No more “proof of concept”: Why outcome-based AI models are revolutionizing the IT landscape

The end of naivety: Why we need to completely recalculate the economic viability of artificial intelligence

While Silicon Valley is experiencing a gold rush and billions in venture capital are flowing into generative AI, disillusionment is spreading in the boardrooms of European companies. The discrepancy is alarming: on the one hand, there is the revolutionary promise of the technology; on the other, a balance sheet that can hardly be justified using conventional methods. Many companies are finding that their expensive AI initiatives, while technically impressive, are economically disappointing.

The problem, however, lies not in the technology itself, but in how we measure and manage its value. For decades, executives have learned to calculate IT investments such as SAP implementations or CRM systems—deterministic projects with a clear beginning, end, and definable benefits. But AI follows different rules: it is volatile, probabilistic, and dynamically evolving. Anyone attempting to navigate this new world with the old maps of traditional IT procurement risks sinking massive budgets into the "sunk cost trap" without ever seeing measurable returns.

This situation is particularly critical for German SMEs and European corporations. Wedged between the innovation-driven capitalist power of the USA and China's state-directed scaling, Europe risks falling behind. The answer, however, cannot be to blindly invest more money. Instead, a radical paradigm shift is needed: away from paying for infrastructure and licenses, and towards rewarding actual results.

The following article analyzes the structural deficiencies of traditional investment models, uncovers the hidden cost drivers of AI projects, and outlines a way out that minimizes risk and guarantees value creation from day one. It is a guide for decision-makers who want to understand AI not as a technological toy, but as a profitable competitive advantage.

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• Unframe.AI: Enterprise AI ROI: Why Most Calculations Are Wrong

Why traditional investment models in Europe are doomed to failure and how a radical realignment can secure access to global markets

The current discrepancy between massive investments in artificial intelligence and the real-world returns it generates represents one of the most pressing problems for business leaders worldwide. While US private equity and venture capital firms pumped over $100 billion into the sector in 2024 alone, European companies—particularly German SMEs—face a sobering reality. A large proportion of ROI calculations for enterprise AI are proving to be flawed. This isn't due to a lack of mathematical rigor, but rather to fundamentally incorrect assumptions. The technological infrastructure and the financial models built upon it, developed over decades for deterministic IT systems like ERP or CRM, are collapsing under the volatility and probabilistic nature of modern AI systems. Anyone still trying to manage generative AI with the same KPIs as an SAP implementation is essentially navigating an ocean with a road map.

The structural incompatibility of classic IT metrics

The core problem with traditional investment calculations lies in misunderstanding the nature of AI projects. Four dynamics fundamentally distinguish these investments from conventional software implementation, leading to standard ROI models systematically producing inaccurate forecasts.

First, there is a serious timeline problem. The classic ROI assumes a defined implementation phase followed by a phase of measurable returns. However, AI projects rarely behave linearly. A project planned as a six-month pilot often evolves into a fourteen-month experimental phase. Production readiness, which was supposedly only weeks away, remains a theoretical goal even a year later. While the denominator in the ROI equation steadily increases due to ongoing costs, the numerator – the return – remains at zero.

Secondly, AI projects are subject to extreme variability in scope. While traditional IT projects often follow rigid specifications, AI use cases evolve dynamically. A document processing system might transform into a knowledge retrieval platform during development, only to be replaced by an agent-based workflow solution shortly before rollout. Since the technological foundations—models, frameworks, and tools—change with a half-life of just a few months, solutions must be continuously adapted to avoid becoming obsolete upon deployment.

Third, the attribution problem presents finance departments with seemingly insurmountable challenges. Even if an AI system generates value, isolating that value is complex. Is the increase in revenue attributable to the new AI recommendation engine, the revamped sales team, or simply to favorable economic conditions? Unlike deterministic software, where causality is often clear, with AI, one frequently measures only a contribution to an outcome, not its sole cause.

Fourth, the sunk cost trap often leads to irrational decisions. Most enterprise AI projects require significant upfront investments: infrastructure provisioning, data cleansing, model training, and integration. Added to this are management costs for AI observability, since models, unlike static software, are subject to performance degradation, known as drift, and must be continuously monitored. The point at which it can be validated whether the investment is worthwhile is often so late in the project that the majority of the budget has already been irretrievably spent.

The global context and Europe's specific locational disadvantage

These inherent risks encounter a particularly fragile ecosystem in Europe. While US companies are often backed by risk-tolerant venture capital and cultivate a "fail fast" culture, the European market operates in an environment of high risk aversion and strict regulation. Although the European Union's AI Act provides legal certainty, it imposes significant compliance costs on small and medium-sized enterprises (SMEs). Estimates suggest that compliance testing for a single high-risk AI system can cost up to €400,000 if no established quality management systems are in place.

