AI Consulting in Denmark: A Practical Guide for Businesses

AI Consulting in Denmark: A Practical Guide for Businesses

Introduction

Artificial intelligence is moving from experimentation into everyday business work. Companies are using AI to analyse information, create content, support customers, automate routine work, forecast demand and improve decision-making. Denmark is well placed for this change because it has a strong digital economy, high levels of digital adoption and a long history of using digital systems in both business and the public sector.

For many organisations, however, the difficult part is not finding an AI tool. The difficult part is deciding what to use, how to connect it to existing systems, how to protect data, how to measure the result and how to make sure employees use it correctly. This is where AI consulting becomes useful. An AI consultant helps an organisation move from an idea such as “we should use AI” to a clear business case, a working solution and a plan for long-term use.

Denmark's AI landscape is also becoming more structured. The Danish government has promoted responsible AI through national strategies, while the 2026–2029 Joint Government Digital Strategy puts responsible use of new technology, including AI, at the centre of public-sector digitalisation. At the same time, the EU AI Act is entering active enforcement stages, making governance and compliance important parts of AI projects.

This guide explains what AI consulting means in the Danish market, why companies use it, which services consultants normally provide, where AI can create value, what organisations should consider about regulation and data, and how to choose an AI consulting partner.

1. What Is AI Consulting?

AI consulting is professional support for organisations that want to plan, introduce, improve or govern artificial intelligence. It can cover strategy, data, software, machine learning, generative AI, automation, security, compliance and employee training.

A consultant does not necessarily build a completely new AI model. In many projects, the better solution is to combine existing AI services with the company's own data and software. For example, a business may use a large language model to search internal documents, an AI service to classify customer requests, or a machine-learning model to forecast demand. The consultant's role is to identify the right approach and make sure it works in the organisation's real environment.

Good AI consulting starts with the business problem rather than the technology. A consultant should first ask what the organisation is trying to improve: reduce manual work, respond to customers faster, improve forecasting, find information more easily, reduce errors, or create a new service. Only after the goal is clear should the team decide whether AI is the right answer.

2. Why AI Consulting Matters in Denmark

Denmark has a strong foundation for AI adoption. Statistics Denmark reports that in 2026, 88% of companies with more than 250 employees used at least one AI-based technology, while the figure for companies with 10–49 employees was 55%. AI use was especially high in the IT and media industry, where 89% of companies used AI. These figures show that AI is not limited to a small group of technology companies; it is becoming part of normal business operations.

The increase also creates a practical challenge. Companies at different stages of adoption need different types of support. A small company may need help selecting a secure generative-AI tool and setting employee rules. A larger company may need help integrating several AI systems, building a data platform, managing model risk and monitoring AI use across departments.

Denmark also has a clear public policy direction. The Danish Agency for Digital Government describes a broad political agreement to accelerate AI development and use while keeping people and democratic values at the centre. Denmark's digitalisation strategy includes a strategic AI initiative and a regulatory sandbox for AI. The government has allocated DKK 740 million for digital development under the 2024–2027 digitalisation strategy.

This creates an environment where responsible AI is not simply a technical preference. It is increasingly connected to business strategy, public policy, data protection and regulatory requirements.

3. Denmark's AI Strategy and Digital Direction

Denmark's first National Strategy for Artificial Intelligence was published in 2019. It focused on four broad goals: building a responsible and human-centred basis for AI, supporting AI research, encouraging Danish businesses to develop and use AI, and using AI in the public sector to improve services. The strategy also identified healthcare, energy and utilities, agriculture, and transport as priority areas.

The direction has developed further. Denmark's 2024 Strategic Approach to AI emphasises fundamental rights and Danish values, global competitiveness for Danish companies, and an ambition to be a leading country in public-sector AI use. It introduced initiatives including a Digital Taskforce for AI, a Centre for Artificial Intelligence, work on Danish language resources and easier access to Danish text data.

The Joint Government Digital Strategy for 2026–2029 continues this direction. It highlights responsible and ethical use of technologies such as AI, with the goal of supporting citizens while freeing public-sector employees to focus more time on core tasks. It also stresses the importance of protecting Danish values in a digital environment increasingly influenced by large international technology companies.

For organisations considering AI consulting, this policy environment is important. It means that successful AI projects in Denmark need to balance innovation with trust, security, transparency, data protection and human oversight.

4. What AI Consulting Services Include

AI strategy and opportunity assessment

The first service is often an AI readiness or opportunity assessment. The consultant reviews the organisation's goals, processes, data, technology and skills. The result is a practical list of AI opportunities ranked by value, complexity, risk and expected effort. This prevents companies from spending money on AI projects simply because a technology is popular.

Data assessment and preparation

AI depends heavily on data. Consultants can review where data is stored, whether it is accurate, how it is accessed, and whether it can legally be used for the intended purpose. They may also help clean datasets, establish data pipelines, define access controls and create processes for data quality.

