Podcasts about Piyush

In part one of our conversation with John Godlove and Piyush Agarwal, co-founders of Fusion Hive, we talked about starting with the workflow instead of the AI. Before choosing a model or building an agent, you need to understand the problem. You also need to understand the process, the data, and what you're trying to improve. In part two, we took that conversation further. We looked at what happens when you move from an AI demo into something a business actually depends on. That's when governance, data privacy, testing, guardrails, monitoring, and cost become much more important. Piyush made a comment that sums this up well. If your workflow is already broken and you throw AI at it, you're probably going to accelerate the chaos. About John Godlove and Piyush Agarwal John Godlove is co-founder of Fusion Hive and has more than a decade of experience helping organizations from startups to Fortune 500 companies build and deploy automation. His background includes work in the Salesforce ecosystem, AI, data, and technology implementation. Piyush Agarwal is co-founder of Fusion Hive and has more than a decade of experience in artificial intelligence and machine learning. His experience includes financial fraud detection, document intelligence pipelines, generative AI multi-agent architectures, and production machine-learning engineering. Together, John and Piyush founded Fusion Hive to bring enterprise AI and automation experience to small and mid-market businesses. Their approach focuses on practical workflows, measurable outcomes, governance, and production-ready AI systems. AI Has a Way of Exposing Problems You Already Had We've been talking all season about AI exposing the cracks in businesses and software development. This conversation was a great example of what we mean. Many businesses don't have perfectly documented processes. The official procedure may say one thing, while the person doing the job follows a slightly different process because they know what works. Sometimes that critical business knowledge isn't documented anywhere. It may be sitting in someone's head, buried in an email, or handled through an informal process. Humans are pretty good at filling in those blanks and making judgment calls. Software needs us to be much clearer about the rules. John explained that small and mid-market businesses don't always have mature governance structures. Regulated organizations tend to have more structure because regulations require it. Other companies may not discover these governance problems until they start looking seriously at AI. The AI project doesn't necessarily create the crack. It often shines a light on something that was already there. That can actually be a good thing. An AI project may force you to understand how your business really works. It can push you to document processes, identify ownership, and clean up your data. You may get significant value from that work before the first AI workflow ever reaches production. AI Governance Is Bigger Than AI When I asked John and Piyush about governance and protecting customer data, one thing became pretty clear. You can't treat AI governance as something that exists by itself. Your business already has systems, users, data, security policies, vendors, and access controls. The AI system has to live inside that environment. Piyush explained that Fusion Hive tries to work within the systems a customer already uses. They don't automatically introduce another platform. A business may already be heavily invested in Microsoft, Google, Azure, AWS, or another ecosystem. Those existing systems may already provide infrastructure that can support the new workflow. John added that internal IT departments and managed service providers also need to be part of the conversation. AI governance overlaps with data governance, cybersecurity, access controls, and existing policies. You can't build one without considering the others. I think that's an important way to look at this. AI may feel like an entirely new technology problem, but it's still another component in your business systems. You still need to know who can access information, where that information goes, and who owns the process when something fails. Know Where Your Data Is Going Data privacy becomes even more important once AI enters the conversation. Before connecting business information to a model, find out where that data goes and what happens to it. Does the provider retain it? Can the provider use it for model training? Does the platform provide the protections your business needs? Those questions become critical in healthcare, financial services, and other regulated environments. Piyush talked about cases where businesses may need specific configurations or agreements with providers. These protections can prevent providers from using company data for model training. He also pointed out that not every use case requires a frontier model. A smaller or open model may work for some problems. That option may also give the business more control over where and how it processes information. The exact solution depends on the business. The important thing is to ask these questions before implementation. You don't want to connect company data to an AI system and then ask where that information went. The Wrong Use Case Can Make AI Expensive Fast When I asked John and Piyush about mistakes they've seen businesses make with AI, John's answer wasn't some crazy technical failure. One of the biggest mistakes is simply choosing the wrong use case. A business leader hears that the company needs AI. Somebody gets a directive to "get AI live," and the team builds something impressive for a demo. But nobody defines what business metric the project should improve. John suggested working backward from the business outcome. Maybe you want to improve margins or reduce turnaround time. Maybe you want to shorten lead qualification or improve the lead-to-cash process. Once you know the target, you can trace that goal back to the workflows that influence it. This is where I think many AI projects can get into trouble. "We implemented AI" isn't a business result. If you don't know what you're trying to change, you may spend money without knowing whether the investment helped. You Need a Baseline Before You Can Measure ROI Of course, that creates another problem. Some businesses don't know how their existing processes perform today. They know people are busy and a workflow feels slow. But they haven't measured the time, exceptions, rework, or actual cost. John talked about measurements such as cycle time, quote turnaround, lead qualification, and lead-to-cash timing. Those numbers give you something to compare after you introduce automation. If a process took 40 hours before AI and takes 20 afterward, you have a measurable improvement. The same applies if errors drop or turnaround improves. Without a baseline, you're guessing. Six months later, you might have a cool AI system. But when somebody asks what it saved the company, the answer can't just be that everyone seems to like it. Don't Automate a Broken Process This is where Piyush made one of the strongest points of the interview. If the workflow is already broken, AI will accelerate whatever already exists. That includes the chaos. Bad data doesn't suddenly become good because an LLM reads it. Unclear ownership doesn't disappear because an agent performs the task. If nobody can tell you whether the result is correct, you're probably not ready to hand that process over to AI. Before you automate, you may need to clean the data or document the rules. You may need to fix the process or determine who owns the outcome. That work can slow down an AI project at the beginning, but I'd rather find those problems before production than after. This reminds me of what we see in testing and automation. Automating a bad test or a bad process doesn't make it better. It just lets you run the wrong thing faster and more often. AI gives us more capability, but that basic engineering principle hasn't changed. Evaluation, Guardrails, and Monitoring This was probably my favorite technical part of the conversation because it lines up with my testing and integration background. Piyush identified three areas that matter when moving an AI workflow toward production: Evaluation Guardrails Monitoring Before production, you need to evaluate the workflow against known data. Piyush described maintaining a holdout or "golden" dataset with known expected results. You can run the workflow against that data and check whether the system produces acceptable results. For developers and testers, this should sound familiar. We've been doing versions of this forever. Give the system a known input, define the expected output, run the test, and compare the results. AI changes how predictable some outputs may be. It doesn't remove the need to test them. In fact, I think AI's variability makes evaluation even more important. Put Guardrails Around What AI Can Do Testing the output is only part of the problem. Once an AI system can take actions, you need to decide what it's allowed to do. We've already seen examples of public-facing AI systems producing ridiculous results because nobody properly constrained them. Guardrails should define where the AI has authority and what requires validation. They should also define when the system needs to stop and ask for help. Some decisions may require deterministic rules, while others may need human approval. This is another place where I don't think we should look at the problem as AI versus humans. A better solution may combine AI, deterministic software, validation rules, and people. Give each part of the system the job it's actually good at. Deployment Isn't the Finish Line Piyush's third piece was monitoring. I think this will become increasingly important as more companies put AI into production. You don't test the system once, deploy it, and forget about it. You need to watch what happens after release. What questions are users asking? What does the system return? What new exceptions appear? Where does it get confused? Those production interactions can become part of the feedback loop that improves the system. That's not very different from what we've learned from building software for years. Production teaches you things your test environment never will. AI can make the behavior less predictable, which makes monitoring and ownership even more important. Don't Forget the People Using the System You can choose the right use case, design the right architecture, and test everything correctly. The implementation can still fail if nobody uses it. John talked about enablement and training as a critical part of the rollout. This becomes especially important when AI changes an existing business process. Employees need to understand what changed, how to use the new system, and why the organization made the change. You also need to deal with the obvious concern that employees may think AI is there to replace them. If that's what people believe, don't be surprised when adoption becomes difficult. We've dealt with change management in software projects forever. AI hasn't removed the human side of implementation. The Most Powerful Model Isn't Always the Right Model Our bonus conversation moved into another area that more businesses are starting to discover: running AI in production can get expensive. Most people first experience AI through ChatGPT, Claude, Copilot, or another chat interface. Production workflows can look very different. An agent might ingest emails, read PDFs, extract information, transcribe documents, and call other systems. Now imagine doing that thousands of times. John explained that businesses need to understand these cost drivers because AI expenses can vary much more than a traditional monthly SaaS bill. Piyush made another good point here. The biggest and most capable model isn't automatically the right model for every task. If you're doing a simple email summary, do you really need the most expensive frontier model available? Probably not. Route the Work to the Right Tool This brought us back to something we discussed in part one. Sometimes the right solution isn't AI at all. Piyush described task-based routing, where the system decides what capability a particular task needs. A simple request might go to a smaller model. A complex reasoning problem could go to a more capable model. A deterministic task might go directly to traditional code. This approach can improve cost, speed, and predictability. John also talked about making systems LLM agnostic. You don't want to lock an entire business process into whichever model happens to be popular today. This market moves incredibly fast, and today's best choice may not be the best choice a year from now. That's more than a technical architecture decision. If you're building something the business expects to run for years, you need to think about changes in pricing, vendors, and technology. Build for the Business, Not the AI Trend The biggest thing I took away from this second part of our conversation is that successful AI implementation looks a lot like good engineering. Understand the problem, know where your data comes from, establish ownership, and define the expected outcome. Then test the system, add controls, monitor production, and make sure the people using it understand the change. AI gives us some incredible new capabilities, but it doesn't make those fundamentals disappear. If anything, it makes the cracks more obvious when we skip them. Before you automate that next process, take a hard look at what you're actually automating. If the process is broken, the data is bad, nobody owns the outcome, and you can't measure success, AI probably isn't your first problem. Otherwise, you may not be automating your business. You may just be automating the chaos. Stay Connected: Join the Developreneur Community

