Healthtech, Product Management & tech frontiers

  India probably does not need to compete head-on with NVIDIA or OpenAI to build a large AI industry. Its more natural opportunities are further up the value chain. Tier 1 — India's strongest existing advantages 1. AI services & implementation TCS, Infosys, HCLTech, Wipro, Tech Mahindra, LTIMindtree, Persistent etc. 2. AI consulting & transformation Big Four, Accenture and Indian IT-services companies. 3. Data engineering & analytics Fractal, Tiger Analytics, LatentView, Mu Sigma and others. 4. Enterprise AI Customer service, automation, enterprise copilots and workflow AI. Tier 2 — High-growth opportunities 5. Agentic AI AI agents that actually execute enterprise workflows. 6. Vertical AI Healthcare, pharma, BFSI, manufacturing, telecom, retail etc. 7. AI governance Especially important in regulated industries. 8. AI managed services Operating and continuously improving enterprise AI after implementation. 9. AI-enabled workforce transformation Training, adoption and r...

 Key agentic AI risks in user stories; Human-in-the-loop controls for AI Key Agentic AI Risks to address in User Stories Every AI-enabled user story should explicitly address these risks and define mitigation controls. 1. Hallucination Risk The AI may generate recommendations, summaries, diagnoses, codes, explanations, or actions that are not supported by source data. User Story Considerations: AI responses must reference source data. Confidence scores must be visible. Unsupported recommendations must be flagged. Human review required for high-impact decisions. 2. Patient Safety Risk Incorrect AI recommendations can impact clinical care. User Story Considerations: Define safety-critical workflows. Establish escalation paths. Require clinical approval for high-risk recommendations. Prevent autonomous execution of clinical decisions. Examples: Medication recommendations. Care plans. Diagnostic suggestions. Triage recommendations. 3. Data Quality Amplification Risk AI can spread and m...

The shift in data retention and privacy approaches highlighted by Anthropic’s introduction of Enterprise Frontier Safeguards (EFS)—which moves away from centralized 30-day retention toward decentralized, automated safety monitoring on client-controlled infrastructure—sparks the creation and evolution of several specialized job roles. Ref https://www.cnbc.com/2026/09/01/anthropic-data-retention.html Balancing rigorous AI safety (such as detecting multi-turn jailbreaks and advanced cyber threats) with strict enterprise data sovereignty and privacy regulations creates a demand for professionals who can bridge these two worlds. Key job roles emerging or growing out of this evolving approach to data include: 1. Enterprise AI Trust & Safety Architect / Engineer * What they do: Unlike traditional trust and safety teams that review content manually on a third-party server, these professionals configure and manage decentralized safety frameworks. They implement systems like Enterprise ...

 A developer in a department working on AI, Data science, Platform engineering , in a well known company having a good career progression , told me these Hackathos were useful. Also some upcoming ones are given. You can check the organizers and participate in coming months/ years. It doesn't matter if your project wins or not, what matters is that you learn to - build a product, product management, MVP requirements. (1) AI Verse Hackathon. It is Amrita Vishwa Vidyapeetham’s month‑long national AI competition.   - Organizer: Amrita School of AI, Coimbatore with Tensor AI Club, IETE‑SF, Intel IoT Club   - Tracks: Generative AI; Agentic AI; AIoT   - Format: Online build phase (Dec 22–30, 2025) + on‑campus finale (Jan 8, 2026, Coimbatore)   - Team size: 2–4 members   - Fee: ₹590 per team (incl. GST)   - Eligibility: Students from any college/university; inter‑college teams allowed   - Prizes: ₹50,000 (1st), ₹30,000 (...

 With ref to news https://www.businesstoday.in/jobs/story/ai-layoffs-in-india-66-of-ai-and-ml-workers-expect-job-cuts-within-3-6-months-engineers-remain-least-worried-548225-2026-08-10 The article’s headline is attention-grabbing, but the interpretation needs considerable caution . The most interesting finding is actually the apparent contradiction: AI/ML professionals are among the most worried about AI-driven job cuts, while engineers are reportedly among the least worried. 1. The 66% figure does not mean 66% will lose their jobs The wording matters. “66% expect job cuts within 3–6 months” means expectation/perception , not an observed probability of individual job loss. There are at least three very different propositions: 66% believe their team may see headcount reduction. 66% believe their own job is at risk. 66% will actually lose their jobs . Only the first appears to be claimed. Headlines can easily make readers interpret it as the third. 2. Why would AI/ML...

Corporate culture has developed an uncomfortable habit: turning ordinary work into emergencies. “Can you do this by EOD?” “Need this ASAP.” “Please prioritize this.” “Can we have it in the next two hours?” Sometimes these requests are genuinely urgent. But increasingly, urgency is communicated simply because someone wants faster execution—not because the work itself has a time-critical consequence. The result is a workplace where speed is confused with productivity. The better response to an urgent-looking request is not always “yes.” Sometimes the most professional response is to pause, understand the requirement, assess the consequences of rushing, and agree on a realistic timeline. The problem with “EOD culture” Imagine an employee is working on an important product specification when a manager sends a message: «“Need a quick analysis by EOD.”» The employee immediately switches tasks. Twenty minutes later, another message arrives: «“Can you also review this presentation urgently?”» ...