AI consultant

The deliverable depends on the task. It can be a verbal sparring, a workshop, a written analysis, a proof of concept, or an actual implementation plan. Content, scope, price, and responsibility are always agreed clearly before the work begins.

What does an AI consultant do?

An AI consultant helps a business understand, assess, and apply artificial intelligence in a way that fits the business's actual needs. That can sound broad, and it is. The term covers strategic advice, teaching, choosing tools, analysing workflows, and support for concrete trials.

In practice, one of the most important tasks is to distinguish between what is technically possible and what makes business sense. A language model can, for example, draft an email in a few seconds. That does not necessarily mean the whole business's email handling should be automated. The gain depends, among other things, on the volume of enquiries, how uniform they are, how sensitive the information is, and how much checking is required.

AI advice is therefore not only about technology. It is just as much about workflows, employees, quality, responsibility, and economy. A solution that looks impressive in a demonstration can be worthless if it does not fit into everyday life. Conversely, a very simple change — for example a fixed method for using ChatGPT to structure meeting minutes — can give noticeable value without a larger IT project.

My role is to help make the possibilities concrete. I ask about the task, look at the current process, and assess with you whether AI is relevant. When the answer is yes, we define a realistic next step. When the answer is no, I say that too.

AI advice without exaggerated promises

AI is often presented as a technology that automatically reduces costs, removes routine work, and improves all results. It is not that simple. The tools are strong, but they make mistakes, misunderstand instructions, and can phrase incorrect information in a convincing way. The result also depends on data, instructions, context, the user's experience, and the specific task.

So I do not promise particular time savings or financial results before a workflow has been examined and possibly tested. In many cases AI can make the first part of the work faster. That can be, for example, making a draft, categorising information, suggesting a structure, or finding patterns in a defined material. But the quality must be assessed, and for significant decisions a human should still hold the responsibility.

An advisory engagement should make it easier to make an informed decision. It can end with a recommendation to proceed, to start smaller, to choose a different tool, or to wait. All four outcomes can be valuable if they prevent a wrong investment.

Where can businesses use AI in practice?

There is no single universal list of the best AI tasks. A use that makes sense in a consultancy can be irrelevant in a manufacturing business. There are, however, a number of areas that are often relevant to investigate.

Text, drafts, and communication

Generative AI can help produce first drafts of emails, newsletters, guides, product texts, job postings, and internal messages. It can also be used to shorten, rewrite, or structure text. The tool should not be seen as a finished copywriter, but as an assistant that can make it faster to get from a blank page to a usable draft.

It remains important to check facts, tone, legal wording, and any claims. The more specialised or sensitive the text, the more important the professional review. If you want to dig deeper into how AI-generated content affects quality and visibility in both Google and AI answers, I have written more about it.

Customer service and internal support

AI can help employees find answers in guides, summarise a long case, or suggest a reply to a customer enquiry. It can also be used to identify the topic of an enquiry or create a short summary for the next employee.

That is not the same as a chatbot answering all customers without control. For complaints, financial questions, personal data, and complex cases, a wrong answer can do more harm than good. Here an assistant function, where the employee approves the answer, can be a more responsible first step.

Documents and information retrieval

Many businesses spend a lot of time finding information in manuals, procedures, quotes, reports, and other documents. AI can in some cases make it easier to search a defined set of documents, extract particular information, or make an overview.

The quality depends, among other things, on the structure, currency, and scope of the documents. If the source material is outdated or contradictory, an AI tool will not automatically solve the problem. Part of the work can therefore be to clean up the knowledge base and decide which sources should be authoritative.

Meetings and minutes

AI can help transcribe, summarise, and structure meetings. It can give a faster draft of decisions, tasks, and open questions. The solution should, however, be chosen with an eye on consent, confidentiality, and the data processing terms that apply to the tool.

An automatic set of minutes should as a rule be reviewed before it is shared or used as documentation. Names, numbers, decisions, and technical terms can be reproduced incorrectly.

Analysis and structuring

AI can help group answers from questionnaires, create an overview of free text, suggest categories, or explain complex data in more understandable language. For statistical analyses and business-critical conclusions, though, the method should be verifiable. A nice explanation is not in itself documentation that the calculation or conclusion is correct.

