When does artificial intelligence create real value? A guide for companies on whether to use managed AI or not.

The reality behind the AI ​​hype

The discussion about artificial intelligence in German companies has reached a turning point. While just two years ago, the technology was primarily viewed as an experimental tool, today 91 percent of German companies view AI as mission-critical for their future business model. This dramatic shift in perception is also reflected in concrete figures: Currently, 40.9 percent of companies are already using AI in their business processes – a significant increase from 27 percent last year.

Nevertheless, a crucial question remains: When does AI actually create real value, and how can this success be measured? The sobering reality shows that despite billions of dollars invested, the overwhelming majority of AI projects fail to deliver the expected return on investment. An MIT study reveals that 95 percent of generative AI pilot projects in companies fail and fail to achieve any measurable return on investment.

This discrepancy between expectation and reality highlights that the success of AI initiatives depends less on the technical performance of the models, but rather on the strategic integration into existing business processes and the ability to continuously optimize based on feedback from practice.

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Identify and measure real added value

Quantitative evaluation criteria for AI success

The added value of AI applications manifests itself at various levels, all of which require systematic measurement. The classic ROI formula forms the foundation: Return on Investment equals total benefit minus total costs, divided by total costs, multiplied by 100 percent. However, this simplistic view is insufficient for AI investments, as both costs and benefits exhibit more complex structures.

The cost side includes not only obvious expenses for licenses and hardware, but also hidden expenses for data cleansing, employee training, and ongoing system maintenance. Particularly critical are the often underestimated change management costs that arise when employees have to learn new workflows.

On the benefit side, various categories can be distinguished: Direct monetary benefits through cost savings or increased sales are the easiest to quantify. For example, one retailer achieved an ROI of 380 percent within three years through AI-assisted inventory optimization. Less obvious, but often valuable, are indirect benefits such as improved decision quality, reduced error rates, or increased customer satisfaction.

Operational key figures as indicators of success

In addition to financial metrics, operational metrics play a crucial role in evaluating the added value of AI. Process efficiency can be measured by time savings on repetitive tasks. For example, Microsoft was able to reduce manual planning processes by 50 percent and increase on-time planning by 75 percent through AI-supported supply chain optimization.

Error reduction is another key indicator. AI systems can surpass the accuracy of human decisions in many areas, which translates directly into reduced costs through fewer rework and complaints. A financial services provider achieved an ROI of 250 percent within one year through AI-based fraud detection.

The scalability of AI solutions offers a particular advantage: Once implemented, they can often be expanded to cover larger data sets or more use cases without a proportional increase in costs. These economies of scale significantly increase the long-term ROI.

Qualitative added value dimensions

Not all of the benefits of AI can be immediately quantified. The improved decision-making quality enabled by data-driven analytics can create significant long-term value, even if this value is difficult to quantify. Companies report improved strategic planning when they use AI-powered market analyses and forecasts.

Employee satisfaction can increase when AI takes over repetitive tasks, allowing employees to focus on more value-added activities. This leads to reduced turnover and increased productivity, the value of which can ultimately be quantified in monetary terms.

Innovation and competitiveness represent further qualitative dimensions. Companies that successfully use AI can develop new products and services or personalize existing offerings. These innovation effects are difficult to predict but can have transformative effects on the business model.

Managed AI as a strategic option

Definition and differentiation of Managed AI Services

Managed AI Services offer an alternative to the independent development and implementation of AI solutions. A specialized service provider assumes responsibility for the entire AI lifecycle: from initial conception through model development to continuous optimization and maintenance in production.

This approach differs fundamentally from traditional software-as-a-service offerings, as it encompasses not only the provision of ready-made AI tools but also strategic consulting, data preparation, and adaptation to specific business requirements. The managed AI provider assumes both technical and operational responsibility for the AI ​​applications.

Advantages and challenges of Managed AI

The main advantage of Managed AI is the reduction of technical complexity for the company using it. Instead of building their own AI expertise, companies can draw on the specialized know-how of the service provider. This reduces both the initial investment and the risk of implementation errors.

The flexibility and scalability of Managed AI Services allows companies to adapt their AI usage to meet their needs. This is particularly beneficial for small and medium-sized enterprises that lack the resources for extensive in-house AI departments.

However, managed AI also presents challenges. Dependence on external service providers can lead to a loss of control over critical business processes. Companies must carefully consider which AI applications they can outsource without jeopardizing their competitiveness.

