Chief Data & AI Officer

Managing the recruitment of a Chief Data & AI Officer (CDAO) or Chief Data Officer requires a precise understanding of their role. While outlined here in general terms, the position must always be adapted to the organization’s specific context.

The CDAO is responsible for the organization’s Data & AI strategy, driving data-driven transformation and creating value for business units, external partners, and customers. This role focuses on growth (top line), innovation, and operational excellence (bottom line) while fostering a strong Data & AI product culture.

Core Responsibilities

a. Define and Lead the Data & AI Strategy

Strategic Alignment: Develop a Data & AI roadmap aligned with business priorities, in collaboration with leadership and business units.
Value Creation: Prioritize high-impact initiatives (new products, process optimization, customer experience) and measure long-term value delivered.

b. Data Governance and Compliance

Governance Framework: Establish standards for data governance (quality, security, accessibility, metadata).
Compliance & Ethics: Ensure adherence to regulations (GDPR, AI Act) and ethical standards, in partnership with legal and compliance teams.

c. Organization and Leadership of Data & AI Teams

Team Structuring: Build and lead central and decentralized Data & AI teams, defining key roles (Data Stewards, Product Owners, local CDAIOs).
Talent Development: Attract, motivate, and upskill top talent in data science, engineering, and AI.

d. Product Culture and Collaboration

Data & AI Product Culture: Drive adoption of a product mindset across discovery, delivery, adoption, and customer success.
Cross-Functional Collaboration: Work with technical, business, and external partners; animate internal communities to foster innovation.

e. Execution and Delivery of Solutions

Lead the CoE: Direct cross-functional teams (Data Scientists, Engineers, ML Engineers, Product Owners) to deliver high-value use cases.
Performance Monitoring: Manage delivery timelines, budgets, and KPIs measuring adoption and business impact.

f. Innovation and Partnerships

Technological Innovation: Track trends (generative AI, edge computing) and evaluate applicability.
Partnerships: Build alliances with technology providers, startups, and academia.
Stakeholder Alignment: Present strategy to executives, investors, and regulators; showcase best practices and successes.

Key Skills

a. Strategic and Business Acumen

Ability to align Data & AI with overall company strategy.
Strong understanding of industry-specific challenges and value creation levers.

b. Technical Expertise

Data Science & AI: Mastery of machine learning, deep learning, NLP, MLOps, and related tools (Python, TensorFlow, PyTorch).
Data Engineering & Cloud: Knowledge of modern architectures (data mesh, data fabric, cloud) and platforms (Databricks, Snowflake).
Governance & Security: Expertise in data management, security, and regulatory compliance.

c. Leadership and Soft Skills

Transformational Leadership: Inspire and align multidisciplinary teams.
Communication & Influence: Translate technical complexity for executives and business units.
Change Management: Drive adoption of Data & AI transformation and manage resistance.

Examples of Achievements

Data-Driven Transformation: Unified data platform implementation, cutting costs through automation and predictive analytics.
AI Solution Deployment: Industrialization of ML models to optimize supply chains or personalize customer experiences, delivering measurable revenue growth.
Product Culture & Adoption: Roll-out of Data & AI product roadmaps with strong adoption by business units.
Governance & Compliance: Deployment of a governance framework ensuring security and regulatory alignment.