Business Ops Directorate team-Data & AI Director

Overview

Name

Job Title

Data & AI Director

Job Grade

20

Reports To

Chief Operating Officer

Direct Reports

TBC

Indirect Reports

TBC

Working Relationships

Internal

Works closely and ExCo and Senior Leaders across People's Partnership, Corporate Board and Trustees

External

Agencies, Consultant companies, Suppliers and contractors

Information and Service Providers

Linked 3rd party organisations

Accountability Level

5

Behaviours Level

Senior Leader (Please refer to the Behaviour Framework on Gateway)

FCA prescribed Responsibility

Please refer to the Fit & Proper Policy and Procedure

Main Purpose

The Director of Data & AI is responsible for defining and delivering an enterprise-wide data and artificial intelligence strategy that ensures responsible and secure use of data and AI, achieves meaningful improvements in data governance and control, and drives measurable business value and innovation.

The role requires not only deep experience in data and AI but also a continuous outward-looking perspective - actively engaging with industry peers, partners and thought leaders to understand best practice and the evolving "art of the possible," and translating this into pragmatic, business-relevant opportunities for the organisation.

Reporting directly to the Chief Operating Officer, the Director will perform an enterprise-wide role and work closely with the Chief Information Officer who holds accountability for technology platforms, information security and architecture underpinning AI, the Chief Risk Officer who is the accountable data protection officer as well as the Strategy Director. The role also collaborates closely with the rest of the Executive Committee and senior leadership to understand use cases, and embed data and AI into strategic decision-making, and with Trustees and the Board to ensure both data and AI strategies align to risk appetite, regulatory expectations and long-term organisational objectives.

The Director will sponsor key data and AI projects, in alignment with the Enterprise Transformation Office and wider Change processes.

A critical aspect of the role is managing the inherent tension between the cautious and responsible use of AI, and the need to be bold and ambitious in capturing its benefits. The Director must balance the risks associated with adoption (e.g. ethics, bias, governance, regulatory compliance) with the equally material risk of non-adoption - ensuring the organisation remains competitive, efficient and future-ready. As a result, the Director will build organisational confidence in both the opportunities and risks of AI adoption.

The role is as much about establishing strong foundations as it is about enabling transformation - setting standards, frameworks and governance – ensuring responsible, ethical and compliant use of data and AI, while also driving innovation, experimentation and value delivery through AI and advanced analytics. The role holder should recognise that the barriers to adoption are related to governance, technology, behaviour and trust.

The Director leads the organisation’s data foundations and AI capabilities as two distinct but interdependent domains:

• Data governance and strategy - ensuring trusted, well-governed, secure and accessible data assets
• AI - leveraging those assets to deliver automation, insight, augmentation, business and customer experience transformation

The Director ensures these capabilities operate in alignment, recognising that AI effectiveness depends on strong data governance, architecture and accessibility, while data strategy must anticipate AI‑led use cases and future innovation.

Key Responsibilities

1. Strategic Leadership

• Define and own the enterpriseData & AI strategy, aligned to business objectives and transformation priorities
• Act as a senior advisor to the Executive Committee and business leaders on data and AI opportunities
• Translate organisational priorities into a clear roadmap of data and AI initiatives
• Drive measurable benefit from data and AI investments

2. Data Leadership

Data Strategy

• Define and implement enterprise-wide data strategy and operating model
• Ensure scalable, secure, and future‑ready data platforms (warehousing, lakes, integrations)

Data Governance

• Establish robust data governance frameworks covering quality, ownership, lineage and compliance
• Lead the Data Governance team
• Sponsor key data projects
• Ensure regulatory compliance (e.g. GDPR) and ethical data use
• Lead data and particulate in data governance forums

Data Management & Accessibility

• Improve data consistency and accessibility across the organisation through centralised frameworks
• Enable self-service analytics and reliable reporting

• Establishing and ensuring compliance with policies governing data retention, archiving, storage volumes, and secure data destruction.

Data Culture & Literacy

• Drive adoption of data-driven decision making across all business areas
• Build capability in data literacy, tools and insights usage

3. AI Leadership

AI Strategy & Innovation

• Define and deliver an enterprise AI strategy aligned to business outcomes
• Identify and prioritise high‑impact AI use cases (automation, customer experience, risk, productivity)

Responsible & Ethical AI

• Ensure AI solutions are transparent, fair, and compliant with regulatory expectations
• Establish governance around model use, bias, explainability and risk

AI Capability & Adoption

• Drive adoption of AI tools and embed AI into business processes

4. Data & AI Integration

• Ensure tight alignment between Data and AI strategies, recognising their interdependence
• Prioritise AI initiatives based on data readiness, availability and quality
• Ensure data platforms are designed to support AI workloads and innovation
• Prevent siloed delivery by establishing shared governance, standards and roadmaps

5. Operating Model

• Ensure effectiveness of a combination of federated model,
• with data owners and stewards across the business
• with AI agent citizen development hubs across the business
• Analytics capability hubs across the business

and central model (BI capabilities in IT, accountability for infrastructure, security, architecture in IT) by defining clear accountabilities, interfaces and joint delivery mechanisms

• Review appropriateness of federated model regularly
• Develop talent pipelines and capability frameworks

• Adhere to Company Risk Management policy and procedures, including reporting of incidents or breaches.
• Apply and promote Consumer Duty principles, putting customers at the heart of decision‑making and ensuring the delivery of good customer outcomes, while demonstrating the organisation's behaviours and values.
• Adhere to Company Diversity & Inclusion policy.
• This role may support hybrid working. To make use of this arrangement, employees must have an appropriate home working environment, including a private workspace and reliable, secure high‑speed internet that enables them to perform their duties effectively.

If FCA prescribed responsibility please check and add the required responsibilities

Behaviours Required

Understanding People:

• Asking questions
• Actively listening
• Learning from feedback

Create Simplicity:

• Challenging to improve
• Working together
• Creating straightforward solutions

Keeping our Promises:

• Taking ownership to deliver
• Setting clear expectations
• Respecting others

Functional/ Technical Skills

Skill/ Experience

Essential/ Desirable

Leadership & Strategy

• Proven senior leadership experienced in the transformation of data, analytics, and AI, ideally within regulated or customer‑centric industries
• Track record of setting and delivering enterprise‑wide data and AI strategies aligned to business outcomes
• Experience building and leading multi‑disciplinary teams, including senior leaders (e.g. Heads of Data and AI)
• Strong stakeholder management skills - able to influence at Executive Committee and Board level

Data

• Deep expertise in data management disciplines, including:
• Data governance and stewardship models
• Data management and controls
• Master and reference data management
• Metadata management and data lineage
• Experience establishing and scaling a data governance function (policies, frameworks, controls, ownership models)
• Strong understanding of data architecture principles, including modern data platforms
• Experience driving data democratisation while maintaining appropriate controls
• Knowledge of regulatory and compliance considerations (GDPR, data protection, audit requirements)

Artificial Intelligence

• Demonstrable experience leading or scaling AI / ML capability, including:
• Use case identification and prioritisation
• Strong understanding of emerging AI technologies, including generative AI and large language models and automation and intelligent decisioning systems
• Experience embedding responsible AI principles, including bias mitigation, transparency and explainability, ethical use and governance of AI
• Ability to translate AI capability into practical business applications that deliver measurable value

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