Podcasts about enterprise ai

What does it take to transform a brilliant AI model from a research paper into a product customers can rely on? We're joined by Elizabeth Lingg, Director of Applied Research at Contextual AI (the team behind RAG), to explore the immense challenge of bridging the gap between the lab and the real world. Drawing on her impressive career at Microsoft, Apple, and in the startup scene, Elizabeth details her journey from academic researcher to an industry leader shipping production AI. Elizabeth shares her expert approach to measuring AI impact, emphasizing the need to correlate "inner loop" metrics like accuracy with "outer loop" metrics like customer satisfaction and the crucial "vibe check." Learn why specialized, grounded AI is essential for the enterprise and how using multiple, diverse metrics is the key to avoiding model bias and sycophancy. She provides a framework for how research and engineering teams can collaborate effectively to turn innovative ideas into robust products. Check out:Register now: Closing the AI gap: Exceeding executive expectations for AI productivityFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Contextual AI: Contextual.ai WebsiteFollow Contextual AI on Social Media: LinkedIn | X (formerly Twitter)Connect with Elizabeth: LinkedInReferenced in today's show:Throwing AI at Developers Won't Fix Their ProblemsWhy language models hallucinatei ran Claude in a loop for three months, and it created a genz programming language called cursedSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

To get to the benefits that AI offers, organizations have to address their technology infrastructure in ways that are much broader than historical approaches. Senior analyst Greg Macatee joins host Eric Hanselman to delve into what's required and what enterprises are identifying in the recent Voice of the Enterprise AI and Machine Learning study. Enterprises are struggling with raising the success levels of AI projects. Over 60% report moderate to severe challenges in achieving AI success. Bringing together the computational power and the right quality data in the right locations can be complicated in the hybrid environments that more are operating. It's not just a matter of being more selective with use cases, AI requires a set of organizational skills that have to be honed. Starting small and iterating can reduce risk while building competency.  Infrastructure has to shift in new ways, as well. Data management processes that can build the necessary data pipelines to feed AI applications bring together a broader set of tech disciplines. There are new wrinkles in AI infrastructure ecosystems, with new providers looking to address supply chain constraints, like the Neocloud or GPU as a Service  (GPUaaS) providers. Even hyperscalers are looking to them to meet surging demand in a tight market. Those new options offer new choices, but enterprises need to match them with their AI goals. More S&P Global Content: Navigating the AI infrastructure landscape The path from LLMs to agentic AI Next in Tech | Ep. 225: Security for MCP For S&P Global Subscribers: AI infrastructure strategies evolve amid widespread data challenges – Highlights from VotE: AI & Machine Learning Generative AI Market Monitor & Forecast AI infrastructure: Trends, thoughts and a 2025 research agenda Credits: Host/Author: Eric Hanselman Guest: Greg Macatee Producer/Editor: Adam Kovalsky Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

The Information's Jing Yang talks with TITV Host Akash Pasricha about the latest developments in US-China trade negotiations in Spain, where TikTok and NVIDIA are now central to the talks. We also talk with reporters Cathy Perloff & Ann Gehan about Perplexity's cautious approach to advertising and e-commerce, and we get into AI valuations with Menlo Ventures' newest partner Deedy Das. Lastly, we talk with You.com's CEO Richard Socher about his company's pivot to AI enterprise search.Articles discussed on this episode: https://www.theinformation.com/articles/search-has-its-goliath-could-richard-socher-be-its-davidTITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to:- The Information on YouTube: https://www.youtube.com/@theinformation4080/?sub_confirmation=1- The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

Discover how Sema4.ai is redefining enterprise AI with a platform built to help businesses build, operate, and scale SAFE AI agents. In this conversation, CTO and co-founder Ram Venkatesh explains why simply generating insights isn't enough and why enterprises need AI that can act on those insights reliably, securely, and at scale. If you want to understand the future of agentic AI and how to safely scale AI across your organization, this episode is a must-watch. Stay Updated:Craig Smith on X: https://x.com/craigssEye on A.I. on X: https://x.com/EyeOn_AI (00:00) The Data-to-Action Gap (00:38) Ram's Big Data Background (03:22) Why RPA Failed & Agents Win   (04:39) Conversational Agents vs Manual Workflows   (06:28) The Power of a Semantic Layer   (08:20) Runbooks: Capturing Intent, Not Just Steps   (11:16) Connecting Data Across Systems   (15:12) How Sema4.ai Keeps AI Secure   (17:37) From 20 to 2,000 Agents: Scaling the Fleet   (20:20) Choosing the Right Agent Platform   (26:22) Process Architects: The New Role in AI   (29:00) Why Finance & Healthcare Lead in Adoption   (30:04) Sema4.ai's Pricing & Adoption Playbook   (41:29) Scaling Faster with Snowflake Deployment   (43:10) ISVs & Domain Experts as Agent Builders  

A full founder's arc: starting small, building global SaaS companies from Hyderabad, taking one to IPO, another to a billion-dollar exit, and then choosing to begin again (and again).Kiran Darisi began at Zoho, founding team member of Freshworks at 25, and stayed twelve years till the company went public. Today he is building Atomicwork, reinventing service management in the AI era. Sreedhar Peddineni started with Host Analytics back when SaaS was still called application service provider, went on to create the customer success category with Gainsight, and is now on his third venture with GTM Buddy.In this episode, we talk about what it takes to build companies that last for decades. We discuss how startups can find the “Goldilocks zone”,why smaller teams are creating more value than ever, and the mistakes founders often make when moving from SMB to enterprise.Both founders share how AI is reshaping every layer of SaaS, why it's both eating the pie and expanding it and what's left for entrepreneurs when the biggest AI companies are chasing every vertical.This conversation looks back at some of India's iconic SaaS companies, shares lessons from two decades of building, and looks ahead to the future of SaaS from India.0:00 — Atomicwork x GTM Buddy1:17 — Why They Chose to Be Founders Again8:27 — How to generate pipeline predictability at a startup?16:46 — Becoming Freshworks' Co-Founder at 2519:43 — How Atomicwork Co-Founders Connected & Chose Their Problem23:25 — Building Companies That Last for Decades27:18 — Why Smaller, High-Quality Teams Win30:21 — 1st vs 2nd Founders: What They Get Wrong31:56 — Scaling: SMB → Mid-Market → Enterprise33:36 — Category Creation at Gainsight40:03 — Disrupting vs Expanding Large Categories44:08 — How to Choose the Right Market49:08 — Why Atomicwork Chose This Category53:11 — The 'Goldilocks Zone' for a Startup Category57:11 — Can Salesforce Be Replaced?58:26 — Neon Fund x Atomicwork1:01:27 — Neon Fund x GTM Buddy1:03:44 — If Big AI Goes Everywhere, What's Left for B2B SaaS?1:07:36 — What to Build in the AI Era?1:10:35 — Is AI Expanding the Pie While Eating It?1:17:03 — How Useful Are Custom GPTs for Companies?1:20:34 — Workflows vs AI Workforce-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