AI is everywhere right now. Businesses are being told they need to adopt it, developers are being asked to build with it, and almost every software product seems to be adding some kind of AI feature or AI Workflow Automation. But just because you can put AI into a process doesn't mean you should. That was one of my biggest takeaways from our conversation with John Godlove and Piyush Agarwal, co-founders of Fusion Hive. Both have spent more than a decade working with automation, data, machine learning, and enterprise technology. What I liked about this conversation was that we weren't talking about AI as some magical solution. We kept coming back to something much more practical: understanding the problem, understanding the workflow, and then deciding where AI actually makes sense. About John Godlove and Piyush Agarwal John Godlove is co-founder of Fusion Hive and has more than a decade of experience helping organizations from startups to Fortune 500 companies build and deploy automation. His background includes work in the Salesforce ecosystem, AI, data, and technology implementation. Piyush Agarwal is co-founder of Fusion Hive and has more than a decade of experience in artificial intelligence and machine learning. His experience includes financial fraud detection, document intelligence pipelines, generative AI multi-agent architectures, and production machine-learning engineering. Together, John and Piyush founded Fusion Hive to bring enterprise AI and automation experience to small and mid-market businesses. Their approach focuses on practical workflows, measurable outcomes, governance, and production-ready AI systems. The Best AI Project Might Be the Boring One When most people think about an AI project, they tend to think about the flashy stuff. They want a chatbot, an agent, or something they can demo and say, "Look what our AI can do." John brought up a different type of project: the boring back-office workflows that nobody really wants to do. Think about someone receiving an email with a PDF attached, opening the document, finding a few pieces of information, entering that information into another system, and then doing the same thing again. Maybe they do it 20 times a day. Maybe they do it hundreds of times a week. John described these as "swivel chair" workflows. Employees are constantly moving between systems, re-entering information and turning unstructured data into structured data. These aren't exciting processes, but they can consume an incredible amount of time. That's exactly why they can be great automation candidates. If you can save five or ten minutes on something that happens hundreds of times, the value starts adding up pretty quickly. Not Everything Needs AI This was probably one of my favorite parts of the conversation because it goes back to something I've seen throughout my career in software development, integration, and testing. Sometimes you don't need AI. Sometimes you just need a script. Piyush made a great point about being deterministic where you can. If you're extracting something from a document and a regular expression, parser, Python script, or existing library can reliably do the job, why send it through an LLM? We've had these tools for years. They work, they're predictable, and they're usually cheap to run. The interesting part is figuring out where that deterministic approach stops working. Maybe you're dealing with text that needs interpretation. Maybe the documents aren't consistent. Maybe you're trying to understand intent or classify information that doesn't fit neatly into a set of rules. That's where AI may start making sense. The answer doesn't have to be traditional software or AI. A good system might use both. Build the Workflow First Piyush described their approach at Fusion Hive as workflow first, and I think that's an important distinction. Don't start by asking, "Where can we use AI?" Start by asking, "What are we actually trying to accomplish?" Walk through what people are doing today. Where does the information come from? Where does it go? Who touches it? What decisions are being made? Where are people wasting time? Where are mistakes happening? Once you understand that, you can start deciding which pieces should be automated. Some steps might be traditional software. Some might use AI. Some might disappear completely. And some may still need a human. That's a much different approach than dropping AI on top of an existing process and hoping it makes everything better. Human in the Loop Is Only Part of the Solution We've talked a lot this season about keeping humans involved with AI, but this conversation made me think about it a little differently. It isn't always just AI versus human. You may actually have three or four options for every step in the process: deterministic software, traditional automation, AI, or a person. The key is deciding which one makes sense for that particular task. A predictable data transformation probably belongs in code. A text-heavy classification problem might be a good fit for AI. A rare exception involving money, compliance, or an important business decision might still need a person. John also brought up something that is easy to overlook when you're designing these systems: the happy path isn't the whole workflow. What happens when something doesn't fit? Does another automation handle it? Does another AI agent look at it? Does it go to another department? Does a person need to review it? Those exceptions are part of the system too, and if you don't think about them before you automate the workflow, you're probably going to discover them the hard way later. Don't Automate a Process Just Because It Exists Another point John made really stood out to me. When you introduce AI or automation, you shouldn't automatically recreate every step of the existing process. He used the example of a law firm where documents might pass through several levels of review before eventually reaching a partner. If you build an AI-assisted process, evaluate it properly, and reach a point where you trust the output, maybe all of those intermediate approvals aren't necessary anymore. That's an important difference. You're not just asking: How do we automate this process? You're asking: What should this process look like now? I've seen this in software projects for years. Companies sometimes spend a lot of money automating a bad or outdated process. They end up doing the wrong thing faster. Before you automate something, make sure the process itself still makes sense. It Still Comes Back to Data At one point in the conversation, I joked that I kept bringing everything back to data. But after years of working with integrations, testing frameworks, healthcare systems, file parsing, and automation, it's hard not to. AI still needs data. If your information is scattered across emails, PDFs, spreadsheets, databases, and systems that don't communicate with each other, throwing AI at the problem doesn't magically clean that up. Piyush described the possibility of eventually creating an intelligence layer over that data—a kind of company brain where people can interact with business information through natural language. That's powerful, but the information still has to be accessible and usable underneath it. Sometimes the first step toward implementing AI isn't implementing AI at all. It might be cleaning up the data, connecting systems, building an integration, or finally automating a process that should have been automated years ago. And that's okay. You're solving the business problem, not trying to win an award for using the most AI. Six Signals That You May Have a Good AI Workflow Piyush gave us a practical framework for identifying a good first workflow. Instead of randomly picking an AI project, Fusion Hive looks for six signals. Volume: Does this happen often enough that saving a few minutes each time will actually matter? Repeatability: Does the process follow roughly the same pattern each time? Measurable cost or delay: Can you point to hours, dollars, turnaround time, or another metric that could improve? Accessible data: Are the inputs already available somewhere, such as a database, email, PDF, or form? Manageable exceptions: Are the unusual cases a small enough percentage that they can reasonably be routed to another process or a person? A clear owner: Is there someone who understands the outcome well enough to say whether the new process is actually working? I like this framework because none of those questions start with AI. They start with the business. How Will You Know It Worked? This is another area where businesses can get themselves into trouble. "We implemented AI" isn't a success metric. What changed? Did the process go from 60 hours a week to 40? Did turnaround time improve? Did the error rate drop? Did employees stop entering the same information into three different systems? Are people able to spend more time on work that actually requires their knowledge? If you don't know what you're trying to improve before you start, it's going to be difficult to prove that the AI actually helped. That also means you need a baseline. Understand what the process costs you today before you change it. Otherwise, six months from now, you may have a cool AI system and no idea whether it saved the business anything. Start With the Problem There is a lot of pressure right now to adopt AI quickly. I understand it. The technology is changing incredibly fast, competitors are talking about it, customers are asking about it, and nobody wants to feel like they're falling behind. But moving quickly doesn't mean you should skip the fundamentals. Understand the problem. Map the workflow. Find the data. Identify the exceptions. Figure out what can be deterministic. Decide where AI actually provides value. Determine where humans still need to be involved. Then decide how you're going to measure success. Then build it. That might not sound as exciting as "put AI everywhere," but it's much more likely to produce something that actually works. And sometimes the best AI project isn't the flashy one everyone wants to demo. It's the boring process everyone wishes they didn't have to do anymore. Stay Connected: Join the Developreneur Community

In May this year, India's top court recognised timely trauma care for road accident victims as part of the Right to Life under Article 21. SaveLIFE foundation was instrumental in paving the way for this milestone. In this episode of Unusual Suspects, founder & CEO of SaveLIFE Foundation, Piyush Tewari and Vikram Bhat, the CEO of SCALE, share their learnings on turning a people's movement into public systems change. Can successes like SaveLIFE's be replicated and adapted to new geographies? What can be repeated and what cannot? Piyush and Vikram bring their deep insights into all this and more in a chat with host Gaurav Choudhury.

Piyush Shah, a legendary Indian Founder of 2 Unicorns (InMobi & Glance) shares why India's AI strategy has to look nothing like Silicon Valley's or Beijing's, and what founders should build instead.00:00 Introduction00:48 Why the Country That Invents AI Rarely Wins02:16 US vs China vs India: India's AI Opportunity03:55 How China Is Winning the AI Race09:38 AI Startup Ideas for India12:52 Consumer AI vs Enterprise AI14:20 Why Consumer AI Can Bankrupt You16:45 Why Enterprise AI Will Win First19:57 Distribution Is Still the Biggest Moat21:43 How AI Startups Should Go to Market25:14 Physical AI Startup Opportunities28:37 What US Enterprises Want From AI Startups32:45 Why Purpose Is a Competitive Moat35:58 How InMobi Uses AI Internally37:47 AI Sales, AI Agents & Enterprise Automation42:16 How Leaders Should Drive AI Adoption44:50 AI Won't Replace You. Fear Will.46:09 The Fastest Way to Learn AI47:32 My AI-Native Chief of Staff48:52 How AI Changed My Personal Life52:38 Final ThoughtsEvery general purpose technology gets invented once and scaled somewhere else. Germany invented the printing press. Britain built an industry on it. America turned that industry into an economy. Piyush Shah, Co-founder of InMobi and Glance, thinks the same pattern is repeating with AI right now, and he's convinced most founders are drawing the wrong lesson from it.On this episode of the Prime Venture Partners Podcast, Amit Somani sits down with Piyush to make the case that copying America's invention-first approach or China's industrialisation-first approach would be the wrong move for India entirely. Instead, Piyush lays out what he believes India's real opportunity looks like: not the smartest AI for the world's most sophisticated users, but AI that raises the floor for a billion people who've never had access to a tutor, a doctor, or a financial advisor in their life.The conversation doesn't stop at strategy. Piyush and Amit get into why consumer AI has a cost problem nobody's fully solved (he calls it inference CAC), why distribution remains the real moat even in an AI-native world, and why "purpose" might be the one advantage that's genuinely hard to copy. Piyush also opens up about how InMobi and Glance have rebuilt themselves around AI internally, and how AI has changed the way he runs his own life, from an 18-year-old AI-native chief of staff to using AI to understand his family's health reports.If you're building an AI startup, thinking about India's AI strategy, or trying to figure out whether your business model can survive the AI shift, this conversation offers a real framework, not another hype-cycle.Connect with Piyush ShahLinkedIn: https://www.linkedin.com/in/piyush-shah-0583412/Follow Amit SomaniX (Twitter): https://x.com/amitsomani?lang=enLinkedIn: https://www.linkedin.com/in/thesomani/About Prime Venture PartnersPrime Venture Partners is an early-stage venture capital firm backing exceptional founders building category-defining technology companies across SaaS, fintech, AI, healthcare, consumer internet and enterprise software.Learn more: https://www.primevp.in/Follow Prime Venture Partners:LinkedIn: https://www.linkedin.com/company/2780448/admin/dashboard/X (Twitter): https://x.com/Primevp_inInstagram: https://www.instagram.com/primevp_in/Learn more aboutInMobi: https://www.inmobi.com/Glance: https://glance.com/?c=BAU&pid=Social_X_bio&shortlink=6md9fteh&source_caller=ui

India-UK CETA promises duty-free access from 15 July, but the determining factor is procedural, rather than a high-level meeting. Watch #Economix with ThePrint Consulting Editor (Economics) Bidisha Bhattacharya:   To read full report: https://theprint.in/opinion/piyush-goyal-says-india-is-on-track-for-1-trillion-exports-fta-kite-has-a-string-problem/2979349/

A monumental structural policy day for Indian markets as the Nifty reclaimed 24,176. Commerce Minister Piyush Goyal electrified export-oriented sectors by announcing that the historic India-US trade pact is 99% complete and scheduled for a mid-July rollout, closely followed by the comprehensive India-UK FTA coming into force on July 15. Join tonight's wrap-up as we analyze the multi-billion dollar tariff advantage Indian manufacturers are set to secure over global rivals.

