage Personalkosten

Important: In practice, the BSFZ groups the individual aspects (novel, creative, uncertain, systematic, reproducible) into three overarching criteria: novelty, risk/uncertainty and systematic approach. "Uncertain" is primarily captured in the risk/uncertainty criterion.

Risk/uncertainty (requirements):

• Requirements for the Forschungszulage

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Uncertain = Technical Risk, Not Commercial Risk

Technical risk (context):

• Statistics: success rates, industries, company sizes

Typical misconceptions:

• "We don't know whether customers will buy it" → commercial risk, does not count.
• "We don't know whether the technology can meet the requirements" → technical risk, does count.

Rule of thumb: Uncertainty must relate to scientific/technical obstacles that could in extreme cases lead to failure.

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Practical Examples: When "Uncertain" Is More Likely Met

Practical examples:

• Example projects

1) Software & AI (Innovation Rather Than Standard IT)

Software/AI context:

• Can I get public funding for my software project?

More likely uncertain (eligible for funding possible):

• Development of an AI-powered real-time analysis at the production line, where it is unclear whether detection + latency + robustness under real conditions can be achieved simultaneously.
• Development of reproducible prompting strategies for consistent image series, when stability and transferability are not technically assured (e.g. drift across models/versions).
• Development of a low-latency platform with synchronisation technology, when it is unclear whether the architecture can handle scaling and consistency under high load.

More likely not uncertain (usually not eligible for funding):

• Standard software development with known frameworks without a technical hurdle
• pure integration/customising of known tools
• routine debugging after the development phase has concluded

2) Prototypes, Pilot and Test Systems

Uncertainty usually met, as long as the prototype/system serves to:

• test technical hypotheses,
• validate parameters,
• and develop further improvements.

Once the testing phase is complete and it is "only" moving towards production/zero series, the eligible part often ends.

3) Data Collection & Analysis

Can be uncertain, if data collection/analysis is an integral part of the technical solution, e.g.:

• Development of new methods for data collection or evaluation
• new measurement/labelling methods that first make the technical innovation possible

Usually not uncertain, if:

• data is only collected for market analyses (known methods, no technical knowledge gain)

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How to Demonstrate Uncertainty Effectively in Your Project Description

Help with the project description:

• 4 calls to the Forschungszulage
• Application process

Describe not only "what you are building", but why it is technically uncertain:

• Technical obstacles: What prevents a direct solution?
• Hypotheses/approaches: Which solution paths are you testing – and why are they not obvious?
• Experiment/test design: How do you verify whether an approach works?
• Metrics & conflicting objectives: e.g. accuracy vs. latency vs. robustness
• Iterations: What happens if approach A fails?

If you wish, you can often structure your uncertainty in 2–3 sentences like this:

1. Starting point/state of the art (what is certainly achievable today?)
2. Technical gap (what is unresolved?)
3. Risk/uncertainty (why is the outcome open?)
4. Validation plan (how do you proceed methodically?)

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Why the Forschungszulage Is a Strong Option for Innovation Projects

The Forschungszulage is a tax-based innovation incentive for companies – technology-neutral and close to practice. The process is typically two-stage:

1. BSFZ certificate (substantive eligibility of the project)
2. Application to the tax office via ELSTER (assessment/offset)

If you clearly work out the criterion "uncertain", the path to the certificate often becomes considerably easier.

More on this (internal):

• zeitmaker.com
• Examples of funded innovation projects

Official information:

• BSFZ – FAQ on the Forschungszulage
• BMF – information on the Forschungszulagengesetz

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