AI & Data Subsidie
AI & Data Subsidies, Invest in AI for Your Business
Dutch SMEs investing in AI and data science can get up to 50% back. The WBSO covers 36-50% of development costs, the MIT R&D AI scheme provides up to €350,000 for AI collaboration projects. Stratalytic builds your AI solution and handles the full subsidy application.
Updated: May 2026
€350.000
Max MIT R&D AI
€20.000
Max MIT feasibility
35%
Subsidy percentage
75%
SME digital target
Quick answer
- Dutch SMEs investing in AI/data can recover 35-50% via stackable subsidies (WBSO + MIT + SLIM).
- MIT R&D AI: up to €350,000 (35%) for two-party AI collaboration. MIT feasibility: up to €20,000 (35%) solo.
- WBSO: 36-50% payroll-tax credit on developer hours. €1.8 bn budget in 2026, no competition, year-round application.
- SLIM: up to €25,000 for AI literacy training (mandatory under EU AI Act per 2 Aug 2026).
- Stratalytic engineers the AI solution (Python, ML, LLMs) and handles all subsidy paperwork. One partner.
Status & timing
When can you apply?
For most schemes the opening date is what counts, the budget is often gone on day one. Here's the live status.
MIT R&D-samenwerking AI
Closed, next round- Next opening
- April 7, 2027 (estimated)
- Allocation
- tender, assessed on quality
- Start prep
- ~12 weeks ahead
- Rate
- 35%
- Max
- €350.000
- Budget
- €3,55 miljoen
The deadline is firm, but quality beats speed. Start a strong dossier well ahead, submitting early gives no advantage.
WBSO
Year-round- Apply
- Year-round
- Allocation
- monthly applications
- Start prep
- ~2 weeks ahead
- Rate
- 36% / 50%*
- Budget
- €1,817 miljard
You can apply every month (by the last day of the month for the next month). Apply before your R&D work starts, retroactive claims aren't possible.
Don't miss the opening day
Leave your region and email. We'll reach out personally, well before MIT R&D-samenwerking AI opens, to check whether you qualify and prepare your application so it's ready on day one.
01
Which AI subsidies are available?
The Netherlands offers several subsidy schemes for AI projects. The MIT R&D AI scheme provides up to €350,000 for collaboration projects between two or more SMEs. For individual companies, the MIT feasibility study offers up to €20,000 to investigate whether AI works for your use case.
Additionally, the WBSO provides a 36-50% R&D tax credit on development hours, and the Digital Europe Programme offers EU funding for AI initiatives. The MIT feasibility study is the most accessible option for a single SME looking to get started with AI.
New in 2026: the SLIM subsidy now covers AI literacy training for your team (60% of costs, up to €25,000). The MIT R&D AI track has a dedicated window opening April-May 2026 with up to €350,000 for AI collaboration projects. For larger-scale AI ventures, the Innovatiekrediet offers financing for scaling AI solutions that already work. Cross-border programmes like Interreg Art-IE even provide free AI implementation support in partnership with universities.
With the EU AI Act AI literacy deadline arriving in August 2026, subsidies can also cover your compliance costs, making it doubly worthwhile to act now.
02
AI applications for SMEs
AI is no longer exclusive to big tech. SMEs can benefit from predictive maintenance, demand forecasting, automated document processing (invoices, contracts), chatbots and virtual assistants, image recognition for quality control, and recommendation engines.
Other valuable applications include anomaly detection for fraud prevention and natural language processing for customer feedback analysis. These solutions pay back quickly and are well suited for subsidy-funded projects.
03
AI technologies and tools
Modern AI projects rely on a rich ecosystem of established technologies. For prediction and classification tasks, such as customer churn, demand forecasting or lead scoring, we use Machine Learning libraries like scikit-learn and XGBoost. These tools deliver fast, interpretable results on structured business data.
When the problem involves complex patterns in images, audio or text, we turn to Deep Learning frameworks such as TensorFlow and PyTorch. For Natural Language Processing (NLP), think chatbots, document processing, sentiment analysis and text classification, we leverage Hugging Face transformer models and OpenAI APIs.
Computer Vision enables applications like automated quality inspection on production lines, document scanning and image-based product categorisation. All models are built on solid Data Engineering foundations using Python, SQL and Apache Spark for data pipelines. When these technologies are applied in an innovative context, solving a problem that has no off-the-shelf solution, the development work qualifies for WBSO tax credits.
04
AI ROI: when does AI pay off?
A common question before investing in AI is: when will it pay for itself? Typical ROI indicators include time savings exceeding 20 hours per month, error reduction of more than 30%, or a revenue increase above 5%. In practice, well-scoped AI projects break even within 6 to 12 months after deployment.
Consider a concrete example: a demand forecasting model that reduces overstock and stockouts saves a mid-sized retailer roughly €40,000 per year in inventory costs. With MIT and WBSO subsidies covering a large share of the project investment, the break-even point is reached within the first year.
The MIT feasibility study is specifically designed to assess this ROI before you commit to full implementation. A structured feasibility phase, of which up to 35% is subsidised via MIT, gives you a data-driven answer to the question "is AI worth it for my business?" before making a larger commitment.
