AI & Data Strategy
We design data and AI strategies through well-defined use cases that transform how your organization operates
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Why it matters
Most AI initiatives fail not because the technology doesn’t work – but because the strategy does.
AI & Data Strategy helps you define what matters, what’s feasible, and what delivers measurable value.
It bridges the gap between ambition and reality – making your organization ready to lead, not follow.
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What you’ll gain
- Strategic clarity around AI and data initiatives
- Prioritized use cases with business impact
- Readiness score: data, systems, people, and processes
- Phased roadmap with measurable milestones
- Compliance with EU AI Act, GDPR and ISO standards
- Alignment between business and tech teams
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Our approach
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Vision and strategic framing.
We define how AI and data can deliver tangible value aligned with your goals.
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Use case identification and prioritization.
We target initiatives with high business relevance and impact.
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Capability assessment.
We evaluate the current state of data, systems and internal processes.
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Strategic roadmap.
A phased implementation plan that balances quick wins with long-term outcomes.
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Regulatory and ethical alignment.
Ensuring compliance and responsible innovation from the start.
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When AI & Data Strategy is the right step
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You are exploring AI but lack a clear strategic path
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You need to align technology investments with business value
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You need to align with the EU AI Act or upcoming regulatory frameworks - ensuring responsible AI use, proper risk management and audit-ready documentation.
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You have scattered initiatives that require structure and prioritization
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You aim to unify business and tech teams under one shared vision
From Vision to Results
Scenarios we transformed into successful change
AI isn’t just a trend - it’s a turning point.
Let’s make sure your strategy reflects that.
Optimizing Demand and Product Scheduling
Sector: Construction Industry / Paving Stone Manufacturing
Challenge:
The client was producing over 400 paving stone models in various colors and shapes, but lacked predictive insights into seasonal demand.
The warehouse was overloaded with slow-moving inventory, while popular products were often delayed in delivery.
Our Approach:
Through an AI & Data Strategy, we analyzed historical order data, seasonality, weather patterns, and regional demand.
We developed a predictive model to classify products into three categories: fast-moving, seasonal, and static.
We optimized the warehouse logic and proposed an AI-driven production scheduling system based on demand forecasting.
Outcome:
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18% reduction in warehouse inventory
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25% faster delivery of high-demand products
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Automated production shifts introduced based on demand prediction
Boosting Sales Through AI Analytics
Sector: Retail / Fashion & Accessories
Challenge:
A company with multiple retail locations lacked visibility into what influenced the performance of its sales teams.
There were major discrepancies in results, and marketing campaigns were launched blindly, without data insight.
Our Approach:
Through a series of AI workshops, we identified which data could support analysis of customer behavior and campaign effectiveness.
We integrated data from POS systems, the CRM database, and online behavior.
A predictive model for purchase intent was developed, along with a KPI dashboard to monitor activity by location.
Outcome:
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30% increase in campaign conversion rates
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Two of the lowest-performing teams moved into the top 3 after targeted interventions
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Automated cross-sell and upsell recommendations per client
AI for Quality Control in Manufacturing
Sector: Metal Components Production / Automotive Industry
Challenge:
Quality issues were detected only at the end of the production process, leading to costly returns and delivery delays.
Inspection was manual and relied on subjective human judgment.
Our Approach:
As part of an AI & Data Strategy, we defined key quality parameters that could be monitored using sensors.
We proposed implementing visual inspection with AI-powered defect recognition models.
We worked on data preparation, model training strategy, and data governance to ensure auditability and compliance with the EU AI Act.
Outcome:
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70% reduction in defects reaching final inspection
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50% faster detection of recurring issues
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Infrastructure in place for further quality automation