Nitin Kempanahalli

Just concluded a mandate to help define and operationalize the AI Product Strategy for CX, for an early stage tech co. Key learnings for an org which is extremely dynamic in nature and operating in a highly-regulated vertical - 1. Build for today but actively anticipate for future scenarios 2. Stress on the human-in-the-loop (HITL), esp due to the regulations involved 3. Combine CX with account management/ upselling, when the HITL kicks-in 4. Redefine how metrics look in an AI-first world Deployments like these have the added benefit of forcing companies to chart their customer journey and bring the focus back to the user.

2

Launching: Chat with Priyank (beta) today on https://priyank.tech A few weeks ago, I launched priyank.tech as a single home for my work, projects, and notes. Today I’m adding a faster way to get to the signal: Skip the scrolling, ask.  “Chat with Priyank” answers questions about my projects, views, and skills by pulling the most relevant context and giving you a concise, useful response. What’s under the hood (and why): - Human‑verified access - Conversations start only after the system knows it’s interacting with a person, not a bot. - Scope & guardrails - It only answers questions about me. When a query is out of scope or uncertain, it responds with a polite “I don’t know.” - Context engineering first (no RAG, yet!) - An analysis by industry veterans suggested that GPT-5 class models can correctly use ~85% of the provided context. I want to see how far careful, prompt, and context design can go before adding retrieval. I’ll evaluate this over the next few days and decide before graduating from beta. - Prompt caching enabled - To keep costs under control (and responses snappy), repeat prompts and shared context are cached. - Early memory layer - A lightweight short-term memory layer powers more personal, consistent answers across sessions. Try it on: https://priyank.tech   If it’s your first visit, do subscribe to my newsletter—I’ll share build notes, decisions, and lessons learned there. Starter prompts: “Tell me about your AI and Generative AI expertise “How can you help me become an AI first organization?” Feedback is welcome, especially edge cases and failure modes.  #AI #product #buildinpublic #LLM

31

2 Comments

The Great GCC Paradox: We’re Hiring, but Losing India’s GCCs are expected to cross $100B by 2030. But here’s what no one’s openly talking about: We’re leaking talent at the top, and hiring at the bottom. 1. A new study by Mercer shows mid-career attrition in tech has doubled since 2022. 2. CXO-level hiring cycles in GCCs have stretched beyond 6 months (Zinnov, 2024). 3. Returnee talent with global exposure is still asked: Q) “Why do you want to come back?” Q) “Are you too senior for this role?” We’ve mastered delivery. But we’re stuck in a talent loop: - Entry-level upskilling is celebrated - Strategic leadership hiring is feared - Internal mobility is sluggish - Culture fit outweighs culture transformation The result? India’s most capable talent is being interviewed by Singapore, Berlin, and Dubai, while local GCCs are still refining JD drafts. What if the real growth limiter isn’t infrastructure or policy… …but our inability to bet on catalytic leadership? You can’t build a $100B ecosystem with a $1M mindset. Let’s talk: What one mindset shift would unlock India’s next-gen GCC play? #GCC #LeadershipHiring #IndiaTalentCrisis #DigitalTransformation #ReturneeTalent #LeadershipGap #CXOHiring #Mercer #Zinnov #StrategicIndia #FutureOfWork #TalentFlight #HyderabadTech #GCCReset Zinnov Achyut Menon "AK" LinkedIn News India Reshab Shaw Vishwanadh Raju Viswanathan K S GCC Pulse™ Korn Ferry KPMG India

84

19 Comments

Consumers are speaking, questioning, and deciding in the wild. Everyday. Everywhere. On Everything. When insight systems are designed to understand context, emotion, and trade-offs together, clarity emerges well before the market consensus does. At BioBrain, we process millions of unprompted UGC signals through our proprietary Web Intelligence Engine which combines large-scale digital listening, AI-led narrative clustering, and rigorous signal filtration to isolate decision-grade meaning. Our models don’t just track sentiment. They map belief systems, credibility filters, ethical thresholds, and purchase trade-offs as they form in real time. Authentic, deep, consumer-grade insights. This is how we see the beauty category reorganising itself, away from claims and toward proof, coherence, and restraint, long before these shifts harden into demand curves. #BioBrain #Insights #research #beauty

3