Discover how Glean AI is transforming enterprise productivity with AI-powered search and intelligent agents.About the episode:Join Nataraj as he explores the evolution of enterprise AI with Arvind Jain, CEO of Glean. From its roots as an AI-powered search solution, Glean has transformed into a comprehensive AI agent platform, helping companies like Zapier, Carta, and Grammarly boost productivity. Arvind shares his journey, the challenges of building a universal AI assistant, and his vision for the future of AI at work. Discover how Glean is helping enterprises leverage AI to streamline workflows and enhance employee efficiency. Learn how Glean ensures AI delivers value safely and securely.What you'll learnUnderstand the evolution of Glean from an AI-powered search tool to a comprehensive AI agent platform.Discover how Glean helps enterprises address productivity challenges by providing quick access to internal knowledge.Learn about the techniques Glean employs to reduce hallucinations and ensure accurate, reliable AI-driven insights.Explore the diverse use cases of AI agents in sales, customer service, engineering, and legal departments.Gain insights into Arvind Jain's vision for the future of work, where AI proactively assists employees in their daily tasks.About the Guest and Host:Arvind Jain: CEO of Glean, work AI platform, and co-founder of Rubrik.Connect with Guest:→ LinkedIn: https://www.linkedin.com/in/jain-arvind→ Website: glean.comNataraj: Host of the Startup Project podcast, Senior PM at Azure & Investor.→ LinkedIn: https://www.linkedin.com/in/natarajsindam/→ Substack: ⁠https://startupproject.substack.com/⁠In this episode, we cover(00:01) Introduction to Arvind Jain and Glean AI(01:13) What Glean does: AI-powered search and conversational AI assistant(03:43) The origin story of Glean: Solving productivity challenges in fast-growing companies(06:46) The evolution from search to an AI assistant(09:45) The advantages of tackling hard problems in startups(12:37) Techniques to reduce AI hallucinations and ensure accuracy(17:31) Model Hub: The different models Glean uses(20:16) Use cases for AI agent platforms across various departments(24:42) Workflow agents and the importance of integrations(31:59) The future of work: Proactive AI companions(37:14) Glean's cross-platform vision(39:07) How AI is changing the business of fast-growing startups(43:39) How Glean is becoming more AI-first internally(47:04) Ideas Arvind would explore if starting over with AI(49:49) Key metrics Arvind watches at Glean AIDon't forget to subscribe and leave us a review/comment on YouTube Apple Spotify or wherever you listen to podcasts.#GleanAI #EnterpriseAI #AISearch #AIAgents #FutureofWork #Productivity #ArtificialIntelligence #Innovation #SaaS #Startups #BusinessInsights #Technology #AIPlatform #WorkflowAutomation #MachineLearning #DeepLearning #AIStrategy #DigitalTransformation #AIinBusiness #TechPodcast

Jay Alammar is Director and Engineering Fellow at Cohere and co-author of the O'Reilly book “Hands-on Large Language Models.” Subscribe to the Gradient Flow Newsletter

Enterprise AI Agents for Work, Service and Process: www.kore.ai Kore.ai founder and CEO Raj Koneru breaks down how enterprises are moving beyond chatbots into agentic AI that actually ships. We get into the no-code tooling behind multi-agent workflows, agentic RAG, guardrails that keep outputs in scope, and why a control layer for governance is now essential. Raj shares real scale numbers, the three Kore.ai product lanes for customer and employee experience, and how partnerships with Microsoft and AWS let teams build where they already run. If you care about building secure, explainable AI agents that integrate fast and scale cleanly, this one is for you. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) Raj Koneru's Journey & The Birth of Kore.ai (03:10) From Chatbots to Enterprise-Grade Agents(06:33) Security, Scale & Proof in the Market(07:04) What Agentic AI Really Means(12:16) Building & Governing AI Agents(17:26) Kore.ai's Product Lines & Differentiation(20:22) Industry Applications & Case Studies(28:17) User Experience & Change Management(34:46) Governance, Identity & Cost Controls(39:56) Adoption Timelines & Market Outlook(43:51) Roadmap & Partnerships(47:38) Future of the Enterprise AI Landscape

Lindsey S. Mignano is the founder of SSM Legal, an entrepreneurial Silicon Valley corporate lawyer representing emerging technology companies and industry-adjacent firms and small businesses. Her practice spans technology company business formation and expansion into US markets, M&A (flips, entity or asset sales), commercial and technology transactions, and venture financing. Lindsey has been recognized as a “Rising Star” by Super Lawyers every year from 2016-2024, an honor awarded to only 2.5% of attorneys under the age of 40. In 2025, she was awarded the Super Lawyers distinction for the first time at the age of 40, an honor awarded to only 5% of attorneys. Separate from her law practice, Lindsey speaks often about diversity issues in the fields of law, tech, and venture. In 2023, Lindsey founded Venture Betches, a venture fund of funds, and Syndicate Betches, a real estate syndicate fund of funds, both with a social justice mission to bring investment opportunities to historically underrepresented accredited limited partners who identify as female and/or BIPOC/minorities.

On Mission Matters, Adam Torres interviews Smriti Kirubanandan, a technology executive, during the Milken Global Conference. Smriti shares her journey from robotics and public health to enterprise AI, the barriers companies face in scaling, and why responsible governance and cultural readiness are crucial. She highlights how AI can simplify healthcare, improve outcomes, and create sustainable impact when scaled responsibly. This interview is part of our Global Milken Conference series. Big thank you to Milken Institute! Follow Adam on Instagram at https://www.instagram.com/askadamtorres/ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: https://missionmatters.lpages.co/podcastguest/ Visit our website: https://missionmatters.com/ More FREE content from Mission Matters here: https://linktr.ee/missionmattersmedia Learn more about your ad choices. Visit podcastchoices.com/adchoices

On Mission Matters, ⁠Adam Torres⁠ interviews ⁠Smriti Kirubanandan⁠, a technology executive, during the Milken Global Conference. Smriti shares her journey from robotics and public health to enterprise AI, the barriers companies face in scaling, and why responsible governance and cultural readiness are crucial. She highlights how AI can simplify healthcare, improve outcomes, and create sustainable impact when scaled responsibly. This interview is part of our Global Milken Conference series. Big thank you to Milken Institute! Follow Adam on Instagram at ⁠https://www.instagram.com/askadamtorres/⁠ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: ⁠https://missionmatters.lpages.co/podcastguest/⁠ Visit our website: ⁠https://missionmatters.com/⁠ More FREE content from Mission Matters here: ⁠https://linktr.ee/missionmattersmedia⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

