AI
Roadmap: Design AI
Welcome to the Design AI Roadmap, the starting point for an exciting journey of discovery into the world of artificial intelligence in design. This...
Roadmap: Data AI
Renee Muller, Brand strategist
Jul 29, 2025
Welcome to Part 2 of the Data AI Roadmap, a carefully curated guide designed to take you on an educational journey through the world of data-driven artificial intelligence. This guide is specifically for anyone interested in how AI can be used to extract value from data, whether you are a data scientist, business professional, or just an AI enthusiast. Part 1 of this roadmap can be foundhere.
The Power of Data in AI
Data is the lifeblood of artificial intelligence. In this roadmap, we explore how the right data analytics can transform AI systems into powerful tools for insights, prediction and decision-making. Whether you're new to the world of AI or want to deepen your knowledge, this roadmap provides a solid foundation.
1. Collect data.
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Identify what data you need to answer the objectives you established in question 1
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Identify possible sources where you can collect the necessary data. This can be internal sources (such as company databases) or external sources (such as public datasets, social media, APIs).
2. Legal and Ethical Considerations:
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Make sure you comply with all legal and ethical standards related to data collection, especially regarding privacy and data protection.
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Do not enter secret data into free tools
3. Selection of AI Technology and Tools:
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Research which AI technologies (e.g., machine learning algorithms) and tools (e.g., AI platforms) best fit your goals.
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Consider partnerships with AI vendors that specialize in the SME segment (Blue Field Agency 😉 ). We can provide customized solutions and support.
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Make sure the data you want to process is supported by your chosen AI technology.
4. Data preparation
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Collect the data from the identified sources. This can be manual or automatic, depending on the source and amount of data.
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Provide appropriate storage for the collected data. This can be a database, data warehouse, or cloud storage, depending on the size and type of data.
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Check the collected data to remove inaccuracies, duplicates, and irrelevancies. This process may also include converting data formats and normalizing data.
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Make an estimate of what results you will expect
5. Have a small piece of the data analyzed
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Perform a preliminary analysis to gain insight into the data. This can help identify patterns, trends, or anomalies.
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Does the data analysis look as you expected or else see why it doesn't.
6. Start a Pilot Project:
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Choose a small, manageable project for your first AI implementation. This could include developing a simple chatbot for frequently asked customer questions.
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Set up a cross-functional team to lead this project, with members from relevant departments (such as IT, customer service, and sales).
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Plan for regular review moments to measure progress and address issues quickly.
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See if the results are consistent with previously defined KPIs