A monumental structural policy day for Indian markets as the Nifty reclaimed 24,176. Commerce Minister Piyush Goyal electrified export-oriented sectors by announcing that the historic India-US trade pact is 99% complete and scheduled for a mid-July rollout, closely followed by the comprehensive India-UK FTA coming into force on July 15. Join tonight's wrap-up as we analyze the multi-billion dollar tariff advantage Indian manufacturers are set to secure over global rivals.

A monumental structural policy day for Indian markets as the Nifty reclaimed 24,176. Commerce Minister Piyush Goyal electrified export-oriented sectors by announcing that the historic India-US trade pact is 99% complete and scheduled for a mid-July rollout, closely followed by the comprehensive India-UK FTA coming into force on July 15. Join tonight's wrap-up as we analyze the multi-billion dollar tariff advantage Indian manufacturers are set to secure over global rivals.

In this episode of The Brand Called You, host Ashutosh Garg sits down with Piyush Bagaria, Co-founder of SalarySe, a pioneering salary-powered fintech platform from India.Discover how SalarySe is redefining financial wellness for middle-class salaried Indians by leveraging technology, AI, and India's digital public infrastructure. Piyush shares insights from his journey across technology, investment banking, private equity, philanthropy, and entrepreneurship, revealing the challenges faced by India's workforce and how SalarySe empowers both employees and employers.Learn about the unique value SalarySe offers, the misconceptions surrounding financial wellness platforms, and how AI and UPI integration are transforming access to credit, flexible benefits, and financial planning for millions. Whether you are an HR professional, a fintech enthusiast, or a salaried employee, tune in to gain actionable leadership lessons and understand the future of financial wellness in India.

We kick off season 3 with a conversation about what makes the Codex Desktop app compelling as an AI workspace. Nikhil and Piyush break down the idea of an AI harness, why heartbeat automations matter, how shared browser context changes the feedback loop, and why moving beyond terminal-first workflows makes AI feel more usable for everyday work. The episode also explores how Codex is shifting from a developer-focused tool toward a broader end-user productivity platform, and why trust, identity, and safety become more important as AI agents get more capable and more persistent.= ⏰ CHAPTERS =00:10: Introduction: Season 3 Kickoff01:23: How We Both Became Codex Converts08:23: What Is an AI Harness?12:36: Codex as Your Second Brain17:28: Heartbeat Automations Explained22:08: Why I Switched from the Terminal to the Desktop App35:40: Security and Identity in AI Agents41:36: Levels of Autonomy: A Framework for Trusting AI=

It was a day of consolidation for Indian equities as macro pressures mounted. Join us as we analyze Commerce Minister Piyush Goyal's latest comments on inter-governmental coordination to stop the Rupee's decline and the upcoming high-level trade talks with the US delegation. We discuss the potential impact of additional currency swaps and foreign dollar inflows on emerging market volatility. Get the facts here.

It was a day of consolidation for Indian equities as macro pressures mounted. Join us as we analyze Commerce Minister Piyush Goyal's latest comments on inter-governmental coordination to stop the Rupee's decline and the upcoming high-level trade talks with the US delegation. We discuss the potential impact of additional currency swaps and foreign dollar inflows on emerging market volatility. Get the facts here.

It was a day of consolidation for Indian equities as macro pressures mounted. Join us as we analyze Commerce Minister Piyush Goyal's latest comments on inter-governmental coordination to stop the Rupee's decline and the upcoming high-level trade talks with the US delegation. We discuss the potential impact of additional currency swaps and foreign dollar inflows on emerging market volatility. Get the facts here.

Piyush Mishra brings his full self to this live Jashn-e-Rekhta session, sharp humour, raw memories, music, poetry, and the kind of honesty that refuses to sound rehearsed.In this conversation, he opens up about love, heartbreak, and why today's fast-moving Gen Z relationships often miss the patience and depth of old-school romance. He speaks about acting as imagination, not just technique, and shares hard-earned truths from his early Mumbai days, when survival, writing, theatre, and cinema were all part of the same struggle.The discussion moves from NSD and the myth of instant fame to the emotional cost of chasing art in a city like Mumbai. Piyush Mishra reflects on guilt, ego, Vipassana, alcoholism, truth, and the personal battles that shaped his voice as an artist. He also recalls Anurag Kashyap, Kay Kay Menon, and the creative integrity behind Black Friday.Alongside the conversation, the session carries powerful musical moments and memories connected to Ek Bagal Mein Chand Hoga, Aarambh Hai Prachand, and Husna. From Bhagat Singh and Faiz Ahmad Faiz to Partition pain and the loneliness of the artist, this episode is a rare mix of laughter, confession, music, and literary fire.Listen to Piyush Mishra at his most unfiltered, poetic, funny, wounded, and alive.

We're LIVE from DSS 2026 in Atlanta! Dave catches up with Piyush Chaudhari, CEO of Salsify, live from the show floor.Piyush unpacks what's driving conversation at the event—AI—and how Salsify is helping brands navigate the shift from digital shelf to agentic shelf, focusing on product discovery, measurement, and efficiency.He highlights Salsify's latest AI innovations, including Angie and the Intelligence Suite, and speaks to the growing It'sRapid–Salsify partnership and new capabilities around sell sheets, flip books, and B2B industrial use cases.Connect with Piyush on LinkedInFollow Beyond the Shelf on LinkedInLearn More about It'sRapidGet the It'sRapid Creative Automation PlaybookTake It'sRapid's Creative Workflow Automation with AI surveyEmail us at sales@itsrapid.io to find out how to get your free AI Image AuditTheme music: "Happy" by Mixaud - https://mixaund.bandcamp.comProducer: Jake Musiker

A joyous, lighthearted Life, at ease with everything around him. We have had many memorable conversations with Piyush. He could have a warm fireside chat with a million people because he understood the power of packaging and more importantly the pulse of Bharat. We will cherish his memories dearly. Our heartfelt condolences and blessings to all bereaved by his passing. -Sg Conscious Planet: ⁠⁠⁠⁠⁠⁠https://www.consciousplanet.org⁠⁠⁠⁠⁠⁠ Sadhguru App (Download): ⁠⁠⁠⁠⁠⁠https://onelink.to/sadhguru__app⁠⁠⁠⁠⁠⁠ Official Sadhguru Website: ⁠⁠⁠⁠⁠⁠https://isha.sadhguru.org⁠⁠⁠⁠⁠⁠ Sadhguru Exclusive: ⁠⁠⁠⁠⁠⁠https://isha.sadhguru.org/in/en/sadhguru-exclusive⁠⁠⁠⁠⁠⁠ Inner Engineering Link: isha.co/ieo-podcast Yogi, mystic and visionary, Sadhguru is a spiritual master with a difference. An arresting blend of profundity and pragmatism, his life and work serves as a reminder that yoga is a contemporary science, vitally relevant to our times. Learn more about your ad choices. Visit megaphone.fm/adchoices

A joyous, lighthearted Life, at ease with everything around him. We have had many memorable conversations with Piyush. He could have a warm fireside chat with a million people because he understood the power of packaging and more importantly the pulse of Bharat. We will cherish his memories dearly. Our heartfelt condolences and blessings to all bereaved by his passing. -Sg Conscious Planet: ⁠⁠⁠⁠⁠⁠https://www.consciousplanet.org⁠⁠⁠⁠⁠⁠ Sadhguru App (Download): ⁠⁠⁠⁠⁠⁠https://onelink.to/sadhguru__app⁠⁠⁠⁠⁠⁠ Official Sadhguru Website: ⁠⁠⁠⁠⁠⁠https://isha.sadhguru.org⁠⁠⁠⁠⁠⁠ Sadhguru Exclusive: ⁠⁠⁠⁠⁠⁠https://isha.sadhguru.org/in/en/sadhguru-exclusive⁠⁠⁠⁠⁠⁠ Inner Engineering Link: isha.co/ieo-podcast Yogi, mystic and visionary, Sadhguru is a spiritual master with a difference. An arresting blend of profundity and pragmatism, his life and work serves as a reminder that yoga is a contemporary science, vitally relevant to our times. Learn more about your ad choices. Visit megaphone.fm/adchoices

