Ai value realisation engineer

The Manager: AI Value Realisation Engineer is responsible for measuring, validating, and optimizing the business value generated by AI platforms and AI-enabled initiatives across the organization.

The role establishes standardized value measurement frameworks, tracks realized benefits against approved business cases, identifies value leakage, and enables leadership to make evidence-based decisions regarding the scaling, optimization, or retirement of AI initiatives.

The position serves as the independent custodian of AI value management, collaborating with business, finance, product, and engineering teams to ensure AI investments deliver measurable and sustainable business outcomes.

Key Performance Areas (KPAs) 1. AI Value Framework & Measurement Design Define, implement, and maintain a standardized AI value realization framework across the organization. Develop methodologies to quantify business value generated through AI, including: Revenue growth Cost reduction Risk mitigation and loss prevention Productivity improvements Operational efficiency gains Ensure value metrics are: Consistent across AI initiatives Defensible and auditable Aligned with Finance and Strategy standards. 2. Benefits Tracking & Validation Measure realized AI benefits against: Approved business cases Expected Key Performance Indicators (KPIs) Original business assumptions Validate outcomes in collaboration with: Business owners Finance teams Performance management teams Differentiate between: Direct AI-attributed value Indirect or correlated business benefits Maintain transparent reporting of realized versus expected outcomes. 3. Value Leakage Identification & Optimization Identify variances between: Forecast and realized value Technical performance and business impact Diagnose root causes of value leakage, including: Low user adoption Ineffective change management Process inefficiencies Cost overruns Data quality issues Model performance limitations Recommend corrective actions and continuous improvement initiatives to maximize business value. 4. AI Investment & Portfolio Analytics Support investment decisions through: Return on Investment (ROI) analysis Break-even assessments Payback period modelling Scale, optimize, or retire recommendations Provide portfolio-level insights into: High-performing AI initiatives Underperforming or high-risk projects Investment performance trends Enable objective prioritization of AI investments using measurable evidence. 5. Executive Reporting & Business Transparency Develop concise and credible reporting on: Business value delivered by AI Value at risk Expected versus realized benefits Portfolio performance Translate technical AI outcomes into executive and board-level business insights. Support governance forums, steering committees, and executive decision-making with objective performance data. 6. Continuous Value Governance Embed value measurement throughout the AI delivery lifecycle. Ensure benefit realization continues beyond production deployment. Monitor long-term business outcomes and operational performance. Continuously refine value realization frameworks to reflect evolving AI capabilities, organizational maturity, and regulatory expectations. Job Requirements Education

Bachelor’s or Master’s Degree in one of the following disciplines:

Finance Economics Business Analytics Engineering Data Science Computer Science Information Systems Or a related field Experience Minimum of 5 years’ experience in one or more of the following areas: Value realization Business performance management Business analytics Corporate finance Strategy Demonstrated experience measuring and quantifying value from digital, analytics, technology, or transformation initiatives. Experience working within highly regulated industries is advantageous. Exposure to Artificial Intelligence, Advanced Analytics, Data Platforms, or Digital Transformation initiatives is preferred. Technical Competencies Financial modelling and ROI analysis Benefits realization methodologies KPI design and performance measurement frameworks Cost attribution and operational efficiency analysis AI delivery lifecycle and operating models Portfolio performance management Business case evaluation Data-driven decision support Skills Strong analytical and quantitative reasoning Financial analysis and business modelling Executive-level reporting and data storytelling Ability to challenge assumptions using objective evidence Cross-functional collaboration with business, finance, product, and engineering teams Excellent communication and presentation skills High attention to detail and analytical discipline Behavioural Competencies Objective and evidence-based decision maker Commercially astute Detail-oriented and methodical Credible and trusted advisor Outcome-focused Highly accountable Strong integrity and professional judgement Continuous improvement mindset Authorities Operate within the organization’s Delegation of Authority (DOA) framework. Collaboration Internal Stakeholders Executive Leadership Business Units Finance Strategy Product Management AI Engineering Teams Data & Analytics Teams Technology Leadership Enterprise Architecture Risk & Compliance Performance Management Teams External Stakeholders Audit and Assurance Partners Performance Management Consultants Analytics and Technology Partners External Advisory and Consulting Firms