Vitesh Kohli

"Ethical AI isn’t a destination – it’s a discipline." As AI systems become increasingly embedded in more decision-making processes, governance shifts from a compliance requirement to a strategic advantage. In a recent interview with CMR, Ashish Heda (Data Science Technology Partner at Tiger Analytics) shares his perspectives on the evolving practice of Ethical AI, diving into: ⬩ Mitigating bias across the AI lifecycle ⬩ Balancing performance and explainability in regulated industries ⬩ The need for robust data privacy frameworks ⬩ Cultural shifts for responsible AI adoption ⬩ Emerging technologies like LLMOps, AgentOps, and AIOps For leaders at the intersection of AI, governance, and impact, the interview offers practical insights on what’s next for intelligent systems and how to get there responsibly. Read the full article: https://lnkd.in/gcNeP4fc #EthicalAI #AIGovernance #ResponsibleAI #BiasInAI #DataPrivacy #TigerAnalytics #LLMOps #AgentOps #EnterpriseAI

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Enterprises have moved from AI curiosity → AI committees → real AI adoption. Khadim Batti (Co-founder & CEO of Whatfix) explains how CIOs and large companies are: ✔️ Building AI task forces ✔️ Running low-risk AI pilots ✔️ Preparing for wide adoption in the next 12–18 months 🎙 Watch the full episode here: 👉 YouTube: https://lnkd.in/dbGPmvQC  🎧 Listen on Spotify: https://lnkd.in/dyZk4g7g  Apple Podcasts: https://lnkd.in/dJWiUsQt #AI #ArtificialIntelligence #EnterpriseAI #DigitalTransformation #SaaS #EnterpriseSoftware #AIAdoption #PrimeVenturesPartnersPodcast

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AI Readiness vs. AI Risk Readiness   Every large enterprise claims to be “AI-ready.” In this year's Q4 board reviews, that phrase is becoming a red flag. Because the question has shifted from “Where are we using AI?” to “How are we governing it?”   The emerging fault line:
 AI Readiness is about pilots, use cases, and adoption velocity.
 AI Risk Readiness is about exposure, accountability, and evidence on demand.   One gets you a budget; the other determines if you keep it.   In boardrooms, the friction is no longer model accuracy — it’s proof of control: ·     Who owns each model’s outcome? ·     What risk tier does it fall under? ·     How fast can compliance evidence be produced? ·     Does our governance cadence match our innovation cadence?   These are no longer "AI ethics" discussions. They are funding eligibility criteria. And they are deciding which programs make it into 2026 budgets.   The pattern is clear: AI initiatives that can't produce governance evidence on demand are being reclassified from "innovation" to "uncontrolled risk." That single label shift is enough to freeze next year’s spend.   For the programs that can answer these questions, the result isn't just approval — it's accelerated approval and trusted access to the risk budget, not just the innovation budget.   Most enterprises have an AI Center of Excellence (AI CoE).
 The ones securing 2026 funding are building an AI Risk Center of Excellence — not as a watchdog, but as the control tower for model development, compliance, and board assurance.   We've codified this into a 90-day AI Risk Readiness Sprint, used by renowned firms to secure board sign-off. It's built around a validated diagnostic that benchmarks your risk posture in under 30 minutes.   If your AI governance answer is still "We’re drafting the policy," it's time to pressure-test your readiness against board-level scrutiny.
   When the board asks for your proof of control, what will you show them? The market has spoken: AI Readiness gets you in the race, but AI Risk Readiness determines who finishes it. Transform Partner – Your Strategic Champion for Digital Transformation Image Source: NIST

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