Piyush Jain, Founder and CEO of Simpalm and co-founder of Ducknowl, is on a mission to solve real-world challenges by combining technology and entrepreneurship. With over 15 years of experience building custom software solutions, Piyush helps businesses turn complex ideas into practical applications by blending technical depth, business acumen, and a strong problem-solving mindset. We explore Piyush's AI Ideation Framework—Validate idea, Proof of concept, Design, Competitor analysis, and Feature selection—a practical approach to building software in the post-AI era. Piyush explains how AI can help teams better understand user personas, validate product assumptions, and rapidly prototype ideas, while human expertise remains essential in design, architecture, and production-grade development. He also shares how prompt engineering, peer-reviewed prompting, and a right-shoring delivery model can help businesses build smarter, faster, and more cost-effectively. — 3D Print Your Software with Piyush Jain Good day, dear listeners. Steve Preda here with the Management Blueprint, and my guest today is Piyush Jain, the Founder and CEO of Simpalm, a custom software development company, and the co-founder of Ducknowl, a candidate screening and assessment application business for high-volume recruiting. Piyush, welcome to the show.  Thank you, Steve. Thanks for inviting me.  Well, I’m very curious about the stuff that you have to share with us, and I’d like to ask first about your personal purpose. What is your “why,” and how are you manifesting it in your business?  Yeah, so that’s a very interesting question. And I think for every entrepreneur or tech founder, really, that's the motivation—why you want to do certain things. So for me, if I look at it, my personal “why” is: why are we not solving challenges? Or why are we not solving them the right way? Why are we not transforming our lives? I grew up in India and then came to the US, so I've seen many different parts of the world—from Asia to North America. I see people face different challenges, but then we are not focusing on solving those problems. A lot of it I see is there’s a lot of challenges in the world because I believe there are not enough entrepreneurs. Because entrepreneurs are the ones who really take risks, combine everything, and create solutions. That was like me, right? That’s what I learned growing up, that I think I can do that, right? I can combine the technical knowledge and the business acumen and create solutions that people like, solve their challenges. Growing up, like I'm more on the technical side.Share on X I was inclined more toward science and technology, but then as I got into my undergrad and grad school, I realized that I have that entrepreneurship aspect, but it's still around science and technology. That’s when I realized that, you know what, I cannot be a pure scientist or maybe a pure entrepreneur, but I can be someone who can combine these two, because my main driving factor is problem-solving. I can combine these two and then live my life, be very happy with what I do. That has been my motivation. I like it. So solving challenges and being an entrepreneur, and kind of combining the two—being the technical expert and the entrepreneur in one. Now, one of the things that we always talk about on this podcast is frameworks. And you have developed a really good one for AI ideation, which I think is something that everyone needs to do these days or use these days, and it helps you create business apps and other business applications. Can you share with me how that framework works, and what are the steps in it?  Sure, yeah, definitely. So just to give you a brief background, we've been building software for the last 15 years. Some companies have used different frameworks, whether it's Agile or Waterfall in SDLC, in building the software, right? There are different methodology that companies have used, and they've been good, successful—they've played their role. But now, with the advent of AI, things have changed. We had to figure out, in our organization, how to use AI, and that's how this framework was built. My team helped me building this framework as well.Share on X But we realized that we were losing business—we were losing clients—since we didn't have an AI framework that would fit our clients. Again, for me, it's a challenge. So anytime I see a challenge, it create brain juice in me, right? So I said, okay, let's figure out how we create this framework. How did you do it?  So really, we built this framework—very interesting. A lot of the steps are similar, but then a lot of things are different.Share on X Whenever client comes to us and says, “Hey, we want to solve this challenge,” what we do is we do enough research. And now we use a lot of AI tools to really understand the problem better and understand the user persona. When you build any software application, there is a person who's going to use that. Sometimes we used to do user research or focus studies to understand that. Now, with the help of AI, we can get a lot of ideas about the user persona. For example, maybe we are building a healthcare application for an anesthesiologist. I don’t know much about that. I know, I mean, because I have been through some medical surgery and all that, but I can't fully understand their user persona or their requirements with respect to the application we're building. But now, with AI, I can actually ask different AI models, “Hey, we are building this app for anesthesiologists. What are their pain points? How would they see it?” So all that deeper mindset and psychology we can get using AI.  You are validating the idea by interrogating AI applications.  What users are going to like and all that. So I will always use this term earlier. In software engineering, now we have this pre-AI and post-AI, right? If you read history, we talk about before Christ and after Christ, right? Yeah. So it's a similar thing now. Yeah, exactly. Or before Covid, after Covid. Before AI, after we did all the user research and everything and created a requirements document, we would usually do design, create like a visual design of the software. But now, with the AI framework, we don't do that. That's not the next step. What we do instead is create a quick prototype using AI platforms.Share on X  So there are a lot of AI platforms—like Lovable, Claude. Now ChatGPT launched Codex for coding, and Replit. Depending on what kind of application you're building—for example, maybe if you're building a web-based application—then I recommend using Lovable or Replit. They're very good at creating that. Whatever software you want to build, whatever user personas that you’re addressing, you can feed into that and it’ll create like a prototype application. Okay.  So what that does is actually, then this prototype, clients can just take it to their customers or internal users and get feedback. A picture is better than a thousand words. Organizations discussing an idea is very different from when they actually see something. Then everybody starts chipping in—“Oh yeah, I see this in the prototype, but I don't want this,” or “I want to move things around,” or “This is what I want.” Basically, building a prototype on AI platforms is much faster than building wireframes and design prototypes like we used to do earlier. So that has changed. So you're 3D printing your software, right?  Yes, exactly. There you go. Well, that’s a very good way you put it together. Yeah. So, yeah, exactly. You’re just 3D printing the software, right? So you can see it, visualize it, and then once you go through that, it creates a lot of better ideas about the software in faster time. So once you have that, then you go into UI/UX design. So in that also, there are two steps. One is wireframing. Wireframing is like creating the flow in black and white. It's like creating a skeleton of your software. It does not have the color, the font, or the branding, but you just create all the different user journeys, the screens, the flow, and the fields that will be there on the screen. So we have integrated AI into that step as well. Earlier, it used to be created by a designer or a business analyst. Now we are using software like Uizard or UX Pilot, where we define what we want—what kind of user journey, flows, and screens—and it creates that. It spins out those wireframes in minutes. So really that has reduced now. The time it used to take to create wire frames is faster now.  So you're designing the wireframes with AI?  Yes, but it's just the wireframe part of it, and it's still guided by our expert VA or designer—someone who knows how to really visualize things and has done a lot of wireframes and sketches. So they know what to tell the AI. Prompting is very important. It's very important that you know how to prompt—what to ask for—so that you can get variations and differentiation in the wireframes. You don't want a standard AI-created wireframe. Everybody can recognize AI-generated images now, right? If I show you one, you'd say, “Oh yeah, it's AI-generated.” I know that, right? Yeah. So again, we keep the human intelligence. We're not asking AI to create the full software end-to-end. It never works—it'll never work. It just doesn't. I know that's a strong statement, but I'm saying that based on experience and an understanding of human behavior and psychology. So AI agents will not be able to code software, in your opinion?  No, they can do the coding, but they cannot build the whole software end-to-end—a production-deployed software. Because these software are being used by humans. You have to have human intelligence to understand and define what you need and how it works.Share on X You can maybe create some software, but it doesn't work very well. Even if you use all these platforms, you can cut down your production time and cost by 30%, 40%, 50%, right? That's the number we are seeing—30 to 50% reduction, depending on the software you're building and the objectives. So just to recap—you validate the idea by interrogating Claude and ChatGPT, asking about the needs of that customer, the psychology of the customer—that's step number one. Step number two is 3D printing the software with Lovable or Replit—so proof of concept. And then you design the wireframes. And then what's next after you design the wireframes? What's the next step?  So that’s a good thing. That’s it. Now I'm going to talk about the human element—some people listening to this podcast will be surprised. Now it comes to visual design, right? So you've created the skeleton, and now you have to add the skin, the tone, the color, the emotion to the design, to the workflow. Now, we have tried AI, but it doesn't work. It's very monotonous. So we use an experienced visual designer, a UX designer, for that step—to give it emotion. When you use AI—I wish I could show you some examples—it creates very similar kinds of designs for apps and software. So what we did is we gave it three different apps with very different objectives and everything, and the designs it came up with were very similar—blocks, buttons—very monotonous. So there's no differentiation. And design is the main thing that becomes the differentiator, right?  Yeah.  So that's what we learned from our experience. And I say that very categorically in all of my talks—that visual design, final UX, has to be human, not AI.Share on X Because you are communicating emotions, right? And AI is still not there to communicate emotions.  Yeah. It doesn’t have emotions.  Well, some people will argue with you and say, “No, it can understand if you're sad or unhappy.” But my response to that is—it's because we've programmed it that way. But things change based on situation, context, ethnicity, culture, fear—how people express nervousness, fear, and all that—it's very different. So there was this AI video interviewing company five or six years ago. They were sued by the Department of Justice because they were trying to detect emotions of people like anxious, nervous, when the interview was happening.  It turned out their model was trained only on one race—they didn't account for other races or ethnicities. So their model failed, and they were sued by Department of Justice for that. So yeah, emotions is something—maybe they have unlimited dimensions, we don't know. So it's hard to program that. So basically: ideation, prototype, wireframe, and then final visual design—that's the discovery and design framework. Now, when it comes to development framework, this is where AI has been a game changer—the coding part. But again, you have to be very careful about how you use AI in your coding pattern with your coding team. It depends on the application, it depends on the tech stack, right? Every platform has its own strengths and weaknesses. For example, if you want to build a web-based application in the React JS framework, then Lovable is great. That's very good—very efficient and cost-effective. Then Claude is there. Claude has been really good in software engineering. I would say it has been built and designed mostly for coding, right? Anthropic—their idea, their starting point—was coding, how to make coding and software engineering better.  So they've been a front runner in the race. ChatGPT is trying to catch up using Codex, and Copilot is great. Copilot is mostly used by enterprises who are on the Microsoft stack. They use Copilot a lot for coding in .NET and enterprise-level applications. They’re used to co-pilot. It’s because they feel comfortable with Microsoft security policies and all that. That’s fine. But in general, we see Claude to be at the top—from our perspective. We've also built a framework for software coding. In software development, there's a popular process called peer review. So when you create source code, you get it reviewed by your peer—your colleague.Share on X  Is this what happens on GitHub?  Yeah, yes. So basically anywhere—any source code repository—you can do that. So your team members can help you make your code better and more efficient.  Yeah, I understand. But now we have a step called prompt peer review. When you're using prompts to build software, those prompts get reviewed by team members. Because if your prompts are not very specific or good enough all the way through the SDLC, you can run into a lot of challenges trying to fix the code. Because now you have a situation where you have code that you have not written fully, and when you ask AI to change something in the code, sometimes it ends up changing a lot of things that you don't want it to change. Yeah.  That's what we've seen, and that's why we evolved. Before we build any software, we create maybe a 10-, 20-, 30-page prompt document, where we go through each screen and function and write it out. It's very sophisticated—it has evolved really well. But the thing is, it takes a few days to do that within the team, because we know if we do it right, the next step is faster and more accurate. So really, the prompt document—think of it more like an architecture document. Earlier, we used to create a solution architecture document, defining all the tools, the design, everything.  But now it's more like an AI-driven solution architecture document with prompts, which get reviewed by team members. So we do that, and then we run that, and we get the code and everything. So I have a CTO club—I run a CTO Club in Maryland—and I was talking to CTOs. They're all using this, but some of them are so advanced that they actually define the test cases in the beginning. They define, “Okay, this is what I want, this is the function I want, and these are the test cases I want it to pass.” That's even more advanced. If you can do that, you can have very efficient code.  Yeah, I love it.  So is that the end? You have your test cases, you design the prompt, you peer-review the prompt, and you already had the prototype, so now you're coding the software—what's the last step?  Yeah. Then there’s an integration as well. So AI doesn’t do the integration so well. You can do the front-end coding, you can do the back-end coding, you can probably create the APIs. APIs require a lot more human intervention. But once you have that, then you have to connect it, right? You have to connect the front end with the backend. A lot of that is still done by the programmer. It's hard to rely on AI for doing that. And again, it depends on the application. Maybe if it's a smaller application, maybe you can have AI do that. But if it's a bigger application—we mostly build bigger applications—then integration, then final QA and testing, and deployment.  So all that is there. But in each of these steps, you can use some sort of AI tool to speed up the process. But the key is you still have to have your architecture, the process. You have to know the steps more. You have to be a good, experienced developer to use AI efficiently if you want to build a production-ready application. You can build a prototype. Anybody can build a prototype on Replit or Lovable, but it's not going to be production-ready that you can give to your customer and charge them money. So that’s the differentiator.  Yeah, I understand. So Piyush, I’d like to switch gears here. I understand the AI ideation framework—that's great. We talked about the technical part of it, the curiosity, the technical challenges. Let’s talk about the entrepreneurship part, which is also part of your profile. So what drives the growth of your business? What would you say drives it?  For us, there are multiple factors that drive the growth of our business. The first is, again, our problem-solving attitude. Any client that comes to us we communicate in that modelShare on X The problem, the challenge, the solution, the business part, the value proposition we bring. And the second factor is our location. We are here in Maryland, and we have another office in Chicago. So being here, we have a global shoring model—that's a main driving factor of our business from the entrepreneurship perspective. So what the global shoring model is: our client-facing team, the senior team, is here—solution architects, sales engineers, designers, project managers, business analysts—they are here in the US, client-facing. And our dev team and testers are in our offshore locations.  Some people call it hybrid shoring. I call it right shoring. The reason I call it right shoring is because in this model, you have the right people at the right shore, so you get the most value. Here, you have people who understand the culture, the product, the context—because products are used by people in a certain culture. And if you are not in that culture, if you haven't experienced it, it's always harder to design the right software solution. I was one of the first people to start that model here in the DMV area for mid-size and smaller companies. This model existed before, but mostly for large enterprise companies. They have used that. But I started to offer that 16 years ago to smaller companies. Either companies were just going offshore, or they were doing onshore, right? I introduced this hybrid—or right-shoring—model, and it has been well received by our customers. So that’s it.  So what is one thing that you’re trying to figure out in your business right now?  Right now, what I'm trying to figure out in my business is scaling. I mean, we have built solutions for many different industries. We have built solutions for different clients in fintech, healthcare, education, nonprofit, startups, IoT, construction. But now what we are trying to figure out is how do we create some off-the-shelf solutions for different industries? Because one challenge we see is that, from the client's perspective, getting custom software built takes time and money. But in certain use cases, we can have off-the-shelf, industry-specific solutions, and then customize those based on the client's needs.  So that's what we are trying to figure out—across different industries, what those solutions can be—so we can scale and also make it easier. And these are more like AI-driven, off-the-shelf solutions that are customizable. So think of it like Salesforce—its core is off-the-shelf, but then you can customize the front end and a lot of other things. Not exactly like Salesforce, but more like industry-specific solutions for different use cases—nonprofit, construction, right? With those, overall, we can build solutions faster.  That’s fascinating. So how has the offshoring—or right shoring, as you call it—model evolved over the past 10 years? Is it different now than it was 10 or 20 years ago?  Yeah, I think that's a great question. It has evolved and changed. Earlier—maybe 10, 12 years ago—when we were talking about hybrid shoring, we were mostly talking about the US and Asia. But now we have different players. We have the nearshore model, which has become quite popular as well—like South America. We have team members in nearshore locations as well, in South America, because we want to leverage different time zones, resources, and culture. And we've seen very positive results. Then you have Eastern Europe. We have competition from countries like Ukraine, Belarus, Romania, Poland. I think it’s the part of the globalized world, right? It's like energy flowing in different spaces—it's not limited to one place, which is great. That's one way it has evolved.  I also know some companies working in Kenya—there are developers there. Some companies are setting up in East Africa, West Africa. So different places are playing roles now. That’s one thing I see. And now, with the help of AI, what's going to happen is it will play two roles. One— in many situations, with AI, you can do more things onshore. That’s one aspect of it. And second—with AI, someone sitting offshore who knows how to use AI can become very competitive as well. We don't have enough data yet to fully see how this will evolve, but maybe in a year or so, we'll see how it plays out.  But I also find that with these simultaneous translation tools—like Apple, I think an iPhone can now translate in all languages. Essentially, another barrier falls that if the language and knowledge of your offshore contractor is not perfect, they can understand things much more clearly because of simultaneous translation. Even on Zoom, you can now flip a switch and they can read what's being said in their own language during a conversation. So that's amazing, I think.  Yeah. That’s amazing. That’s amazing. They can understand more about the culture and mindset. So that's something have to see. Again, I think it depends on the use case, the application, the problem we're solving. But in some cases, it might be great news for onshore—we can keep more dollars here. But keeping dollars here with AI also means a lot of that spend is going to AI, right? So that's one thing—we have to be very careful. Yesterday, in our tech breakfast, our presentation was about how to optimize your AI tokens. There are some companies spending $150,000 per year per employee on tokens. Wow.  That's like the salary of one employee.  Yeah.  A mid-level developer—$150K—they're spending that much. And then they’re trying to figure out how to optimize it. And on top of that, they have cloud costs, right? AWS, Azure—those costs are still there—and then you add AI. So it's a lot of money. You really have to be very smart about understanding and optimizing it. That’s why the prompting is so important, right? It's not just about getting the right software—it's also about getting the cost down.  Yeah. Again, you need expert people who can prompt well, because it's about being able to communicate well. Prompting is about communication—it's about clarity, brevity, security, all that stuff. So, Piyush, we're coming close to the end of the recording. If someone would like to learn more about the applications you develop, how you're using AI, and how you can help their business develop technology, where can they find you? What's the best way to get in touch with you? Sure, there are many ways people can reach out to me. They can go to my website, www.simpalm.com—we have a contact form there. They can submit the form, or they can reach out to me via email directly at contact@simpalm.com. They can also connect with me on LinkedIn. I'm on LinkedIn—message me there if somebody needs anything. I always like discussing problems and what the solutions can be. If anybody reaches out to me, I'm always very quick to respond.  That's awesome. So Piyush Jain, the CEO of Simpalm—and we didn't even talk about your other business, Ducknowl—thank you for coming, and thank you for sharing your insights and your framework on how to build an ideation framework for AI. So thanks for sharing that. And if you're listening and you enjoyed this conversation, then stay tuned, because every week we have another entrepreneur sharing their insights and frameworks with you. So make sure you follow us on YouTube, subscribe, and give us a review on Apple Podcasts. So thanks for coming. Thank you, Steve. It was a pleasure talking to you. Important Links: Piyush's LinkedIn Piyush's website

