Sriman Ravi

🚀 Stop celebrating output. Start measuring outcomes 🚀 Many teams still equate “busy” with “impact.” But real progress isn’t measured by how many features we ship — it’s measured by the value we create and the problems we solve. When we focus on outcomes, we build with purpose, prioritize what truly matters, and create meaningful change for our users and our business. 👉 Read full article here: (https://lnkd.in/eh5KabrU) Let’s build value, not only features

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The Ultimate Guide to Cyber Threat Profiling Cyber threat profiling is the process of creating detailed, data-driven profiles of potential cyber adversaries to better understand their tactics, techniques, and procedures (TTPs). This method helps organizations anticipate, detect, and respond to specific threats by mapping out the behavior patterns and motivations of cybercriminals or advanced persistent threats (APTs). Profiling involves gathering intelligence from various sources, including threat reports, network traffic analysis, and incident data, to identify key indicators of compromise (IOCs) and trends. By creating threat profiles, cybersecurity teams can tailor their defenses to address the most likely attack vectors, prioritize vulnerabilities, and optimize response plans. The ultimate goal of cyber threat profiling is to proactively defend against attacks, minimize damage, and ensure a more resilient security posture. Stay connected to @Anuj Arora for content related to Cybersecurity.    #LinkedIn #Cybersecurity #Cloudsecurity #AWS #GoogleCloud #Trends #informationprotection #Cyberthreats #CEH #ethicalhacker #hacking #cloudsecurity #productmanagement #cybersecurity #appsec #devsecops #Cloudstrategy #cloudgovernance #ITIL #Azure #Datasecurity 

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Most people think AI will take jobs someday. That framing is already wrong. What we’re seeing now isn’t a recession or a downturn. It’s something economists are quietly calling a job lockdown. Here’s what the data actually says: • Goldman Sachs: ~25% of work tasks exposed to AI • MIT: AI already matches tasks across ~40% of jobs • Gartner: Middle management is being flattened, fast This isn’t about job loss in the future. It’s about job redesign happening right now. Roles are merging. Departments are shrinking. Work is being rebuilt around AI. The people under pressure aren’t the least skilled. They’re the ones still competing against machines instead of directing them. I’m curious: Which roles do you think are most exposed right now — and why?

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Transform support with AI-driven customer experience. Hear how Meijer, an American supercenter chain, uses AI to shift from reactive to proactive support, boosting availability, compliance, and customer trust. Learn how SAP’s proactive engagement helps identify and mitigate risks before they impact the customer experience (CX), enabling smooth operations and resilient CX across cloud solutions from SAP. Add the session to your agenda today: https://imsap.co/6048AjEAE

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AI Agents are no longer a futuristic concept—they're here, and enterprises are embracing them at lightning speed! 🚀 For years, we've talked about the potential. Now, we're witnessing the practical application. The biggest shift isn't just *if* they'll be adopted, but *how fast*. These intelligent agents are: ✅ Automating complex workflows. ✅ Boosting operational efficiency. ✅ Freeing human teams for strategic, creative tasks. ✅ Revolutionizing everything from customer support to data analytics. This isn't just tech evolution; it's a fundamental redefinition of business operations. Enterprises are realizing the competitive edge these autonomous systems provide. The future of work is being shaped right now. Where do you see AI agents making the biggest impact in your industry? Share your thoughts below! 👇 #AIAgents #EnterpriseAI #DigitalTransformation #FutureOfWork #Innovation

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FABCON 2026 in Atlanta was nothing short of mesmerizing-where bold ideas, breakthrough tech, and real-world impact came together seamlessly. An electrifying blend of vision, innovation, and execution that set the tone for the future of data and AI. Now, it’s a wait-and-watch moment to see how much of it proves truly real, scalable, and competitively differentiated. ** AI Foundry: Fabric IQ is positioned as a capability that makes data AI-consumable for agents and AI apps via OneLake ** OneLake Transform transformations are applied on top of shortcut or mirrored data (zero‑copy, zero‑ETL), preserving the original source while exposing a transformed view in OneLake. New Connectors have been added (SharePoint in preview). ** Data and Catalog mirroring / zero copy options across open table formats from snowflake and Databricks into Fabric ** SAP Fabric and Salesforce Fabric version with zero copy and write back functionality ** Availability of CoPilot across various platforms within Fabric exclusively designed to support that platform only ** Fabric AI and Data Agents ** Zero ETL Data Mirroring onto Onelake from Azure data for PostgreSQL. Analytics on Operational workloads made simple. ** Building declarative AI Pipelines made simpler with dspy and pixeltable Thanks to Cognizant leadership for the opportunity. Rajesh SreepadaSatish Hegde FABCON & SQLCON - The Microsoft Fabric & SQL Community Conferences Microsoft Fabric

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We've heard the phrase "this changes everything" way too often in reference to technology innovation. But this time, with AI, is it actually true? I watched a recent talk by Mr. Nandan Nilekani, Chairman of Infosys, where he shared his insights on AI and how it's forcing us to rethink and repriotize how we work, how we do business and how we invest our resources. Here's my take on this topic and a summarization of Mr Nilekani's views. Most of us have lived through numerous tech “turning points” - Mainframes. PCs. The web. Mobile. Cloud. But here’s the uncomfortable truth about AI - it isn't just another technology turning point. When the cloud came along, we could lift and shift our workloads. When mobiles arrived, we could create new front-end experiences. But this time, it’s a "root-and-branch surgery" of our existing mental models. AI is forcing us to rethink how businesses actually run. Customer journeys. Operating models. Talent. Even how we define “work.” AI reached scale quickly because the internet and smartphones already paved the road. But the real challenge isn’t the models getting smarter. It’s the deployment gap. ⭐ Technology is sprinting. Enterprises are walking. Under the hood, most large companies are still carrying decades of tech debt. Mainframes from the 70s. Data silos from the 90s. Undocumented systems only two people understand. We’ve been postponing cleanup for years. That is no longer an option! ⭐ This isn’t an opportunity gap. It’s an execution gap. The winners won’t be the ones chasing the latest models. They’ll be the ones modernizing their core, designing for flexibility, reskilling talent, and leading real change. ⭐ AI won’t reward curiosity. It will reward discipline. The future isn’t about who adopts AI first. It’s about who actually makes it work. #AIFromTheCore #RethinkingBusiness #RethinkingWork

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I've been really interested in Model Context Protocol (MCP) and its applications around integrations with AI. This article by my colleague, Murtuzaali Surti provides a deep dive into the applications and implementation. MCP is essentially the "USB-C for AI" - a universal connector that standardizes how AI models interact with external data sources, tools, and systems. Why is this revolutionary? - It eliminates the need for custom integrations for each AI agent or data source - It creates a uniform way for AI to connect to various business systems, databases, and tools - It enables real-time, context-aware interactions between AI and external systems - It allows for capability discovery, letting AI dynamically learn what systems can do For enterprises looking to scale AI adoption, MCP solves many integration challenges. Instead of building custom connectors for each AI model and business system, MCP gives us a standardized protocol that works across environments. As we implement more AI solutions, this kind of standardization will be critical for maintainability, security, and scalability. I expect MCP to become as fundamental to AI integration as REST APIs have been for web services. What are your thoughts on MCP? Are you already implementing it in your systems?

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