AI Solutions Lead

This role is a Global role, Candidates do not need to be based in Bonn (Remote working)

Job Purpose:

At DHL, the AI Solutions Lead ensures we focus on solving the right business problems — not just building sophisticated technology. This role sits at the intersection of business strategy and AI delivery, translating ambiguous business demand into clear, actionable problem statements that data science and engineering teams can execute against.

Operating across commercial, operations, customer experience, and network efficiency domains, you identify where AI can create meaningful business impact, shape high-value use cases, and drive them from initial discovery through to adoption.

You are not managing a single product roadmap. You are responsible for continuously identifying, shaping, and prioritizing the most impactful problems — ensuring the team builds solutions that are used, adopted, and deliver measurable outcomes.

Differentiator:

• A highly experienced problem-solver who combines structured thinking with strong product intuition and a bias toward action.
• Operates effectively in ambiguity, making decisions with incomplete information and focusing on progress over perfection.

• Understands when to invest in further discovery and when to move quickly to building and testing real solutions.

Key Tasks:

Business Discovery & Problem Framing

• Lead structured discovery with commercial leaders, operations teams, and regional stakeholders to uncover the real business problem behind stated requests
• Challenge assumptions and apply strong judgment to determine whether AI, analytics, automation, or a simpler solution is the right intervention.

• Drive alignment on clear problem definitions, focusing on decisions to be improved, users impacted, and measurable business outcomes.

Translating Demand into Buildable Briefs

• Convert business needs into crisp, build-ready problem briefs that clearly articulate:
• The problem to be solved and why it matters
• Target users and decisions being supported
• Success metrics and measurable outcomes
• A realistic first version that can be delivered in 3–4 weeks
• Ensure technical teams receive sufficient context to build effectively, without over‑specifying solutions prematurely

Rapid Discovery & Early Validation

• Run rapid discovery sprints with data scientists and engineers, combining stakeholder input, data exploration, and early prototyping.
• Maintain a strong bias toward tangible outputs — prototypes, early insights, or simple working versions — that stakeholders can react to quickly.

• Continuously refine both problem definition and solution approach based on feedback and learning. Delivery

Delivery Oversight & Value Realization

• Stay closely engaged through development to to ensure solutions remain aligned with business needs and adapt as requirements evolve.
• Define success metrics upfront and lead user acceptance testing (UAT) ensuring solutions are usable and relevant in real operational contexts.

• Drive adoption by working with stakeholders to embed solutions into workflows and decision-making processes.

Demand Management & Prioritization

• Own and manage the pipeline of incoming AI and analytics demand across the organization.
• Partner with leadership to prioritize initiatives based on business value, feasibility, and strategic alignment.

• Make clear decisions on what gets built, deferred, or stopped — maintaining focus on high-impact outcomes over activity.

Stakeholders:

• Act as a primary interface for senior business stakeholders exploring AI-enabled solutions.
• Set clear expectations on what AI can and cannot deliver, and guide stakeholders toward pragmatic, high-impact use cases.

• Represent the AI team with credibility, combining business understanding with practical knowledge of AI capabilities and constraints.

Management Responsibility:

• Manage a department or a small unit that includes multiple teams

Skills:

Problem structuring, product thinking, stakeholder management, AI/analytics literacy, business case development, prioritization, facilitation, communication, data-driven decision-making, user-centric design, change management.

Strong ability to translate between business needs and technical execution, and to balance speed, feasibility, and impact.

Qualifications & Key Requirements:

Education Level

Bachelor’s degree in business, Engineering, Data/Analytics, Computer Science, Economics, or a related field

Experience Level:

5–8 years’ experience in management consulting, strategy, product operations, or adjacent roles requiring structured problem-solving and delivery in ambiguous environments Proven ability to translate complex business problems into actionable initiatives and to work closely with data, analytics, or engineering teams. Hands-on exposure to AI or advanced analytics is strongly preferred, with a practical understanding of capabilities and limitations.