Idea development and sparring

Generative tools are often useful as a sparring partner. They can suggest angles, ask counter-questions, challenge an outline, or generate possible scenarios. The value does not necessarily lie in the first answer, but in an iterative dialogue where the user assesses and improves the suggestions.

It is an area where employees can often get value quickly once they learn to give sufficient context and ask precise questions.

ChatGPT, Copilot, Claude, or Gemini – which should you choose?

The choice of AI tool should not be decided by which name fills the most media space. The best-known platforms evolve quickly, and features, prices, integrations, and data terms change continuously. So the choice should be based on your workflows and IT environment.

ChatGPT is a broad, generative assistant that can be used for many types of text, analysis, and idea tasks. Microsoft Copilot can be relevant for organisations that already work intensively in Microsoft 365, but the concrete value depends on licences, setup, permissions, and the quality of the data it gets access to. Claude is known, among other things, for working with longer texts and documents, while Gemini can be relevant in environments that use Google's services. There are also many specialised tools for particular industries and tasks.

None of the platforms is automatically the right choice for everyone. In some cases employees can meet the need with an ordinary business licence. In others an integration or a more defined solution is required. And sometimes the problem is not a lack of AI at all, but a lack of structure, unclear processes, or data that is not available in a usable form.

As part of the advice, we can set up requirements and compare the options based on, for example:

• Which tasks the tool should support
• Which data types are to be processed
• Requirements for access control and administration
• Integration with existing systems
• The option to disable or limit the use of data
• Price per user and expected usage
• The need for teaching and support
• Requirements for documentation and control

It is important to check the suppliers' current terms before a decision is made. An assessment should therefore be based on the information and licences that apply at the time the solution is chosen.

Data security, personal data, and confidential information

When employees use AI tools, it is crucial to know which information they enter, how the information is processed, and who can get access to it. This applies especially to personal data, customer data, contracts, internal financial information, source code, and trade secrets.

A free private account and a managed business solution can have different terms and control options. So you should not assume that all versions of a tool process data in the same way. Settings, subscription type, supplier terms, and the specific setup all matter.

I do not provide legal advice, but I can help map the workflow, identify the places where questions should be asked, and make the technical matters understandable. When sensitive or regulated information is processed, it may be necessary to involve a data protection adviser, lawyer, or other relevant specialist.

A practical AI policy can, among other things, describe:

• Which tools the business has approved
• Which information employees must not enter
• When an AI answer must be checked
• Who is responsible for the finished result
• How AI-generated material may be used externally
• How errors or accidental sharing are handled
• Where employees can get help

The policy should be short enough to actually be read, but concrete enough that employees know what to do in practice.

Employees are an important part of the AI effort

A tool does not create value on its own. Employees must understand what it can do, what it cannot do, and how it fits into their work. If a solution is introduced without involvement and teaching, there is a risk that it will not be used, will be used incorrectly, or will create duplicate work.

A workshop can therefore be a good first step. Here we can work with the business's own, non-sensitive examples and examine how different instructions affect the result. Participants learn to give context, describe a desired format, ask follow-up questions, and check the answers.

The teaching should be adapted to the audience. A management workshop can be about prioritisation, risks, and investments. An employee workshop can be more practical and focus on daily tasks. A technical team may need to understand APIs, access control, logging, and integrations. Read more about talks and teaching if a workshop is what you are missing.

The goal is not for everyone to be AI specialists. The goal is for the relevant employees to be able to use the chosen tools safely and sensibly.

When does a proof of concept make sense?

A proof of concept is a defined trial meant to show whether an idea is technically and practically realistic. It is not a finished production solution. The purpose is to gain knowledge before the business invests in a larger implementation.

It can, for example, be relevant to test whether AI can:

• Classify a particular type of enquiry with sufficient quality
• Extract fixed information from a uniform document type
• Suggest answers based on a limited knowledge base
• Create usable summaries of internal documents
• Help employees with a concrete, recurring writing task

Before the test, we should agree on what counts as a usable result. It can be quality, time spent, number of errors, or the employees' assessment. Without clear criteria it is easy to be impressed by a demonstration without knowing whether the solution can actually be used.

