Innovation Hub Manager at Kaelo

Role Purpose

• The Innovation Hub Manager builds and runs Kaelo’s research and development capability. The department exists to identify, prove and industrialise new product concepts, agentic and AI-based capability, and emerging technology that can create commercial advantage for the organisation. It works ahead of the production estate, taking ideas from opportunity through to a proven, de-risked solution that graduates into Systems Solutions for production ownership.
• This is a management role rather than a hands-on engineering role. The incumbent supports the IT Executive in executing the AI and innovation strategy, acting as an enabler, and is accountable for the quality of the innovation pipeline, the rigour with which ideas are tested and the commercial return the portfolio generates.
• They achieve this by building a capable team from a largely greenfield base, empowering the AI Architect to own technical direction, and holding the portfolio to account through stage-gated governance, documented operating procedures and honest reporting of results.

Key Responsibilities

Managing & Team Capability

• Manage the Innovation Hub, setting into action the direction set by the IT Executive, prioritising initiatives, establishing ways of working and maintaining a culture in which disciplined experimentation and early stopping are treated as productive outcomes.
• Manage the team from a largely greenfield base; develop and retain scarce AI, analysis and design skills.
• Delegate technical ownership appropriately, empowering the AI Architect to set reference architecture and engineering standards.
• Set objectives, manage performance and manage departmental budget, capacity, partners and vendors.
• Develop and maintain standard operating procedures (SOPs) for the Innovation Hub, including stage-gate, experimentation and handover procedures.
• Operationalise and enforce Kaelo’s policies within the department, including responsible AI, information security and data protection policies, and address non-compliance.

Innovation Strategy & Portfolio

• Translate the AI and innovation strategy set by the IT Executive into a sequenced portfolio of initiatives balancing near-term return against longer-horizon capability.
• Run stage-gated governance through the Project Manager so that initiatives are funded incrementally and stopped decisively when evidence does not support them.
• Horizon-scan emerging technology, particularly in artificial intelligence, and assess its applicability to health insurance and financial services.
• Report portfolio progress, learning and realised benefit to the IT Executive and business leadership.

Opportunity Identification & Commercial Validation

• Engage business and executive stakeholders to identify opportunities of genuine commercial or member value.
• Ensure every initiative has a framed use case, value hypothesis, success criteria and measurement approach before build effort is committed.
• Ensure realised benefit is measured honestly after solutions reach production.
• Build relationships with external partners, vendors, start-ups and academic institutions where they accelerate capability.

Responsible AI & Risk

• Establish responsible AI practices covering fairness, bias, explainability, human oversight, data provenance and model risk.
• Ensure the use of member or production data in experimentation is governed, minimised, anonymised where possible and formally approved under POPIA.
• Ensure third-party and model supply chain risk from AI vendors and foundation model providers is assessed and managed.
• Ensure security requirements are considered from the outset, in partnership with the Security Engineer, rather than retrofitted at handover.

Graduation to Production

• Agree with the Systems Solutions Manager the acceptance criteria and boundary of responsibility for solutions entering production.
• Ensure graduating solutions carry the documentation, test coverage, observability and operational runbooks required for production acceptance.
• Ensure productionisation work is planned and resourced jointly with Systems Solutions.

Stakeholders & Governance

• Act as a trusted partner to the business, building demand for the Hub’s work while setting realistic expectations of what AI can deliver.
• Identify, record and escalate delivery, ethical, regulatory and vendor risks.
• Maintain the records required to evidence governance of the innovation portfolio to internal audit and regulators.

Alignment, Reporting & Escalation

• Align the department’s work to, and execute, the direction and strategy set by the IT Executive.
• Communicate progress to the IT Executive, and escalate issues, risks and decisions requiring direction promptly and transparently.

Qualifications

REQ-02

Qualifications & Experience

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science or a related field.
• Postgraduate qualification, or formal training in artificial intelligence, product or innovation management, advantageous.

Essential Experience

• Minimum 8 years’ experience in technology, including at least 3 years managing teams in a research, innovation, product or advanced engineering function.
• Demonstrated experience taking new technology concepts from idea through proof of concept to production adoption.
• Practical, current knowledge of the AI landscape, including large language models, agentic systems and machine learning operations.
• Experience establishing governance for exploratory work, including stage gating and portfolio-level decision making.
• Track record of engaging executive stakeholders and securing sponsorship for speculative investment.

Preferred Experience

• Financial services, health insurance, medical schemes or healthtech.
• Experience implementing responsible AI or model risk governance frameworks.
• Experience managing partnerships with technology vendors, start-ups or academic institutions.

Competencies

Management Competencies

• Decisiveness — backs promising initiatives and stops unpromising ones without sunk-cost bias.
• Managing Through Others — empowers the AI Architect and team to own technical detail and holds them to account.
• Stakeholder Influence — builds executive sponsorship and organisational appetite for change.
• Team Development — attracts, develops and retains scarce AI and innovation talent.
• Intellectual Honesty — reports results as they are, including negative ones.

Working Technical Understanding

• AI and machine learning landscape — LLMs, agentic systems, classical ML
• AI reference architecture and platform principles
• Responsible AI, model risk, bias and explainability
• Innovation portfolio management and stage-gated governance
• Rapid prototyping, product discovery and experimentation methods
• AI data readiness and data platform principles
• Vendor, partner and model supply chain management
• Benefit measurement and portfolio reporting
• SOP development and policy enforcement

Key Performance Indicators

• Realised commercial or member value from initiatives that reached production.
• Throughput of the innovation pipeline from idea through to graduation or stop decision.
• Timeliness of stage-gate decisions and proportion of initiatives correctly stopped.
• Quality of handover to Systems Solutions, measured by post-graduation rework.
• Maturity of the responsible AI governance framework.
• Delivery of the departmental build-out against the approved resourcing plan.
• Team engagement, capability growth and retention.
• Coverage and currency of departmental SOPs, and adherence to SOPs and policies, measured through audits and spot checks.

Regulatory, Risk & Information Security Responsibilities

• Comply with POPIA in the handling of personal and special personal information, including member health information, and with the Cybercrimes Act, reporting any suspected unlawful access or compromise without delay.
• Adhere to Kaelo information security policies and the frameworks the organisation aligns to, including FSCA Joint Standard 2 of 2024, ISO/IEC 27001, the CIS Controls and the NIST Cybersecurity Framework.
• Maintain the evidence required to support the ISMS and internal, external and certification audits within the area of responsibility.
• Escalate security incidents, control weaknesses and operational risks promptly, and complete all mandatory compliance and awareness training.

Additional Information

Personal Attributes

• Accountability
• Action orientated
• Collaborative
• Communication
• Business writing skills
• Manage complexity
• Problem solving skills
• Decision thinking
• Encourages engagement
• Resilient and adaptive
• Resourceful
• Demonstrates self-awareness
• Purpose driven
• Significance and values orientated

Start date : January 2027