AI Project Management
Your AI strategy deserves expert execution - with business focus, regulatory rigor and zero compromise.
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Why it matters
AI projects often start strong, with vision, ambition, and momentum.
But too often, they falter when strategy meets delivery:
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Business goals get lost in translation
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Scope expands into technical complexity
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Timelines slip
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Compliance is an afterthought
You don’t need another vendor managing the build.
You need a strategic partner who ensures that what gets built delivers business value - ethically, efficiently, and in full alignment.
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What you’ll gain
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✔ A neutral project leader focused on your business interests, not technical bias or vendor loyalty
✔ Translation of your strategic goals into technical execution, with cross-functional coordination
✔ KPIs and outcomes that are clear, measurable, and enforced
✔ Regulatory compliance embedded into every phase (EU AI Act, GDPR, ISO/IEC 42001)
✔ Early identification of risks, blockers, and blind spots – before they escalate
✔ Faster, safer and smarter implementation through planning, orchestration, and reporting
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How we work
We serve as the bridge between business and tech – translating needs, aligning stakeholders, and managing delivery end-to-end.
Our role is not to code – but to ensure that what gets coded creates value.
From roadmap execution and team coordination, to risk governance and reporting, we oversee your AI initiative with clarity, discipline and strategic focus.
We work side by side with your teams and solution vendors – always representing your interests exclusively.
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When AI Project Management is the right step
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You’re ready to implement your AI roadmap, but need neutral project leadership
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Multiple teams are involved – internal, external, global or hybrid
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You’re operating in a high-stakes, regulated environment
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You want to avoid scope creep, over-engineering and wasted resources
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Success means business outcomes, not just technical delivery
From Vision to Results
Scenarios we transformed into successful change
AI can bring great value, but only with the right safeguards
Sector: Fintech
When a client planned to implement a proprietary AI model for financial recommendations, we flagged hidden risks and led a full contract revision. Outcome: zero reputational or legal exposure, and a reusable AI governance toolkit.
AI delivery without business discipline is just expensive experimentation.
We help you stay on course, mitigate risk, and ensure that strategy becomes reality.
AI Implementation with Multi-Stakeholder Orchestration
Sector: Retail chain / Cross-departmental operations
Challenge:
The company initiated the implementation of an AI model for demand forecasting and procurement optimization. Several teams were involved – IT, logistics, and an external vendor from abroad – but coordination was lacking. The model produced inaccurate predictions, leading to increased inventory costs and growing tensions between departments.
Our Approach:
We assumed leadership as an independent AI project manager. Our first step was an urgent validation of input data. We aligned communication across departments and introduced a decision-tracking methodology. Key KPIs were redefined, and a Change Control Board was established for all major decisions.
Additionally, we implemented early warning mechanisms to monitor and respond to any degradation in model performance.
Outcome:
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The model was optimized and retrained on validated data
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Inventory surplus was reduced by 15% during peak season
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Technical insights were translated into actionable dashboards for the leadership team
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Project was completed 6 weeks ahead of the revised deadline
Risk-led Project Governance to Protect Client Interests
Sector: Fintech / Data-driven user services
Challenge:
The client planned to implement an AI system for personalized financial recommendations. Early in the project, we identified that the vendor was using a proprietary model with no clear explanation of how recommendations were generated, no auditing mechanisms, and no documented methodology.
The contract did not include provisions granting the client access to the model's logic or output variability.
Our Approach:
As an independent AI project manager, we conducted a risk assessment based on EU AI Act and GDPR criteria. We identified a potentially high reputational and legal risk (e.g., discriminatory outcomes, lack of explainability).
We initiated a requirement redefinition process, which included:
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Mandatory explainability component
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Contractual right to test and independently evaluate the model
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Inclusion of a model card and risk log as part of the deliverables
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Mechanism for user recourse in case of adverse outcomes
Outcome:
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Contract modified without additional cost
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Vendor retained under stricter oversight
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Client now owns a risk governance toolkit for all future AI projects
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Project completed with zero reputational or regulatory exposure
From Chaos to Clarity - Recovering a Stalled AI Initiative
Sector: Manufacturing / Quality Control Automation
Challenge:
An AI project aimed at defect detection on the production line had started with great enthusiasm, but without a clear owner. After 8 months of development, the model was non-functional, the budget had been exceeded, and no one could pinpoint the core problem.
Our Approach:
We introduced structure through a Project Recovery Audit. We analyzed all previous activities, conducted a gap analysis between original goals and current status, and established a control checkpoint with clear go/no-go criteria.
The project scope was redefined: part of the original objective was postponed, and one product segment was prioritized for a pilot phase. A three-iteration plan was created, with the option to stop after each phase if no progress was observed.
Outcome:
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Functional AI pilot completed within 6 weeks
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Management gained transparency into what was actually happening in AI projects
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An evaluation framework for AI initiatives was implemented for future use
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Project moved from “shut it all down” to a controlled, phased continuation