AI Adoption and Transformation Lead

Job Description

Own finera.’s AI strategy, adoption roadmap, and governance, and the practical delivery of AI-enabled business solutions. This is a hands-on transformation role working across business, technology, security, data protection, legal, compliance, operations, HR, and senior leadership. Key objectives include setting and maintaining the AI adoption roadmap, identifying and delivering high-value use cases, enabling controlled adoption of generative AI and automation, building the governance layer, and keeping all initiatives aligned with security, privacy, compliance, and regulatory expectations.

Job Requirements

Job Requirements

• AI adoption, digital transformation, automation, or technology delivery in a business or regulated context.

• Practical generative AI, LLM, agent, and workflow-automation knowledge; translating business needs into technical solutions.
• Running pilots through to production; working with senior stakeholders and cross-functional teams.
• Evaluating vendors, building business cases, and managing third-party risk.
• Governance, risk, security, or compliance; creating policies, frameworks, or operating models.

Skills & Competencies
Technical / AI:

• Strong grasp of generative AI, LLMs, agents, automation, and data-driven decision support.
• Understanding of prompt engineering, RAG, workflow design, and human-review models.
• Ability to evaluate platforms and their limitations; working knowledge of APIs, integrations, access control, and cloud.

Business / Transformation:

• Strong business analysis and process optimisation; sharp prioritisation and ROI focus.
• Roadmap, business-case, and operating-model development.
• Solid project management across planning, scheduling, dependency tracking, risk/issue management, and stakeholder reporting.

Governance / Risk:

• Understanding of responsible AI, privacy, security, and operational risk; ability to assess data sensitivity and regulatory exposure.
• Familiarity with security- and privacy-by-design; comfort working with legal, risk, and security stakeholders.
• Regulated-environment experience highly desirable.

Communication / Leadership:

• Excellent written and verbal communication; able to explain complex AI in plain business language.
• Able to influence senior stakeholders, lead workshops, and drive governance discussions.
• Strong documentation discipline with a functional mindset ensuring concepts translate into execution.

Qualifications:

• Degree in Computer Science, Information Systems, Data Science, Engineering, Business Technology, or a related field — or equivalent experience.
• Advantageous certifications: AI governance / responsible AI, cloud, project management, or business analysis.
• Practical and delivery-oriented; analytical, curious, and innovation-driven.
• Risk-aware without being restrictive; balances speed, control, and value.
• Strong stakeholder management; able to challenge unrealistic AI expectations.
• Disciplined in documentation; high integrity and confidentiality.

Nice-to-Have / Preferred:

• GDPR, EU AI Act, ISO 27001, PCI DSS knowledge in a financial services or fintech context.
• Third-party and model risk management; internal audit experience.
• Enterprise AI platforms; vector databases and RAG; low-code/no-code automation.
• Secure software development practices.

Job Responsibilities

Strategy & Roadmap:

• Develop the AI strategy in line with business priorities and risk appetite.
• Maintain a practical roadmap from short-term pilots to long-term maturity, underpinned by an AI maturity model.
• Define KPIs, ownership, timelines, and governance checkpoints; report progress to senior management.

Use-Case Delivery:

• Work with department heads to surface opportunities; assess each on value, complexity, data sensitivity, regulatory impact, risk, and ROI.
• Lead pilots, PoCs, and controlled rollouts; translate requirements into functional specs.
• Coordinate internal teams, vendors, and business users ensuring every use case has a clear owner, defined process impact, measurable benefit, and human accountability model.

Governance, Risk & Compliance:

• Create an AI system inventory (tools, use cases, owners, data types, vendors, risk ratings) and approval workflows.
• Coordinate AI risk assessments across privacy, security, confidentiality, bias, explainability, resilience, and regulatory exposure.
• Partner with Legal, Security, and Data Protection to align with GDPR, EU AI Act, ISO 27001, PCI DSS, DORA, NIS2, and internal policies.
• Embed human-in-the-loop controls for high-impact use cases.

Secure Adoption:

• Prevent confidential, regulated, or personal data from reaching public AI tools.
• Mitigate AI-specific risks: prompt injection, data leakage, hallucination, insecure integrations, uncontrolled agents, and vendor lock-in.
• Ensure platforms ship with SSO, RBAC, DLP, logging, audit trails, encryption, contractual safeguards, and incident escalation.
• Support secure internal assistants built on approved data sources and controlled retrieval.

Tools & Vendors:

• Evaluate AI tools, LLM providers, automation platforms, and agents; produce comparisons, business cases, and recommendations.
• Support procurement and due diligence across security, privacy, data processing, hosting, contracts, and model-training practices.
• Onboard vendors through third-party risk management; monitor performance, cost, and risk.

Recruitment & Enablement:

• Help HR define AI roles and skills; build JDs, interview questions, and scoring criteria.
• Create training, playbooks, and awareness programmes promoting responsible use.
• Help teams redesign workflows around AI without introducing unacceptable risk.

Stakeholder Management & Change:

• Act as the central coordination point for AI adoption across all functions and leadership.
• Facilitate workshops and risk reviews; build confidence through low-risk, high-value wins.
• Communicate clearly to technical and non-technical audiences; manage resistance so adoption supports rather than disrupts people.

Measurement & Reporting:

• Define adoption KPIs and value metrics tracking impact on productivity, cost, quality, cycle time, and decision support.
• Report to senior management on roadmap progress, use-case delivery, risk, tool usage, policy compliance, shadow-AI indicators, and vendor performance.
• Use data and feedback to continuously refine the roadmap.

Job Benefits

• Competitive salary package aligned with experience and market standards.
• Medical Insurance starting from day 1.
• Access to training resources and development opportunities that support your professional growth.
• Well-stocked office with snacks, drinks, and refreshments available daily.
• A multinational organisation that promotes a strong, collaborative culture
• Regular team-building events and company activities that strengthen collaboration across teams. 
• Employee Recognition Program celebrating our "Employee of the Month" with special perks.