A 14-year journey from bootstrap to scale.Exotel's story is one of India's most remarkable SaaS journeys. Shivakumar Ganesan, started Exotel in 2011, bootstrapping it from the ground up. In 2012, he raised a seed round of ₹2.5 crore, but for the next eight years, the company grew without any external funding. Then came COVID and revenue went from $10M to $5M and what followed were bold strategic moves.3 funding rounds, 2 major acquisitions, and the decision to stay focused on the Indian market despite advice to go global first. Today, Exotel powers calls for delivery executives, cab drivers, and banking relationship managers across the country.In this conversation, Shivku shares what it's like to tackle India's unique AI challenge of building voicebots in 30+ languages, and how automation could reduce contact center jobs by as much as 80%. He talks about the tough transition from serving SMBs to enterprise customers and how he has built a ₹2500 crore+ business without leaving India.If you're interested in B2B companies built from India, this episode is full of insights on timing, reading the market, and creating deep moats in overlooked opportunities.0:00 – Trailer0:42 – Exotel enabling 2 Billion Calls Monthly5:04 – 4 Fundraises & 2 M&A's in 18 Months12:06 – How Acquisitions Affect Company Finances18:11 – Why 90% of M&As Fail22:02 – Why Acquisitions Are Extremely Hard22:59 – How AI Will Change Customer Relations26:46 – How Customer Spending Will Shift with AI29:10 – AI Could Reduce 80% of Jobs30:27 – Where AI Offers Hope31:47 – India's Unique AI Challenges34:60 → Actually 35:00 – Building in India for the US Market38:17 – Why Exotel Didn't Enter the US Market39:49 – Indian SaaS Co's Should Go Public42:50 – The Mega Cycles of Tech Transformation45:37 – Customer Segments: SMBs to Startups to Enterprise56:45 – Find Large Uniquely Indian Markets to Solve59:44 – India's Shift from Price to Quality Is 20 Years Away-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

In this episode, Anant Bhardwaj, CEO of Instabase, provides a pragmatic guide for AI practitioners building enterprise solutions. Subscribe to the Gradient Flow Newsletter

Maisa AI is built on the premise that enterprise automation requires accountable AI agents, not opaque black boxes. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Here's the thing. Most enterprise AI talk today starts with chatbots and ends with glossy demos. Meanwhile, the data that actually runs a business lives in rows, columns, and time stamps. That gap is where my conversation with Vanja Josifovski, CEO of Kuma.ai, really comes alive. Vanja has spent two and a half decades helping companies turn data into decisions, from research roles at Yahoo and Google to steering product and engineering at Pinterest through its IPO and later leading Airbnb Homes. He's now building Kuma.ai to answer an old question with a new approach: how do you get accurate, production-grade predictions from relational data without spending months crafting a bespoke model for each use case? Vanja explains why structured business data has been underserved for years. Images and text behave nicely compared to the messy reality of multiple tables, mixed data types, and event histories. Traditional teams anticipate a prediction need, then kick off a long feature engineering and modeling process. Kuma's Relational Foundation Model, or RFM, flips that script. Pre-trained on a large mix of public and synthetic data warehouses, it delivers task-agnostic, zero-shot predictions for problems like churn and fraud. That means you can ask the model questions directly of your data and get useful answers fast, then fine-tune for another 15 to 20 percent uplift when you're ready to squeeze more from your full dataset. What stood out for me is how Kuma removes the grind of manual feature creation. Vanja draws a clear parallel to computer vision's shift years ago, when teams stopped handcrafting edge detectors and started learning from raw pixels. By learning directly from raw tables, Kuma taps the entirety of the data rather than a bundle of human-crafted summaries. The payoff shows up in the numbers customers care about, with double-digit improvements against mature, well-defended baselines and the kind of time savings that change roadmaps. One customer built sixty models in two weeks, a job that would typically span a year or more. We also explore how this fits with the LLM moment. Vanja doesn't position RFM as a replacement for language models. He frames it as a complement that fills an accuracy gap on tabular data where LLMs often drift. Think of RFM as part of an agentic toolbox: when an agent needs a reliable prediction from enterprise data, it can call Kuma instead of generating code, training a fresh model, or bluffing an answer. That design extends to the realities of production as well. Kuma's fine-tuning and serving stack is built for high-QPS environments, the kind you see in recommendations and ad tech, where cost and latency matter. The training story is another thread you'll hear in this episode. The team began with public datasets, then leaned into synthetic data to cover scenarios that are hard to source in the wild. Synthetic generation gives them better control over distribution shifts and edge cases, which speeds iteration and makes the foundation model more broadly capable upon arrival. If you care about measurable outcomes, this episode shows why CFOs pay attention when RFM lands. Vanja shares examples where a 20 to 30 percent lift translates into hundreds of thousands of additional monthly active users and direct revenue impact. That kind of improvement isn't theory. It's the difference between a model that nudges a metric and a model that moves it. By the end, you'll have a clear picture of what Kuma.ai is building, why relational data warrants its own foundation model, and how enterprises can move from wishful thinking to practical wins. Curious to try it yourself? Vanja also points to a sandbox where teams can load data and ask predictive questions within a notebook, then compare results against in-house models. If your AI plans keep stalling on tabular reality, this conversation offers a way forward that's fast, accurate, and designed for the systems you already run.

The software that powers 25% of India's e-commerce transactions, processes a billion orders each year, and in 2025 alone fulfilled 20 million quick-commerce orders: Unicommerce sits at the core of India's digital retail ecosystem. It is one of the few SaaS companies from India to go public, doing so after nearly a decade of steady growth without fresh primary capital until its IPO in 2024.In this episode of The Neon Show, we sit down with Kapil Makhija, CEO of Unicommerce, the company quietly running India's $60B E-retail market (set to hit $2 Trillion in the next two decades). The conversation goes beyond the company's journey to unpack how perceptions of Indian SaaS customers are changing: from the old belief that they “don't pay” to a more nuanced reality where they value communication, support expectations, and long-term relationships define success.We also look ahead to the future of SaaS in India: from the impact of AI, to the challenges of scaling from zero to $100M, to the balance between pricing and value, and identifying the sectors most ready for building large SaaS companies.This episode is for anyone curious about the story of SaaS in India, from how it is being built, scaled & the opportunities ahead.00:00 – Trailer01:15 – India makes you product-ready & pressure-tested03:45 – GTM: India doesn't reward size, it rewards focus05:00 – Joining Unicommerce the week Snapdeal acquired it10:10 – Digital-first brands vs. traditional brands12:36 – Why Excel and manual ops were the real competition?14:25 – The acquisition of Shipway17:02 – How will the company achieve 1000 Cr Revenue?19:40 – How the decision to go public was made23:40 – Success in SaaS isn't sign-ups, it's retention29:31 – The myth that Indian customers don't pay33:30 – Do Indian customers want Enterprise support but SMB pricing?36:40 – The impact of AI on SaaS39:10 – Founder vs. CEO: Is there a difference?47:29 – White spaces in e-commerce waiting to be built50:35 – Q-commerce vs. E-commerce: where are brands betting?52:07 – How SaaS companies decide if they're IPO-ready?55:10 – Can India build billion-dollar SaaS companies at home?58:15 – How long does the 0 → $100M journey really take?01:01:15 – How to build a ₹100 Cr SaaS company today?01:09:53 – Are pricing advantages in SaaS sustainable?01:12:22 – How much do brands actually spend on tech?-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

