Setting up an AI strategy? Here's what you need to know in 2025!

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From AI experiments to an AI strategy

AI is already being used extensively in the workplace. Every day, employees ask questions to ChatGPT, use marketing teams Content creation AI, developers are using AI when coding, and operational processes are increasingly supported by smart automations. But despite these separate applications, an overarching one is often missing AI strategy.

A plan is missing. Using separate tools without a clear purpose means that AI does not perform optimally. The result? Standalone AI solutions that do not contribute to business goals. A well-thought-out AI strategy is about a coherent approach. It ensures that AI actually contributes to shared goals and better decision-making.

Read on to learn how to develop a successful AI strategy within your organization.

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What is an AI Strategy?

An AI strategy is a plan that determines how and where AI technology is used to achieve business goals. It's not just about using AI tools, but about a structural approach that links AI to concrete goals, processes and decision-making. Without a clear AI strategy, AI remains a separate experiment with no measurable impact. Here are a few examples of separate experiments:

• An AI chatbot for customer queries
• Personalized recommendations with AI
• Predicting maintenance and inventory with AI
• AI-generated content for marketing purposes
• The development of AI models for automatic image recognition
• Using Machine Learning to automate time-consuming tasks.

These are not bad initiatives, quite the contrary. The applications mentioned above show how AI can be deployed in various companies. But without an overarching AI strategy, these remain separate experiments with limited impact. To really get more out of AI, it is crucial to use AI in a structured and targeted manner.

Get more out of AI

The applications mentioned above are interesting, but they are not concrete enough. You have to ask yourself what you want to achieve before you can use AI. Your AI strategy should include the following 4 pillars:

1. Linking AI solutions to business goals Instead of implementing a chatbot without a clear goal, AI can be used to increase customer satisfaction or reduce the turnaround time for support requests.
2. Integrating AI with existing systems An AI model for automatic image recognition can be more effective when linked to inventory management or quality control in the production chain.
3. Actively involving employees in the use of AI AI can support processes, but without support within the organization, it won't get off the ground. Training and clear guidelines help with successful implementation.
4. Making results measurable An AI solution is only really valuable when the impact is clear. Set KPIs, measure improvements and continuously optimize based on data.

In short, AI only becomes really interesting if it does not just stick to a separate tool, but becomes a strategic pillar within the organization. In the next section, we'll discuss how to build a strong AI strategy and what steps are needed to successfully implement AI.

The three building blocks of an AI strategy

An AI strategy requires a coherent approach that integrates AI structurally and measurably into the organization. To achieve this, there are three crucial building blocks that form the basis for a successful implementation. Unfortunately, this is not a checklist that you can tick off or a step-by-step plan that you can go through. These are strategic sessions where you determine the scope and objectives.

1. Vision and Strategic Goals

• Vision — What is the long-term strategy for AI within the organization?
• Strategic objectives — How does data and AI contribute to business strategy and growth plans?
• KPI structure — Which measurable indicators help monitor progress and impact?

2. Ambition level and maturity analysis

• Aspiring benchmark — What is the desired level of AI and data maturity within the organization?
• Current vs. desired maturity — Where is the organization now, and what is needed to reach the next phase?

3. Evaluation of business competences

• Business Competence Analysis — Which AI and data capabilities are already present and which are still missing?
• Gaps and opportunities — What are the shortcomings and what opportunities are there to accelerate data-driven work?
• Defining the ideal IT landscape — What does the ideal architecture look like to make the most of AI and data?

Now that we've discussed the three building blocks of an AI strategy, it's time to see what this looks like in practice. Because how do you translate a vision and strategic goals, a maturity analysis and an evaluation of business competencies into a concrete one? AI application?        

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Practical example of AI strategy

For a leasing company, AI for claims processing is a promising opportunity. It is possible to use AI models for the automatic assessment of claims. This allows claims to be processed more quickly and customer satisfaction can increase. This contributes to efficiency and customer satisfaction.

1. Define the KPIs

The first step is to determine KPIs. What does' success' mean in this case? For example:

• Reduced claim processing time by 30%
• Increase in customer satisfaction scores by 15%
• Reducing the number of false claim rejections to 1 in 20

2. Maturity Analysis

When the KPIs are clear, it is necessary to determine what is needed to reach this level. The question is: Does the leasing company have the right data and processes to apply AI? For example, an analysis may show:

• Claims are processed in various systems, so data is fragmented
• Vehicle damage footage is not always captured consistently

Without data, there is no AI. This means that the leasing company cannot implement AI directly, but must first ensure streamlined and uniform data collection.