05
From idea to working AI solution
Stratalytic follows a fixed approach: (1) data audit: what data do you have and is it clean enough, (2) proof of concept: a small test to validate the AI approach, (3) production model: build a robust solution, (4) integration: connect to your existing systems.
This approach aligns perfectly with the MIT feasibility study (steps 1-2) and follow-up implementation, maximizing both your chances of success and subsidy coverage.
06
Why start AI with a subsidy?
AI projects carry inherent uncertainty, will the model actually work for your data? Subsidies reduce this financial risk. The MIT feasibility study is literally designed for this: investigate whether AI works for your use case before committing to full implementation.
If the proof of concept succeeds, you have solid evidence to invest further. If not, the subsidy has covered a significant portion of the exploration cost. Either way, you make an informed decision.
The Dutch government's digital agenda targets 75% SME digitalisation by 2030. Schemes like MIT, WBSO and SLIM are specifically designed to lower the adoption barrier for technologies such as AI and data science. With the EU AI Act AI literacy deadline in August 2026, investing in AI skills now ensures both compliance and competitive advantage. By acting now, you benefit from generous subsidy budgets while gaining a competitive edge over businesses that postpone their AI investments.
Process
How does it work?
Determine AI potential
We analyze your data and business processes to determine where AI adds the most value.
Subsidy & project plan
Based on the analysis we choose the right subsidy and write a compelling project plan.
Build & implement
We develop your AI solution iteratively: from proof-of-concept to production-ready application.
FAQ
Frequently asked questions
Do I need a lot of data for AI?+
That depends on the application. Some AI models, such as classification models built with scikit-learn, need only hundreds of well-structured data points. Deep learning models for image or text analysis typically require thousands. During the first conversation we assess whether your data is sufficient in volume and quality. If gaps exist, data cleaning and feature engineering are standard parts of our project scope and fully eligible for WBSO subsidies, meaning the cost of preparing your data is also partially covered.
What if my data isn't clean?+
That is completely normal and exactly where Stratalytic excels. Most companies have data spread across multiple systems with issues like missing values, duplicate records, inconsistent date or currency formats, and outdated entries. Data cleaning and engineering are a core part of every AI project we deliver. We use Python-based pipelines to standardise, deduplicate and enrich your data before any model is trained. All of this preparatory work qualifies as technical development under the WBSO, so 36-50% of these costs are covered by the subsidy.
Is the MIT R&D AI only for collaboration projects?+
Yes, for the large MIT R&D AI (up to €350k) you need at least one SME partner. For individual projects, the MIT feasibility study (up to €20k) is suitable.
Which AI technologies do you use?+
We select technology based on your specific use case. For tabular data and prediction tasks we use Python with scikit-learn or XGBoost. For complex pattern recognition in images or text we use deep learning frameworks such as TensorFlow and PyTorch. For natural language processing, chatbots, document analysis, text classification, we leverage Hugging Face transformer models and OpenAI APIs. Computer vision tasks like quality inspection or document scanning use specialised image recognition libraries. All solutions are built on robust data pipelines using Python, SQL and where needed Apache Spark for larger datasets.
How long does an AI project take?+
A typical AI project follows several phases. The initial data audit and feasibility assessment takes 2-3 weeks. A proof-of-concept, where we build and validate the first model, takes 4-6 weeks. Full implementation, including model optimisation, testing and integration with your existing systems, takes 8-16 weeks depending on complexity. This phased approach fits perfectly within the MIT subsidy timeline and allows for clear go/no-go decisions at each stage.
What if the AI model doesn't perform well enough?+
That is precisely why we start with a feasibility study funded by the MIT scheme. AI development is inherently iterative: we train a model, evaluate its performance, adjust features and hyperparameters, and retrain until we reach acceptable accuracy. If after thorough experimentation the results are not convincing for your use case, we report that honestly with full documentation of what was tested and why it fell short. The subsidy is designed to cover exactly this research risk, so you never pay the full cost of exploration. This transparent approach ensures you make a well-informed decision before committing to a larger investment.
Can I use the SLIM subsidy for AI training?+
Yes, since 2026 the SLIM subsidy covers AI literacy and AI skills training for your employees. You receive 60% of the costs back, up to €25,000. This is ideal if your team needs to learn how to work with AI tools or if you want to build internal AI competencies alongside a technical AI project.
Does the EU AI Act affect my business and can subsidies help with compliance?+
Yes, from August 2026 the AI literacy obligation under the EU AI Act applies to all organisations deploying AI systems. Compliance costs, such as risk assessments, documentation and training, are eligible for subsidy funding through schemes like SLIM. Stratalytic helps you combine compliance and AI implementation in a single project.
What is the MIT R&D AI track and when can I apply?+
The MIT scheme now includes a dedicated AI track for R&D collaboration projects. It offers up to €350,000 for joint AI development between at least two SMEs. The application window opens in April-May 2026. Stratalytic can help find a collaboration partner and write the application.
Sources & official references
Verified information from authoritative sources
Discover what AI can do for your business
Schedule a no-obligation conversation and we'll explore the possibilities together. From data analysis to working AI, with subsidies it becomes achievable.