Cost structures and ROI considerations for Managed AI

Managed AI services typically operate on subscription models that allow for predictable monthly or annual costs. This facilitates budget planning and reduces financial risk compared to in-house developments, which often involve unpredictable cost increases.

The ROI calculation for managed AI differs from that for in-house developments. While the initial investment is usually lower, there are ongoing operating costs. A multi-year total cost analysis often shows that managed AI services can be more cost-effective, despite higher ongoing costs, because they are implemented faster and carry lower risks.

Independence versus managed services

The autonomy debate in AI applications

The decision between independent AI development and managed services raises fundamental questions about digital sovereignty. Many German companies are skeptical about their dependence on external AI providers, especially those from the US or Asia. A recent Bitkom study shows that 78 percent of companies in Germany find their dependence on US cloud providers problematic.

These concerns are not unfounded. Cloud-based AI services pose risks related to data protection, compliance, and strategic control. At the same time, they also provide access to sophisticated AI models that would be difficult to replicate internally.

Local AI as an alternative to cloud dependency

On-premises AI implementations, where data is processed exclusively on in-house servers, offer an alternative to cloud dependency. These approaches ensure GDPR compliance and maximum control over sensitive corporate data.

The advantages of local AI include low latency, as no data transfer to external servers is required, as well as independence from external service providers and their potential failures. Local AI can be a better choice, especially for real-time applications or data-sensitive areas.

However, on-premises AI also presents challenges. The expertise required for implementation and maintenance is considerable, and the initial investment in hardware and personnel can be significant. Furthermore, scalability is often limited compared to cloud-based solutions.

Hybrid approaches as a compromise

Many companies opt for hybrid solutions that combine the advantages of both approaches. Critical and data-sensitive applications are run locally, while less critical or compute-intensive tasks are outsourced to cloud services.

This hybrid strategy allows you to maintain control over key business processes while benefiting from the performance and cost-effectiveness of cloud services. However, the complexity of the architecture increases significantly, requiring corresponding management capacities.

Managed AI Platform - Image: Xpert.Digital

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Scalability as an indicator of success

From pilot projects to company-wide implementation

The ability to scale AI applications is considered one of the most important indicators of real added value. Many companies remain stuck in the pilot phase without successfully transitioning their AI initiatives into regular operations. Only about 5 percent of pilot projects make the leap into scaled production.

Successful scaling requires more than just technical excellence. Organizational adaptations, employee training programs, and integration into existing business processes are equally critical. Companies must establish AI governance that defines standards for data quality, model validation, and risk management.

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Infrastructure requirements for scaling

Scalable AI systems require a robust IT infrastructure that can keep pace with growing data volumes and more complex requirements. Cloud-based solutions often offer advantages through their inherent scalability, while on-premises systems may require additional hardware investments.

Data architecture plays a crucial role in scalability. AI systems are only as good as the data they work with. Companies must invest in high-quality data management systems that ensure both data quality and accessibility.

Metrics for successful scaling

The success of AI scaling can be measured by various metrics. The number of use cases that have successfully transitioned from the pilot to production phase is a direct indicator. Equally important is the speed with which new AI applications can be implemented.

User acceptance within the organization is another critical factor. High adoption rates among employees demonstrate that AI solutions actually create added value and are not just technical gimmicks.

Economic scaling is reflected in the development of costs per use case or per processed data point. Successful AI implementations exhibit decreasing marginal costs because fixed costs can be spread across more applications.

Industry and size-specific success factors

AI adoption by company size

The use of AI varies significantly depending on company size. While 56 percent of large companies use AI, the figure is just 38 percent for small and medium-sized enterprises and just 31 percent for micro-enterprises. This discrepancy can be explained by different resource availability and economies of scale.

Large companies have more extensive financial, technological, and human resources, which facilitate AI investments. They also benefit more from economies of scale, as the high initial investment costs are amortized more quickly with larger production volumes.

Small businesses, on the other hand, face resource-related constraints that hamper the adoption of innovative technologies. Limited financing options, a lack of qualified personnel, and the challenge of high initial investments represent significant barriers.

Industry-specific application patterns

AI usage varies considerably across industries. In advertising and market research, 84.3 percent of companies already use AI, followed by IT service providers with 73.7 percent and the automotive industry with 70.4 percent.

These differences reflect both the affinity for digital technologies and the specific application possibilities. Industries with large amounts of data and standardized processes can often implement and benefit from AI more easily.