In this episode of Campus Technology Insider Podcast Shorts, hosted by Rhea Kelly, we explore the latest in higher education technology. Highlights include Tripti Sinha taking over as president and CEO of Internet2, a report from Anthropic on how faculty utilize generative AI, and an MIT Media Lab report revealing the shortcomings of enterprise AI investments. For more in-depth stories and analysis on higher ed tech, visit campustechnology.com. 00:00 Introduction to Campus Technology Insider 00:15 New Leadership at Internet2 00:55 Generative AI in Academia 01:34 Enterprise AI Reality Check 02:05 Conclusion and Further Resources Source links: Internet2 Announces a New President and CEO to Step Up in October Top 3 Faculty Uses of Gen AI MIT Report: Most Organizations See No Business Return on Gen AI Investments   Campus Technology Insider Podcast Shorts are curated by humans and narrated by AI.

Enterprise AI is still in its infancy, with less than 1% of enterprise data currently used to fuel AI, according to Raj Verma, CEO of SingleStore. While consumer AI is slightly more advanced, most organizations are only beginning to understand the scale of infrastructure needed for true AI adoption. Verma predicts AI will evolve in three phases: first, the easy tasks will be automated; next, complex tasks will become easier; and finally, the seemingly impossible will become achievable—likely within three years. However, to reach that point, enterprises must align their data strategies with their AI ambitions. Many have rushed into AI fearing obsolescence, but without preparing their data infrastructure, they're at risk of failure. Current legacy systems are not designed for the massive concurrency demands of agentic AI, potentially leading to underperformance. Verma emphasizes the need to move beyond siloed or "swim lane" databases toward unified, high-performance data platforms tailored for the scale and complexity of the AI era.Learn more from The New Stack about the latest evolution in AI infrastructure: How To Use AI To Design Intelligent, Adaptable InfrastructureHow to Support Developers in Building AI Workloads Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Drawing upon insights from the ebook, “Critical Success Factors of Enterprise AI ”, we'll explore key strategies for prioritizing AI use cases, demonstrating business value, optimizing resources, ensuring data quality, and driving AI adoption. The discussion will provide actionable takeaways for business leaders looking to navigate the complexities of AI implementation and maximize its transformative potential. The ebook is available at: https://forms.workday.com/en-us/ebooks/critical-success-factors-to-enterprise-ai-adoption/form.html?refCamp=7014X000002XN5WQAW&step=step1_default

AGNTCY - Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support. In this episode of Eye on AI, we sit down with Leon Song, VP of Research at Together AI, to explore how open-source models and cutting-edge infrastructure are reshaping the AI landscape.    From speculative decoding to FlashAttention and RedPajama, Leon shares how Together AI is building one of the fastest, most cost-efficient AI clouds—helping enterprises fine-tune, deploy, and scale open-source models at the level of GPT-4 and beyond.   We dive into Leon's journey from leading DeepSpeed and AI for Science at Microsoft to driving system-level innovation at Together AI. Topics include: The future of open-source vs. closed-source AI models Breakthroughs in speculative decoding for faster inference How Together AI's cloud platform empowers enterprises with data sovereignty and model ownership Why open-source models like DeepSeek R1 and Llama 4 are now rivaling proprietary systems The role of GPUs vs. ASIC accelerators in scaling AI infrastructure   Whether you're an AI researcher, enterprise leader, or curious about where generative AI is heading, this conversation reveals the technology and strategy behind one of the most important players in the open-source AI movement. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI

This week's World of DaaS LM Brief looks at new research from MIT, which finds that 95% of generative AI pilot programs inside companies are failing to deliver meaningful financial impact.The issue is not model quality but enterprise integration and a steep organizational learning gap. The report shows ROI is strongest in back-office automation even as most budgets flow to sales and marketing, and that success comes from buying specialized solutions and forming strategic partnerships rather than building in-house.Listen to this short podcast summary, powered by NotebookLM.

The 21st century has shattered old assumptions about diplomacy.Relationships between nations are no longer guided by ideology or morality, but driven by pragmatism and national interest.This week, former diplomat Rajiv Sikri who served 36 years in the Indian Foreign Service, offers a deep dive into how global power dynamics are shifting. We discuss why the United States still remains the only true great power, yet its tariff policies are reshaping global trade and forcing countries like India to rethink their strategies. And explores how the Russia–Ukraine conflict has reshaped security and political choices. For India, Russia remains a vital partner, while Europe has chosen to cut ties despite its heavy dependence on Russian energy and Britain continues to commit billions to Ukraine even with its own economy struggling. Rajiv also examines China's growing alignment with Pakistan and what this means for India's long-term security and economic positioning. Rajiv also argues that a future global conflict may not involve every country, but rather regional conflicts with worldwide consequences.This episode provides a clear-eyed analysis of global diplomacy; its complexities, evolving alignments, and the choices India faces in navigating an increasingly multipolar world.0:00 – Why US remains the world's true great power3:35 – Has the WTO collapsed?5:23 – How US Tariffs have destabilised the world7:12 – Can India become an Agri-exporter?11:32 – Why Trump puts the MAGA base first13:21 – The Russia-Ukraine war explained22:24 – Diplomatic relationships are no longer based on ideology25:22 – Why Europe cutting ties with Russia may backfire27:52 – Why Britain funds Ukraine despite its weak economy29:57 – Did Operation Sindhoor reveal open Chinese support to Pakistan?33:32 – What China risks from India's rise?37:24 – Why morality doesn't exist in global politics38:30 – Will China's attitude towards India change?39:28 – How China dominates global manufacturing44:40 – Why global investors should bet on India49:20 – Israel's War Acts53:17 – How will WW3 actually be?56:07 – Can the world create an organisation not dominated by the West?59:29 – Why India must act cold-blooded in its national interest1:01:13 – Are India's global moves headed in the right direction?1:03:43 – Lessons from 36 years as a diplomat-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

AI projects often fail not due to technology, but because organisations struggle with change. In this episode, we explore practical strategies for enterprise leaders to drive successful AI adoption through structured change management. Using the SHIFT framework, we cover aligning strategy with purpose, managing human emotions, integrating robust frameworks, fostering psychological safety, and turning resistance into momentum. Designed for managers, consultants, and transformation leaders, this episode provides actionable insights to accelerate adoption, build trust, and deliver measurable business impact. 

