S&C GN - TS&T – Digital Foundations - AI Op Model & Governance - Manager

Job description

AI Op Model & Governance — Manager Join our team in Technology Strategy & Transformation for an exciting career opportunity to enable our most strategic clients to realize exceptional business value from Data Management, Governance, and AI Operating Model design. Practice: Technology Strategy & Transformation, Global Network Areas of Work: AI & Data Operating Model & Governance Level: Manager Location: Bangalore / Gurugram / Mumbai / Pune / Chennai / Kolkata / Hyderabad Years of Experience: 10 to 15 years Explore an Exciting Career at Accenture Do you believe in creating an impact? Are you a problem solver who enjoys working on transformative strategies for global clients? Are you passionate about being part of an inclusive, diverse and collaborative culture? Then, this is the right place for you! Welcome to a host of exciting global opportunities in Accenture Technology Strategy & Transformation. The Practice — A Brief Sketch The Technology Strategy & Transformation Practice is a part of Accenture Strategy and focuses on the clients' most strategic priorities. We help clients achieve growth and efficiency through innovative R&D transformation, aimed at redefining business models using agile methodologies. As part of this high-performing team, you will work on scaling Data & Analytics — and the governance that makes it trustworthy — to power every single person and every single process. You will be part of our global team of experts who work on the right scalable solutions and services to help clients achieve their business objectives faster. AI Operating Model Design: Define structural and process changes required to move from AI pilots to enterprise-scale AI operating models, including AI Centre of Excellence design, org design, and ways of working. AI Governance & Risk Management: Design pragmatic AI governance frameworks that define policy and guardrail structures across Responsible AI, model risk, privacy, security, compliance, human oversight, and third-party AI usage. Data Operating Model & Data Management: Design governance structures, policies, data stewardship frameworks, and accountability models for enterprise data at scale, including regulatory compliance (GDPR, DPDP, CCPA) and data ownership frameworks. Define MDM architecture, golden record strategy, entity resolution, and data domain ownership across the enterprise using leading MDM platforms. Establish data quality frameworks including data profiling, data cleansing, data validation, DQ monitoring, and remediation processes to ensure trusted data at scale. Design and implement metadata management, data lineage, data discovery, business glossary, and enterprise data catalog capabilities using platforms such as Collibra and Alation. Bring Your Best Skills Forward to Excel in the Role AI Operating Model Design: Define the structural, governance, and process changes required to move from AI pilots to enterprise-scale adoption.Shape AI target operating models covering AI Centre of Excellence design, federated vs. centralized ownership, talent and capability model, operating cadence, and cross-functional ways of working across business, data, technology, risk, and legal teams. Establish pilot-to-scale frameworks including use-case intake and prioritization, value tracking, lifecycle management, model ownership, MLOps/LLMOps handoffs, business adoption, and change enablement. Drive role clarity, process integration, and service management models to embed AI into enterprise delivery and business operations at scale. AI Governance & Risk Frameworks: Design pragmatic AI governance frameworks that balance innovation, control, and regulatory readiness. Define policy and guardrail structures across Responsible AI, model risk, privacy, security, compliance, human oversight, and third-party AI usage. Establish governance processes for use-case triage, risk classification, approval workflows, documentation standards, testing and validation, monitoring, incident response, and periodic review. Advise clients on controls for GenAI and traditional AI across data access, prompt and output risk, explainability, transparency, bias and fairness, traceability, and model lifecycle accountability. Translate emerging regulatory expectations and enterprise risk requirements into actionable governance operating mechanisms, roles, forums, and reporting. Data Operating Model & Data Management: Define a Data & Analytics Operating Model to manage data across organizations, with working exposure to core data management disciplines including data governance, stewardship, accountability, master data concepts, and data quality practices. Support the design of data ownership models, governance forums, policy structures, quality controls, and domain-based roles. Bring a practical understanding of concepts such as golden record, reference data, entity resolution, profiling, monitoring, remediation, and trust enablement as good-to-have capabilities in broader AI and data transformation engagements. Establish frameworks for effective data governance across multi-speed implementations. Define data ownership, data policy, data stewardship roles, accountability frameworks, regulatory compliance (GDPR, DPDP), and associated processes. Drive Data Capability Maturity Assessments for clients. Design and implement metadata management capabilities, data lineage, data discovery, business glossary, and enterprise data catalog solutions. Demonstrate expertise in tools such as Collibra, Alation, and data observability platforms. Data Platform & Cloud Strategy: Demonstrate a fair understanding of data platform strategy for data-on-cloud migrations, big data technologies, large-scale data lake, and DW-on-cloud solutions. Utilize strong expertise in cloud data platforms — Google BigQuery, Azure Synapse, AWS Redshift — or equivalent. Executive Advisory & Stakeholder Engagement: Lead discovery workshops and executive design sessions to identify Data & AI opportunities, clarify business pain points, and align stakeholders on strategic priorities. Demonstrate consulting-led problem structuring, hypothesis-driven thinking, and the ability to translate ambiguity into actionable recommendations, roadmaps, and investment cases. Engage confidently with CIO, CTO, CDAO, and business leadership on target state architecture, AI governance, operating model decisions, transformation sequencing, and value realization. Use strong presentation, storytelling, and facilitation skills to influence senior stakeholders and drive alignment across business and technology teams. Budgeting & Proposals: Manage budgeting and forecasting activities and own proposals response for AI Op Model engagements. Read more about us. The rise of forerunners Recent Blogs Your Experience Counts! MBA from a tier 1 institute preferred. 5 – 7 years of Strategy Consulting experience at a consulting firm. 3+ years of experience on projects showcasing skills across: data governance, data policy, data stewardship, accountability frameworks, data quality, data profiling, data cleansing, DQ monitoring, master data management, MDM, golden record, data domain, reference data, entity resolution, metadata management, data catalog, data lineage, data observability, AI operating model, and org design. Proven experience leading cross-functional discovery and design workshops, driving structured discussions to extract insights, align diverse stakeholders, and translate inputs into clear value propositions, recommendations, and actionable roadmaps. At least 3+ years of experience designing solutions for these domains: Data Quality, Master Data (MDM), Metadata Management, and Data Catalog. Experience in one or more technologies in the data governance space: Collibra, Alation, Talend, Informatica, SAP MDG, Stibo, Alteryx, or equivalent. Experience in at least two MDM technologies — Informatica MDM, Reltio, SAP MDG, or equivalent. Deep understanding of data supply chain and building value re