Infor is hiring a Senior Director, Analysis & Engagement to compress the time from business intent to outcome: to shape what Infor’s internal AI capability takes on, lead the business analysts who scope and manage that work with an AI-first method of discovery acceleration, and carry the analysis function through a tectonic shift from BI-centric reporting to AI-enabled ways of working. This is an accountable executive role. The leader owns the capability’s demand and prioritization, informed by the AI Council and held accountable through the PMO, and carries the work to the executive leadership team, including the CEO, CFO, and CIO. Success is the quantity of high-quality, highly visible work the capability delivers and executives value, not the volume of requests it fields.

The leader operates as a principled entrepreneur and as one of three capability leaders who succeed together, collaborating so that prioritized demand, the data it needs, and the platform that serves it move as one. They run the enablement that turns AI capability into adoption the business can feel, and they represent the capability’s data and AI point of view across Infor and Koch, bringing outside knowledge back in. The role calls for the judgment and communication of an accountable executive in a Principle-Based Management environment: comparative advantage, a contribution-motivated mindset, intellectual honesty under disagreement, and the clarity to carry value to leaders both technical and not. Their first priority is the team: they inherit an established analysis organization with real strength and real distance to travel, and are accountable for growing it into one that works the new way.

Data & AI is Infor IT’s integrated capability for internal AI: Trusted Data Products, AI Engineering & Platform, and Analysis & Engagement. Scope is internal AI for Infor employees and operations, not customer-facing product AI. Operating principles: choose the work by the value it returns and deliver it at a quality and volume executives notice, not by the requests it clears; allocate talent by comparative advantage against clear time horizons, success criteria, and accountabilities; ship outcomes, not slides. Infor is actively investing in and scaling this capability through 2026 and beyond.

Analysis & Engagement is the demand side of the capability: it translates business intent into prioritized, well-scoped AI and data work through AI-first discovery, feasibility analysis, and scope validation; runs the enablement that helps the business adopt AI; and carries the capability’s point of view across Infor. The work compounds when the capability takes on what matters and delivers visibly, and when AI lands as a new way of working rather than as tools nobody uses. The leader sets that operating model, leads close to the work and to the business, and has the executive presence to be trusted in the room while keeping current delivery whole through a transition that should change how the analysis organization works, not only what it uses.

• Lead and grow the team. Lead, mentor, and develop the business analysts, data analysts, and engagement leads; allocate the work by comparative advantage; and bring an established analysis organization to the frontier, not only keep it running.

• Own demand and prioritization: decide what the capability takes on and in what order. Coordinate the AI Council, which informs priorities; collaborate with the PMO, which holds the work accountable; and escalate to the VP, Data & AI only when priorities cannot be resolved. Choose for value and visibility, and make the tradeoffs explicit.

• Accelerate discovery with an AI-first method: business analysts scope and manage projects, data analysis supports feasibility studies, scope validation, and excavation of the real requirement, and collaboration with the data product teams keeps scoped work landing on certified data.

• Deliver a flow of high-quality, executive-valued work: a steady volume of visible wins the business feels, not a backlog of requests fielded. Set the standard for how findings and outcomes are framed and defended to senior leaders.

• Lead the BI-to-AI transition as a change in ways of working: reorient the analysis organization with named role-level conversion targets, executed quarter by quarter, so the team works the new way while current delivery stays whole.

• Run AI enablement an