In this article
The key idea
A qualification is a starting point. What matters is a useful application, clear responsibilities and the ability to support a solution in operation.
An AI solution needs to do more than work in a demonstration. It should make a specific task easier, work with the available data and have someone responsible for its operation. An AI consultant connects these questions with implementation.
A qualification can provide a foundation. It does not replace practical experience or an assessment of a particular company's requirements.
What an AI consultant contributes
Start with a manageable task: sorting documents, making existing information easier to find or preparing a recurring workflow step. Define the goal, data sources and boundaries together.
The work includes:
- Assessing use cases: Which step takes time, and how would an improvement be measured?
- Reviewing data and interfaces: What information is available, who may use it and which systems need to connect?
- Defining responsibility: Who reviews results, handles exceptions and responds to errors?
- Supporting adoption: How will people learn the workflow, and how will its value be evaluated?
From an idea to a testable pilot
A pilot needs clear boundaries. Test one process step with representative data before attempting a larger automation. Include incomplete documents, ambiguous inputs and situations where the AI cannot produce a dependable answer.
Agree acceptance criteria, human review and ongoing costs before launch. These make it possible to decide whether to expand, change or stop the solution.
AI literacy and regulatory requirements
The European AI Act addresses AI literacy and responsibilities in the use of AI. Relevant requirements depend on the system, the organisation's role and the context of use. A general qualification alone does not establish compliance for a particular project.
The European Commission's AI literacy information and the current regulation on EUR-Lex provide starting points. Project-specific legal questions require the appropriate professional assessment.
What should remain after the consulting phase
A useful initial assessment goes beyond a list of AI tools. It makes a decision about one process easier: with a clear goal, a bounded use case and an explanation of where people continue to make decisions.
These deliverables are particularly useful for implementation:
- Process outline: Inputs, processing, outputs and handovers to other systems.
- Data overview: Sources, access rights, quality issues and responsible owners.
- Test plan: Typical cases, deliberately difficult cases and criteria for rejection or manual review.
- Operating plan: Ownership, ongoing effort and the response to outages or incorrect results.
The documents should fit the project. A small pilot may need only a few clearly written pages. What matters is that the team and developers can examine the same assumptions.
Practical example: Preparing incoming documents
Suppose a team transfers information from incoming documents into an internal system. A possible pilot extracts selected fields and presents them to a responsible person for review. Data is transferred only after approval. This is an illustrative workflow, not a client reference.
Tests should include readable and incomplete documents. The team records which fields are extracted reliably, how much correction is needed and when processing must stop. These observations support the next decision: expand the scope, improve the inputs or reject the approach.
Organise collaboration inside the company
The operational team, IT and the people responsible for privacy or legal questions contribute different perspectives. The operational team knows everyday exceptions. IT evaluates interfaces and operation. Other specialists assess requirements within their remit. An AI consultant helps bring these questions together early and record decisions.
Introduction also needs a plan: who explains the controls, collects feedback and reports errors? Who can stop processing? A pilot becomes a useful working tool when these responsibilities hold up in everyday use.
What to ask an implementation partner
Ask how data is processed, which providers are involved, how outputs are tested and what happens during an outage. A useful proposal explains assumptions and limits alongside potential benefits.
At theconcept technologies, we connect this planning with AI integration and automation and product development. Our own products illustrate different applications. We discuss the specific requirements of your project in an initial conversation.
Frequently asked questions
Is a certificate enough for a successful AI project?
No. Appropriate data, technical implementation, clear ownership and operational support are also necessary.
Where should a company start?
With one clearly defined process, a measurable goal and a decision about where people need to review outputs.
How can practical experience be assessed?
Ask for understandable examples, a concrete test plan and an explanation of running costs. Willingness to explain limitations matters alongside technical depth.