Management Consulting, Head of AI and Technology

Role purpose

The Head of AI and Technology is accountable for the direction, delivery and adoption of AI across Teneo Management Consulting and for ensuring that its broader technology environment supports the needs of the business. The role sets the AI strategy and investment priorities, leads the AI Engineering team, owns the strategy and priorities for Management Consulting-specific technology, software, data, knowledge-management workflows and subscriptions, and acts as the principal Management Consulting stakeholder for Teneo IT on centrally managed technology, data platforms and services.

The role connects Management Consulting's priorities with Teneo's central AI team, Teneo IT and other control and enabling functions. It translates business needs into Management Consulting-owned AI and technology initiatives or clear requirements for central teams, moves high-value AI and software solutions from experimentation into reliable use, and gives consultants the skills and support required to use approved AI tools effectively.

It is primarily an internal capability role. The role is accountable for the allocation and utilisation of the AI Engineering team, including support for selected client engagements, and for the Management Consulting-specific AI and broader technology within its remit. Teneo IT and Teneo's central AI team remain accountable for the centrally managed services and capabilities, while Management Consulting engagement leaders retain accountability for client relationships, overall engagement delivery and revenue targets.

Key responsibilities

AI strategy and priorities

• Own the Management Consulting AI strategy, roadmap and priorities, aligned with the business strategy and Teneo-wide AI direction. Consult Teneo's central AI team on new solutions to reuse firm-wide capabilities, avoid duplication and manage enterprise dependencies.
• Identify and assess AI opportunities across consulting delivery, business development, proposition development, internal operations and knowledge work.

Leadership of the AI Engineering team

• Lead the day-to-day work of the Management Consulting AI Engineering team, including objectives, prioritisation, resource allocation, workforce planning, utilisation, delivery oversight and performance management.
• Balance the team's capacity across client engagements, internal tooling development, enablement of the broader MC team, and technical capability improvement, while maintaining sustainable workloads.
• Set and enforce engineering and product standards for Management Consulting AI solutions, consistent with mandatory Teneo-wide architecture, AI, security and technology standards.
• Define clear demand, resourcing and decision-making processes between the AI Engineering team and key stakeholders, including Resourcing, Operations, Finance, project leads and the wider client-facing team.
• Build the team's capability through recruitment, coaching, feedback, career development and targeted use of external partners where justified.
• Maintain high practical AI fluency and sufficient technical understanding to create simple prototypes, automations and demonstrations, evaluate their outputs and translate business needs into clear engineering requirements, without becoming the bottleneck for implementation.

AI service lifecycle

• Oversee the path from discovery and prototyping to production, adoption, support and retirement for Management Consulting AI solutions.
• Ensure solutions are designed for reuse where appropriate and integrate with approved data, identity, security and collaboration environments.
• Establish proportionate evaluation and monitoring for quality, reliability, user experience, cost and risk, including controls for generative AI outputs.
• Define ownership and support arrangements before launch, and ensure that technical debt, maintenance and business continuity inform investment and prioritisation decisions.

Business partnership and consulting integration

• Partner with Management Consulting leadership and practice leads to translate business priorities into a focused AI agenda and secure decisions on investment, risk and operating change.
• Support proposition and methodology development by identifying where approved AI capabilities can improve the quality, speed or differentiation of consulting work.
• Advise case teams on feasible and responsible use of AI, deploying the AI Engineering team where specialist support is justified. Own the team's allocation and utilisation; engagement leaders remain accountable for client relationships and engagement delivery.
• Represent Management Consulting in relevant Teneo forums and maintain productive relationships with external technology and delivery partners.

Teneo IT partnership and centrally managed technology

• Act as the principal Management Consulting stakeholder for Teneo IT, representing the business's needs and priorities in relation to centrally managed technology, data platforms and services.
• Translate Management Consulting requirements into clear input for firm-wide technology strategy, roadmaps and investment decisions, and communicate central plans, constraints and decisions to Management Consulting leadership.

Management Consulting technology, knowledge management and subscriptions

• Own the strategy, roadmap and priorities for Management Consulting-specific technology, software, data and subscriptions, aligned with business and AI priorities and Teneo-wide standards.
• Maintain a coherent view of Management Consulting applications, data sources, knowledge assets, licences and subscriptions, reducing duplication and addressing material capability gaps.
• Own selected technology-enabled knowledge-management workflows, including the capture, organisation, permissioning, retrieval and reuse of knowledge for consulting and AI-enabled workflows.
• Work with client-facing teams and designated knowledge owners to establish clear accountability for the quality, permissions and appropriate use of knowledge assets, while acting as a key stakeholder in wider knowledge-management decisions.
• Lead business cases, supplier evaluation, procurement and renewals for Management Consulting-specific tools and services. Manage approved expenditure, licences and consumption within delegated authority and the relevant central approval processes.

Training enablement and adoption

• Set the AI capability-building plan for Management Consulting, based on the needs of different roles, practices and levels of experience.
• Direct practical training, guidance, demonstrations, office hours and communities of practice that help colleagues apply approved tools to real consulting workflows.
• Expand the network of leaders and super-users who can reinforce good practice, surface demand and support adoption within their teams.

Responsible AI governance and risk

• Consult Teneo's central AI team on proposed solutions to identify existing capabilities, avoid duplication and maintain alignment with Teneo-wide AI direction.
• Work with Cyber Security, Risk, Legal and IT to translate firm-wide requirements into practical delivery and usage controls.
• Ensure new AI solutions receive the required Cyber Security and Risk approvals prior to implementation, with clear records of decisions, exceptions and accountable owners.
• Promote safe handling of client and firm information and address emerging model, vendor and regulatory risks as the technology changes.

Experience and capabilities

• Substantial experience setting and delivering an AI and technology strategy in a professional services, consulting or similarly knowledge-intensive environment.
• Demonstrable experience leading multidisciplinary technical teams and taking AI or software products from discovery into secure, supported production use, with the judgement to challenge decisions on architecture, integration, evaluation, security and operational support.
• High practical AI fluency, demonstrated through regular use of generative AI tools and the ability to create and evaluate simple prototypes, automations or workflows. Production coding experience is not required, but the candidate must recognise when specialist engineering support is needed.
• Evidence of converting ambiguous business needs into prioritised initiatives, investment cases, delivery plans and measurable outcomes.
• Experience of technology and data governance, including responsible AI, information security, privacy, vendor risk and approval processes.