On episode 52 of Generationship, Rachel Chalmers sits down with Piyush Agarwal to explore how developer behavior reveals far more about buying intent than traditional sales signals. They discuss why most dev tool GTM strategies fail, how to distinguish curiosity from real demand, and what it takes to engage developers at exactly the right moment.

On episode 52 of Generationship, Rachel Chalmers sits down with Piyush Agarwal to explore how developer behavior reveals far more about buying intent than traditional sales signals. They discuss why most dev tool GTM strategies fail, how to distinguish curiosity from real demand, and what it takes to engage developers at exactly the right moment.

India’s trade talks with the United States have sparked a big question. Are Indian farmers being protected, or are their interests being traded away?See omnystudio.com/listener for privacy information.

This week begins Chase's brand new season (season 11), dealing with the theme "21 Questions." Chase discusses how every item necessary for our existence can be found in Genesis 1:1. Work Cited:1 Patel, Piyush.“What Elements Are Present in the Human Body?” ScienceABC, 19 Oct. 2023,www.scienceabc.com/humans/what-elements-are-present-in-the-human-body.html#google_vignette.Description Visit our linktree: https://linktr.ee/scatteredabroadnetwork Visit our website, www.scatteredabroad.org, and subscribe to our email list. "Like" and "share" our Facebook page: https:// www.facebook.com/sapodcastnetwork Follow us on Instagram: https://www.instagram.com/ the_scattered_abroad_network/ Subscribe to our Substack: https://scatteredabroad.substack.com/Subscribe to our YouTube channel: The Scattered Abroad Network Contact us through email at san@msop.org. If you would like to consider supporting us in any way, don't hesitate to contact us through this email.

This week begins Chase's brand new season (season 11), dealing with the theme "21 Questions." Chase discusses how every item necessary for our existence can be found in Genesis 1:1. Email me at tcgreen008@yahoo.com. Work Cited:1 Patel, Piyush.“What Elements Are Present in the Human Body?” ScienceABC, 19 Oct. 2023,www.scienceabc.com/humans/what-elements-are-present-in-the-human-body.html#google_vignette.Description Visit our linktree: https://linktr.ee/scatteredabroadnetwork Visit our website, www.scatteredabroad.org, and subscribe to our email list. "Like" and "share" our Facebook page: https:// www.facebook.com/sapodcastnetwork Follow us on Instagram: https://www.instagram.com/ the_scattered_abroad_network/ Subscribe to our Substack: https://scatteredabroad.substack.com/Subscribe to our YouTube channel: The Scattered Abroad Network Contact us through email at san@msop.org. If you would like to consider supporting us in any way, don't hesitate to contact us through this email.

