How do you identify the truly high-value AI use cases for your company?

An AI strategy is more than a list of projects: it defines which use cases have priority, what the data and IT architecture must look like, which governance rules apply and how return on investment is measured. It connects business goals with technical capabilities and creates a decision basis for investments.

For your company, such a strategy is relevant because it reduces uncertainty. Instead of many isolated experiments, you get a structured plan that shows which projects deliver value in the short term and which need to be built up over the longer term.

The strategy also helps identify and remove organizational obstacles: missing data, team or skill gaps, required integrations and compliance needs. This way technical measures are translated into operational reality.

In short: an AI strategy creates focus, reduces risk and makes investments measurable. Without it, there's a risk of directing resources to projects that are neither scalable nor economically viable.

The time to first results depends on maturity and complexity. In many cases, initial prototypes or feasibility checks can be delivered within days to weeks, especially when we have clear, tightly scoped use cases and available data.

For substantial business results, such as process automation with demonstrable cost savings or improved customer experiences, we typically plan a timeframe of 3–9 months, including the pilot phase, validation and initial production.

Our AI PoC offering (€9,900) is explicitly designed to quickly verify technical feasibility and provide clear decision-making foundations. This helps you avoid expensive, lengthy projects without validated hypotheses.

It is important that we work in short iterations: small, fast experiments validate assumptions before larger budgets are released. This reduces time-to-value and increases decision certainty.

Our prioritization is based on a transparent scoring model that weights impact, feasibility, time-to-value and risks. Impact considers monetary benefit, efficiency gains and strategic relevance; feasibility assesses data availability, integration effort and technical hurdles.

We combine quantitative analyses (e.g., potential estimates, cost forecasts) with qualitative inputs from workshops and interviews. This produces a heatmap that quickly shows which use cases deliver immediate value and which should be established long-term.

For each prioritized idea we create a small business case with KPIs, effort estimates and alternative scenarios. This enables decision-makers to steer investments deliberately and, if needed, reallocate resources.

Prioritization is not static: we renew the assessment at regular intervals and after relevant learning loops from pilots to respond to new insights.

Data is the foundation of every AI initiative. Data readiness includes aspects such as quality, consistency, completeness, latency and access. Without clean and suitable data, models are not reliable and production rollouts are risky.

Our Data Foundations Assessment analyzes existing data sources, identifies gaps and quantifies the effort required for preparation, enrichment and governance. We prioritize measures that provide the largest leverage for prioritized use cases.

With poor data we follow pragmatic approaches: data-driven cleansing pipelines, complementary labeling initiatives, synthetic data generation or switching to business-friendly metrics. Sometimes the better route is to adjust processes so that relevant data is produced in the first place.

It is important that data remediation is not postponed indefinitely. We propose concrete milestones to improve data quality step by step while simultaneously running pilots that can deliver valid results with minimal data requirements.

Our governance models are pragmatic and risk-based. They define responsibility, decision rights, review processes and policies for data, models and deployment. The goal is not bureaucracy but reliable standards that reduce risks and enable scaling.

Typical elements of a governance framework are roles (model owner, data steward), evaluation and approval processes, monitoring requirements, explainability standards and audit mechanisms. We incorporate compliance requirements such as GDPR, industry-specific regulations and internal policies.

Governance is operationalized pragmatically: checklists for model approvals, template processes for risk assessments and dashboards for continuous monitoring. This ensures rules are practiced, not just documented.

We support the implementation of these governance elements and train responsible teams so that compliance is sustainably ensured without blocking innovation speed.

The co-preneur mentality means we take on outcomes like co-founders, not like external observers. That means: we work in the P&L team, are accountable for implementation and success and remain involved until real results are achieved.

Unlike classic consulting, we don't deliver abstract roadmaps that wither unattended. We build prototypes, validate business cases and design implementation plans that are operable. Speed and result orientation are central.

Technically we bring engineering capacity so that the handover to internal teams is pragmatic. We ensure knowledge transfer, documentation and enablement so your organization can operate the solutions long-term.

This approach reduces the risk of the implementation gap — the phase where many consulting projects fail — and ensures that strategy leads directly to measurable results.

Costs vary greatly depending on scope, maturity and number of prioritized use cases. An initial readiness assessment and use case discovery are comparatively inexpensive and provide the decision basis. Our AI PoC offering at €9,900 is an example of a standardized entry point to test technical feasibility.

For developing and scaling production solutions, budget ranges depend on infrastructure, data effort, team size and integration needs. Smaller pilots can be implemented in the mid five-figure range, while enterprise-wide rollouts can reach the six- to seven-figure range.

It is important to plan budgets iteratively: start with clear, small investments for quick validation and increase funding only after a proven business case. This minimizes total expenditure and maximizes ROI.

We help you model budget proposals and provide transparent effort estimates and scenario analyses so investment decisions can be made on a solid basis.