AI efficiency without an AI strategy as a prerequisite? Why companies shouldn't blindly rely on AI

Rethinking AI: AI is not a tool – moving from installing software to strategy

The reality in German companies is sobering: Although 63 percent of companies are already using AI, only 6 percent have actually developed a well-thought-out AI strategy. This discrepancy explains why many AI initiatives fizzle out in pilot projects or are discontinued after a short period of time. The reason rarely lies in the technology itself, but rather in a lack of strategic preparation.

Companies often treat AI like a regular software implementation, but this is a fatal misconception. AI is more than a tool—it's a paradigm shift that transforms processes, roles, decision-making, and the entire work culture. A Rand study shows that in 80 percent of cases, AI implementations fail not because of the technology, but because of a lack of strategic preparation, a lack of cultural change, and inadequate change management.

Why do companies build the roof before the foundation?

This approach—building the roof before the foundation—is manifesting itself concretely in several areas: First, seven out of ten employees use AI tools without their company's approval. This so-called shadow AI has increased by as much as 250 percent in some industries. Second, its unstructured use leads to significant security risks.

The consequences are already visible: Unsecured digital "hubs" are often used through which AI tools communicate with each other and exchange data. If these are unprotected, hackers can intercept all data traffic. Researchers identified a critical vulnerability in such an interface with an extremely high risk score of 9.6 (out of 10), which allows attackers to remotely execute their own malicious code. Experts like Docker warn of a "security nightmare" that exposes companies to the risk of data loss, the takeover of entire systems, and attacks on the digital supply chain.

How dangerous are prompt injection attacks?

Prompt injection attacks represent a particularly perfidious form of manipulation. They can occur both directly and indirectly. In indirect attacks, attackers hide malicious instructions in emails, PDF documents, or on websites. For example, white text on a white background in PDFs is invisible to the user, but is processed by AI and can trick it into performing unwanted actions.

A scientific study documented over 208,095 unique attack attempts by 839 participants in a realistic email scenario. These attacks can, at best, lead to academic papers performing better in chatbot evaluations, but at worst, can reveal trade secrets.

What are the risks of shadow AI?

Shadow AI refers to the unauthorized use of AI tools by employees without approval from IT or data governance teams. This practice poses several critical risks: data breaches due to uncontrolled data processing, inconsistent decision-making due to disparate tools, and regulatory compliance violations.

A typical scenario: A customer service representative uses an unauthorized chatbot to answer customer inquiries instead of consulting official company resources. This can lead to incorrect information, misunderstandings with customers, and security risks if sensitive company data is embedded in the inquiry.

What are the risks to trade secrets?

The unstructured use of AI endangers trade secrets on multiple levels. Direct input of sensitive information by employees into AI systems can result in it remaining in the system permanently and being used for training. Inferences through pattern recognition enable AI systems to reconstruct confidential content from seemingly harmless data.

This becomes particularly critical when AI systems have been trained directly with internal company data. This poses the risk of "data leakage" – the unintentional disclosure of trade secrets. Legally, this means that if trade secrets are entered into AI systems, this is considered impermissible disclosure, which can have serious consequences, including the loss of protected status.

Why are technical solutions alone not enough?

The security vulnerabilities go beyond purely technical aspects. Unprotected digital interfaces without user authentication or data encryption create significant security risks. Researchers found 492 such unprotected systems that allow attackers direct access to sensitive corporate data. A successful attack can lead to a complete system takeover.

At the same time, many companies lack fundamental governance structures. Forty percent of technology leaders consider their existing governance measures insufficient to ensure security and compliance in AI projects. Fifty-three percent of enterprise architects are concerned about data breaches and security risks.

How should an AI strategy be developed?

A successful AI strategy begins with clear organizational structures. The AI ​​Governance Framework (DAGF) developed by Databricks comprises 43 key areas of action, divided into five pillars: organizational integration with clear alignment between AI goals and strategic corporate guidelines; legal compliance to ensure regulatory compliance; risk management for the systematic assessment and management of AI risks; ethical responsibility as the basis for trustworthy AI use; and technical governance for secure and controlled implementation.

The strategy must be interdisciplinary. An AI governance framework requires the interaction of various departments: IT security, data protection, compliance, risk management, and other departments must work together in a coordinated manner. The compliance function can act as an advisory, coordinating, and consolidating authority.

What legal framework must be observed?

With the AI ​​Act and the still-valid GDPR, companies face a dense web of legal obligations. The AI ​​Regulation follows a risk-based approach: high-risk applications are subject to strict requirements, and critical systems are already prohibited. At the same time, the GDPR remains fully applicable when personal data is processed.

