AI Strategy & Implementation

The honest answer is: when you have a real problem AI can solve and you have tried to solve it yourself without success. That sounds simple but most AI consulting engagements start in the wrong place, with a technology solution looking for a problem rather than a business problem looking for a solution.

The most common trigger we see: leadership has committed to an AI strategy but no one internally can define what that means in practice. A consultant comes in, runs a use-case workshop, and usually finds that 80 percent of what is labelled AI could be solved with simpler automation, and two or three genuinely valuable use cases where generative AI or ML would make a real difference.

Other common triggers: a competitor has shipped something AI-powered and you need to understand if it changes your market position. A supplier or partner is requiring AI capabilities you do not have. A specific operational bottleneck (document processing, customer support, content production) is costing you enough that a proper AI build would pay for itself within a year.

What AI consulting is not the right answer for: replacing your entire technology strategy, or building AI features because the board expects to see them in a slide deck. Those projects tend to produce expensive demos that no one uses.

A good AI consultant starts with a use-case audit, not a technology pitch. They sit with the people doing the actual work, map the processes that take the most time, and identify where AI has a genuine productivity advantage. Most companies have two or three of these. A consultant who opens with a demo of their preferred LLM wrapper is skipping the most important step.

Good AI consultants are clear about confidence levels. The field is moving fast and anyone who claims certainty about what will work in your specific context is either very experienced with your industry or overconfident. The honest framing is: here is what we know works, here is what we think will work but needs a proof of concept, and here is what is interesting but too early to commit budget to.

Good consultants build for maintainability, not impressiveness. The best AI project I have seen at a Danish SME was a document extraction tool that saved a finance team three hours per week. It was not impressive. It used a well-documented API, ran on infrastructure the client already had, and had a junior developer comfortable maintaining it six months later.

Good consultants are honest about the failure modes. AI systems fail in specific, often surprising ways. A good consultant documents the failure modes upfront, builds monitoring that catches them, and does not pretend the system is more reliable than it is.

Warning signs: They build on models or APIs that are not yet stable in production. They cannot explain what happens when the AI gets something wrong. Their proof of concept uses synthetic data, not your actual data. They scope in months without a defined outcome for the first six weeks.

Ask for a project where the AI did not work as planned and what they did about it. Every honest AI project has at least one failure mode that had to be managed. A consultant who cannot describe one has either not shipped anything real or is not being straight with you.

Check their stack independence. The AI tool market is changing fast. A consultant with a strong commercial relationship with one vendor (Microsoft, OpenAI, Google) will naturally recommend that vendor. Ask what they would recommend if they had no commercial relationships. The answer tells you whether they are advising you or selling you something.

Make sure they understand Danish data residency requirements. GDPR and data residency rules affect which AI tools you can use with customer data and personal data. A consultant who has not worked with Danish companies before may not know the constraints.

Ask about the maintenance model. AI systems drift: model updates change outputs, data distributions shift, edge cases accumulate. Ask what happens 6 months after the project ends. The honest answer involves monitoring and an agreed process for retraining or adjusting prompts. The bad answer is a shrug.