About the AI Market Unit
The AI Market Unit unifies Cognizant’s AI expertise and best practices to turn our strongest capabilities into repeatable, packaged offerings for clients – positioning us to compete more effectively, win larger AI opportunities, and deliver stronger outcomes across the APJ region.
It is not a delivery factory or a generic centre of excellence. It is a lean, elite, purpose-built squad whose charter is to enable Cognizant to become the most credibleAI Builderin the APJ market and to win a higher share of AI-first deals. The Market Unit focuses on:
- Engaging business leaders across our clients’ organisations, not only IT
- Shaping demand through packaged AI solutions and outcome-based commercial models
- Targeting opportunities across the three Vectors – driving productivity, modernising core systems, and agentifying workflows
- Strengthening Cognizant’s AI brand through showcases, client narratives, and thought leadership
- Bringing regional depth across ANZ, Japan, ASEAN+, and India, respecting each market’s distinct AI dynamics, and driving AI fluency uplift across APJ
Role Summary
The AI Builder / Strategist is a senior client-facing advisor responsible for helping clients identify, prioritise, justify, and scale AI-led transformation initiatives. The role acts as the bridge between business strategy and AI execution, engaging C-suite executives to develop AI vision, operating models, investment roadmaps, business cases, and transformation programs.
The role combines management consulting, AI strategy, industry expertise, and commercial acumen to help clients move from AI experimentation to enterprise-wide value realisation. The consultant works closely with clients, industry leaders, AI ecosystem partners, hyperscalers, solution architects, data scientists, AI engineers, and consulting teams to shape strategic AI opportunities and accelerate adoption.
Key Responsibilities
1.Executive&CXO Engagement
- Engage CXOs to identify AI-driven business opportunities
- Facilitate AI strategy workshops and executive briefings
- Translate AI capabilities into measurable business outcomes
2.AI Strategy&Roadmap Development
- Assess AI maturity, readiness, and organisational capabilities
- Develop enterprise AI roadmaps aligned to business objectives
- Define AI operating models, governance frameworks, and adoption strategies
- Prioritise AI initiatives based on business value, feasibility, and risk
3.Business Case&Value Realisation
- Develop AI investment cases, ROI models, and value-realisation frameworks
- Quantify benefits including productivity, cost reduction, revenue growth, and customer experience improvements
- Identify high-value use cases across business functions and industries
4.Solution Shaping&Deal Orchestration
- Craft and orchestrate end-to-end AI solutions for clients across service lines and industries, mapped to Cognizant’s AI Builder strategy
- Create appropriate AI commercial and pricing models for PoCs and RFPs
5.Solution Delivery
- Lead small teams to deliver PoCs and consulting assignments
- Produce client-ready materials – business cases, value propositions, and demo narratives
6.Sales, Growth&Thought Leadership
- Define the AI go-to-market plan for the assigned industry – opportunity areas and solution plays
- Research industry trends, pain points, and adoption drivers; craft industry AI use-case narratives
- Contribute to market thought leadership and AI consulting offerings
- Drive AI-enabled revenue growth and strategic client relationships
Key Performance Indicators
- Bookings TCV / Influenced Bookings TCV
- Billability: 30%
Experience&Skills
The capability profile below is common to both levels. Levelling is differentiated solely by depth and breadth of experience, as set out in the levelling matrix that follows.
- Proven AI strategy expertise, including Generative AI, Agentic AI, AI operating models, governance, Responsible AI, enterprise adoption, and AI-led transformation programs
- Extensive CXO engagement experience, with the ability to facilitate executive workshops, influence senior stakeholders, and present complex AI concepts in business terms
- Strong business case and value-realisation skills, including development of ROI models, investment cases, cost-benefit analyses, and executive funding proposals
- Experience developing AI roadmaps and transformation strategies – from opportunity identification and use-case prioritisation through to enterprise-scale implementation
- Strong understanding of modern AI technologies, including LLMs, GenAI, AI Agents, RAG, AI platforms (Azure AI/OpenAI, AWS Bedrock, Google Vertex AI), and enterprise data ecosystems
- Dominant industry expertise in one or more sectors such as Insurance, Mining, Telecommunications, Financial Services, Retail, Transport, Hospitality, Public Sector, or Healthcare