Role Summary
A large-cap Private Equity sponsor is appointing a Chief Data & AI Officer (CDAO) into a scaled Agri-Food portfolio company to build enterprise-wide data foundations and industrialise AI as a core value-creation lever. The role will own the data strategy, AI roadmap, governance, and delivery of high-impact use cases across the end-to-end value chain—farm/inputs → production → quality → supply chain → commercial → finance—with clear linkage to EBITDA uplift, working-capital improvement, resilience, and improved decision-making cadence.
The CDAO will operate as a hands-on executive leader: shaping strategy, building capability, and delivering measurable outcomes at pace, consistent with PE timeframes and board-level expectations.
Key Objectives (First 3-6 Months)
1) Establish the Data & AI operating model
Create a clear enterprise data vision and operating model (central vs federated), including ownership, stewardship, governance forums, and delivery cadence.
Define the “single source of truth” for critical domains (e.g., product, customer, supplier, inventory, yield, quality, cost).
2) Build the data foundations to scale AI
Stabilise and modernise the data platform (cloud, lakehouse/warehouse, integration, master data, lineage, observability).
Improve data quality, timeliness, and accessibility across core operational and commercial systems (ERP/MES/WMS/TMS/CRM, manufacturing/quality systems, IoT/plant systems where relevant).
3) Deliver a prioritised AI value roadmap
Identify and deliver a pipeline of AI/advanced analytics use cases tied to value creation, for example:
Demand forecasting & S&OP optimisation (service levels, inventory reduction, waste reduction)Yield / throughput optimisation (process parameters, bottleneck management)Predictive maintenance (downtime reduction, asset reliability)
Quality & food safety analytics / computer vision (defect detection, compliance)
Procurement & commodity insights (pricing, hedging signals, supplier risk)
Commercial analytics (pricing, promo, mix optimisation, trade spend)
Traceability & compliance enablement (farm-to-fork visibility, audit readiness)
4) Embed responsible AI and risk management
Implement pragmatic, PE-friendly AI governance: model risk, bias testing, explainability, security, vendor risk, and regulatory compliance.
Role & Responsibilities
Strategy & Value Creation
Own the enterprise Data & AI strategy aligned to the sponsor’s value-creation plan and management priorities.
Translate business goals into a quantified use-case roadmap with benefits cases, milestones, and owners.
Establish KPI reporting tied to realised outcomes (not “activity metrics”).
Data Platform & Architecture
Lead the evolution of data architecture, integration, and data products across core systems.
Ensure the data estate supports scalable analytics/AI (reliable pipelines, governance, standards, metadata, lineage).
Drive simplification and interoperability across fragmented systems and spreadsheets.
AI Delivery & MLOps
Build repeatable delivery capability (product-based delivery, agile ways of working, MLOps, model monitoring).
Partner with Technology/Engineering teams to deploy models into production, not just proof-of-concepts.
Governance, Security & Compliance
Establish data governance (policies, access controls, quality thresholds, master data).
Implement AI governance (model approval, monitoring, auditability, vendor controls).
Ensure alignment with relevant food sector expectations (quality, traceability, safety) and broader privacy/security requirements.
Leadership & Culture
Build and lead a high-performing Data & AI function (data engineering, analytics, data science, data product).
Upskill business leaders and create a culture of data-driven execution—decision rights, dashboards, and performance routines.
Stakeholder Management
Influence at C-suite and Board level; communicate simply and commercially to senior stakeholders.
Work closely with CFO, COO, CIO/CTO, Supply Chain, Manufacturing, Quality, Commercial, and Procurement leaders.
Serve as a trusted partner to the PE sponsor/operating team (as required).
Candidate Profile (Must-Haves)
Proven Data/AI executive leadership experience (CDAO/CDO/VP Data & AI or equivalent) delivering measurable outcomes, not just platforms.
Experience in Private Equity-backed environments (compressed timelines, EBITDA focus, board cadence).
Agri-Food domain knowledge: traceability, food safety/quality, seasonal variability, commodity dynamics, yield/waste drivers.
Track record building data foundations in operationally complex environments: manufacturing, supply chain, logistics, FMCG/CPG, food production, or adjacent asset-heavy sectors.
Demonstrable ability to create a prioritised AI use case portfolio, deliver at pace, and embed solutions operationally.
Strong grasp of modern data stack principles (cloud data platforms, integration, governance, MDM, analytics engineering, MLOps).
Excellent commercial storytelling—able to engage a PE board and translate technical delivery into value.