How to Increase AI Infrastructure Productivity

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

Every leader I speak with is asking the same question: “𝘏𝘰𝘸 𝘥𝘰 𝘐 𝘪𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦 𝘈𝘐 𝘪𝘯𝘵𝘰 𝘮𝘺 𝘵𝘦𝘢𝘮 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘭𝘰𝘴𝘪𝘯𝘨 𝘵𝘳𝘶𝘴𝘵, 𝘤𝘰𝘯𝘵𝘳𝘰𝘭, 𝘰𝘳 𝘲𝘶𝘢𝘭𝘪𝘵𝘺?” The common mistake is treating AI as a tool instead of a workflow shift. Here are 𝟓 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐭𝐨 𝐛𝐨𝐨𝐬𝐭 𝐰𝐨𝐫𝐤𝐟𝐨𝐫𝐜𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐰𝐢𝐭𝐡 𝐀𝐈 without overwhelming your people: 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝗦𝗺𝗮𝗹𝗹: 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 “𝟱-𝗠𝗶𝗻𝘂𝘁𝗲 𝗧𝗮𝘀𝗸𝘀” Every employee has 5–10 repetitive tasks that eat hours per week. 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: drafting follow-up emails, summarizing meeting notes, creating reports. Map these first. Automate the grunt work before the strategic work. 2️⃣ 𝗖𝗿𝗲𝗮𝘁𝗲 𝗧𝗲𝗮𝗺-𝗟𝗲𝘃𝗲𝗹 𝗔𝗜 𝗣𝗶𝗹𝗼𝘁𝘀 Don’t force company-wide adoption on Day 1. Instead, launch 1 pilot team per department (sales, ops, HR). Document wins and failures → scale what works. 3️⃣ 𝗣𝗮𝗶𝗿 𝗛𝘂𝗺𝗮𝗻𝘀 + 𝗔𝗜 (𝗻𝗼𝘁 𝗛𝘂𝗺𝗮𝗻𝘀 𝘃𝘀. 𝗔𝗜) Best practice: AI drafts, humans refine. Teach your team the “80/20” rule → AI produces 80%, humans polish the critical 20%. Builds speed and confidence. 4️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 AI success ≠ “we’re using ChatGPT.” AI success = measurable savings in time, errors reduced, decisions improved. Ask: “What % of work is now AI-assisted vs. manual?” 5️⃣ 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗔𝗜 𝗟𝗶𝘁𝗲𝗿𝗮𝗰𝘆 Tools will change. What won’t change? Your team’s ability to think with AI. Leaders who train people in prompting, critical evaluation, and creative use cases are building future-proof capacity. This shift with AI upgrades your team’s daily habits so they operate at a higher strategic level. Companies that adopt this mindset will: ✔️ Deliver faster than competitors. ✔️ Free up bandwidth for growth and innovation. ✔️ Retain top talent who want to work in future-ready organizations. 👉 If you’re trying to figure out how to introduce AI in a way that sticks, creates wins fast, and builds adoption instead of fear, that’s where I help leaders design real AI workflows that actually save time and money. www.biginnovates.com ♻️ Share this so more leaders can unlock AI’s strategic edge 🔔 Turn on notifications to stay ahead with daily insights 🤓 Follow Jeff Eyet 🔑✨ for practical strategies on AI and business growth

Portkey doesn’t use any other proxy service under the hood. Why? When building an AI Gateway, conventional wisdom would suggest extending existing API infrastructure. After all, why reinvent the wheel when proven solutions exist? We explored this path feverishly in the beginning, but discovered they just don't work. 🔄 AI requests push traditional infrastructure to its limits. Think - streaming calls, high latency, long request-response windows, and more. A blocking architecture would struggle with throughput at scale, leading to higher infrastructure costs and degraded performance. Irregular traffic spikes to different AI providers can overwhelm these systems. By building our own architecture, we now manage 1k rps with all features enabled easily on a machine with 2 vCPUs - performance that would require 5x the resources with traditional solutions. 📊 Traditional API monitoring operates in binaries - success (2XX) or failure (4XX/5XX). AI introduces a spectrum of outcomes where an API can succeed but the response could be problematic. Existing monitoring solutions struggle to capture these nuances, leading to missed issues and false positives. Teams end up flying blind on actual AI performance. Our observability layer now tracks 50+ AI-specific metrics per request, giving teams real-time insights into hallucinations, token optimization, and response quality - things traditional API metrics never considered. 🌐 Legacy API infrastructure was optimized for ingress & collocated services. But AI providers are distributed across regions and cloud providers. You need the AI gateway to be lightweight enough to run across regions, to minimize roundtrip latencies. We achieved this by building a compact ~120kb gateway that runs entirely on the edge! 🔌 AI infrastructure requires deep integration with specialized components - evals, guardrails, security policies, and provider-specific optimizations. We'd have ended up building plugins for plugins, which is just absurd. Today, our purpose-built plugin architecture has enabled teams to deploy custom guardrails and security policies in production within hours instead of weeks, while maintaining enterprise-grade reliability. 💰 AI infrastructure costs follow fundamentally different patterns than traditional API costs. Existing solutions focus on bandwidth and compute optimization, missing the biggest cost factor in AI - token usage and model selection. Our integrated cost optimization has helped teams reduce their AI spending by 30-50% through automatic token optimization, smart routing, and dynamic model selection - all without any code changes. Today, our infrastructure handles millions of AI requests while providing granular observability, sophisticated routing, and cost optimization - all without compromising on performance or developer experience. Building from scratch wasn't the easy choice, but it was the right one for delivering the next generation of AI infrastructure!

AI Agents - or shifting chat bots into do bots, is the next big thing AI development currently in the hype stage. This article discusses a responsible framework. Taking the leap from having generative AI to do simple tasks to exploring workflows is one step. But AI agents goes beyond that to automating a department or team workflow. That requires some readiness steps including: 1) Identify Repetitive Tasks for Automation: Identify routine and time-consuming tasks that AI agents can handle. These might be some of the simple tasks that you are using generative AI for right now. But you want to put those in the context of a whole workflow using process mapping. 2) Small Controlled Team or Dept. Pilot: Identify a pilot that is low-risk. Better places to start are on internal workflow processes. Identify a metric for success - time savings or work quality improvement? 3) Ensure Human Oversight: While AI agents can handle many tasks autonomously, it's crucial to maintain human oversight, especially for tasks requiring nuanced judgment or ethical considerations. These should be identified during process mapping. And, once the pilot is up and running, set up bias checks, audits, and steps to address issues. 4) Invest in Training and Development: Equip people with the necessary skills to work alongside AI agents. This includes training in prompting, data management, and understanding AI functionalities. Agents are not a pot-roast, set it and forget technology. They require preparation, planning, and monitoring. https://lnkd.in/gh5rXDfH