Digital & AI Transformation Director Asset Management

Role description

The Asset Management Digital & AI Transformation Director is accountable for rewiring how Asset Management delivers value to clients by embedding digital, data, and AI capabilities into core delivery workflows, decision‑making, and operating rhythms. This role is about changing how work gets done to make better decisions, reduce risk, improve quality and outcomes, and scale what works across Asset Management.

• Set the Digital & AI direction for the Service Line
• Create an Asset Management specific digital & AI strategy in the context of our 3yr-out vision and strategy of the Asset Management, and the strategic priorities of Industries & Sectors.
• Define and regularly update “digital must-win” areas by conducting competitor analyses and gap analysis within existing portfolio of tools and digital products and partnerships.
• Identify which Asset Management delivery domains and workflows must be redesigned.
• Setup collaboration rhythm with Digital Intelligence, Investment governance, pipeline management approach and prioritization framework, change network, CoPs, etc.
• Set clear objectives for the transformation that link to overall Asset Management performance metrics.
• Own the Opportunity Portfolio
• Collect and pro-actively source digital opportunities and use cases within the organization, in close collaboration with peers in other Service Lines and Industries.
• Kick‑start and actively participate in AI studios around the most promising opportunities, to quickly validate and potentially prototype, ideally in real client settings.
• Work closely together with Digital Intelligence counterparts to make buy/build/partner decisions, as well as start/continue/stop decisions, following pre-set guidelines.
• Actively stop or kill initiatives that do not demonstrate clear value, adoption potential, or strategic fit.
• Participate regularly in client conversations to better understand client needs.
• Act as primary interface and product sponsor to product managers, e.g., help get funding, set requirements, mobilize business SME involvement, anything needed to deliver at pace.
• Redesign end‑to‑end Asset Management delivery workflows to embed digital and AI where they materially improve outcomes.
• Work with Service Line leadership to support adjustments to new ways of working.
• Build a local network of digital & AI champions (change agents and catalysts) within each Service Area Group.
• Strengthen foundational digital skills and AI usage within the organization (e.g., Drive Citizen Development participation, measure and steer on AI usage, make part of SPO).
• Setup adoption campaigns around new tooling / digital products, and ways of working, incl. training programs and updated global procedures.
• Turn global data strategy into an actionable plan within the service line, with clear focus around the priority areas (digital must-wins).
• Create knowledge and data libraries around discipline areas, managed within Communities of Practice.
• Setup data governance (who collects, who structures and maintains) and measure adherence and progress.
• Define any other pre‑requisites for digital innovation and bake into transformation plan.
• Own the Asset Management Digital and AI innovation backlog and roadmap, in partnership with Digital Intelligence, ensuring alignment to strategic priorities and measurable value delivery.
• Drive investment prioritisation and portfolio transparency within the Innovation governance framework, enabling clear decision‑making and optimal allocation of resources.
• Champion digital adoption across the Asset Management workforce and project delivery, working closely with Services leadership to embed tools, behaviours, and ways of working that maximise impact.

• Extensive leadership experience building and scaling global digital, AI, automation or technology‑enabled capabilities within the AEC or related professional services industry, with clear accountability for capability maturity, business value and enterprise impact.
• Experience embedding responsible AI principles, including governance, transparency, human oversight, risk mitigation and regulatory alignment into enterprise AI…