•⁠ ⁠சுனாமி பேரலை தாக்கியதன் 21வது ஆண்டு நினைவு தினம் •⁠ ⁠வேங்கைவயல் சம்பவம்: 3ம் ஆண்டு•⁠ ⁠கீழ்வெண்மணி நினைவு தினம்!•⁠ ⁠தேவாலயத்திற்குள் சென்று கொலைவெறி கூச்சலிடும் இந்துத்துவா கும்பல்... ViralVideo •⁠ ⁠அசாமில் கிறிஸ்துமஸ் அலங்காரங்களை சூறையாடிய VHP மற்றும் பஞ்ரங் தள் அமைப்பினர் கைது •⁠ ⁠உன்னாவ் பாலியல் வன்கொடுமை வழக்கின் குற்றவாளிக்கு தண்டனை நிறுத்தம் - உ.பி. அமைச்சர் வரவேற்பு•⁠ ⁠உ.பி பிராமணர்கள் MLA கூட்டம் நடந்ததற்கு BJP எதிர்ப்பு?•⁠ ⁠``சிறுபான்மையினருக்கு எதிரான வெறுப்பு 74% அதிகரித்திருக்கிறது" - முதல்வர் ஸ்டாலின் •⁠ ⁠கள்ள மௌனம் சாதிப்பதேன்?- சீமான்•⁠ ⁠TVK: கிறிஸ்துமஸ் தாக்குதல்; "இந்தியாவின் எந்த மூலையிலும் நடைபெறக் கூடாது" - தவெக அருண்ராஜ்•⁠ ⁠“அன்புமணிக்கு பாமக தலைவர் என்ற உரிமை இல்லை.. மீறி பயன்படுத்தினால் நடவடிக்கை எடுக்கப்படும்” - ராமதாஸ்•⁠ ⁠ராமாதஸ் பொதுக்குழு - அன்புமணி தரப்பு போலீஸில் புகார்•⁠ ⁠திட்டமிட்டபடி பொதுக்குழு - ஜி.கே.மணி உறுதி•⁠ ⁠பதவி வழங்காமல் புறக்கணித்ததால் வேதனை: தவெக பெண் நிர்வாகி தற்கொலை முயற்சி •⁠ ⁠அஜிதாவை அழைத்து விஜய் பேசியிருக்க வேண்டும்; பெண்களுக்கு அரசியலில் போராட்டமாகவே இருக்கிறது - தமிழிசை சௌந்தரராஜன்•⁠ ⁠பியூஷுக்குப் பிடிகொடுக்காத எடப்பாடி பழனிசாமி - பகலில் பற்றவைத்த வதந்`தீ', இரவில் அணைந்தது ஏன்?•⁠ ⁠தேர்தல் அறிக்கை தயாரிக்க அதிமுக குழு: எடப்பாடி கே.பழனிசாமி அறிவிப்பு•⁠ ⁠திமுக தேர்தல் அறிக்கை தயாரிக்க AI?•⁠ ⁠தமிழ்நாடு சட்டமன்ற கூட்டம் எப்போது? •⁠ ⁠தோழர் நல்லகண்ணு 101!•⁠ ⁠விஜயகாந்தின் 2ஆம் ஆண்டு குருபூஜைக்கான அழைப்பிதழை முதல்வர் மு.க.ஸ்டாலின் & எடப்பாடியிடம் வழங்கிய சுதீஸ்?•⁠ ⁠யாருடன் கூட்டணி இன்னமும் முடிவு செய்யப்படவில்லை - பிரேமலதா•⁠ ⁠மும்பை: 28 ஆண்டுக்கால கனவு நனவானது; பயன்பாட்டிற்கு வந்த நவிமும்பை சர்வதேச விமான நிலையம்!•⁠ ⁠ரயில் கட்டண உயர்வு: இன்று முதல் அமல்•⁠ ⁠இந்தியாவில் ஒரு லட்சம் பெட்ரோல் நிலையங்கள்?•⁠ ⁠மாவோயிஸ்ட் முக்கியத் தலைவர்கள் உள்பட 6 பேர் சுட்டுக்கொலை?•⁠ ⁠சீன-இந்திய உறவுகள் குறித்து அமெரிக்காவின் பென்டகன் வெளியிட்ட அறிக்கைக்கு சீனா கடும் எதிர்ப்பு!•⁠ ⁠ஹிந்துகளுக்கு சொந்தமானது வங்கதேச மண் - 17 ஆண்டுகளுக்கு பிறகு நாடு திரும்பிய கலீதா ஜியா மகன்

This week, Monika breaks down the surprising 8.2% GDP growth print for India's July–September quarter, and why the cheer hasn't shown up in the stock market. She revisits the GDP formula and unpacks the drivers — strong private consumption, resilient services growth, a manufacturing boost from PLI schemes and GST cuts, and a surge in MSME credit that signals broad-based momentum. With India on track for ~7.5% growth this year, the fastest among major economies, Monika explains why this is solid, real activity rather than price-led nominal growth, and how that strength is built on rural spending, good monsoons and recent tax breaks.Monika then turns to the puzzle of why markets remain flat despite a booming economy. She outlines three reasons: money being pulled into IPOs, gold and silver instead of the secondary market; exceptionally low inflation reducing the GDP deflator and therefore nominal growth (the number that drives corporate revenues, profits and market valuations); and stretched valuations that still need cooling. Even so, she emphasises that long-term investors should stay disciplined, maintain asset allocation across debt and equity, and rebalance if needed — the structural growth story remains intact. She also previews next week's discussion on the RBI's rate decision and what it means for households.In listener questions, Shivam asks whether to prepay a low-rate education loan or invest aggressively, and how he and his wife should structure ₹1 lakh of monthly SIPs; Monika explains why, given their stability, keeping the loan and investing for growth works. Piyush writes in from a severe debt trap with home loans, personal loans and card dues far exceeding income; Monika urges him to involve family, liquidate assets and seek structured help through a debt resolution service. Anup, writing from Germany, wants to sell two residential plots and eventually buy a home in India; Monika points him toward Sections 54 and 54F, suggests consulting a CA on capital-gains planning, and outlines why a short-term loan followed by staggered asset sales may be practical.Chapters:(00:00 – 00:00) Why GDP Is Hot but Markets Are Cool(00:00 – 00:00) Understanding the Drivers Behind India's Surprising Q2 GDP(00:00 – 00:00) EMI vs Investing: Navigating a Low-Interest Education Loan(00:00 – 00:00) Escaping a Debt Trap: Practical Steps When Repayments Overwhelm(00:00 – 00:00) Selling Plots or Taking a Loan: Smart Strategies for Buying a Home in Indiahttps://freed.care/ https://cleartax.in/s/section-54-capital-gains-exemption#h6If you have financial questions that you'd like answers for, please email us at ⁠mailme@monikahalan.com⁠ Monika's book on basic money management⁠⁠⁠⁠⁠https://www.monikahalan.com/lets-talk-money-english/⁠⁠⁠⁠⁠Monika's book on mutual funds⁠⁠⁠⁠https://www.monikahalan.com/lets-talk-mutual-funds/⁠⁠⁠⁠Monika's workbook on recording your financial life⁠⁠⁠⁠⁠https://www.monikahalan.com/lets-talk-legacy/⁠⁠⁠⁠⁠Calculators⁠⁠⁠⁠⁠https://investor.sebi.gov.in/calculators/index.html⁠⁠⁠⁠⁠You can find Monika on her social media @monikahalan. Twitter ⁠⁠⁠⁠⁠@MonikaHalan⁠⁠⁠⁠⁠Instagram ⁠⁠⁠⁠⁠@MonikaHalan⁠⁠⁠⁠⁠Facebook ⁠⁠⁠⁠⁠@MonikaHalan⁠⁠⁠⁠⁠LinkedIn ⁠⁠⁠⁠@MonikaHalan⁠⁠⁠⁠Production House: ⁠⁠⁠⁠www.inoutcreatives.com⁠⁠⁠⁠Production Assistant:⁠⁠⁠⁠ Anshika Gogoi⁠⁠⁠

Product marketing—marketing's favorite misunderstood stepchild or just expensive project management in disguise? Pranav Piyush (ex-Dropbox, ex-Bill, founder of Paramark) joins the crew to drop some inconvenient truths: most PMMs are stuck doing thankless work because nobody knows who actually runs the business. We're talking hypothesis-driven thinking, why talking to customers isn't optional, the statistical traps that make your research garbage, and why that rebrand probably won't save your pipeline. Also:The "HIPPO problem" destroying 90% of PMM effectivenessThe three data pitfalls that make your research worthless (cherry-picking is just the start)Why statistics courses should be mandatory for every marketerThe hypothesis-based approach that turns opinions into provable strategiesWhy measuring creative team productivity is a complete waste of timeThe incrementality blind spot: 99% of B2B orgs have no clue about their marketing ROIActivity metrics you should ignore vs. the engagement signals that actually matterIf you've ever felt like a glorified PowerPoint factory or wondered why your data never wins arguments, this episode will either validate your existence or make you question everything. Either way, you'll finally understand why the role exists in the first place.TIMESTAMPS:00:00 Introduction and Host Intros00:37 Introducing the Guest: Pranav Piyush00:46 Pranav's Background and Career Highlights01:25 Personal Anecdotes and Adventures02:40 Origins of the Podcast03:37 The Role of Product Marketers07:04 Challenges in Product Marketing17:40 The Importance of Data in Marketing24:00 Understanding Positioning and Messaging24:45 Qualitative vs Quantitative Research in Messaging25:04 The Role of Customer Research30:13 Activity Metrics: What Really Matters?34:29 Creative Work and Measurement37:31 The Importance of Incrementality43:58 Rebrands: Are They Worth It?47:11 Final Thoughts and Podcast PromotionSNOW NOTES:Pranav's LinkedIn ParamarkElena VernaStatistical significanceHosted by Ausha. See ausha.co/privacy-policy for more information.

Pre-IPO door shut for Mutual Funds, equity market braces for a big IPO wave next month, the fallout from Russian oil sanctions, what China's 15th five-year plan entails and the genius that was Piyush Pandey - all this and more in the day's edition of Moneycontrol Editor's Picks newsletter.

10/22/25: Robbie Saner Sullivan, N'ton at-large school committee candidate. CDH Surgeon Dr. Michelle Helms on Breast Cancer Awareness Month. Brian Adams w/ Grow Food Northampton's Farm & Land Mgr, Piyush Labhsetwar, & Co-Dir, Michael Skillicorn: what we grow & why. Chuck Collins "Burned by Billionaires: How Concentrated Wealth and Power Are Ruining Our Lives and Planet.”

Piyush Sharrma is the co-founder and CEO of Tuskira, a pioneering threat defense platform leveraging an AI-powered security mesh, which launched out of stealth mode with $28.5 million in funding. In this episode, he joins host Scott Schober to discuss the announcement, what the funding will be used for, and more. • For more on cybersecurity, visit us at https://cybersecurityventures.com.