3. Selecting AI Technology

Now that the KPIs and data foundation are in order, the next step is: which AI technologies are best suited to this application? Not every AI solution is suitable, so it's important to choose technologies that meet the goal: faster and more accurate claims processing. For claims processing, there are various AI technologies that can add value:

• Machine Learning (ML) — This is an AI technique that learns from previous claims. The model looks at patterns in previous approved and rejected claims and can predict whether a new claim can be approved immediately or needs additional control.
• Computer Vision — This is an AI model that can 'view' and analyse damage photos. By comparing damage images with previous photos, AI can estimate how extensive the damage is and what the repair costs will be.
• Natural Language Processing (NLP) — This is AI that understands written text, such as emails, claims, and reports. This allows AI to automatically extract the most important information from documents, so that employees don't have to do manual work.
• Robotic Process Automation (RPA) — This is not an 'intelligent' AI, but a smart way to automate repetitive administrative tasks. For example, RPA can automatically process approved claims into the system without an employee having to enter them manually.

To make the claims process faster, more efficient and with fewer errors, 4 AI technologies are already needed! Although these technologies have a lot of potential, you can imagine that setting up an AI-driven claims process requires a lot of time and investment.

Do you dare to take on the AI adventure?

In the long term, investing in AI offers major benefits. For the leasing company above, AI can make claims processing faster, more efficient and less error-prone. This reduces operational costs, reduces workload and increases customer satisfaction. Before you can get started, data must be properly collected and integrated, AI models must be trained and tested, and employees must get used to a new way of working. This requires a phased approach. Contact us

Find out if you're ready for AI!

AI can transform your business, but implementing it successfully requires a well-thought-out strategy. As you saw in our example case, AI is not a ready-made solution that you simply “turn on”. It requires a clear vision, a solid data foundation, and the right technologies.

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Frequently asked questions about AI strategy

               What is an AI Strategy?

An AI strategy is a plan that determines how and where AI is used within an organization to achieve business goals. It's not just about implementing AI tools, but about a structural approach that links AI to processes, data and decision making. It connects to the AI act and is more than just a collection of separate experiments with limited impact.

               Who Owns the AI Strategy?

The AI strategy must be anchored at a strategic level, usually under the responsibility of a Chief Data Officer (CDO), Chief Technology Officer (CTO), or an AI governance team. At the same time, AI shouldn't just be an IT issue. AI affects the entire organization, so business units, compliance, and operational teams must also be involved.

               Which AI platforms are available?

There are many AI platforms that help companies develop and implement AI solutions. Some of the well-known AI platforms include:

• Microsoft Azure A.I — AI tools and machine learning models in the cloud
• Google Cloud AI — Machine learning and AI services such as Vertex AI.
• AWS AI & Machine Learning — Cloud-based AI solutions such as Amazon SageMaker.
• IBM Watson — AI platform for companies with a focus on NLP and automation.
• OpenAI — Developer of advanced AI models such as ChatGPT and DALLE ·.

Choosing an AI platform depends on your business goals, IT architecture, and data maturity.

               How is an AI model trained?

An AI model is trained by large amounts dates to process and recognize patterns. This process consists of several steps:

1. Data collection & cleaning — AI models need qualitative, structured data.
2. Feature engineering — Identify and optimize important variables in the data.
3. Model training — The AI model learns to recognize patterns using machine learning algorithms.
4. Validation & testing — The model is tested with new data to verify that it performs well.
5. Implementation & monitoring — The model is integrated into the business processes and optimized based on new data.

An AI model is never 'finished'. It needs to be updated and updated continuously to stay accurate.

               What is a generational AI strategy?

One AI strategy generation (or generative AI strategy) focuses on using generative AI, such as ChatGPT, DALL·E, and Midjourney. These AI models can generate new content, such as text, images, code, and even videos.

An effective generative AI strategy includes:

• Identifying use cases where generative AI adds value (such as content creation, chatbots, or process automation).
• Ensuring data security and compliance with regulations such as the AI Act.
• Training and supervising employees in the use of generative AI tools.

               What is an example of AI?

This is what we describe very well in our blog: 7 AI applications for service companies.

AI is used in various ways in companies. A few practical examples:

• Chatbots & virtual assistants — AI such as ChatGPT supports customer service and internal communication.
• Predictive maintenance — AI analyses sensor data to predict when machines need maintenance.
• Credit assessment — Banks and financial institutions are using AI to assess customers' creditworthiness.
• Marketing personalization — AI analyses customer behavior and makes targeted recommendations (such as Netflix or Spotify).
• Document automation — AI helps generate and analyze documents, for example in the legal and financial sectors.

The possibilities with AI are endless, but without a strategic approach, AI remains a separate tool instead of a valuable business driver.

E-book: Applying AI to your company

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