The aftershocks of GPT-5's chaotic rollout continue as OpenAI scrambles to address user backlash, confusing model choices, and shifting product strategies. In this episode, Paul Roetzer and Mike Kaput also explore the fallout from a leaked Meta AI policy document that raises major ethical concerns, share insights from Demis Hassabis on the path to AGI, and cover the latest AI power plays: Sam Altman's trillion-dollar ambitions, his public feud with Elon Musk, an xAI leadership shake-up, chip geopolitics, Apple's surprising AI comeback, and more. Show Notes: Access the show notes and show links here Timestamps:  00:00:00 — Intro 00:06:00 — GPT-5's Continued Chaotic Rollout 00:16:03 — Meta's Controversial AI Policies 00:28:27 — Demis Hassabis on AI's Future 00:40:55 — What's Next for OpenAI After GPT-5? 00:46:41 — Altman / Musk Drama 00:50:55 — xAI Leadership Shake-Up 00:55:55 — Perplexity's Audacious Play for Google Chrome 00:58:32 — Chip Geopolitics 01:01:43 — Anthropic and AI in Government 01:05:17 — Apple's AI Turnaround  01:08:09 — Cohere Raises $500M for Enterprise AI  01:10:57 — AI in Education This episode is brought to you by our Academy 3.0 Launch Event. Join Paul Roetzer and the SmarterX team on August 19 at 12pm ET for the launch of AI Academy 3.0 by SmarterX —your gateway to personalized AI learning for professionals and teams. Discover our new on-demand courses, live classes, certifications, and a smarter way to master AI. Register here. This week's episode is brought to you by MAICON, our 6th annual Marketing AI Conference, happening in Cleveland, Oct. 14-16. The code POD100 saves $100 on all pass types. For more information on MAICON and to register for this year's conference, visit www.MAICON.ai. Visit our website Receive our weekly newsletter Join our community: Slack LinkedIn Twitter Instagram Facebook Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

While an Anthropic spokesperson confirmed that the AI firm did not acquire Humanloop or its IP, that's a moot point in an industry where IP lives in the brain. And what Humanloop's team is bringing to Anthropic is experience developing the tools that help enterprises run safe, reliable AI at scale. Also, India's Rapido begins testing food delivery to take on Swiggy, Zomato. Rapido's beta food delivery service has popped up in three key localities in Bengaluru before a broader rollout. Learn more about your ad choices. Visit podcastchoices.com/adchoices

NLW recently collaborated with KPMG on a 7-part enterprise AI-focused series called You can with AI. On this Saturday bonus preview, we share episode 7 of the series, all about the trends shaping the AI-ready organization of the future. Featuring Steve Chase, KMPG Global Head of AI and Digital Innovation.Learn more about the series: https://www.kpmg.us/aipodcasts

Ashok Atluri founded Zen Technologies in 1993, bootstrapping from Hyderabad at a time when India was importing 70% of its defence equipment and private players contributed just 5% of procurements. It took Zen five years to win its first contract from the Indian Army in 1998.Today, the company builds simulators and anti-drone systems, and has grown its market cap from ₹40 crore to over ₹13,000 crore.Ashok shares that India needs to make it easier for private, self-funded R&D companies to succeed in defense tech and why the focus should be in building technology with India's own IP. We also discuss the policy shifts he has seen in India's defense tech over the last 32 years, and how policies like IDDM and Make-II have reshaped India's defense manufacturing.This is an episode with a founder who has spent over three decades turning India's defence technology from an import-dependent sector into one that can build defense tech with its own IP.0:00  – Trailer  1:18  – Why entering defense tech must be easier  8:48  – Building simulators for the army  10:53 – Zen's entry into anti-drone systems  12:26 – 400x growth in 12 years  13:41 – Policy shifts in defense tech  15:42 – How Indian-owned IP can transform defense?  19:24 – How big is India's defense simulations market?  22:06 – From ₹60 Cr to ₹930 Cr in 4 years  25:27 – How are simulations built for future weapons?  29:15 – India's defense budget (foreign tech vs. local tech)  30:23 – The entry barriers in the 1990s and even today  31:43 – Is doing business with the government harder for some sectors?  36:06 – Surviving 32 years being financially conservative  37:29 – How Indian government is pushing exports in defense tech  40:35 – Zen's anti-drone systems used in Operation Sindhoor  42:31 – Will there be an India–China conflict?  43:15 – Where does China stand in defense tech?  44:08 – How India should back its wealth creators  49:12 – Policies that are enabling Indian defence companies today  49:37 – Parrikar's influence on private sector role in defense tech-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

OpenAI's rollout of GPT-5 came with some bumps for users, but we dig into how the new model could be boosting the company's enterprise business. 

Shay Levi (@shaylevi2, CEO @UnframeAI) & Larissa Schneider (COO @UnframeAI) discuss the complexities of building an enterprise-grade AI platform. Topics include what an AI platform is, the advantages of adoption, and the efficiencies gained.SHOW: 948SHOW TRANSCRIPT: The Cloudcast #948 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK:  http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT OUR OTHER PODCAST:  "CLOUDCAST BASICS"SPONSORS:[VASION] Vasion Print eliminates the need for print servers by enabling secure, cloud-based printing from any device, anywhere. Get a custom demo to see the difference for yourself.[DoIT] Visit doit.com (that's d-o-i-t.com) to unlock intent-aware FinOps at scale with DoiT Cloud Intelligence.SHOW NOTES:Unframe websiteTopic 1 - Shay & Larissa, welcome to the show!  Give everyone a brief introduction and a little about your background. Topic 2 - Today, we're discussing AI Security and Enterprise Platforms. What are the problems or issues you see with AI development today?Topic 3 - Is this where an AI platform comes into play? I'm seeing more and more about this term and wondering what it truly means to be a platform. What is your definition of a platform, and what are the advantages?Topic 4 - Shay, considering your background in APIs and API security, how does that knowledge transfer into this space?Topic 5 - Larissa, with your background in operations, where do you see the inefficiencies in AI development and lifecycle management of the AI models and the datasets?Topic 6 - Let's talk about Unframe. Give everyone an overview. Is this a SaaS service? How and where does it fit into your typical AI development stack?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

FirstMark Capital's Rich Heitzman talks with TITV Host Akash Pasricha about the IPO market & big tech's AI investments. We also talk with Writer's May Habib about enterprise AI agents and Kajabi's Ahad Khan about the creator economy, and we get into the AI search wars with Profound's James Cadwallader.Articles discussed on this episode:https://www.theinformation.com/articles/people-power-sap-remakes-ai-eraTITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

