Developing an AI strategy

Basics

What is an AI strategy?

An AI strategy is a structured plan that defines how a company uses artificial intelligence to achieve defined business goals. It covers goal definition, technology selection, data management and organisational change.

Distinction from the IT strategy

The AI strategy is not an appendix to the IT strategy. It addresses specific questions such as model selection, data quality and ethical guardrails that do not appear in classic IT strategies.

Vision and target picture

The AI vision describes what role artificial intelligence should play in the company in the long term. It sets the direction and forms the basis for all further decisions.

Use-case portfolio

The use-case portfolio contains concrete use cases, prioritised by business value and feasibility. It ensures that AI is used where the greatest benefit arises.

Data and infrastructure plan

The infrastructure plan defines requirements for data quality, storage and computing capacity. It clarifies whether cloud solutions, on-premise systems or hybrid approaches are used.

Organisational model

The organisational model defines roles, responsibilities and the necessary skills building. It determines whether AI is controlled centrally or anchored decentrally in the departments.

Value contribution

Why does a company need an AI strategy?

Without an AI strategy AI projects remain isolated experiments without measurable business value. A strategy ensures that AI initiatives contribute to the company goals and that resources are used efficiently.

Risks without a strategy

Companies without an AI strategy often invest in pilot projects that are never scaled. The result: high costs without lasting benefit.

In addition, without a strategic framework there is no basis for data protection and ethical guardrails. AI projects then quickly come into conflict with regulatory requirements such as the GDPR or the EU AI Act.

Prioritisation of AI projects

With a strategy, AI projects are selected by business value and feasibility. That prevents resources from flowing into ventures that make no measurable contribution.

Resource efficiency

Budget, data and staff are used where they offer the greatest leverage. Without strategic control, investments are spread across too many individual initiatives.

Compliance from the start

Ethical and regulatory requirements are integrated into the planning from the outset. That reduces the risk of expensive rework on data protection or the EU AI Act.

Approach

Which steps lead to an AI strategy?

Developing an AI strategy follows a structured process of analysis, goal definition, use-case selection, roadmap creation and piloting. The exact order depends on the company's AI maturity.

Analyse the starting point

The first step is an honest inventory of the current AI maturity. That includes existing data sources, technical infrastructure and the skills of the employees.

Define goals and expectations

AI goals must be concrete and measurable. “Increase efficiency” is not enough – better: “Reduce the processing time of customer enquiries by 40%”.

Identify and prioritise use cases

The analysis yields potential use cases. These are evaluated and prioritised by business value, feasibility and data availability.

Create a roadmap

The AI roadmap defines milestones, responsibilities and budgets. It starts with quick wins and scales step by step to more complex projects.

Start pilot projects

A pilot project tests the selected use case under real conditions. Results flow back into the strategy and sharpen the next steps.

Use cases

How do you find the right AI use cases?

The right AI use cases come from the intersection of high business value, technical feasibility and available data. Not every process benefits from AI – the selection decides the success of the strategy.

Evaluate business value

Every potential use case is evaluated by what measurable benefit it brings to the company. Only use cases with a clear value contribution make it onto the roadmap.

Check data availability

Without sufficient data quality every use case fails. Before implementation it has to be checked whether the necessary data is available in structured and accessible form.

Assess technical feasibility

The technical feasibility clarifies whether implementation is realistic with the available means. That includes existing infrastructure, available AI models and the know-how in the team.

Typical entry-level use cases

Many companies start with document processing, customer communication or internal knowledge search. These areas often offer high data quality and quickly visible results.

Another common entry point is the automation of recurring tasks in accounting, HR or logistics. Here the ROI can be quantified particularly clearly.

Data basis

What role does the data strategy play?

The data strategy is the foundation of every AI strategy. Without structured, accessible and high-quality data, even the best AI models remain ineffective.

Ensure data quality

AI models deliver results only as good as the data they work with. Data cleansing, standardisation and ongoing quality control are mandatory.

Establish data governance

Data governance regulates who may access which data, how data is stored and which data protection requirements apply. In Germany the GDPR is the central regulatory framework.

In addition, the EU AI Act has defined requirements for high-risk AI systems since 2024. Companies have to check whether their planned use cases fall under this regulation.

Stumbling blocks

Which obstacles endanger an AI strategy?

The most common obstacles are poor data quality, a lack of AI knowledge at management level and a strategy that is not aligned with the business goals. Recognising these risks early prevents expensive wrong turns.

Missing data basis

Many companies underestimate the effort for data preparation. Without clean, structured data, AI projects fail as early as the pilot phase.

Lack of understanding at C-level

If management does not understand AI, the necessary support for budget, resources and organisational change is missing. AI literacy at management level is a prerequisite for the success of the strategy.

External AI consulting can help close this knowledge gap and develop the strategy together with management.

Strategy without an implementation plan

An AI strategy that only describes goals but defines no concrete measures remains ineffective. A roadmap and clearly assigned responsibilities are mandatory.

Measuring impact

How do you measure the success of an AI strategy?

The success of an AI strategy is measured by concrete KPIs that are defined before the project starts. Typical metrics are time savings, cost reduction, quality improvement and adoption rate.

Define KPIs

Every AI use case needs its own success metrics. These have to be measurable, scheduled and tied to business goals.

Process KPIs

Process KPIs measure the operational impact of AI on workflows. Typical metrics are processing time, error rate and throughput.

Financial KPIs

Financial KPIs quantify the economic effect of an AI initiative. They include cost savings, revenue impact and return on investment.

Adoption KPIs

Adoption KPIs show whether employees actually use the AI solution. Relevant metrics are usage rate, employee satisfaction and training progress.

Evaluate and adjust regularly

AI strategies are not static documents. Quarterly reviews check whether the set goals are being met and whether new technologies or market changes require an adjustment.

FAQ

Frequently asked questions about AI strategy