More traditional industries such as hospitality, food production, and textile manufacturing are still hesitant about AI adoption. This is partly due to lower levels of digitalization, but also due to a lack of awareness of relevant use cases.

Risks and obstacles to success

Technical and organizational barriers

The most common causes of AI project failure lie less in the technology itself than in organizational deficiencies. Inadequate data, lack of data availability and quality, and unclear responsibilities often lead to project stalls.

Silo structures in companies hinder successful AI implementation because they prevent holistic process thinking. AI projects require interdisciplinary collaboration between IT, business departments, and management.

Lack of transparency in benefit measurement represents another obstacle. Without clear KPIs and success criteria, progress can neither be measured nor improvements identified. This leads to dwindling management support and ultimately project termination.

Compliance and governance challenges

With the EU AI Regulation coming into force in August 2024, compliance requirements have become a critical success factor. Companies must ensure that their AI applications comply with regulatory requirements, which creates additional complexity and costs.

Establishing appropriate AI governance structures requires clear responsibilities, standards, and control mechanisms. Many companies underestimate the effort required for these organizational adjustments.

Ethical guidelines and transparency in AI decision-making are becoming increasingly important, both for compliance and for acceptance among employees and customers. Building the necessary competencies and processes requires time and resources.

Future prospects and trends

Development of the German AI market

The German AI market is experiencing significant acceleration. Companies' willingness to invest is growing continuously: 82 percent plan to increase their AI budgets in the next twelve months, more than half by at least 40 percent.

This development is driven by the growing realization that AI is no longer optional, but is becoming a prerequisite for competitiveness. 51 percent of companies now believe that companies have no future without the use of AI.

Technological developments and new fields of application

Multimodal AI systems that can process different data types such as text, images, and audio in combination are on the verge of a breakthrough in widespread use. These technologies open up new fields of application and can significantly improve existing solutions.

Automated machine learning and no-code platforms are democratizing access to AI technologies. Even companies without deep technical expertise can increasingly benefit from AI.

The integration of AI into DevOps processes, known as AIOps, is transforming the way IT operations are managed. By predicting and automating IT processes, companies can increase efficiency and reduce downtime.

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Strategic recommendations for companies

Companies should align their AI strategy with long-term value creation rather than short-term efficiency gains. Investing in data quality and organizational adjustments is often more important than selecting the best algorithms.

Developing internal AI skills remains critical, even when using managed services. Companies need to understand how AI works and which use cases are relevant to their business.

An iterative approach with small, measurable steps reduces risks and enables continuous learning. Pilot projects should be designed for scalability from the start.

Selecting the right partners, whether for managed services or consulting, often determines success or failure. Companies should look for proven expertise and industry-specific experience.

Practical implementation and measurement concepts

Development of an AI ROI framework

A structured framework for ROI measurement begins with clearly defining business objectives and translating them into measurable KPIs. This should include both leading indicators that provide early signals of success or failure and lagging indicators that measure long-term effects.

Baseline measurements prior to AI implementation are crucial for subsequent success assessment. Without precise knowledge of the initial situation, improvements cannot be quantified.

Regular reviews and adjustments to the measurement concept are necessary, as both AI systems and business requirements are continuously evolving. ROI measurement should be viewed as an iterative process, not a one-time activity.

Implementation strategies for different company types

Small and medium-sized businesses should start with clearly defined use cases that enable rapid success. Cloud-based solutions or managed services can help limit initial investments.

Large companies can launch parallel pilot projects in different areas to identify synergies and develop best practices. Establishing a central AI competency can accelerate company-wide scaling.

Regardless of the company's size, the involvement of business departments from the outset is critical. AI projects should not be viewed as purely IT initiatives, but rather as business-driven transformation projects.

Artificial intelligence has the potential to fundamentally transform German companies and create new competitive advantages. However, success depends not solely on the chosen technology, but rather on the strategic approach, organizational implementation, and continuous measurement and optimization. Managed AI services can represent a valuable option, especially for companies that want to benefit from AI quickly without building extensive internal expertise.

The decision between in-house development and external services should be made based on specific business requirements, available resources, and strategic goals. More important than the technology decision is a consistent focus on measurable business value and a willingness to continuously adapt and improve AI systems.

Konrad Wolfenstein

I would be happy to serve as your personal advisor.

contact me under Wolfenstein ∂ Xpert.digital

call me under +49 89 674 804 (Munich)

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