AI Lead, Technology Risk at KPMG South Africa

Role Purpose

• The Lead for Artificial Intelligence in Technology Risk will provide strategic and operational leadership for the development, adoption and commercialisation of AI capabilities across the Technology Risk practice.
• The role will be responsible for positioning Technology Risk at the forefront of Trusted AI, AI governance, advanced data analytics and technology-enabled assurance. The successful candidate will lead the development of innovative AI-enabled solutions, support the responsible adoption of AI across engagements, and build a scalable capability that improves client outcomes, delivery quality, efficiency and profitability.
• This leader will combine deep technical expertise with strong commercial judgement, client credibility and people leadership. The role will help address the strategic risk of insufficient AI adoption by embedding AI across Technology Risk competencies, strengthening the Technology Risk AI Centre of Excellence, and developing the skills, methodologies and assets required tcompete successfully in an evolving market.

Strategic Context

• Organisations are accelerating investment in artificial intelligence, advanced analytics and enterprise data transformation.
• This is creating increased demand for services relating tAI governance, assurance, security, data risk and responsible AI implementation.
• Within Technology Risk, there is an opportunity to:
• Build a differentiated Trusted AI and AI assurance offering.
• Embed AI and automation in the delivery of technology audit and advisory engagements.
• Improve engagement quality, consistency, turnaround time and profitability.
• Develop reusable AI methodologies, accelerators and intellectual property.
• Support clients in managing emerging AI, data and technology risks.
• Build scarce AI, analytics and technology audit skills within the practice.
• Strengthen market presence and originate new client opportunities.

Key Responsibilities

AI Strategy and Leadership

• Develop and execute the Technology Risk AI strategy, aligned with the broader business strategy and relevant firm-wide AI priorities.
• Translate the AI strategy into practical roadmap covering offerings, people, technology, governance, investment and market development.
• Lead the adoption of AI across Technology Risk competencies and service lines.
• Establish clear priorities and measurable outcomes for AI-enabled transformation.
• Advise Technology Risk leadership on emerging AI developments, risks, opportunities and investment requirements.
• Act as the senior AI subject-matter leader for the Technology Risk practice.
• Promote responsible, ethical and risk-aware adoption of AI in both internal and client-facing activities.

Trusted AI, Governance and Assurance

• Lead the development and delivery of Trusted AI, AI governance and AI assurance services.
• Support clients in assessing and strengthening the governance, risk management and control environments surrounding AI.
• Develop approaches for reviewing AI models, data, security, controls, accountability and responsible use.
• Lead complex engagements involving AI governance, data risk, technology assurance and emerging technology.
• Ensure that AI solutions and methodologies are aligned with relevant firm policies, professional obligations and quality requirements.
• Monitor developments in AI governance and emerging technology risk, translating these into practical client propositions.

AI-Enabled Technology Risk Delivery

• Identify high-value opportunities to embed AI and automation into technology risk, audit and advisory engagements.
• Drive the implementation of AI-enabled audit procedures and automation techniques.
• Reduce manual and repetitive activities through intelligent automation.

Develop AI-enabled solutions supporting areas such as:

• Automated testing
• Data analytics
• Continuous control monitoring
• Workpaper preparation
• Evidence analysis
• Risk identification
• Reporting and insight generation
• Improve the quality, consistency and speed of engagement delivery.
• Work with engagement leaders to incorporate AI into planning, execution and reporting while maintaining appropriate human oversight.
• Monitor the performance, adoption and value delivered by AI-enabled solutions.

Innovation and Solution Development

• Build a structured pipeline of AI and analytics use cases across Technology Risk.
• Lead the design, development, testing and implementation of AI-enabled solutions.
• Create reusable methodologies, templates, prompts, models, accelerators and automation assets.
• Establish appropriate processes for evaluating, prioritising and scaling AI use cases.
• Coordinate proof-of-concept and pilot initiatives before broader deployment.
• Promote innovation while ensuring appropriate governance, security, quality and risk management.
• Collaborate with relevant technical, innovation, data, cyber, audit and sector teams to develop integrated solutions.
• Convert successful internal innovations into client-facing propositions where appropriate.

Commercial Growth and Market Development

• Build and execute a go-to-market strategy for Trusted AI, AI governance, advanced analytics and AI-enabled assurance.
• Identify market opportunities and convert client needs into scalable service offerings.
• Originate new business and support partners and engagement leaders in developing AI-related opportunities.
• Lead or contribute to proposals, presentations, demonstrations and client workshops.
• Build trusted relationships with senior client stakeholders.
• Expand relationships across existing and prospective clients.
• Develop market-facing thought leadership, technical publications and points of view.
• Represent the practice at relevant industry forums, conferences and innovation events.
• Strengthen the practice’s market credibility in Trusted AI, data risk and emerging technology.

People and Capability Development

• Build and lead a high-performing, AI-enabled Technology Risk capability.
• Define the skills, roles and capacity required to deliver the AI strategy.
• Recruit, develop and retain professionals with relevant AI, data, analytics, audit and technology risk skills.
• Establish structured learning pathways for AI, automation, analytics and Trusted AI.
• Develop communities of practice that promote experimentation, knowledge sharing and reuse.
• Improve AI literacy across all levels of the Technology Risk practice, not only within specialist teams.

Performance and Value Realisation

• Define and maintain an AI value scorecard for Technology Risk.
• Track the adoption, utilisation and benefits of AI-enabled solutions.
• Measure improvements in efficiency, quality, turnaround time, colleague experience and engagement profitability.
• Monitor the commercial performance of AI and Trusted AI offerings.
• Report progress, risks, investment requirements and value delivered to Technology Risk leadership.
• Use performance data and engagement feedback to refine the AI strategy and roadmap.

Candidate Profile

Essential Experience

The successful candidate should demonstrate:

• Significant leadership experience in artificial intelligence, data analytics, technology risk, IT audit, digital transformation or a related discipline.
• Experience designing and implementing AI, machine-learning, advanced analytics or intelligent automation solutions.
• Strong knowledge of AI governance, responsible AI, data governance and emerging technology risk.
• Experience leading complex client engagements and multidisciplinary teams.
• A strong understanding of technology risk, controls, assurance and audit environments.
• Experience developing and commercialising technology-enabled professional services.
• Proven ability to translate technical concepts into practical business and risk outcomes.
• Experience working with senior executives and building trusted client relationships.

Preferred Experience

• Experience in a professional services, consulting or assurance environment.
• Experience in technology-enabled audit or risk transformation.
• Experience developing reusable technology assets and accelerators.
• Exposure to cloud-based data and AI platforms.
• Experience with model risk, AI assurance or responsible AI assessments.

Qualifications

Essential

• A relevant bachelor’s degree in one or more of the following:
• Computer science
• Data science
• Artificial intelligence
• Information systems
• Engineering

Technical Knowledge

• The successful candidate should possess strong knowledge across a meaningful combination of the following areas:
• Generative AI and large language models
• Machine learning and predictive modelling
• AI agents and intelligent automation
• AI governance and responsible AI
• Data governance and data quality
• Advanced data analytics
• Cloud data and AI platforms
• Data migration and transformation