Generative AI and automation

Generative AI is now one of the most common entry points for businesses. Consultants can help introduce internal knowledge assistants, document search, customer-service assistants, content workflows, meeting summaries, drafting tools and other applications. The important point is to design these systems around real workflows rather than treating a chatbot as the entire solution.

Machine learning and predictive AI

Some organisations need more specialised models. These can be used for demand forecasting, fraud detection, predictive maintenance, customer segmentation, quality inspection or risk analysis. Consultants can help choose models, prepare training data, evaluate performance and connect models to business systems.

AI integration and implementation

Proof of concept is only useful if it can become part of daily work. Implementation may involve APIs, cloud services, databases, business software, identity management and monitoring. Consultants can help move a tested idea into a production environment while keeping security and reliability in mind.

AI governance, risk and compliance

Governance is becoming a core consulting service. Organisations need clear rules for acceptable AI use, responsibility, data handling, human review, supplier management, documentation and monitoring. A governance framework should be practical enough that employees can follow it in everyday work.

Training and change management

AI changes how people work. Employees need to understand both the benefits and the limits of AI tools. Training can cover safe prompting, checking AI-generated information, handling confidential data, recognising AI errors and knowing when a human must make the final decision.

5. Key AI Use Cases for Danish Businesses

The right use case depends on the organisation's sector and data. Still, several areas are particularly relevant to Danish businesses because they can improve productivity without requiring a complete replacement of existing systems.

• Customer service: AI assistants can answer common questions, search approved knowledge and help employees prepare responses. Human support should remain available for complex or sensitive cases.
• Document and knowledge management: AI can help employees search policies, contracts, manuals and internal information using natural language. Access controls must remain in place so users only receive information they are allowed to see.
• Operations and forecasting: Machine-learning models can support demand forecasting, inventory planning, scheduling and predictive maintenance. These systems are most useful when their predictions are connected to a clear business process.
• Sales and marketing: AI can support customer segmentation, campaign drafting, product recommendations and analysis of customer feedback. Human review is still important for accuracy, brand tone and compliance.
• Finance and administration: AI can assist with document classification, invoice processing, anomaly detection, reporting and repetitive back-office tasks. Automated decisions should be reviewed carefully where financial or legal consequences are significant.
• Healthcare and life sciences: AI can support research, administration, imaging, data analysis and other specialist workflows. These projects usually require stronger governance because the data and decisions can be highly sensitive.

Denmark's national AI infrastructure also supports advanced research and industrial use. The Danish Centre for AI Innovation operates Gefion, Denmark's AI supercomputer, which was launched in 2024. Gefion is intended to support large AI projects in areas including healthcare and life sciences, the green transition and quantum computing. The infrastructure is designed to support data sovereignty and sensitive workloads.

6. AI Consulting and the EU AI Act

Any serious AI consulting project in Denmark now needs to consider the EU AI Act. The Act entered into force in August 2024 and its requirements are being introduced in stages. From 2 August 2026, many of the main provisions and enforcement powers became applicable, although some rules have later transition dates. High-risk rules for certain systems in areas such as employment, education, biometrics, critical infrastructure and other sensitive areas are scheduled to apply from 2 December 2027, while certain AI systems embedded in regulated products have a later date.

The Act uses a risk-based approach. Not every AI application has the same level of regulatory risk. A simple internal productivity tool may face a very different set of obligations from an AI system used in a sensitive or high-risk context. This is why AI consulting should include an early assessment of what an AI system does, who uses it, what data it processes, and what impact it can have on people.

Transparency is also important. From 2 August 2026, certain transparency requirements under Article 50 apply, including requirements relating to informing users when they are interacting with AI and marking certain AI-generated or manipulated content.

In Denmark, the Danish Agency for Digital Government coordinates implementation of the AI Act, with a sector-based supervision structure. Denmark also has an AI regulatory sandbox run by the Danish Data Protection Agency and the Agency for Digital Government. The sandbox can provide project-specific guidance on GDPR and AI Act risk classification for eligible AI projects.

7. GDPR, Data Protection and AI in Denmark

AI projects often involve personal data, which makes GDPR an important part of planning. A company should not assume that data can be sent to an AI provider simply because the provider offers a useful service. The organisation needs to understand what information is processed, why it is processed, where it goes, who can access it and how long it is retained.

Consultants can help organisations assess lawful processing, data minimisation, access controls, supplier arrangements, retention, security and privacy risks. The Danish regulatory sandbox specifically recognises that GDPR principles continue to apply when organisations develop and use AI systems.

For practical projects, this means sensitive information should be handled deliberately. Employees should know which information can be entered into public AI tools and which information must stay within approved company systems. Technical controls are useful, but clear internal policies and training are equally important.