This is leading to a dangerous investment gap. US investments in AI far exceed European ones. China, in turn, is using state-directed integration to force economies of scale in industry. Germany and Europe risk being caught in a sandwich position: technologically dependent on US models and under price pressure from Chinese efficiency. For European C-level executives, this means that AI projects must be not only profitable but also strategically vital. Yet it is precisely Germany's Mittelstand, the backbone of the European economy, that is hesitant. Only about a third of large companies and an even smaller fraction of SMEs have AI in productive use. The fear of incalculable costs and unclear benefits is stifling innovation.

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From abstract promise to measurable reality

To break this deadlock, the business case for AI needs to be radically rethought. Successful organizations don't start by asking about the technology, but about the outcome. The first question must be: What specific business outcome will this AI enable? Vague goals like "increased efficiency" or "promoting innovation" are worthless in this context. A robust business case requires precise metrics that can be tracked weekly on a dashboard.

Good examples of this are concrete and verifiable: reducing contract review time from four hours to twenty minutes, increasing the first-contact resolution rate in customer service from 62 percent to 78 percent, or reducing manual data entry for loan applications by 80 percent. If a goal cannot be formulated in the language of a department head, there is no business case.

The second crucial question concerns validation: How do we know if it works? Traditional models answer this at the end of the project—often after eighteen months. However, AI projects require continuous validation. What do we need to see in week two to confirm the course? What decision point exists in month three where the project can be stopped if indicators are lacking? The best investments are structured to quickly prove their value or fail before significant capital is destroyed.

The invisible capital destroyers in the cost structure

Even if the objective is sound, many calculations fail due to hidden costs that are often ignored in the initial phase. Data preparation consumes around 60 percent of the time and budget in most projects. This involves not only technical cleaning, but also governance, normalization, and the particularly complex legal approval of datasets in Europe.

Another underestimated factor is integration complexity. An AI that functions in an isolated demo environment has little in common with a system embedded in existing security architectures and workflows. This "last mile" of integration often costs more than the AI ​​component itself and is where most projects stall. Add to that the ongoing operating costs. Models require constant monitoring for drift and regular retraining when data patterns change.

Finally, the opportunity cost of time is almost never calculated. Every month an AI project takes to deliver value is a month of lost value creation. A project with an 18-month duration and a 200 percent ROI can be economically worse than a project with a six-week duration and an 80 percent ROI, because the latter generates positive cash flow for 16 months longer. The organizations with the best ROI are not necessarily those with the highest returns, but rather those that achieve measurable value most quickly with the least capital investment.

Beyond CapEx: The paradigm shift towards results-oriented financing models

Given these risks and European reluctance, new pricing and business models that shift risk from buyer to supplier are gaining traction. Providers like Unframe and other progressive players in the market are establishing principles based on pre-commitment validation. This outcome-based pricing approach could be the key to overcoming the investment freeze in Europe.

Instead of buying infrastructure in advance (CapEx) or paying for licenses per user (seat-based pricing) that often go unused, companies here pay for the results achieved. Costs scale with the value captured, not with the resources consumed. This directly addresses the attribution problem and forces vendors to sell only solutions that actually work.

In this model, every engagement begins with a defined use case and a measurable outcome. The customer sees the AI ​​working on their own data and in their environment before making a significant investment. There are no 18-month project durations with the hope of a return on investment at the end. Value creation is prioritized. Furthermore, the massive upfront costs for infrastructure are often eliminated, as modern platforms handle the burden of data preparation and model deployment. This eliminates those hidden costs that can otherwise consume up to 80 percent of the budget.

Another advantage of this model is the move away from user-based licensing models, which in the past penalized widespread adoption. If every additional user incurs costs, the use of the technology is artificially limited. Outcome-oriented models, on the other hand, encourage widespread use, as more users generally lead to more results and thus greater added value.

Strategic implications for European leadership

For decision-makers in Europe, this means that the era of experimental "proof of concepts" without a clear path to value creation is over. Economic reality demands a shift away from technological fascination towards almost surgical precision in defining business outcomes. Companies should not use workshops and pilot phases to learn what AI can do, but rather to isolate the most valuable use case and validate its economic impact.

It is advisable to seek partnerships with providers who are willing to take risks and be measured by results. However, this also requires a change of mindset on the customer side: away from purchasing “IT hours” or “licenses,” and toward entering into value-creation partnerships. In a world where the US and China dominate through massive capital allocation, efficiency in capital deployment is Europe's only chance. The key is not to spend more money, but to invest that money in models that pay for themselves before the bill is due. Anyone still relying on 18-month forecasts has already lost the game. True competitiveness arises where value creation is not promised, but proven from day one.

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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