Strategies for Capacity Development in AI

As a Global Capability Center(GCC) Leader, the Onus Is on You—Will You Drive AI Transformation or Get Left Behind? Most GCCs were not designed with AI at their core. Yet, AI is reshaping industries at an unprecedented pace. If your GCC remains focused on traditional service delivery, it risks becoming obsolete. The responsibility to drive this transformation does not sit with IT teams or innovation labs alone—it starts with you. As a GCC leader, you must push beyond cost efficiencies and position your center as a strategic AI hub that delivers business impact. How to Transform an Existing GCC into an AI-Native GCC This shift requires clear, measurable objectives. Here are five critical OKRs (Objectives & Key Results) to guide your AI transformation. 1. Embed AI in Core Business Processes Objective: Move beyond AI pilots and integrate AI into everyday decision-making. Key Results: • Automate 20 percent or more of manual workflows within 12 months. • Deploy AI-powered analytics in at least three business-critical functions. • Reduce operational decision-making time by 30 percent using AI insights. 2. Reskill and Upskill Talent for AI Readiness Objective: Develop an AI-fluent workforce that can build, deploy, and manage AI solutions. Key Results: • Train 100 percent of employees on AI fundamentals. • Upskill at least 30 percent of engineers in MLOps and GenAI development. • Establish an internal AI guild to drive AI innovation and best practices. 3. Build AI Infrastructure and MLOps Capabilities Objective: Create a scalable AI backbone for your organization. Key Results: • Implement MLOps pipelines to reduce AI model deployment time by 50 percent. • Establish a centralized AI data lake for enterprise-wide AI applications. • Deploy at least five AI use cases in production over the next year. 4. Shift from AI as an Experiment to AI as a Business Strategy Objective: Ensure AI initiatives drive measurable business value. Key Results: • Ensure 50 percent of AI projects are directly linked to revenue growth or cost savings. • Develop an AI governance framework to ensure responsible AI use. • Integrate AI-driven customer experience enhancements in at least three markets. 5. Change the Operating Model: From Service Delivery to Co-Ownership Objective: Position the GCC as a leader in AI-driven transformation, not just an execution arm. Key Results: • Rebrand the GCC internally as a center of AI-driven innovation. • Secure C-level sponsorship for AI-driven initiatives. • Establish at least three AI innovation partnerships with startups or universities. The question is not whether AI will reshape your GCC. It will. The time to act is now. Are you ready to drive the AI transformation? Let’s discuss how to accelerate your GCC’s AI journey. Zinnov Mohammed Faraz Khan Namita Dipanwita ieswariya Mohammad Mujahid Karthik Komal Hani Amita Rohit Amaresh

The most valuable AI asset isn't a wildly intelligent model. It's the capability you build to use it. After observing dozens of AI implementations, a pattern emerges that mirrors another domain near to my heart: trading. The most successful trading desks don't just subscribe to external data feeds—they build proprietary analysis capabilities that transform common information into uncommon insights. Similarly, leading firms in AI adoption aren't merely licensing algorithms; they're developing institutional knowledge that turns vendor solutions into competitive advantage. This capability-building happens across three critical layers: 1️⃣ At the strategic level, cross-functional AI steering committees ensure alignment between technical possibilities and business realities—particularly important in regulated financial environments. 2️⃣ For technical depth, structured upskilling creates "T-shaped" AI professionals who understand both financial context and technical implementation. 3️⃣ On the operations front, internal AI champions translate between quants, technologists, and business stakeholders—bridging the communication gaps that derail most implementations. In capital markets, sustainable AI advantage requires institutional knowledge that can't be purchased off-the-shelf. The most effective vendor engagements deliberately build this knowledge with: → Pilot-as-a-Service projects where your team shadows vendor experts, creating internal runbooks → Hybrid Pod structures pairing vendor technical leads with your domain specialists → Capacity-Ramp Engagements that financially incentivize knowledge transfer by shifting payment from vendor MSAs to internal headcount For executive teams and boards, this approach demands different oversight questions. Does the vendor own integration outcomes with SLA-backed timelines? Is there contractual clarity on explainability and audit trails that satisfy regulators? Does indemnity cover third-party models and user prompts? How many internal staff will shadow the vendor, and for how long? At what capability threshold do we insource or dual-source? Each successful implementation should leave your organization more capable than before — lowering the cost and time required for the next project. This transforms vendor selection from a procurement exercise into a talent strategy that acknowledges the real source of lasting value: not just what the system does, but what your organization learns. Sustainable advantage in financial technology is fundamentally about capability development, not vendor selection. #governance #fintech #ai #startups

