Gagan Bajaj

Optimum’s virtual AI agent (AVA), powered by Google Cloud’s generative AI technology, is a masterclass in using AI to deliver smarter, more empathetic customer experiences. AVA leverages Google’s Customer Engagement Suite, Vertex AI platform and Gemini models, and now handles 50% of customer interactions, underscoring the company’s +26 NPS gain over the past two years. Here’s how:  • Personalized support powered by an intelligent knowledge base • Real-time sentiment analysis to meet customers where they are • Conversational recommendations that flow seamlessly across channels • Deep insights into customer behavior and performance Congratulations to Keith Bowen, Shuvankar Roy, and the whole team at Optimum. Read more: https://lnkd.in/g4XzrHZs #AI #GoogleCloud #Gemini #VertexAI

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Are you struggling to prove the ROI of your AI investments? You're definitely not alone. I've heard from so many leaders that it's their single biggest blocker – 57% of them, in fact. In my latest article for (The Guardian), I'm sharing some real ways to prove demonstrable value. As Customer Zero for our own technology, Salesforce is running what I believe is the world's largest agentic AI deployment, and I'm detailing exactly what we've learned. The article outlines five hard-won lessons on how to demonstrate tangible value and move from a pilot to a truly profitable programme. We've seen firsthand an 83% resolution rate for customer queries and have cut escalations to humans in half – the ROI is absolutely real. For any leader looking for a practical playbook on AI deployment, I hope this provides it. The lessons cover everything from unifying your data to building trust with your users. https://lnkd.in/gx7yA_XH #AIStrategy #ROI #DigitalTransformation

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Perhaps the second most interesting data platform trend this year is around pricing models. 1. Fabric, once boasting a predictable bill, has now introduced some on-demand elements in addition to still needing a Fabric subscription. For clarity: I am not critizing the value this could bring to Fabric customers, but the loss of the “one predictable bill” selling point I think is significant, specially if that continues to become more of the norm. 2. Snowflake’s Gen 1, Gen 2, and data volume based pricing is also creating a lot more “variety” which in itself is not bad, but decreases the clarity of the otherwise clear pricing that I’ve publicly praised them for. 3. If you’ve been a Databricks customer for a while, chances are pricing still remains one of most complex things about Databricks. This is mainly fueled by legacy compute and the concept of “Serverless”. But in my interactions with newer cusfomers, generally compute pricing is clear and becomes less of a stumbling element. Fun times!

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In the consumer world, we want a single interface that can write a poem, plan a vacation, and debug code. In that open world, scale is the only strategy that works. But enterprise workflows don’t live in an open world. Most B2B problems like parsing invoices, routing tickets, classifying clauses operate in closed systems. They have well-defined inputs, explicit outputs, and hard failure modes. In my latest article, I argue that the future of enterprise AI isn't always about getting bigger, it's often about model specialization. The most effective architectures I’m seeing are cascading. They use SLMs for routine volume, and escalate to LLMs only when deep reasoning is required. Read the full breakdown below. https://lnkd.in/gkyJbFWb

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LLMs aren’t good enough yet for the general public to use it for shopping… but they’re getting dangerously close. When AI cracks commerce, traffic behavior will change faster than most businesses are ready for.👇

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Before you invest another dollar in AI infrastructure, it’s worth pausing to ask: Where does your data actually live and how fast can it move? We’re seeing a clear shift toward distributed AI, where intelligence flows between clouds, edges, and partners in real time. The balance of data, compute, and connectivity is changing and interconnection is emerging as the true foundation. In our latest blog, we explore how enterprises can “do AI right” by building a foundation that’s flexible, intelligent, and ready for what’s next.

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