How are leading urologic oncologists using advanced biomarkers and artificial intelligence to refine the diagnosis and management of non-muscle invasive bladder cancer (NMIBC)? In the opening episode of our 2025 NMIBC Creator Weekend™ series, host Dr. Bogdana Schmidt engages in an insightful, in-studio discussion with Dr. Anne Schuckman and Dr. Piyush Agarwal about contemporary strategies and challenges in the diagnosis of non-muscle invasive bladder cancer.---This podcast is supported by an educational grant from Ferring Pharmaceuticals.---SYNPOSISThe doctors emphasize the importance of having an experienced cytopathologist and discuss the use of different biomarkers and imaging modalities in bladder cancer diagnosis. The conversation delves into risk stratification, patient management strategies, and the evolving role of technology and artificial intelligence in enhancing diagnostic accuracy. The experts also share their perspectives on future advancements and their potential impact on clinical practice.---TIMESTAMPS00:00 - Introduction04:05 - Surveillance and Follow-Up Strategies10:10 - Biomarkers in Bladder Cancer18:02 - Blue Light Cystoscopy and Patient Comfort30:56 - Risk Assessment and Counseling42:56 - Future of Bladder Cancer Diagnostics47:00 - Concluding Thoughts---RESOURCESCxBladder Studyhttps://www.sciencedirect.com/science/article/pii/S1078143923000091Lars Dyrsakjot Study on Tumor Markershttps://pmc.ncbi.nlm.nih.gov/articles/PMC7690647/The Bridge Trialhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10515442/

#285 Creative Strategy | In this episode, Dave is joined by Pranav Piyush, Founder & CEO of Paramark, a measurement platform helping B2B and B2C companies run smarter marketing experiments. Pranav is known for turning strategy documents into repeatable creative processes that generate campaigns, ads, and content tied directly to a company's story.Dave and Pranav cover:How to translate your company narrative into concrete marketing campaigns and creative hooks across channelsWhy creative output (not measurement) is the biggest bottleneck for most B2B marketing teams todayThe frameworks like category entry points, jobs-to-be-done, and behavioral psychology that help marketers spark fresh, testable campaign ideas month after monthYou can expect a practical, example-filled conversation on turning strategy into execution and building a creative engine that never runs dry.Timestamps(00:00) - – Intro and audience roll call (05:09) - – Why narratives often get stuck in a Google Doc (08:09) - – Foundational docs you need before creating campaigns (13:09) - – Category entry points and jobs-to-be-done explained (17:09) - – How to feed company inputs into AI tools (23:09) - – Generating ad campaign ideas with real examples (25:09) - – Using analogies (like basketball) to explain complex concepts (29:54) - – Where AI falls short (and why human judgment matters) (32:54) - – Mining sales call transcripts for campaign hooks (36:54) - – Turning customer objections into marketing messages (40:54) - – Repurposing podcasts, presentations, and blog posts into new formats (44:54) - – Systemizing idea generation for repeatable output (48:54) - – Closing thoughts and audience Q&A Send guest pitches and ideas to hi@exitfive.comJoin the Exit Five Newsletter here: https://www.exitfive.com/newsletterCheck out the Exit Five job board: https://jobs.exitfive.com/Become an Exit Five member: https://community.exitfive.com/checkout/exit-five-membership***This episode of the Exit Five podcast is brought to you by Qualified.AI is the hottest topic in marketing right now. And one thing we hear a lot of you marketers talking about is how you can use AI Agents to help run your marketing machine.That's where Qualifed comes in with Piper, their AI SDR agent.Piper is the #1 AI SDR Agent on the market according to G2, and hundreds of companies like Box, Asana, and Brex, have hired Piper to autonomously grow inbound pipeline. How good does that sound?Qualified customers are seeing a massive business impact with Piper: a 3X increase in meetings booked and a 2X increase in pipeline.The Agentic Marketing era has arrived. And if you're a B2B marketing leader looking to scale pipeline generation, Piper the #1 AI SDR Agent is here to help.Hire Piper, the #1 AI SDR Agent, and grow your pipeline today.You can learn more at qualified.com/exit5

In today's edition of Moneycontrol Editor's Picks listen to a key interview with Union Commerce Minister Piyush Goyal as he addresses the landmark GST reforms and India-US ties amid the tariff wars unleashed by Trump. We have a comprehensive coverage on the rate rationalization from the big picture view to its on-ground impact. In other news, learn about Phone Pe's IPO, Starlink's trial run, incentive for India's largest printed circuit board plant, and how Indian GCCs are upskilling employees for AI.

Marketing today should be a team sport. The companies that thrive are the ones who align product and brand around a shared mission. In this week's episode of Growth Talks, Pranav Piyush, CEO of Paramark, joins host Krystina Rubino to share leadership strategies for aligning product, marketing, and finance to drive business success. As a former marketing leader at companies like BILL and Adobe,  Pranav unpacks how to recognize growth plateaus, lead change with clarity, and foster high-trust relationships with finance. From refining your brand-performance connection to activating your community as a true growth engine, this episode provides sharp insights for building a company that lasts.

It can be a draw when setting up a new device. Some info transfers, while some don't. Learn how developers can ensure the best user experience when people backup and restore an Android device. Hosts Tor and Chet are joined by Graham and Piyush on the Android Consumer Experience team  and Alon on the Android Studio team to discuss Android backup and restore.   Chapters: 0:00 - Introduction & the user pain point 1:38 - Why backup fails: Developer challenges 4:00 - Evolution of backup testing: From scripts to Android Studio 7:15 - Beyond testing: Backup for developer workflow 8:50 - Cross-device backup & GMS core integration 10:55 - Understanding backup types: Devices vs. Cloud 14:55 - Data categories & developer control 16:05 - System-level backup: Permissions & credentials 18:35 - Default backup pitfalls & key-value agents 28:15 - Database migrations & backup stress testing 32:04 - Automated testing framework 41:02 - Recap & feedback channels

#259 Paid Ads | In this episode, Dave is joined by Pranav Piyush, founder and CEO of Paramark, a platform helping B2B marketers run real experiments to measure ad performance. Pranav brings a sharp point of view on attribution, channel performance, and how to actually prove what's working across your paid media mix.Dave and Pranav cover:Real-world results from 7 B2B ad campaigns, including branded search, YouTube, billboards, and Performance MaxWhy most marketers are misusing the word “test” and how to run true experiments with lift, control, and causalityHow even small teams can apply experiment design (on a $10K budget or less) to make smarter spend decisionsWhether you're managing a big budget or just getting started with paid campaigns, this episode will help you think more critically, and confidently, about where and how to invest in B2B marketing.Timestamps(00:00) - – Intro (03:08) - – Why Paramark pulled real data from 7 B2B ad campaigns (05:38) - – Attribution vs. experimentation: what most marketers get wrong (09:08) - – Correlation vs. causation explained (with a LinkedIn example) (11:53) - – How to run a real test (hint: you need a control) (13:08) - – Branded search test results: $1M+ saved, no performance loss (18:08) - – Why strong SEO makes or breaks branded search tests (19:08) - – Billboard test: how one brand proved real lift with out-of-home (22:38) - – What “digital out-of-home” looks like in B2B (24:08) - – YouTube ad tests: one big win, one big flop (28:19) - – How to run tests with small budgets ($500–$10K) (32:49) - – Connected TV (CTV) test results from a Series F SaaS brand (34:49) - – What happens when a multichannel test works—but isn't efficient (36:49) - – Paramark's Exit Five sponsorship test (real numbers shared) (40:19) - – Why content needs to drive short-term lift, not just long-term “brand” (43:19) - – How Pranav used LinkedIn to drive inbound from day one (45:19) - – Your attribution model is lying, give your audience more credit (46:49) - – When 7 ad tests fail in a row…and the 8th one works (48:19) - – Performance Max test: why it worked for one brand (50:19) - – How long to run a test? Use data, not gut (52:19) - – Bonus: Pranav's hiring playbook for his first marketing leader (56:19) - – Wrap up and final takeaways Send guest pitches and ideas to hi@exitfive.comJoin the Exit Five Newsletter here: https://www.exitfive.com/newsletterCheck out the Exit Five job board: https://jobs.exitfive.com/Become an Exit Five member: https://community.exitfive.com/checkout/exit-five-membership***Today's episode is brought to you by Knak. Email (in my humble opinion) is the still the greatest marketing channel of all-time.It's the only way you can truly “own” your audience.But when it comes to building the emails - if you've ever tried building an email in an enterprise marketing automation platform, you know how painful it can be. Templates are too rigid, editing code can break things and the whole process just takes forever. That's why we love Knak here at Exit Five. Knak a no-code email platform that makes it easy to create on-brand, high-performing emails - without the bottlenecks.Frustrated by clunky email builders? You need Knak.Tired of ‘hoping' the email you sent looks good across all devices? Just test in Knak first.Big team making it hard to collaborate and get approvals? Definitely Knak.And the best part? Everything takes a fraction of the time.See Knak in action at knak.com/exit-five. Or just let them know you heard about Knak on Exit Five.***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

Is veganism a "white privilege"? Piyush and I delve into it on this episode, covering subjects such as the caste system; the idea of veganism as a "Western" idea; and a form of racism known as the 'bigotry of low expectations'.We also cover the topic of Indians claiming household cows are "treated well" in India, and briefly touch on the recent wave of anti-Indian hatred online.____Piyush's Instagram____Patreon: https://patreon.com/CarnismDebunkedFacebook: https://facebook.com/CarnismDebunkedInstagram: https://instagram.com/Carnism_DebunkedX: https://twitter.com/CarnismDebunkedWebsite: https://CarnismDebunked.com

Piyush Kharbanda, General Partner at Vertex Ventures, discusses his firm's AI investment thesis.

In today's Tech3 from Moneycontrol, Commerce Minister Piyush Goyal's critique of Indian startups sparks fiery rebuttals from Zepto's Aadit Palicha, Ashneer Grover, and others. Meanwhile, Microsoft's AI takes over interviews—grilling Gates, Ballmer, and Nadella in a hilarious tech nostalgia moment. Flipkart's Sachin Bansal opens up about post-exit regrets and his new fintech dream. Plus, Trump's tariff hike shakes up India's IT sector. Tune in for all the updates from startup and tech world.