AI-driven teams scale fast—or crash hard. The real game-changer? IO psychology, and how it rewires your talent engine 👇 Most leaders focus on AI tools and forget the human element. Big mistake. Industrial-Organizational (IO) psychology is the secret sauce for AI success. It's about optimizing human performance in tech-driven environments. Here's how IO psychology transforms your AI teams: 1. Talent acquisition: Use psychometric assessments to identify AI-ready mindsets. 2. Team composition: Balance technical skills with soft skills for cohesive AI units. 3. Learning agility: Foster adaptability to keep up with rapid AI advancements. 4. Change management: Reduce resistance to AI integration through targeted interventions. 5. Performance metrics: Develop KPIs that align human efforts with AI capabilities. 6. Leadership development: Train managers to lead hybrid human-AI teams effectively. 7. Organizational culture: Build a culture that embraces AI as an enabler, not a threat. Remember: Your AI is only as good as the team behind it. Invest in your people's psychology, and watch your AI initiatives soar. Elevate your human capital to match your technological ambitions.

Ilya Sutskever explains a lot of obscure concepts, but this one will drive AI capabilities from linear improvement, to exponential. Most AI labs use agentic platforms to improve models faster than data alone. Here’s how it works. Simple agentic platforms provide access to prebuilt apps and existing curated data sources. In the self-improvement paradigm, new agents are added to build new apps and generate new data sources. 1️⃣ During model training, agents are tasked with identifying training gaps. 2️⃣ They hand those gaps to a prescriptive agent that guesses what tools or datasets will help fill each gap. 3️⃣ App builder and synthetic data agents deliver the proposed training environment. 4️⃣ The training gap agent assesses the model to see if the training gap is narrowing based on the improvement plan. If it isn’t, the cycle repeats itself. The goal isn’t to a single model, but to improve all agents to the point where each does its job effectively. The training environment (or playground) grows to host a massive app and dataset suite. In phase 2, the goal shifts from improving the playground to improving the models’ ability to self-improve. Simply put, the objective shifts from optimizing the playground to optimizing how models use the playground to improve. In phase 3, models are optimized to pass on what they learn. Optimized teacher models deliver the biggest jumps in model capabilities, but are least understood. Near-term AI capabilities were overstated, but long-term AI capabilities are underestimated. Models teaching models and models that self-improve, will accelerate skills, capabilities, and eventually, expertise development. #ArtificialIntelligence #GenAI

Power of Agentic AI: A Roadmap for IT, BPO, and GCC Transformation Agentic AI, with its autonomous decision-making capabilities (Step 1), is revolutionizing how IT, BPO companies, and GCCs need to operate. Reskilling the workforce with a structured approach is critical: The following is a roadmap based on the comprehensive learning journey, detailed in the visual (source - Brij Kishore Pandey): 1️⃣ Lay the Groundwork (Steps 1 & 2): Begin by understanding the core concepts of Agentic AI and building a solid foundation in AI and Machine Learning fundamentals. 2️⃣ Acquire Essential Technical Skills (Steps 3 & 4): Equip your team with proficiency in programming (Python), relevant AI frameworks, and a deep understanding of Large Language Models (LLMs) and their architecture. 3️⃣ Master Core Agentic AI Principles (Steps 5, 6, & 7): Delve into the specifics of AI agents, including their types, memory mechanisms (like RAG), and decision-making and planning abilities. 4️⃣ Advance Your Expertise (Steps 8, 9, & 10): Explore sophisticated techniques like prompt engineering, reinforcement learning for self-improvement, and advanced Retrieval-Augmented Generation (RAG) strategies. 5️⃣ Implement and Scale (Steps 11 & 12): Learn how to effectively deploy AI agents in real-world applications, optimize their performance, and leverage them for tangible business impact. Key Transformation Strategies: - Identify high-impact use cases within your specific industry. - Form specialized AI teams with expertise across these learning steps. - Invest in the necessary infrastructure for development and deployment. - Foster a culture of experimentation and continuous learning. - Prioritize responsible data handling and governance. - Iterate on pilot projects and strategically scale successful Agentic AI solutions. IT, BPO companies, and GCCs have urgent and critical need to empower their employees to harness the transformative power of Agentic AI, leading to intelligent automation, enhanced workflows, and improved decision-making. #AgenticAI #AI #ArtificialIntelligence #Automation #Reskilling #Transformation #IT #BPO #GCC #GCCIndia Abhay Vashistha Srikanth Iyengar Aparna Thakur Frank Pendle Brij kishore Pandey Gustavo Tasner Venkat Thiruvengadam