In this episode from Cisco Live, Maribel Lopez sits down with two Cisco executives, Vijoy Pandey, SVP of Outshift at Cisco and Nathan Jokel, SVP of Corporate Strategy and Alliances at Cisco, to discuss how AI is fundamentally changing enterprise infrastructure over the next year. The conversation explores the evolution from deterministic to probabilistic computing, the emergence of agentic workflows, and practical advice for business leaders navigating the AI transformation.Host: Maribel LopezGuests:Vijoy Pandey, SVP of Outshift at CiscoNathan Jokel, SVP of Corporate Strategy and Alliances at CiscoRecorded at: Cisco LiveEpisode OverviewIn this episode from Cisco Live, Maribel Lopez sits down with two Cisco executives to discuss how AI is fundamentally changing enterprise infrastructure over the next year. The conversation explores the evolution from deterministic to probabilistic computing, the emergence of agentic workflows, and practical advice for business leaders navigating the AI transformation.Key Topics DiscussedThe Three Waves of AI Infrastructure EvolutionWave 1: AI training in public cloud (mostly behind us)Wave 2: AI inference moving to enterprise data centers for control, security, and economic reasonsWave 3: AI moving to the edge with physical and embodied AI requiring new infrastructure for robots and devicesFrom Deterministic to Probabilistic ComputingVijoy explains the fundamental shift happening in computing:Traditional computing: deterministic, machine-speed but limitedHuman intelligence: agentic but slowNew paradigm: AI agents with human-like behavior operating at machine speed and scaleThe Internet of AgentsA collaboration platform where AI agents from different vendors can:Get discovered and authenticatedCompose workflows togetherExecute tasks collaborativelyBe evaluated for performanceReal-world example: Building a sales funnel portal using agentic interfaces from Salesforce, ServiceNow, Microsoft, and Cisco security - all working together without manual UI clicking.AI and Energy ChallengesThe Problem: By 2028, projected 63 gigawatt shortfall for new data center capacitySolutions:Invest in diverse energy sources (nuclear, renewables, battery storage)Build data centers near power sources (e.g., Cisco's Middle East partnerships)Develop more energy-efficient infrastructureFocus on smaller, specialized models instead of racing for maximum parametersCisco's Specialized AI ModelsFoundation SAC 8B: 8 billion parameter model specialized for security policyDeep Network Model: Expert model trained on network configurationsOutshift: Cisco's Innovation EngineCisco's internal incubator tackling problems adjacent to core business in:Space: Areas adjacent to networking, security, observability, collaborationTime/Risk: Higher-risk ventures that can't enter at Cisco scale initiallyCurrent Big Hairy Audacious Goals (BHAGs):Internet of AgentsQuantum Internet - building quantum networks for distributed quantum computing

Enterprise AI leaders from C3 AI, Resolve AI, and Scale AI reveal how Fortune 100 companies are successfully scaling agentic AI from pilots to production and share secrets for successful AI transformation.Topics Include:Panel introduces three AI leaders from Resolve AI, C3 AI, and Scale AIResolve AI builds autonomous site reliability engineers for production incident responseC3 AI provides full-stack platform for developing enterprise agentic AI workflowsScale AI helps Fortune 100 companies adopt agents with private data integrationMoving from AI pilots to production requires custom solutions, not shrink-wrap softwareSuccess demands working directly with customers to understand their specific workflowsAll enterprise AI solutions need well-curated access to internal data and resourcesSoftware engineering has permanently shifted to agentic coding with no going backAI agents rapidly improving in reasoning, tool use, and contextual understandingIndustry moving from simple co-pilots to agents solving complex multi-step problemsSpiros coins new concept: evolving from "systems of record" to "systems of knowledge"Democratized development platforms let enterprises declare their own agent workflowsSemantic business layers enable agents to understand domain-specific enterprise operationsTrust and observability remain major barriers to enterprise agent adoptionOversight layers essential for agents making longer-horizon autonomous business decisionsPerformance tracking and calibration systems needed like MLOps for reasoning chainsCEO-level top-down support required for successful AI transformation initiativesTraditional per-seat SaaS pricing models completely broken for agentic AI solutionsIndustry shifting toward outcome-based and work-completion pricing models insteadReal examples shared: agent collaboration in production engineering and sales automationParticipants:Nikhil Krishnan – SVP & Chief Technology Officer, Data Science, C3 AISpiros Xanthos – Founder and CEO, Resolve AIVijay Karunamurthy – Head of Engineering, Product and Design / Field Chief Technology Officer, Scale AIAndy Perkins – GM, US ISV Sales – Data, Analytics, GenAI, Amazon Web ServicesFurther Links:C3 – Website – AWS MarketplaceResolve AI – Website – AWS MarketplaceScale AI – Website – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AGNTCY - Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support. How does a 150,000-employee global leader make AI work at scale? In this episode of Eye on AI, host Craig Smith sits down with Julia Peyre, Head of AI Strategy & Innovation at Schneider Electric, to explore how the company is pioneering enterprise AI adoption through its AI Hub, hybrid AI systems, and real-world digital twin applications. From breaking data silos and embedding AI into hardware, to partnering with startups and building predictive maintenance solutions, Julia shares a blueprint for bringing AI from pilot programs to full-scale deployment, across both internal processes and customer-facing products. Stay Updated: Craig Smith on X:https://x.com/craigssEye on A.I. on X: https://x.com/EyeOn_AI (00:00) Preview and Intro (01:58) Meet Julia Peyre & Her Role(03:19) Inside Schneider's AI Hub(05:51) AI in Industrial Automation & Robotics(08:43) Internal vs External AI Applications(13:54) Why Schneider Is Ahead in AI Adoption(15:57) Centralized AI Hub Model(19:38) Hybrid AI: Combining Physics & Data(25:44) Early Steps in Multi-Agent Systems(29:54) Breaking Data Silos for AI at Scale(32:02) Predictive Maintenance with Hybrid AI(37:03) Long-Term View on AI Automation(42:37) Advice for Young Professionals in AI(44:21) Framework for Evaluating AI Solutions(50:19) Involving End Users in AI Testing

