AI Transformation Lead

Job Description & Summary

The opportunity

Lead the AI Transformation & Agentic Systems Practice, originate high-value client opportunities and remain accountable for commercial performance, executive relationships and the value delivered by the portfolio.

What you will be doing

·        Set the practice strategy, market positioning, priority sectors and annual go-to-market agenda.

·        Build trusted relationships with boards, CEOs, COOs, CIOs and business-unit executives.

·        Lead major pursuits, executive workshops, strategic alliances and qualification decisions.

·        Sponsor complex client programs and resolve commercial, stakeholder and delivery escalations.

·        Ensure each engagement has explicit business outcomes, accountable owners and value measures.

·        Build a culture that combines consulting quality, engineering excellence, agile delivery and responsible innovation.

What we need from you

·        Significant leadership experience in technology consulting, business transformation or AI-enabled change.

·        Demonstrated success originating and leading complex technology transformation engagements.

·        Strong executive communication, commercial judgment and multidisciplinary leadership.

·        Ability to connect AI, data, cloud and operating-model choices with business economics and risk.

Relevant AI technologies and tooling

·        Executive-level fluency across generative AI, machine learning, agentic systems, retrieval-augmented generation, model evaluation and hybrid AI deployment, sufficient to challenge solution choices and explain their business implications.

·        Awareness of the principal agent-development ecosystems, including LangChain and LangGraph, Microsoft Agent Framework or Semantic Kernel, and OpenAI Agents SDK, together with the ability to remain vendor-neutral when shaping client propositions.

·        Understanding of AI platform economics, including model consumption, data and infrastructure costs, engineering effort, operational support and the implications of cloud, sovereign and on-premises deployment choices.

Measures of success

·        Qualified pipeline and profitable revenue

·        Strategic client relationships and repeat work

·        Portfolio value realized by clients

·        Practice utilization, capability growth and retention

·        Quality and risk outcomes across engagements

Key interfaces

·        Other members of the AI Transformation & Agentic Systems Practice

·        PwC sector, functional, cloud, cyber, risk, Responsible AI and change specialists

·        Client business owners, product owners, technology teams and operational users

·        Technology alliance and implementation partners where relevant

Contribution to the practice

·        Support proposals, client workshops and market development appropriate to seniority.

·        Contribute reusable methods, patterns, code, assets and lessons learned.

·        Coach colleagues and participate in the capability’s continuous learning agenda.

·        Uphold PwC quality, independence, confidentiality and risk-management requirements.

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