1. Translate Business Needs into AI and Data Solutions
Work with business users to understand the decisions they need to make and translate those needs into clear AI and data use cases, requirements and delivery plans.
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Business questions, user needs, pain points and desired decisions are understood and documented.
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Needs are translated into clear problem statements, user stories, process flows, acceptance criteria and measurable outcomes.
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Business, Product, Data Science, Data Engineering, Architecture and AI teams have shared clarity on users, outcomes, scope, data needs and dependencies.
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AI and data product roadmaps remain connected to growth, productivity, efficiency, decision quality and user outcomes.
2. Shape High-Value AI and Data Use Cases
Use business insight, available data and an AI-first mindset to identify and shape practical opportunities that improve decisions, productivity and business performance.
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Use cases are grounded in real business decisions, workflows and user needs rather than technology-led concepts.
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Opportunities are supported by relevant data, evidence, user insight and baseline performance measures.
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Structured analysis helps teams assess value, data readiness, technical feasibility, adoption needs, risk and strategic alignment.
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AI capabilities are translated into responsible, practical and scalable business applications.
3. Provide Engineering Logic and Delivery Clarity
Apply structured engineering thinking to help delivery teams move from a business problem to a solution that is testable, explainable, usable and supportable***.***
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Requirements describe the user journey, decision logic, data inputs and outputs, business rules, exceptions and success measures.
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Data availability, quality, lineage, access, privacy and dependencies are considered early, with gaps clearly documented and escalated.
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Works with engineers, data scientists and architects to clarify functional and non-functional requirements, test assumptions and refine options.
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Supports testing, validation and acceptance to confirm that solutions meet business needs and operate as intended.
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Understands enough of data models, APIs, integration, analytics and AI solution patterns to engage technical teams effectively without being the hands-on technical lead.
4. Enable AI Adoption and Value Realisation
Help users adopt AI-enabled data products and embed them into day-to-day decisions, processes and ways of working.
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Current workflows, decision points and pain points are mapped and translated into practical future-state experiences.
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AI and data experiences are designed to improve efficiency, consistency, user experience and decision quality.
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Trust, explainability, responsible use, training and change needs are identified and addressed alongside delivery.
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Usage, feedback and outcome measures are used to refine products, prompts, workflows and priorities.
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Benefits and adoption are tracked against agreed measures to demonstrate value and inform continuous improvement.
5. Connect Business Users with AI and Engineering Teams
Act as a practical connector between front-facing business teams and the technical teams that design, build and run data and AI solutions.
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Trusted relationships are built with business users, Product Managers, Data Scientists, Data Engineers, Architects and AI specialists.
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Business language is translated into delivery language, and technical choices, constraints and limitations are explained clearly to non-technical stakeholders.
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Dependencies, assumptions, risks and trade-offs are surfaced early and supported towards constructive resolution.
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Teams remain aligned on the user problem, intended outcome, delivery scope, ownership and measures of success.
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Feedback flows continuously between users and delivery teams throughout discovery, build, launch and optimisation.