In this episode of Startup Project, Nataraj interviews Ben Kus, CTO of Box, about the critical role of unstructured data in the AI revolution. They discuss the cost structures of adopting and building with AI, and how AI is transforming enterprise businesses. Ben shares Box's unique perspective, managing over an exabyte of data for 120,000 enterprise customers. Learn how AI can understand, automate, and enhance the value of your unstructured data, turning untapped potential into practical benefits. What you'll learn: Understand what unstructured data is and why it's so critical for AI applications in business.Discover Box's strategic approach to integrating with other platforms and providing AI solutions on top of your data.Learn about the pivotal moment when Box realized the potential of generative AI and how they retrofitted their platform to be AI-first.Explore early AI use cases launched at Box, including chatting with documents and data extraction, and how enterprises are adopting these features.Understand the cost implications of leveraging AI and how Box balances offering AI for free with managing expenses.Dive into Box's perspective on pricing based on usage versus outcomes, and their current subscription model.Learn about Box's approach to AI agents, their definition, and how they are being implemented to solve complex problems.Discover the concept of "context engineering" and its importance in building AI agents that understand user needs.Understand how AI is impacting productivity within Box and the broader enterprise landscape.Find out about the AI models Box is working with and how they ensure security and trustworthiness for enterprise customers.About the Guest and Host:Ben Kus: Chief Technology Officer at Box, previously VP of Product at Box and co-founder of Subspace.Connect with Guest:→ LinkedIn:   https://www.linkedin.com/in/benkus .→ Website: https://box.com/Nataraj: Host of the Startup Project podcast, Senior PM at Azure & Investor.→ LinkedIn: https://www.linkedin.com/in/natarajsindam/→ Twitter: https://x.com/natarajsindam→ Substack: ⁠https://startupproject.substack.com/⁠In this episode, we cover:(00:01) Introduction(00:35) What is unstructured data and why is Box in the center of AI?(04:05) Box's strategy on building new AI tools and features.(06:55) The moment Box realized AI was a big shift.(11:08) Earliest AI use cases launched at Box.(15:17) The cost of leveraging AI and its impact on profitability.(19:24) Pricing based on usage vs. outcomes.(22:47) Abuse prevention and handling unlimited storage.(24:16) AI products targeted for specific knowledge worker persona.(28:18) Being an AI-first company.(30:55) Defining and implementing AI agents within Box.(36:38) Form factors for agents in an enterprise product sense.(39:59) Productivity improvements with AI.(44:07) Progression in junior developers.(46:17) Document parsing and extraction.(49:16) AI models Box is working with.(52:10) Startup ideas in the AI era.Don't forget to subscribe and leave us a review/comment on YouTube, Apple, Spotify or wherever you listen to podcasts.#unstructureddata #ai #artificialintelligence #enterprisetech #cto #box #datamanagement #machinelearning #generativeai #businesstransformation #ainnovation #techleadership #cloudcomputing #datascience #podcast #startupproject #natarajsindam #digitaltransformation #enterprisesolutions #aifirst

What if you could build and deploy AI solutions across your enterprise in just hours — without sharing your data or retraining a model?In this episode of XTraw AI, host Raghu Banda sits down with Shay Levi, CEO & Co-founder of Unframe, a groundbreaking AI platform redefining how enterprises operationalize AI securely, rapidly, and at scale.From AI-native transformation to blueprint-based automation, Shay shares how Unframe is helping some of the world's largest companies modernize their systems and unlock value from previously untapped data — all while maintaining full control over their tech stack.

In this episode of The Digital Executive podcast, Brian Thomas welcomes Fahd Rafi, founder of Noodle Seed, an AI startup transforming how modern organizations operate through intelligent automation. Drawing from a background leading AI and data strategy at Google Cloud and Microsoft, Fahd shares how his mission is to “automate the ordinary and enable the extraordinary” through a new paradigm: Agents-as-a-Service.Fahd explains how Noodle Seed builds agentic systems that go far beyond simple chatbots—by identifying business processes that should be automated, eliminated, or augmented with AI. He breaks down misconceptions around AI implementation, emphasizing the need for outcome-driven design over hype-driven deployment. Instead of charging by user seats or token usage, Fahd advocates for shared-value models where pricing aligns with business results.From eliminating repetitive tasks to empowering enterprise teams with intelligent agents, Fahd offers a compelling vision of the AI-powered future—where human creativity is prioritized, and machines take care of the rest.Like to be a future guest on the show? Apply Here

In this episode of Gradient Dissent, Lukas Biewald sits down with Arvind Jain, CEO and founder of Glean. They discuss Glean's evolution from solving enterprise search to building agentic AI tools that understand internal knowledge and workflows. Arvind shares how his early use of transformer models in 2019 laid the foundation for Glean's success, well before the term "generative AI" was mainstream.They explore the technical and organizational challenges behind enterprise LLMs—including security, hallucination suppression—and when it makes sense to fine-tune models. Arvind also reflects on his previous startup Rubrik and explains how Glean's AI platform aims to reshape how teams operate, from personalized agents to ever-fresh internal documentation.Follow Arvind Jain: https://x.com/jainarvindFollow Weights & Biases: https://x.com/weights_biasesTimestamps: [00:01:00] What Glean is and how it works [00:02:39] Starting Glean before the LLM boom [00:04:10] Using transformers early in enterprise search [00:06:48] Semantic search vs. generative answers [00:08:13] When to fine-tune vs. use out-of-box models [00:12:38] The value of small, purpose-trained models [00:13:04] Enterprise security and embedding risks[00:16:31] Lessons from Rubrik and starting Glean [00:19:31] The contrarian bet on enterprise search [00:22:57] Culture and lessons learned from Google [00:25:13] Everyone will have their own AI-powered "team" [00:28:43] Using AI to keep documentation evergreen [00:31:22] AI-generated churn and risk analysis [00:33:55] Measuring model improvement with golden sets[00:36:05] Suppressing hallucinations with citations [00:39:22] Agents that can ping humans for help [00:40:41] AI as a force multiplier, not a replacement [00:42:26] The enduring value of hard work

From idea to IPO and beyond. What does it take to back a company for nearly two decades?There are no written rules to navigate one of the most important relationships in a startup. One between a founder and an investor.This episode is an inside look at how one of India's longest founder-investor relationships was built and tested, between Yashish Dahiya (Policybazaar) and Sanjeev Bikhchandani (Info Edge).In 2008, a ₹20 crore cheque was signed for 49 percent of the company, based solely on a powerpoint idea.What followed were regulatory challenges, shifting business models, new investors on-board, and moments of disagreement. But through 17 years, six funding rounds, and an IPO, they stayed aligned.These are two entrepreneurs who built their first ventures a decade apart; Sanjeev in 1997, Yashish in 2008 and have seen the Indian startup ecosystem evolve from the ground up.If you are building or funding startups this conversation will resonate with you for its honesty and give takeaways for your own journey.0:00 – Infoedge Ventures X Policybazaar1:08 – Sanjeev's first memories of Yashish before Policybazaar5:33 – Pitching of the Policybazaar idea 11:08 – How Info Edge almost didn't invest in Policybazaar15:56 – What shaped Yashish as Founder & Sanjeev as Investor25:14 – How the founder–investor bond evolved 27:08 – The Boardroom Dynamics at Policybazaar31:08 – Moments of Disagreement: ₹840 Cr raised, ₹700 Cr still in the bank34:38 – What makes an investor-founder relationship work?46:02 – What We've Learned after 17 years of building together49:03 – How India can build Long-term founder-investor bonds-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

Is AI finally ready for the enterprise? In this AI Infra Summit 2025 interview, Luke Norris, CEO of Kamawaza, reveals how Fortune 500 and Global 2000 companies are moving beyond AI experiments to real-world, production-level deployments—saving millions and reshaping industries.Luke shares insights from Kamawaza's groundbreaking work with over 20 Fortune 500 clients, including a live demo with the Department of Homeland Security and massive cost savings for major enterprises. Learn why consulting firms are feeling the heat, how the AI partner ecosystem is evolving, and what's next for enterprise AI—including game-changing breakthroughs in open-source models like Quen 3.0 and the rise of Model Context Protocol (MCP).