Want to accelerate your AI strategy by years? Read this. Johnson & Johnson just gave a rare public look at what it takes to move from early experimentation to true enterprise value with Gen AI. (Link in comments) Yogesh Chavda - Thank you for sharing. To their credit, J&J leaned in early, encouraging teams across the company to experiment and engage directly with the technology. They expected that decentralizing innovation would unleash speed and creativity. Instead, it created fragmentation. Hundreds of use cases popped up, but many lacked clear value, measurable outcomes, executive visibility, and connection to business priorities. Now, J&J is moving toward a more centralized model, complete with governance, curated tools, and a cross-functional steering com. This is a familiar pattern. Early experimentation is important, but without a disciplined approach, momentum stalls. Here’s how to avoid that. It starts with identifying the right use cases. Here’s a simple filter I use with my clients: 1. Start with real tasks: What does your team actually do day to day? 2. Pressure test: Is this task repeatable? Business-critical? 3. Prioritize: Focus on high-impact tasks that create friction 4. AI check: Can GenAI make this faster, smarter, or more effective? If the answer’s no, move on. Then conduct disciplined experimenting. The key word here is disciplined. Here is what that means: ✔️ Define success upfront: Set clear outcomes and a baseline so you can measure real impact. ✔️ Secure a senior sponsor: You need someone with authority to unblock, advocate, and decide. ✔️ Launch within 30 days: Urgency sharpens focus. Avoid over-engineering and just start. ✔️ Progress over perfection: An MVP with the right training is more valuable than a flawless concept no one uses. ✔️ Plan for 90 days: Enough time to learn. Short enough to stay agile. J&J learned it the hard way: experimentation without structure doesn’t scale. Disciplined pilots are what move strategy forward. Are you following these practices or losing time you can’t afford to waste? #WomeninAI #AITrainer #FutureofWork #AIinInnovation #AISpeaker #AIAdvisor

In January, everyone signs up for the gym, but you're not going to run a marathon in two or three months. The same applies to AI adoption. I've been watching enterprises rush into AI transformations, desperate not to be left behind. Board members demanding AI initiatives, executives asking for strategies, everyone scrambling to deploy the shiniest new capabilities. But here's the uncomfortable truth I've learned from 13+ years deploying AI at scale: Without organizational maturity, AI strategy isn’t strategy — it’s sophisticated guesswork. Before I recommend a single AI initiative, I assess five critical dimensions: 1. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Can your systems handle AI workloads? Or are you struggling with basic data connectivity? 2. 𝗗𝗮𝘁𝗮 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: Is your data accessible? Or scattered across 76 different source systems? 3. 𝗧𝗮𝗹𝗲𝗻𝘁 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Do you have the right people with capacity to focus? Or are your best people already spread across 14 other strategic priorities? 4. 𝗥𝗶𝘀𝗸 𝘁𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲: Is your culture ready to experiment? Or is it still “measure three times, cut once”? 5. 𝗙𝘂𝗻𝗱𝗶𝗻𝗴 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Are you willing to invest not just in tools, but in the foundational capabilities needed for success? This maturity assessment directly informs which of five AI strategies you can realistically execute: - Efficiency-based - Effectiveness-based - Productivity-based - Growth-based - Expert-based Here's my approach that's worked across 39+ production deployments: Think big, start small, scale fast. Or more simply: 𝗖𝗿𝗮𝘄𝗹. 𝗪𝗮𝗹𝗸. 𝗥𝘂𝗻. The companies stuck in POC purgatory? They sprinted before they could stand. So remember: AI is a muscle that has to be developed. You don't go from couch to marathon in a month, and you don't go from legacy systems to enterprise-wide AI transformation overnight. What's your organization's AI fitness level? Are you crawling, walking, or ready to run?

