Why an AI Strategy for 2025 Is Not a Luxury
2025 is no longer a distant year — it's the operational window in which companies decide whether to use artificial intelligence to lead markets or be overtaken by them. We see many teams fascinated by technology but working without systematic prioritization: many ideas, few results. From our perspective, the risk is not choosing the wrong technology, but choosing the wrong projects.
That's why leaders need a practical prioritization framework that bases decisions on concrete criteria and brings projects to productive status in weeks rather than months. In this post we present such a framework, explain the five evaluation axes and provide heatmaps, use-case portfolios and decision trees — supported by real examples from our projects with Mercedes Benz, STIHL and Internetstores.
Why Prioritization Is Critical
Companies have limited resources: data experts, development capacity and management attention. A disordered roadmap leads to wasted budgets, siloed projects and disappointing outcomes. We advocate for focused investments that deliver fast learning cycles and measurable business impact.
Prioritization protects against two failure modes: either too much effort for marginal effects or too little focus on high-leverage opportunities. A structured framework creates clarity and enables reproducible decision-making — even when management changes.
The Prioritization Framework: Five Evaluation Axes
Our framework evaluates AI initiatives along five core criteria: Business Impact, Data Availability, Political Feasibility, Integration Complexity and Process Change Behavior. Each axis is scaled (e.g., 1–5), allowing comparable evaluation across different use cases.
1. Business Impact
Here we consider direct financial benefit, time savings, risk reduction and strategic relevance. A high score means the project influences clearly measurable KPIs (e.g., revenue, costs, time) within a defined timeframe.
Assessment: Metrics such as expected ROI, scalability and dependence on manual processes help quantify impact.
2. Data Availability
Data is the fuel of AI. We distinguish between qualitative and quantitative access: Are historical, clean and structured data available? Are there data protection or compliance blockers?
High data availability dramatically reduces development time. If data are missing, a project becomes a data project — which is often a completely different undertaking.
3. Political Feasibility
This axis covers stakeholder acceptance, compliance risks and the political willingness to delegate decisions. Some use cases are technically trivial but fail due to internal resistance or regulatory constraints.
Assessment: We measure management supporters, legal hurdles and the required change communication.
4. Integration Complexity
A model is only as good as its integration into existing systems. Integration effort includes API requirements, legacy systems, authentication and ongoing operations (MLOps).
High integration complexity increases time-to-value and operational risk — which is why this factor is crucial for prioritization.
5. Process Change Behavior
Even good models only deliver value if people and processes adapt. We assess how much processes need to change and how transformation-ready the organization is.
Some initiatives require only a small UI tweak; others need task and role shifts. That affects adoption and therefore success.
Heatmaps and Use-Case Portfolio Visualize Priorities
After use cases are evaluated along the five axes, we aggregate scores and visualize results in heatmaps and a use-case portfolio. This makes priorities immediately visible: where are the quick wins, where the strategic cornerstones?
Heatmap logic: On the X-axis we place combined technical feasibility (data + integration complexity), on the Y-axis the business impact. The quadrants yield:
- Top right: High Impact / High Feasibility — immediate priority
- Top left: High Impact / Low Feasibility — strategic projects (possibly requiring data engineering investment)
- Bottom right: Low Impact / High Feasibility — quick automation or efficiency gains
- Bottom left: Low Impact / Low Feasibility — avoid or review later
Example portfolio (simplified):
- Mercedes Benz recruiting chatbot: top right — high impact, good data foundation (candidate conversations), low integration hurdle (messaging API).
- STIHL chainsaw simulator: top left — high strategic impact, but demanding in product simulation and UX integration.
- Eberspächer noise reduction (operations analytics): top right — direct operational benefit, data available via sensors.
- Internetstores ReCamp personalization (e-commerce): bottom right — high feasibility, moderate impact per use case, but scalable.
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Decision Tree: How We Select MVPs
Heatmaps help set priorities. To select an MVP we need a clear decision tree so product teams don't get stuck in analysis paralysis. Our decision tree is pragmatic and built in three decision stages:
- Stage 1 — Impact Criterion: Is the expected business impact above the defined threshold? If no → Low Priority. If yes → proceed to Stage 2.
- Stage 2 — Data & Feasibility: Are the necessary data available or attainable in the short term? Is integration complexity limited? If yes → MVP possible. If partial → plan a PoC for data/integration (e.g., AI PoC €9,900).