A proof of concept should as a rule use defined data and have a clear end date. After the test we gather the experiences and assess whether the idea should be developed further, adjusted, or stopped.

AI advice and automation are not the same

AI can be part of an automated workflow, but the terms should not be mixed up. AI advice is first about understanding the possibilities, choosing the right approach, and ensuring responsible use. Automation is about getting systems and processes to perform actions with less manual intervention.

An example can be the handling of customer enquiries. The AI advice can uncover whether a language model is good enough to categorise enquiries or suggest answers. An automation solution can then fetch the enquiry, send relevant data to the model, save the result in a system, and create a task for an employee.

Some businesses only need advice and teaching. Others have already identified a concrete process and want to go straight to integration and automation. That is why the services are split across two pages.

Who is AI advice relevant for?

AI advice can be relevant for businesses, associations, and organisations, but the effort must match the size, industry, and needs. Smaller businesses often do not need long strategy reports. They can get more value from a concrete review of three or four workflows and help choosing one tool that employees can actually use.

Medium-sized businesses may need a more structured approach, because several employees already use different AI tools on their own. Here the task can be to create shared guidelines, choose approved platforms, train key people, and prioritise the first projects.

Associations and educational environments can have other questions. It can be how AI is used responsibly by volunteers, how to produce material faster, or how to teach members and course participants without making the technology more complicated than necessary.

Typical situations are:

• You know employees already use ChatGPT, but lack a shared framework
• You are considering Copilot or another licence and want to assess the need first
• You have many recurring text or document tasks
• You want to hold a practical workshop for management or employees
• You have an idea for an AI solution but are unsure how realistic it is
• You want to identify suitable workflows before you invest in development
• You have tried different tools but lack an overall direction

What does an AI consultant cost?

The price depends on the scope and form of the task. A short sparring, a workshop, an analysis, and a proof of concept are different deliverables and therefore cannot be priced the same.

Before the work begins, we agree:

• What is to be investigated or delivered concretely
• Who takes part
• Which material and which systems are involved
• Whether written documentation is to be produced
• Whether the task is done at a fixed price or by time spent
• Which prerequisites and limitations apply

For larger tasks it is described in a SOW. The purpose is to create a shared and understandable basis, not to make the process heavier than necessary.

A responsible approach to AI

Responsible use of AI does not mean that every project should be slowed down by long processes. It means that the most important questions are asked at the right time.

Who is responsible for the result? Which data is processed? What happens if the model gets it wrong? Can the employee see which sources an answer is based on? Is the solution proportionate to the problem? Can it be maintained when models, prices, or systems change?

The answers do not have to be perfect from the start. But they should be good enough that the business knows which risk it is accepting. This applies especially when AI is used in relation to customers, employees, finances, health, law, or other areas where errors can have significant consequences.

I help make these considerations concrete and understandable. For legal, security-related, or industry-specific questions that require specialist competence, the relevant professionals should be involved.

Get started with a defined and useful AI effort

It is rarely necessary to begin with a large transformation project. A better start can be to choose one workflow, one employee group, or one recurring problem. That way it becomes possible to learn, measure, and adjust without making the organisation dependent on an untested solution.

A good first project typically has:

• A clear task
• A defined data basis
• A responsible owner
• A known quality level today
• The option for human control
• A practical way to assess the result

If the test creates value, the effort can be expanded. If it does not, you have still gained knowledge that can be used in the next decision.

Why work with me as your AI consultant?

I work with AI as part of a broader digital and technical reality. My background includes development, WordPress, WooCommerce, Laravel, integrations, support processes, SEO/GEO, and teaching. That makes it possible to see an AI idea in the context of the systems, data, and people that have to make it work.

I am not interested in selling a particular platform to everyone. The recommendation depends on the task. Sometimes an existing standard tool is sufficient. Sometimes a smaller custom solution is required. Other times the process should be improved before AI even becomes relevant.

The collaboration is directly with me. That means the person you talk to about the need is also close to the analysis, the documentation, and any technical solution. You can read more about me and see selected cases.