⚡ Quick Summary
Published by Deloitte India, this December 2024 leader’s guide explains how Global Capability Centres (GCCs) can assess their readiness for generative AI and drive its adoption across their parent organisations. In the foreword, Rohan Lobo and Anjani Kumar describe it as a resource that provides an approach for identifying and prioritising use cases, a high-level approach to implementation and metrics to measure success.
The core mechanism is a GenAI adoption readiness assessment framework organised around two dimensions: ecosystem enablers (strategic alignment, services and processes delivered, leadership buy-in, culture of innovation) and capabilities (technology infrastructure, talent pool, data management capability, change management and communications, risk, compliance and security, partnerships). Plotting a GCC against both dimensions produces four positions: favourably placed, build capability, re-evaluate strategy and initiate groundwork.
The guide then sets out a three-step method for use cases — identification, qualification and prioritisation — a proof-of-concept-to-production implementation path with qualitative and quantitative success metrics, a short section on AI agents and multi-agents, and Deloitte’s Trustworthy AI framework across seven dimensions. It closes with six actions for GCC leaders.
🧩 What’s Covered
The document moves from context to frameworks to implementation, using illustrative figures and Deloitte case studies.
- The role of GCCs in harnessing GenAI capabilities: describes GCCs as strategic partners rather than passive observers and maps GenAI modalities — text, image, code, audio and video — to applications such as chatbots, code generation, translation and video simulation.
- Accelerating GenAI adoption: lists six enablers — strategic alignment, tech-enabled ecosystem, data access and management, talent availability, culture of experimentation, and scalability and flexibility — then presents three Deloitte case studies and GenAI embedded in enterprise systems (SAP Joule, Oracle’s OCI Generative AI Service, ServiceNow Now Assist, Deloitte Ascend).
- Readiness assessment: Figure 1 plots ecosystem enablers against capabilities into four quadrants, and Figure 2 defines low-to-high criteria for all ten parameters, from strategic alignment to partnerships.
- Use case identification: seven guiding questions covering generated, transformed, translated, analysed, personalised and summarised content and conversations, illustrated for finance processes in Figure 3.
- Qualification and prioritisation: qualification weighs data availability, technical feasibility, financial feasibility and risk, compliance and regulatory exposure; prioritisation maps benefits against effort into low-hanging fruits, high-impact investments, incremental gains and resource drains.
- Implementation and scaling: prerequisites for a proof of concept, a golden set of queries, build, train and validate steps, leadership buy-in, go-live, LLMOps, transition to BAU and hypercare; Figure 7 gives technical, business and risk metrics; pitfalls include data quality, runaway costs, silos and change fatigue, with federated, centralised or hybrid Centre of Excellence models.
- AI agents and multi-agents: defines agents as reasoning engines that plan workflows, use external tools and execute multi-step tasks, and multi-agent systems as teams of specialised agents working together.
- Trustworthiness and other considerations: Deloitte’s Trustworthy AI framework dimensions (safe and secure, private, robust and reliable, transparent and explainable, accountable, fair and impartial, responsible), plus operational, organisational and technological considerations.
💡 Why it matters?
Teams standing up or governing GenAI programmes often have to justify investment and choose between competing use cases without a shared yardstick. This guide supplies one: a readiness assessment that can be completed with leadership, a repeatable sequence for identifying and qualifying use cases, and criteria for reviewing a proof of concept before production. Its governance content — a Centre of Excellence with defined roles, responsible and ethical deployment, and trustworthiness dimensions — addresses the structures that keep adoption auditable. It complements rather than cites external regimes: no law, standard or national AI framework is named or mapped.
❓ What’s Missing
The guide stays at the level of method and names no law, regulation or external standard, so readers get no mapping to the compliance obligations or sectoral rules that apply to their GCC or parent organisation. The readiness framework is qualitative: parameters are described as low or high with no scoring, weighting or benchmark, and the qualification and prioritisation steps carry no cost thresholds or decision rules. Figures 3 to 6 are marked illustrative and reuse the same two finance processes. The agents and multi-agents section is conceptual, with no implementation guidance, and the case studies are drawn from Deloitte’s own engagements for GCCs in India.
👥 Best For
Best for GCC leaders and their transformation or strategy teams, who can use the readiness framework and quadrant positioning to frame investment conversations with parent-organisation leadership. It also suits AI Centre of Excellence designers choosing between federated, centralised and hybrid models, and programme leads who need qualification and prioritisation criteria to shortlist use cases and to define POC success and production metrics.
📄 Source Details
A GCC leader’s guide for driving Generative AI adoption, published by Deloitte India and dated December 2024 on the cover, with copyright to Deloitte Shared Services India LLP. The “Connect with us” page lists Rohan Lobo, Anjani Kumar, Saurabh Mathur, Ganeshji Awasthi and Deepak Mowdhgalya, with a further group under “Acknowledgements”. The PDF runs to 42 pages, printed page numbers to 39; the text extraction covered all 42 pages. No URL for the document itself is printed, only a QR code and www.deloitte.com/about.