In this episode, we sit down with Piyush Sharrma, CEO and co-founder of the Tuskira team. They're an AI-powered defense optimization platform innovating around leveraging an Agentic Security Mesh.We will dive into topics such as Platform vs. Point Solutions, Security Tool Sprawl, Alert Fatigue, and how AI can create "intelligent" layers to unify and enhance security tooling ROI.We discussed:What drove Piyush to jump back into the startup space after successfully exiting from a previous startup he helped foundThe industry debate around Platform vs. Point Solutions or Best-of-Breed and the perspectives between industry industry leaders and innovative startupsDealing with the challenge of alert fatigue security and development teams and the role of AI in reducing cognitive overload and providing insight into organizational risks across tools, tech stacks, and architecturesThe role of AI in providing intelligence layers or an Agentic Security Mesh across existing security tools and defenses and mitigating organizational risks beyond isolated vulnerability scans by looking at compensating controls, configurations, and more.Shifting security from a reactionary model around incident response and exploitation to a preemptive risk defense model that minimizes attack surface and optimizes existing security investments and architectures

Kopi Time hits 5 years and 150 episodes! We celebrate the milestone with Piyush Gupta, outgoing CEO of DBS Bank Ltd. Piyush brings his remarkable intellectual breadth in this conversation, in which we touch upon various aspects of the present and future of banking. We begin by discussing how banks in Asia and the West have evolved since the 2008/09 global financial crisis, and their track record in embracing digital banking, financial inclusion, risk management, and relationship with regulators. Piyush then weighs in on conventional banks versus pure-play digital banks. The conversation moves on to the digital asset ecosystem and what that means for both banks and central banks going forward. We also talk about cyber security and banks’ role in green transition. We conclude with Piyush’s take on the promise and challenge of AI, particularly LLMs. What makes him hopeful? It’s the capacity of humanity to absorb all sorts of transformations and cataclysms, and yet move forward. This is not goodbye; we will have Piyush back.See omnystudio.com/listener for privacy information.

In this podcast episode, Pranav Piyush joins the conversation to discuss marketing measurement and incrementality, with a focus on affiliate and influencer marketing. The discussion highlights Media Mix Modeling (MMM) and its growing accessibility through open-source tools and lower costs, positioning it as a key attribution method in the coming decade. The episode explains the difference between attribution and incrementality, contrasting the limitations of multi-touch attribution (MTA) with MMM's cause-and-effect approach. Pranav shares insights on how MMM can help brands assess the impact of various marketing channels, including those that are traditionally difficult to track. Listeners will also gain practical advice on implementing MMM, covering data needs, cost factors, common pitfalls, and real-world success stories. The conversation emphasizes the value of actionable insights and the importance of moving beyond basic dashboards to achieve meaningful business growth.

Pranav Piyush is Co-Founder and CEO at Paramark. Paramark (Pre-Seed) helps CMOs and CFOs invest in marketing with confidence and predictability. Here's what we cover: I see the book Alchemy on Pranav's bookshelf and it's about "chasing the magic" so have to ask what got him into it; How do you incorporate customer research at Paramark? What are some of your goals with this research; What have been the results; Why is customer research important; Challenges with customer research; Pranav asks me his burning question. Pranav on LinkedIn: www.linkedin.com/in/pranavp Paramark: paramark.com For more content, subscribe to Building With Buyers on Apple or Spotify or wherever you like to listen, and don't forget to leave a review if you're lovin' the show. Music by my talented daughter. Anna on LinkedIn: ⁠www.linkedin.com/in/annafurmanov⁠ Website: ⁠furmanovmarketing.com

Dave's guest this week is Piyush Chaudhari, Chief Executive Officer at Salsify.  Piyush joined Salsify this past June after an illustrious career in leadership at prestigious firms including SRS & Co, where was CEO, and IRI, where he was President, Americas and Global Strategy.    Piyush shares the three things changing the retail landscape today (8:02), his experience at Salsify so far (10:03), an overview of the Salsify/It'sRapid partnership (14:42), his POV on retail media (19:58), thoughts on how the new administration will impact Saas and Tech, and how AI will impact content creation (26:22). Connect with Piyush: https://www.linkedin.com/in/piyush-chaudhari-62b29a13/ Follow Salsify: https://www.linkedin.com/company/salsify Take advantage of a special offer from It'sRapid and get a free image, video or banner ad by emailing sales@itsrapid.io with code "BEYOND2024"Learn more about ItsRapid: https://itsrapid.ai/ Theme music: "Happy" by Mixaud - https://mixaund.bandcamp.comProducer: Jake Musiker

In episode 187 of PG Radio, we sit down with Mr. Piyush Goyal  @PiyushGoyalOfficial  , India's Minister of Commerce & Industry, for an engaging and insightful conversation on a range of topics that shape the future of India. Mr. Goyal shares his views on the nation's economic path, the influence of politics on governance, and his experience as a Lok Sabha MP. We also dive into issues like the role of infrastructure in India's growth, the impact of social media on elections, and the challenges posed by caste and religion in the political landscape. Piyush Goyal is an Indian politician, chartered accountant, and prominent member of the Bharatiya Janata Party (BJP), currently serving as India's Minister of Commerce and Industry, Consumer Affairs, and Food and Public Distribution. Known for his strategic economic vision, Goyal has held key portfolios, including Railways and Coal, where he played an instrumental role in transforming India's railway infrastructure and fostering renewable energy initiatives 00:00 - Is Piyush a socialist or a capitalist? 5:34 - Globalizing Indian Education System 8:57 - Are policies delivering? 9:52 - Piyush Goyal on Donald Trump winning the US elections 13:50 - India's Biggest Obstacle 19:57 - Influence of Caste and Religion in Elections 21:23 - Is Infrastructure the answer to India's Growth? 28:55 - Is Revenge Politics Undermining India's Growth 30:14 - Experience of becoming a Lok Sabha MP 41:28 - How Social Media is shaping the Elections 43:30 - Piyush Goyal's message for the People of India 45:40 - Piyush's questions for Prakhar 47:13 - Monologue

This episode is from Drive 2024, our first-ever in-person event for B2B marketers in Burlington, Vermont. Pranav Piyush, Co-Founder & CEO of Paramark, hosted a session on one of B2B's biggest challenges: measurement and attribution.Pranav covers:Why attribution models like first-touch and last-touch are outdatedHow to prove the incremental impact of marketing channels, including search, social, email, podcasts, billboards, and brand.How to present marketing's value to the CFO and CEOTimestamps(00:00) - - Intro to Pranav (06:01) - - Marketing IS Measurable (06:44) - - What Does Attribution Mean? (09:59) - - Do Search Ads Drive Incremental Sales? (11:16) - - How To Find Cause From Search > Impression > Click > Conversion (13:09) - - Google's Conversion Lift (14:36) - - Do Social Ads Drive Incremental Sales? (16:02) - - Tracking Demo Bookings vs. LinkedIn Impressions (18:17) - - Do Emails Drive Incremental Sales? (20:47) - - Do Podcasts Drive Incremental Sales? (23:59) - - Do Billboards Drive Incremental Sales? (26:23) - - How Do You Measure Brand? (31:18) - - Why You Should Experiment to Understand Attribution (33:28) - - The Long Term Impact of Brand Equity (35:13) - - How to Measure the Impact of Videos and LinkedIn (36:26) - - Impressions vs Results (39:36) - - Impressions vs Demos (43:33) - - Wrap Up Send guest pitches and ideas to hi@exitfive.comJoin the Exit Five Newsletter here: https://www.exitfive.com/newsletterCheck out the Exit Five job board: https://jobs.exitfive.com/Become an Exit Five member: https://community.exitfive.com/checkout/exit-five-membership***This episode of the Exit Five podcast is brought to you by our friends at Knak.  Launching an email or landing page in your marketing automation platform shouldn't feel like assembling an airplane mid flight with no instructions, but too often that's exactly how it feels.No more having to stop midway through your campaign to fix something simple. Knack lets you work with your entire team in real time and stops you from having to fix things mid flight. Check them out at knak.com/exit-five/***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

Piyush Tantia, Chief Innovation Officer at ideas42, and Sarah Welch, Associate Managing Director at ideas42, join today's conversation to discuss how behavioral science principles can enhance fundraising efforts. Together, they explore strategies fundraisers can use to stay motivated, manage tasks effectively, and overcome stressors like fear and uncertainty. In this episode, you will be able to: Learn how behavioral science improves fundraising effectiveness. Discover strategies for staying motivated and managing tasks. Understand how fear and uncertainty lead to task avoidance. Explore techniques for reducing burnout and stress. Recognize the power of storytelling in fundraising. Reframe negative emotions like shame and pressure constructively. Streamline communication and manage fundraising's emotional toll. Address resistance by understanding underlying behavioral causes. Design solutions to overcome stress and fear in fundraising. Get all the resources from today's episode here.  Connect with me:  Instagram: https://www.instagram.com/_malloryerickson/ Facebook: https://www.facebook.com/whatthefundraising YouTube: https://www.youtube.com/@malloryerickson7946 LinkedIn: https://www.linkedin.com/mallory-erickson-bressler/ Website: malloryerickson.com/podcast Loved this episode? Leave us a review and rating here: https://podcasts.apple.com/us/podcast/what-the-fundraising/id1575421652 If you haven't already, please visit our new What the Fundraising community forum. Check it out and join the conversation at this link. If you're looking to raise more from the right funders, then you'll want to check out my Power Partners Formula, a step-by-step approach to identifying the optimal partners for your organization. This free masterclass offers a great starting point Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Piyush Khanna, Vice President of Clinical Services at CareFirst BlueCross BlueShield, joins the podcast to discuss the importance of timely data from provider networks. He shares insights on quality reporting requirements, operational challenges, and how advancements in data exchange are shaping the future of healthcare delivery.

In this special episode Ankur introduces the incredible members of the waricrew team. From content creators to data analysts, each team member shares their unique journey, career roles, personal achievements, and valuable life advice. Discover how this remote-working team collaborates, learns, and grows together while achieving financial freedom through multiple income streams and disciplined investments. Don't miss these inspiring stories of young professionals making their mark! 00:00 Introduction to Woice With Warikoo 00:27 Meet the Warikoo Team 02:19 Piyush's Journey 06:40 Shaurya's Story 10:50 Gitanjali's Experience 14:00 Rishwajeet's Role 19:55 Aditya's Path 22:57 Landing the Graphic Design Job 23:17 Working with Waricrew 23:45 Income and Investments 24:18 Best and Worst Parts of the Job 24:38 Advice for Aspiring Professionals 25:01 Introducing Bhavya 25:34 Bhavya's Journey and Insights 27:53 Introducing Shivam 28:56 Shivam's Role and Advice 30:53 Introducing Ria 31:50 Ria's Career Path and Advice 34:15 Introducing Ananya 35:03 Ananya's Experience and Advice 38:37 Introducing Surbhi 39:02 Surbhi's Role and Advice 41:55 Introducing Richie 42:38 Richie's Role and Financial Advice 43:55 Key Takeaways and Final Thoughts