Head of AI Acceleration

The Head of AI Acceleration is responsible for driving AI strategy execution and adoption across DexKo. This role drives the enterprise AI direction and ensures initiatives move from idea to production at speed, delivering measurable business impact.

This leader works across business units, technical teams, and functional stakeholders to align priorities, remove barriers, and ensure consistent, scalable execution of AI initiatives.

This role is accountable for orchestrating efforts across the organization to accelerate progress and outcomes. As AI resources are distributed across the company, this role will need to coordinate and influence resources in other business functions and segments.

Responsibilities

Drive Adoption and Business Impact:

• Ensure AI solutions are not just built, but adopted and embedded into how work gets done
• Partner with business leaders to define changes in roles, processes, and decisions as a result of AI initiatives
• Establish adoption, usage, and impact metrics across all initiatives
• Monitor performance and drive continuous improvement post-deployment

• Translate enterprise strategy into a prioritized, actionable AI roadmap
• Balance quick wins with longer-term, high-impact initiatives
• Continuously refine priorities based on business needs and results

• Support the development of AI capability across the business through AI Business Partners, Training and enablement programs, and communication.
• Foster a culture of experimentation, learning, and responsible AI use

• Collaborate with business, CoE, and external partners on the end-to-end execution of AI initiatives through the pipeline (idea ? use case ? business case ? POC ? implementation ? production)
• Ensure initiatives progress at pace, with clear accountability and measurable outcomes
• Identify and remove barriers to execution across teams and functions
• Apply company-wide value stream lens to portfolio of projects to ensure holistic approach and project connectivity across business processes, functions, segments and geographies

• Align and coordinate across: Central AI / technical resources and CoE, Business Unit AI Business Partners, Business analysts and AI stewards embedded in functions
• Establish clear ways of working across this network to ensure consistency and efficiency
• Drive prioritization across competing initiatives and resources

• Collaborate with and provide feedback to CoE and AI Governance Council regarding standards for: Use case development, Business case rigor, Model deployment and lifecycle management, change management, ROI and benefit tracking
• Partner with governance bodies (e.g., AIGC) to ensure appropriate controls without slowing progress
• Ensure a repeatable, scalable approach to AI delivery

• Deliver clear, concise reporting to ELT and stakeholders on:
• Pipeline status
• Initiative progress
• Business impact
• Risks and dependencies
• Create transparency and accountability across the AI portfolio

• Chief Business Strategy & Transformation Officer
• Business Unit Leaders
• AI Business Partners (dotted-line alignment)
• Central AI / Data / Technology teams
• Functional leaders (Operations, Finance, Purchasing, etc.)

• AI initiatives consistently move from idea to production without stalling
• Clear visibility into pipeline, priorities, and impact across the enterprise
• Strong alignment between business units and technical teams
• Measurable business value delivered at increasing scale
• AI becomes embedded in how work is done—not isolated pilots

Ideal Attributes

• Execution-focused: drives outcomes, not just strategy
• Influential: able to lead without direct authority
• Pragmatic: balances speed with structure
• Credible: trusted by both business and technical stakeholders
• Resilient: able to push through resistance and ambiguity

Years of experience: 7-15 years

• Proven experience leading large-scale, cross-functional initiatives in a complex organization
• Strong track record of driving execution and delivering measurable business outcomes
• Experience working across business and technology teams
• Familiarity with AI, data, and advanced analytics (deep technical expertise not required, but strong fluency expected)
• Experience operating in a matrixed model

• Travel frequency: Up to 25%