AI Transformation Leader

About Our AI Transformation Leader Role

Our organization is a global leader in Business Process Management and digital transformation. We are seeking an experienced consultant to lead our AI practice, driving business outcomes through pragmatic, scalable AI adoption.

This role requires a strategic thinker who can advise the C-suite, architect AI roadmaps, and ensure execution that drives financial impact. The successful candidate will connect traditional AI, GenAI, and intelligent automation to real cost takeout, margin improvement, revenue growth, and competitive advantage.

Key Responsibilities:

- Build trusted relationships with senior business leaders (CEO, CFO, COO, CDO).

- Shape multi-year AI transformation programs with clear financial impact.

- Drive account expansion and originate large-scale AI-led transformation deals.

AI Strategy & Roadmap Design:

- Lead AI maturity assessments, opportunity mapping, and prioritization workshops that link AI initiatives to financial metrics.

- Define enterprise AI strategies across Traditional AI/ML, Generative AI, and Intelligent Automation.

- Create build vs. buy AI roadmaps leveraging partner ecosystems.

Solution Oversight & Execution:

- Oversee design and delivery of AI solutions that directly improve margins and optimize cost-to-serve.

- Ensure AI is embedded into operating models, processes, and tech stacks to sustain financial impact.

- Govern delivery to ensure programs move from PoC to scaled financial results.

Practice Development & Thought Leadership:

- Develop and refine AI consulting IP — frameworks, financial impact models, and accelerators.

- Represent our organization in client forums, analyst briefings, and industry events as a trusted voice on AI's role in value creation.

- Mentor consulting talent to think in terms of financial levers, not just technology adoption.

Required Experience & Skills:

- 10–18 years in top-tier management consulting or equivalent transformation leadership.

- Proven track record of selling and delivering large-scale AI/digital transformation engagements with measurable financial impact.

- Expertise in linking operational changes and AI initiatives to financial metrics valued by boards and investors.

AI Implementation Depth:

- Strong working knowledge of AI/ML lifecycle: data strategy, model development, deployment, MLOps.

- Understanding of traditional AI/ML (predictive analytics, optimization, NLP, computer vision) and Generative AI (LLMs, agentic AI, RAG, fine-tuning, prompt engineering).

- Experience embedding AI into enterprise operations at scale to drive cost takeout and growth.

Technology Depth:

- Strong understanding of the AI technology stack including data ingestion and processing layers, data platforms, feature stores, model development environments, MLOps pipelines, APIs, and cloud-native AI services.

- Proven ability to work with Traditional AI (predictive analytics, optimization, NLP, computer vision) and Generative AI (LLMs, agentic AI, RAG architectures, fine-tuning, prompt engineering).

- Understanding of integration patterns for embedding AI into enterprise applications, workflows, and operating models.

- Familiarity with security, governance, compliance, and model monitoring requirements for AI at scale.

Market Ecosystem Knowledge:

- Deep understanding of the AI solutions landscape — from enterprise-scale offerings by hyperscalers to specialized AI-first products from startups and niche vendors.

- Awareness of key differentiators, commercial models, and partnership strategies across the AI market.

- Ability to evaluate and select the right partner and platform ecosystem based on scalability, speed to value, interoperability, and strategic fit.

- Up-to-date awareness of emerging trends and innovations across Agentic AI, Generative AI, and vertical-specific AI applications.

Sector Expertise (Preferred):

- Healthcare, BFSI, CMT, or Customer Operations — with proven ability to identify EBITDA-linked AI use cases.

Core Skills:

- Exceptional structured problem-solving and executive storytelling.

- Commercial acumen with P&L and cash flow understanding.

- High tolerance for ambiguity and a bias for action.

Education:

- Bachelor's degree in Engineering, Business, Economics, or related field required.

- Master's degree (MBA or MS in AI/Data Science) strongly preferred.