Anand Singhal

Everyone talks about AI, but how do you make it work? EXL's CEO Rohit Kapoor shared some great points with moneycontrol.com on exactly that. By embedding AI directly into workflows, EXL is achieving a 94% success rate in implementation. This isn't just theory; it's driving double-digit growth and transforming operations for our clients. If you're interested in how we are turning data into tangible results, give this a read: https://bit.ly/3KZrBlv

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EXL’s 2025 Enterprise AI Study uncovered critical challenges faced by the U.S. insurance industry in AI adoption, including legacy systems, siloed data, and a lack of generative AI adoption. While over 58% of insurers have started using machine learning AI solutions for claims, automation, or fraud detection, only 38% have adopted GenAI tools for chatbots simulating customer service interactions. “Many insurers aren’t building AI into their workflows,” says EXL’s Sumit Taneja, explaining that many companies are still bound by outdated infrastructures. For example, a single insurer might run on 28 different admin systems. To see real transformation, insurers must modernize their platforms and rethink traditional processes. Are these foundational challenges holding your organization back? Read the full article to learn more: https://bit.ly/4lUK9QH #DataIntegration #AIAdoption

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4:30 AM... Continuously learn the business you serve. Information Technology changes rapidly, and staying current with tooling is necessary. Yet tools are only instruments. Proper data architecture determines whether those instruments produce harmony or noise. Technology executes. Data architecture governs structure. Business understanding gives it purpose.

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Enterprise AI Strategy: Moving Beyond the Hype AI is transforming how businesses operate—but are most enterprises truly ready for it? Despite the buzz, many organizations still lack a clear, unified AI strategy. Scattered pilots, disconnected tools, and siloed teams don’t create long-term value. In this article, I break down what a real enterprise AI strategy looks like, why most companies are missing the mark, and how to build one that drives measurable business impact. From aligning AI with business goals to scaling responsibly and governing intelligently, this goes deeper than the usual surface-level take. 🔗 Article link in the first comment. #AI #EnterpriseAI #DigitalTransformation #ArtificialIntelligence #Strategy #Leadership #ML #DataScience #AIAdoption

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Just wrapped up an incredible three days at the Gartner Data & Analytics Summit! I went to the conference expecting most conversations to center around Agentic AI and new AI products. What surprised me was how many sessions — and vendor booths — focused on data foundations: Unified views, semantic layers, data quality, governance, observability, and MDM. Amid all the AI buzz, it was a clear reminder: the future of AI still runs on strong data foundations. → AI agents are becoming a new class of data consumers. If we can design data products for humans, we’ll need to design them for agents too — with strong metadata, semantics, and context. → Governance as product - Humans can question bad data. Agents usually won’t — they will simply act on it. That means governance must evolve from manual checkpoints to automated guardrails. → We’re moving from assistants to autonomous systems. To make that work, organizations will need a stronger knowledge layer — almost a digital twin of the business. → Semantic infrastructure is becoming critical. Knowledge engineering is quickly turning into the difference between reliable AI and hallucinations. Beyond the sessions, the best part was exchanging ideas with peers, partners, and vendors all navigating this shift toward agentic workflows and AI orchestration. Grateful to the leadership at Blue Cross Blue Shield of Michigan for the opportunity to attend. Now comes the interesting part — turning these insights into real architecture. #GartnerDA #DataStrategy #AgenticAI #DataProducts #ContextEngineering #AI #EnterpriseArchitecture

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As we approach the fourth quarter of 2025, India’s economy appears positioned for a consumption-led recovery. US H-1B Visa Revisions: The policy introduces a $100,000 fee for new applications, effective from the March 2026 lottery cycle, but spares renewals and existing holders. Visa issuances to leading Indian and India-centric firms have decreased from 14,792 in 2015 to 10,162 in 2024, representing less than 1% of their workforce. We anticipate limited overall impact, with potential shifts toward alternative staffing models. This may accelerate offshoring and the expansion of Global Capability Centers in India, supporting sustained growth in the technology services and back-office sectors. GST Overhaul: Implemented on September 22, the reform simplifies the regime into a two-tier structure, reducing rates on mass-consumption goods and phasing out compensation cess on most luxury items. Government estimates suggest an injection of approximately INR 2 trillion into the economy, fostering broad-based consumption. The automobile sector is particularly well-placed to benefit, with rate reductions including small cars and compact SUVs from 29-31% to 18%, two-wheelers from 28% to 18%, and tractors to 5%. These changes are expected to enhance affordability and stimulate demand across urban, semi-urban, and rural markets. Festive Season and Budget Support: Coinciding with India’s festive period—a traditional peak for discretionary spending—the GST adjustments align with the February 2025 Union Budget’s personal income tax reductions under the new regime. These measures aim to increase disposable income for middle-class households, with effects likely to materialize more prominently in the latter half of the year. We foresee positive implications for Q4 2025 earnings in consumer-oriented sectors such as automobiles, retail, and discretionary goods. In aggregate, these developments underpin a constructive view on India’s economic trajectory, with domestic demand offsets potentially mitigating external pressures from visa policy changes.

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A very interesting paper to start my weekend reading. Studying variances in LLM behaviours over most common human decision making use cases like Rock-Paper-Scissors is indeed an interesting way to predict and plan for meaningful divergences, when these LLMs are used for problem solving at scale..

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Thank you Seth Cohen for sharing Procter & Gamble’s blueprint for #leading_technology, a framework I believe is highly relevant far beyond the consumer goods industry. The three insights that resonate most with me: 1️⃣ Start with business capabilities and processes, not technology. Clearly define the capability or process transformation you want to achieve, then work backward to identify the technology required to enable it. 2️⃣ Adopt a platform approach that democratizes access to data, insights, and AI. This allows algorithms, generative AI models, and agentic AI to be scaled across all lines of business — rapidly accelerating capability development. 3️⃣ Reimagine advanced analytics. Move beyond static dashboards and traditional analytical applications toward intelligent, adaptive, and embedded solutions that let people converse with their data and insights. For me Technology leadership today is about aligning capabilities, platforms, and intelligence to unlock new value, not just for efficiency, but for transformation and an enhanced customer experience.

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The rapid rise of generative AI is revolutionizing business operations and driving innovation across industries. However, the Public Sector and K-12 education are trailing in adoption. Discover the opportunities, hurdles, and key concerns of AI adoption in the public sector with our latest whitepaper: https://lnkd.in/g9MbpZPX #AI #PublicSector #K12Education

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