- Stage 3 — Adoption & Governance: Is there stakeholder support and clear KPIs? If yes → start MVP with a co-preneur team. If no → run stakeholder workshops and a change plan before the MVP.
This decision tree reduces subjective assessments and creates a repeatable logic for portfolio decisions.
Concrete Examples: Manufacturing, HR, Marketing, Operations
Practice beats theory. Here we describe how concrete projects delivered productive modules in just a few weeks — based on our co-preneur way of working.
Manufacturing — STIHL: Simulation & Service Tools
At STIHL we worked on several solutions, from saw training to simulator tools. The key to rapid success was combining a clear business case with an existing data foundation (product data, usage profiles). We prioritized use cases that promised high product differentiation while remaining feasible within a manageable integration scope.
Result: Within a few weeks an MVP of the training module with a basic simulation was live, collecting customer feedback and serving as the basis for the product roadmap.
HR — Mercedes Benz: Recruiting Chatbot
The recruiting project at Mercedes Benz shows how a well-scoped use case can quickly deliver impact. The chatbot automated initial candidate communication, collected applicant information and performed automated pre-qualification.
Success factors were clearly defined KPIs (time-to-response, qualified leads) and the available data foundation (candidate conversations). Within weeks a productive bot was live and measurably reduced time-to-hire.
Marketing & E-Commerce — Internetstores
For Internetstores we worked on personalization use cases and product models like MEETSE. Through targeted prioritization we identified personalized recommendations and A/B tests as quick levers with clear KPIs (conversion, cart value).
The modules were introduced in iterative releases: an MVP with simple rules and an embeddings-based product recommendation, later expanded into a scalable service.
Operations — Eberspächer & FMG
In manufacturing we worked with Eberspächer on AI-supported noise reduction — a classic example of strong operational impact. Using sensor data and ML models we were able to recommend preventive measures and reduce downtime.
Similarly, for FMG we built an AI tool for document search: fast, precise results for consultant teams who previously spent hours on manual searches.
Implementation in Weeks: Roadmap for a Productive MVP
Our co-preneur mentality accelerates decisions. A typical 4-week plan looks like this:
- Week 0–1: Scoping & Metrics — use-case workshop, scoring using the framework, definition of KPIs and data mapping.
- Week 1–2: Proof of Concept — minimal prototype with real data, first validation of modeling approaches. (If uncertain: our standard AI PoC offering)
- Week 2–3: User Integration — embedding in a small user group, feedback loops, measuring metrics.
- Week 3–4: Production Planning — architecture, security and compliance checks, scaling plan and rollout roadmap.
In this timeframe speed is crucial, but not at the expense of robustness. We deliver working modules, document decisions and provide a clear production plan.
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Governance, KPIs and Scaling
An MVP is only the beginning. Sustainable success requires governance, MLOps and clear KPIs. We recommend:
- KPI Board: a few clear metrics (e.g., ROI, accuracy, adoption rate)
- Responsible Owner: product ownership instead of “project responsibility”
- MLOps Pipeline: automated deployments, monitoring and retraining plans
- Security & Compliance: data protection review and clear SLA agreements
Reruption supports teams not only during prototyping but also in building and handing over to operations teams — precisely where many projects otherwise fail.
Practical Tips for Leaders
To conclude, three concrete recommendations you can implement immediately:
- Run a quarterly use-case scoring: Use the 5-axis framework to make portfolio decisions based on data.
- Start with 1–2 top-right use cases: Quick wins build credibility and capital for larger initiatives.
- Use co-preneur teams for speed: We recommend leveraging external AI engineering capacity with the co-preneur methodology to bring expertise into the company quickly and deliver results in weeks rather than months.
Conclusion & Call to Action
2025 will be the year when prioritization makes the difference. A clear, practical framework — based on Business Impact, Data Availability, Political Feasibility, Integration Complexity and Process Change Behavior — enables systematic evaluation of AI initiatives and helps get MVPs productively live fast.
If you want to check which of your ideas can deliver real value in the coming weeks, we are happy to help: with our Co-Preneur way of working, concrete heatmaps and a standardized AI PoC program (€9,900) for technical feasibility proofs. Contact us — we help turn your AI strategy 2025 from roadmap into reality.