With its guidelines of June 2025, the German Data Protection Conference (DSK) has created a practical framework for the GDPR-compliant use of AI systems. These guidelines specify the GDPR's basic principles for AI applications and, among other things, call for technical and organizational measures (TOMs) that scale with the risk of the respective AI system.

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How can data protection risks be minimized?

Privacy by design and privacy by default must be integrated into AI systems from the outset. Companies must ensure that the most data-efficient and privacy-friendly settings are always selected. Regular audits of AI systems are necessary to ensure data protection-compliant operation.

A data protection impact assessment (DPIA) is often mandatory for AI systems, especially when they create "high risks" for data subjects, for example through profiling or automated decision-making. The challenge: With self-learning AI systems, the algorithm itself is often no longer comprehensible to its developers—the so-called "black box problem."

What are the concrete steps for implementation?

Successful AI implementation requires a structured approach in three phases: Phase 1 (months 1-3): Preparation and strategy development with goal definition, risk analysis, and establishment of the governance structure. Phase 2 (months 4-9): Pilot project phase with controlled testing of selected use cases and continuous optimization. Phase 3 (months 10-18): Scaling and consolidation with company-wide rollout and established governance processes.

The selection of initial pilot projects is critical. These should focus on areas with high potential and low risk, such as the automation of repetitive tasks in accounting or the optimization of forecasts in inventory management. Clear success criteria and meticulous performance measurement are essential.

How do you successfully engage employees?

Employee training is crucial for AI success. 69 percent of companies see a shortage of AI specialists as a hurdle. This problem can be counteracted through targeted training of existing employees. Interdisciplinary teams that bring together AI experts with domain specialists ensure that AI solutions are developed with practical relevance.

An open culture of error is necessary to reduce fears and encourage employees to actively use and provide feedback. Regular communication about the benefits of AI helps promote acceptance and reduce resistance. At the same time, clear guidelines must be communicated regarding which AI tools may and may not be used.

What role does continuous monitoring play?

AI projects are not a one-time affair; they require ongoing support. Feedback loops must be established to continuously improve AI models. The performance of AI systems must be regularly analyzed and adapted to changing business conditions.

Documenting all AI activities is necessary for both legal compliance and further development. Best practices and learnings must be documented to accelerate rollout to other areas of the company. This requires flexibility – the strategy must be adaptable as needed.

How can the investment be justified?

Willingness to invest in AI is growing continuously, but companies expect measurable results. According to an IW study, AI could triple annual productivity growth in Germany in the long term and save around 3.9 billion working hours by 2030. However, it requires strategic, not blind, deployment.

Clear KPIs and measurable goals should be defined from the outset. These can include cost reduction, revenue growth, or improved customer experience. Successful pilot projects should be gradually scaled to other business areas, leveraging the experience gained from the initial implementations.

What can companies implement immediately?

Immediate measures include the creation of an AI policy that clearly regulates which data may be entered into which AI systems. Non-disclosure agreements for employees who work with AI tools are legally required. Technical security measures such as encryption and strong passwords must be implemented.

Access management should limit the number of employees working with trade secrets using AI to a necessary minimum. Regular training on the safe use of AI tools must be established. System selection must be carefully considered – cloud-based services should be avoided if multiple companies have access to the same system.

Why is now the right time to act?

The gap between AI pioneers and hesitant companies is widening. Companies that act strategically now can secure decisive competitive advantages. The regulatory framework is becoming increasingly clear – with the 2025 DSK Guidelines and the AI ​​Act, practical frameworks are available.

At the same time, the federal government's funding measures, such as AI real-world labs, gigafactory programs, and the innovation-friendly implementation of the AI ​​Act, will quickly be exhausted. Early action can secure decisive competitive advantages here. Waiting is not an option – reality already clearly demonstrates the risks associated with unstructured AI use.

Strategy before technology

Technology alone doesn't guarantee a successful AI transformation. Without strategic preparation, even the most advanced AI tools will remain ineffective or even pose a security risk. Current developments with shadow AI, security vulnerabilities, and data breaches clearly demonstrate that companies must do their homework before embracing AI.

A well-thought-out AI strategy

encompasses organizational structures, legal compliance, risk management, ethical responsibility, and technical governance. It requires interdisciplinary collaboration and continuous development. Companies that lay this foundation can use AI safely and successfully. Those that continue to build the roof before the foundation not only risk the loss of trade secrets but also jeopardize their entire digital transformation.

The first step is always to stop: Analyze your current AI usage, identify shadow AI, and develop a strategic plan. Only then should you press the start button for controlled AI implementation. Investing in a solid AI strategy will pay off in the long run through safe, efficient, and legally compliant AI usage.

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