AI Business Analyst
Pubblicato il 25-08-2026 - NTT DATA Europe u0026 Latam in Reggio Emilia
Who We Are We believe that clarity turns complexity into meaningful outcomes. We are a department within NTT DATA Romania, known as the Agile Transformation Office, your gateway to creating real impact in software projects by keeping the focus on value and alignment.
Who We Are We believe that clarity turns complexity into meaningful outcomes. We are a department within NTT DATA Romania, known as the Agile Transformation Office, your gateway to creating real impact in software projects by keeping the focus on value and alignment.
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If you can bridge business and technology, translating needs into solutions and ensuring team alignment, we look forward to your application for the AI Business Analyst role. We are seeking an experienced Manufacturing Business Analyst – AI to bridge business strategy and artificial intelligence initiatives across Manufacturing, Supply Chain, Sales, Pricing, and Finance. This role partners closely with business leaders, data scientists, AI engineers, and data engineers to identify high-value AI opportunities, define business requirements, validate outcomes, and drive adoption.
The ideal candidate combines strong manufacturing and business process expertise with a practical understanding of AI, machine learning, and generative AI technologies. They can translate business challenges into AI solutions that deliver measurable business value while supporting responsible and ethical use of AI.
What You’ll Be Doing
- AI Opportunity Identification:
Analyze end-to-end business workflows to identify inefficiencies, automation opportunities, decision-support needs, and potential bias risks that can be addressed through AI integration.
- Business Problem Framing: Translate high-level or ambiguous business goals into clear AI use cases, success criteria, and testable AI and data science hypotheses.
- Requirements Definition: Develop business requirements, user stories, acceptance criteria, process maps, and functional specifications for AI-powered solutions.
- Data Feasibility Assessment: Collaborate with data engineers and data scientists to validate whether existing datasets have sufficient volume, quality, coverage, and accessibility to support AI model development.
- AI Solution Validation: Evaluate AI model outputs and recommendations against real-world business outcomes to ensure solutions deliver tangible value beyond statistical accuracy.
- KPI & Value Measurement: Define and track AI success metrics, including efficiency gains, cost savings, revenue impact, model confidence, adoption rates, forecast accuracy, and user satisfaction.
- Stakeholder Communication: Act as a translator between technical teams and business stakeholders, clearly explaining AI concepts, risks, tradeoffs,
and outcomes in business-friendly language.
- AI Product Vision & Roadmap: Define and communicate the AI product vision, align it with business goals and user needs, and help prioritize use cases based on business value, feasibility, and readiness.
- Responsible AI Governance: Support responsible AI practices by identifying risks related to fairness, transparency, explainability, privacy, compliance, and bias mitigation.
- User Adoption & Continuous Improvement: Partner with business users to monitor solution effectiveness, gather feedback, identify enhancement opportunities, and improve adoption of AI-driven capabilities. xysqume
What You'll Bring Along
- Bachelor's degree in Informatics or similar field of study or equivalent working experience is required
- 5+ years of experience as a Business Analyst, Product Owner, Process Analyst, or related role supporting manufacturing and business operations
- Strong understanding of manufacturing operations, supply chain, inventory planning, procurement, sales operations, pricing, and finance processes
- Familiarity with the AI lifecycle, model performance metrics, generative AI capabilities, and responsible AI principles such as fairness, transparency, and bias mitigation
- Experience with requirements gathering, process mapping, stakeholder management, workshop facilitation, user stories, acceptance criteria, and solution validation
- Ability to partner with technical teams to assess data quality, availability, completeness, and fit for AI and analytics use cases
- Strong documentation, workshop facilitation and communication skills in English.