From challenge to use case
We start with the decision or task that needs improving, such as assessing applications or scheduling maintenance. Then we look at what form of AI fits, or whether a rule in the existing system would be enough.
AI consultancy from challenge to production
You’ll know where AI delivers value, what it takes to implement it and how to keep it working after the pilot.
Trusted by leading organisations
AI consultancy from Data Science Lab helps you identify which decisions or processes AI can improve, whether that is feasible with your data, and how to deploy it responsibly in production. Since 2016, our AI consultants have worked with medium-sized organisations and public institutions, from the first use case through to adoption and management.
Why AI often gets stuck in the pilot phase
A team uses a language model to build a demo that summarises incoming customer enquiries and suggests a response. It works well on a handful of emails, and management is enthusiastic. Then little happens: it is not connected to the CRM, no one knows who is responsible for maintaining the model, and no one has asked the customer service team whether a summary would make their work easier. After the summer, the demo is still running only on its developer’s laptop.
Research by RAND among experienced data scientists and engineers identifies the main reasons AI projects fail: misunderstanding the problem, missing data, focusing more on the latest technology than on the user, insufficient infrastructure to manage models, and problems that are too difficult for AI. We see the same in our own work: a pilot rarely fails because of the model, but often because of what is missing around it: a reliable flow of data, a team to maintain the model, and a process in which its output is used.
AI consultancy addresses these questions before anything is built, not just when the project is handed over to IT.
What an AI consultant works through with you
Prioritising by value and feasibility
We assess each potential use case by what it could deliver and what it would require: available data, connections to source systems, risks and the people who will work with it. This shows you which use cases offer substantial value for relatively little effort.
Build, buy or configure
Often, a solution already exists: Copilot in Microsoft 365, an AI feature in your ERP system or an off-the-shelf product. We weigh up how much bespoke work you really need, what each route requires in terms of management and how dependent you would become on a supplier.
Assessing data readiness
We check whether the data for the chosen use case exists, is accessible and is good enough. That includes its history, definitions, ownership and privacy. If the data foundation is missing, we say so and set out what needs to happen first.
Governance and the AI Act
We identify the AI systems you use or want to build, prepare their risk classification and document ownership, controls and agreements. Your legal adviser or compliance department makes the legal assessment.
Adoption from the start
The people who will work with the results are at the table from the first session. We discuss what will change in their work, when they can disregard a model’s recommendation and what training they need.
Have an AI idea, but unsure whether it’s feasible?
Discuss your challenge with one of our consultants. We’ll look at the data, the value and the path to production.
How we take an idea into production
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Define the decision and objective
We identify which decision or process needs improving, who it should benefit and how you'll know whether it works.
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Choose use cases
In a Use Case Discovery session with business, IT and data teams, we identify opportunities and prioritise them based on value, feasibility and risk.
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Proof of concept
We use historical or anonymised data to test whether the approach works technically. If the results fall short, we'll say so and recommend stopping there.
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Pilot in the real-world process
A small group of users works with it in their day-to-day roles. We compare it with the current way of working and incorporate their feedback.
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Production and ongoing management
We set up pipelines, monitoring and access rights, and designate an owner so the application keeps working as data or usage changes.
Data Science Lab’s expertise gave us the confidence from the outset to take a broad approach to the project. They handled everything for us, and we now have a concrete, actionable plan to keep building towards our data ambitions together with Data Science Lab.
From proof of concept to production
A proof of concept answers whether it can work: does the approach work with your data in a controlled environment? A pilot answers whether it helps: do employees use it in their actual workflow, and does it improve their work? Production means the application runs without its builders on hand, with monitored data flows, an owner and arrangements for management. If you call a successful demo a pilot, you skip the hardest step.
Getting to production usually requires a reliable data environment. If you don’t have one, the data foundation belongs on the roadmap. We build on Databricks or Microsoft Fabric, depending on what you already use. That way, every application aligns with your data and AI strategy rather than remaining a standalone initiative.
The AI Act also matters. The ban on certain AI practices has applied since 2 February 2025, as has the obligation to take measures to improve employees’ AI literacy. Following the postponement under the AI omnibus, the obligations for high-risk AI systems listed in Annex III will apply from 2 December 2027. Which requirements apply to you depends on the application and on whether you are a provider or a deployer. Read more on our AI Act page.
About Data Science Lab
Any organisation can start an AI pilot. The challenge comes next: making AI part of the organisation. That’s where Data Science Lab makes the difference.
Learn more about us
Why organisations choose us
10+
years of experience
8.7
client rating
200+
projects completed
Microsoft & Databricks Partner
Frequently asked questions
An AI consultant helps you decide where AI can add value and what it takes to make it work: gathering and prioritising use cases, assessing data and other requirements, choosing whether to build, buy or configure, and agreeing on ownership and risk. Because we also build and manage solutions, we check every recommendation against what it will take to run in production.
Choose a use case where the decision is clear, the data exists and users are asking for a solution themselves. Don’t start with the organisation’s biggest problem. At GVB, we help identify which data use case can have the greatest impact with relatively little effort, while the solutions affect several departments that often haven’t worked together before.
Not all of it, but the data needed for your chosen use case does. A complete data platform isn’t a prerequisite, but without reliable historical data, clear definitions and an owner, a model won’t get past the pilot. A data maturity assessment shows where you stand. At Albert Schweitzer Ziekenhuis, conversations with clinical, policy and IT teams led to a baseline assessment and a concrete path for improvement.
Start with the organisation’s goals and an honest picture of where you stand. At Nationale Opera & Ballet, we assessed data maturity and showed where opportunities were being missed. This led to a growth plan linking data to their mission: full auditoriums, a broader audience and inclusion. You can then make a more informed choice about which AI application will help.
That depends on what the application does and whether you develop it yourself or use an existing system. We help you identify the AI you use, prepare the risk classification and establish governance with clear roles and controls. Your legal counsel or compliance department makes the legal assessment. To read about how we look beyond the law, see Responsible AI.
By involving them from the start, not just when it goes live. Users help define what the application should do, take part in testing during the pilot and retain control over their own decisions. Through training and adoption, we also offer AI training for management and technical training for teams, among other things.
If a rule in your existing system, a cleaned-up data source or a good dashboard solves the problem, you don’t need AI or an AI consultant. If you already know exactly what you want to build and only lack capacity, bringing in an AI engineer or data scientist is more sensible than a consultancy project.
Questions about AI consultancy?
Stef Stoelwinder
Business DevelopmentE-mail info@datasciencelab.nl
Phone +31 (0)20 3636 724
Not sure which AI use case to tackle first?
Tell us which decision or process needs improving. We’ll help you think through feasibility, data and the path to production.
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