Designing #AI applications and integrations requires careful architectural consideration. Similar to building robust and scalable distributed systems, where principles like abstraction and decoupling are important to manage dependencies on external services or microservices, integrating AI capabilities demands a similar approach. If you're building features powered by a single LLM or orchestrating complex AI agents, a critical design principle is key: Abstract your AI implementation! ⚠️ The problem: Coupling your core application logic directly to a specific AI model endpoint, a particular agent framework or a sequence of AI calls can create significant difficulties down the line, similar to the challenges of tightly coupled distributed systems: ✴️ Complexity: Your application logic gets coupled with the specifics of how the AI task is performed. ✴️ Performance: Swapping for a faster model or optimizing an agentic workflow becomes difficult. ✴️ Governance: Adapting to new data handling rules or model requirements involves widespread code changes across tightly coupled components. ✴️ Innovation: Integrating newer, better models or more sophisticated agentic techniques requires costly refactoring, limiting your ability to leverage advancements. 💠 The Solution? Design an AI Abstraction Layer. Build an interface (or a proxy) between your core application and the specific AI capability it needs. This layer exposes abstract functions and handles the underlying implementation details – whether that's calling a specific LLM API, running a multi-step agent, or interacting with a fine-tuned model. This "abstract the AI" approach provides crucial flexibility, much like abstracting external services in a distributed system: ✳️ Swap underlying models or agent architectures easily without impacting core logic. ✳️ Integrate performance optimizations within the AI layer. ✳️ Adapt quickly to evolving policy and compliance needs. ✳️ Accelerate innovation by plugging in new AI advancements seamlessly behind the stable interface. Designing for abstraction ensures your AI applications are not just functional today, but also resilient, adaptable and easier to evolve in the face of rapidly changing AI technology and requirements. Are you incorporating these distributed systems design principles into your AI architecture❓ #AI #GenAI #AIAgents #SoftwareArchitecture #TechStrategy #AIDevelopment #MachineLearning #DistributedSystems #Innovation #AbstractionLayer AI Accelerator Institute AI Realized AI Makerspace

The path ahead is not just about asking what #AI can do for us but about reorienting our approach to how we can strategically design, deploy, and empower our teams to thrive with AI. For those focusing on "Tailored Technologies" (unique to each organization and its context), it's imperative to delve deep into the specific strategic operations that drive your organization, tailoring AI solutions that amplify these areas to achieve peak effectiveness. On the other hand, "Pervasive Proficiencies" (ubiquitous across all employees) demands a democratization of AI knowledge, ensuring that every employee is equipped and empowered to elevate their work. By embracing this dual approach - addressing both #TailoredTechnologies and #PervasiveProficiencies - leaders can unlock a future where AI does not replace humans but instead, expands human potential exponentially. A Dual Approach to AI Integration Tailored Technologies: How can we strategically tailor AI solutions to seamlessly integrate with and enhance our core business functions while addressing specific organizational goals? Pervasive Proficiencies: How do we cultivate an environment where every employee, regardless of their role, is equipped and motivated to utilize AI tools to enhance their productivity and innovation? #GenAI #ChangeSuccess #ChangeManagement #AIAdoption

𝟲% 𝗼𝗳 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗿𝗲𝗽𝗼𝗿𝘁 "𝗔𝗜 𝘂𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗶𝗻 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀". What they do differently: 𝟭) 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗮𝗻𝗱 𝘁𝗼𝗽𝗹𝗶𝗻𝗲 𝗴𝗿𝗼𝘄𝘁𝗵 Of the companies that expect to see cost savings from AI and GenAI in 2024, roughly half anticipate more than 10% in cost savings. 𝟮) 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝘀𝘆𝘀𝘁𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 Leading companies ensure that teams know how to use GenAI most effectively. Almost half of their workforce will need to be reskilled in GenAI over the next three years. 𝟯) 𝗕𝗲 𝘃𝗶𝗴𝗶𝗹𝗮𝗻𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝘀𝘁 𝗼𝗳 𝘂𝘀𝗲 With GenAI’s rapidly broadening accessibility, companies can expect swift adoption—and rising related costs. 𝟰) 𝗕𝘂𝗶𝗹𝗱 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 AI leaders understand that the technology and the solutions it makes possible are moving fast. They are actively building a partnership ecosystem to gain access to cutting-edge technology and create near-term value. 𝟱) 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 (𝗥𝗔𝗜) 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 The sheer speed of GenAI adoption makes RAI more important than ever. Organizations must be proactive in addressing RAI issues, no matter where they are on their AI journey. (Source: BCG "From Potential to Profit with Generative AI" survey among 1,400+ leaders) #ArtificialIntelligence #MachineLearning #GenerativeAI #DigitalTransformation #IntelligenceBriefing