8. A Practical AI Consulting Process

A good consulting engagement should be structured, but it should not become unnecessarily complicated. A typical project can be organised into the following stages:

1. Discovery: Understand business goals, current processes, systems, data and pain points.
2. AI opportunity assessment: Identify realistic use cases and compare expected value, effort and risk.
3. Data and compliance review: Check data quality, privacy, security, supplier requirements and relevant AI Act obligations.
4. Proof of concept: Build a small test that answers an important business question before making a large investment.
5. Evaluation: Measure accuracy, time saved, cost, user satisfaction, reliability and risks against agreed targets.
6. Production implementation: Integrate the solution with business systems, identity controls, monitoring and support processes.
7. Training and adoption: Teach employees how to use the system safely and effectively.
8. Continuous monitoring: Review performance, costs, data quality, security and regulatory requirements as the system changes.

This staged approach is especially useful for smaller organisations. Instead of starting with a large and expensive AI transformation, a company can test one valuable use case, learn from it and then decide where further investment makes sense.

9. Challenges Danish Companies Should Prepare For

Data quality

Poor data can produce poor AI results. Historical data may be incomplete, inconsistent or stored in systems that do not work well together. Data preparation often takes more time than the first AI demonstration suggests.

Skills and internal ownership

An AI project should not depend completely on an external consultant. The organisation needs people who understand the business process and can own the solution after implementation. Consultants should therefore transfer knowledge, document the system and work with internal teams.

Trust and accuracy

Generative AI can produce convincing but incorrect answers. Predictive models can also become less accurate when conditions change. Important workflows therefore need testing, human review and monitoring rather than assuming that an AI system is always correct.

Security and supplier risk

AI introduces additional questions about access, prompts, data leakage, model providers, third-party APIs and system dependencies. A supplier review should be part of the project, especially when confidential or personal information is involved.

Employee adoption

Even technically strong AI systems can fail if employees do not trust them or do not understand how to use them. Clear guidance, training and simple workflows usually create better adoption than simply giving employees access to a new AI tool.

10. How to Choose an AI Consulting Partner in Denmark

Choosing a consultant should be based on more than technical skills. Organisations should look for a partner that understands both the technology and the business environment in which it will operate.

• Relevant experience: Ask for examples of projects similar to your sector, company size or problem.
• Business-first approach: The consultant should be able to explain the expected business value, not only the model or platform being used.
• Data and security capability: Ask how personal, confidential and commercially sensitive information will be protected.
• Regulatory knowledge: The partner should understand GDPR and the EU AI Act and know when specialist legal or compliance advice is needed.
• Clear delivery method: Look for a defined process from discovery and proof of concept through implementation and monitoring.
• Knowledge transfer: Make sure internal employees will be able to operate and manage the solution after the consulting engagement.

It is also useful to ask how the consultant measures success. A strong answer should include measurable outcomes such as reduced processing time, lower manual workload, better forecast accuracy, faster customer response or improved service quality. “Using AI” is not itself a business result.

11. The Future of AI Consulting in Denmark

AI consulting in Denmark is likely to become less about introducing AI as a new technology and more about integrating AI into normal business operations. As adoption grows, organisations will need support with AI governance, model monitoring, secure use of generative AI, data architecture and the redesign of business processes.

The growth of sovereign and local AI infrastructure may also matter for organisations that have strict requirements around data and computing. Gefion is one example of Denmark investing in domestic AI capacity. DCAI describes the infrastructure as suitable for secure and sovereign workloads, including healthcare, pharma and public-sector use.

Public-sector adoption will remain another important area. Denmark already has a highly digital public sector, and the 2026–2029 joint digital strategy explicitly supports responsible use of AI to help citizens and free staff time for core tasks. This creates opportunities for AI projects in administration, citizen services, healthcare and other public functions, while also increasing the need for strong governance and human oversight.

At the same time, regulations will become a normal part of AI delivery. Businesses will increasingly need clear records of how AI systems are selected, tested, used and monitored. Consultants that can combine technical delivery with governance and business understanding will therefore be in a stronger position to provide long-term value.

12. Conclusion

AI consulting in Denmark is not simply about helping companies buy or build AI tools. It is about helping organisations use AI in a way that creates measurable business value while protecting data, people and trust.

Denmark's high level of digitalisation, growing business adoption of AI, national AI initiatives and strong public-sector digital infrastructure provide a solid foundation for further AI growth. At the same time, the EU AI Act and GDPR make responsible implementation an essential part of the process.

For a Danish organisation starting its AI journey, the best approach is usually practical: identify a real business problem, assess the available data, check the risks, build a focused proof of concept, measure the result and scale only when the value is clear. A good AI consulting partner can guide this process and help turn AI from an interesting technology into a useful part of everyday work.

Key Takeaways

• AI adoption is growing across Danish businesses, including small and medium-sized companies.
• AI consulting can cover strategy, data, implementation, governance, compliance and employee training.
• The strongest projects begin with a business problem and measurable outcome, not with a technology trend.
• GDPR and the EU AI Act should be considered early in projects involving personal data or regulated AI use.
• Denmark's national AI strategy, digitalisation initiatives and AI infrastructure support further responsible AI adoption.
• A successful AI solution needs ongoing monitoring, employee adoption and clear ownership after implementation.