What happens after AI helps you write code faster? You create a bottleneck in testing, security, and operations. In part two of their conversation, SADA's Simon Margolis and Google Cloud's Ameer Abbas tackle this exact problem. They explore how Google's AI strategy extends beyond the developer's keyboard with Gemini Code Assist and Cloud Assist, creating a balanced and efficient software lifecycle from start to finish. We address the burning questions about AI's impact on the software development ecosystem: Is AI replacing developers? What does the future hold for aspiring software engineers? Gain insights on embracing AI as an augmentation tool, the concept of "intentional prompting" versus "vibe coding," and why skilled professionals are more crucial than ever in the enterprise. This episode offers practical advice for enterprises on adopting AI tools, measuring success through quantitative and qualitative metrics, and finding internal champions to drive adoption. We also peek into the near future, discussing the evolution towards AI agents capable of multi-step inferencing and full automation for specific use cases. Key Takeaways: Gemini Code Assist: AI for developer inner-loop productivity, supporting various IDEs and SCMs. Gemini Cloud Assist: AI for cloud operations, cost optimization, and incident resolution within GCP. AI's Role in Development: Augmentation, not replacement; the importance of human agency and prompting skills. Enterprise Adoption: Strategies for integrating AI tools, measuring ROI, and fostering a culture of innovation. The Future: Agents with multi-step inferencing, automation for routine tasks, and background AI processes. Relevant Links: Blog: A framework for adopting Gemini Code Assist and measuring its impact Gemini Code Assist product page Gemini Cloud Assist product page Listen now to understand how AI is shaping the future of software delivery! Join us for more content by liking, sharing, and subscribing!

Building AI Agents that work is no small feat.In Agents in Production [Podcast Limited Series] - Episode Six, Paul van der Boor and Sean Kenny share how they scaled AI across 100+ companies with Toqan—a tool born from a Slack experiment and grown into a powerful productivity platform. From driving adoption and building super users to envisioning AI employees of the future, this conversation cuts through the hype and gets into what it really takes to make AI work in the enterprise.Guest speakers:Paul van der Boor - VP AI at Prosus GroupSean Kenny - Senior Product Manager at Prosus GroupHost:Demetrios Brinkmann - Founder of MLOps Community~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]

AGNTCY - Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support.   Grammarly is no longer just a writing assistant. It's building an AI productivity platform that could rival Microsoft Copilot. In this episode, Luke Behnke, VP of Enterprise Product at Grammarly, shares how the company is moving beyond grammar correction into intelligent agents, enterprise workflows, and real-time AI tools.   We dive into Grammarly's new Authorship feature, why AI fluency is becoming essential at work, how Grammarly is integrating tools like Coda and Superhuman, and what the future of multi-agent systems looks like.   If you're curious about where AI at work is really heading, this conversation will give you a clear and powerful glimpse. (00:00) Preview and Intro (03:37) Meet Luke Behnke(05:00) Grammarly's Origin Story and Early Vision(09:11) Grammarly's UX Advantage(13:30) Competing With Microsoft Copilot and Built-In Assistants(17:48) What Is “Authorship” and Why It Matters(20:31) AI Detection vs Authorship Tracking(25:05) The Future of AI Transparency(27:43) Why AI Fluency Will Be a Job Requirement(32:04) Grammarly's Agentic Vision(34:11) The Rise of Context-Aware Enterprise Agents(38:24) Use Cases: Automating Tasks Across Tools with AI(40:21) The Coda Acquisition & Building the Agent Platform(44:48) The Future of Interoperable AI Agents(47:43) Why Agent Oversight Is Crucial in Enterprise AI(55:57) Measuring Grammarly's ROI in the Enterprise

Sam Johnson, Chief Customer Officer of Jamf, discusses the implementation of AI built on Amazon Bedrock that is a gamechanger in helping Jamf's 76,000+ customers scale their device management operations.Topics Include:Sam Johnson introduces himself as Chief Customer Officer from Jamf companyJamf's 23-year mission: help organizations succeed with Apple device managementCompany manages 33+ million devices for 76,000+ customers worldwide from MinneapolisJamf has used AI since 2018 for security threat detectionReleased first customer-facing generative AI Assistant just last year in 2024Presentation covers why, how they built it, use cases, and future plansJamf serves horizontal market from small business to Fortune 500 companiesChallenge: balance powerful platform capabilities with ease of use and adoptionAI could help get best of both worlds - power and simplicityAI also increases security posture and scales user capabilities significantlyCustomers already using ChatGPT/Claude but wanted AI embedded in productBuilt into product to reduce "doorway effect" of switching digital environmentsCreated small cross-functional team to survey land and build initial trailRest of engineering organization came behind to build the production highwayTeam needed governance layer with input from security, legal, other departmentsEvaluated multiple providers but ultimately chose Amazon Bedrock for three reasonsAWS team support, large community, and integration with existing infrastructureUses Lambda, DynamoDB, CloudWatch to support the Bedrock AI implementationAI development required longer training/validation phase than typical product featuresReleased "AI Assistant" with three skills: Reference, Explain, and Search capabilitiesParticipants:Sam Johnson – Chief Customer Officer, JamfFurther Links:Jamf.comJamf on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

The global strategy consulting market stands at $39.5 billion, with Asia commanding $9.1 billion. India contributes just $1.09 billion. This is despite having the talent; Indians run global back-offices for McKinsey, BCG, Bain, Deloitte, and other consultancies. Yet, India continues to outsource strategy to the Big 4.Sanjeev Sanyal, PM Modi's Economic Advisor joins us to break this down.We discuss the factors helping and hindering India's growth opportunities. Sanjeev has long worked on improving the process reforms with the belief that this country needs small reforms that will bring huge impact.We also discuss AI, with a policymaker who strongly believes unregulated AI will be catastrophic. Sanjeev shares his opinions on what could be the government's approach to regulation, with acceptance of the limited predictability of future with AI.If you want to understand India from a policymaker's eye this episode is for you.0:00- Trailer0:55 – Why India Needs Many Small Reforms2:50 – Was WFH Technically Illegal Until 2000?3:57 – India as the GCC Capital for the world7:02 – How did India go from filing 6,000 to 1 Lakh Patents?13:45 – Why India Can't build Its Own Big 4+317:40 – When professional bodies in India don't work together21:05 – What happens when branding is banned?24:08 – Restrictions That need to stay27:11 – How India's IT Sector Grew Without a Governing Body30:06 – Are we risking catastrophic failure with Unregulated AI?36:10 – Can We Regulate AI Like the Stock Market?41:39 – Why India Must Shut down Population Control47:10 – Will AI Replace Lawyers and Accountants?49:14 – What India Isn't Ready For?51:31 – India as a historically risk taking nation54:31 – Why are professional bodies holding onto protection?56:55 – The Business Culture Problem in Kolkata58:32 – Sanjeev's Work in Agroforestry-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

Send us a textHow do you build AI governance that scales without becoming the innovation police? In our final conversation with tech lawyer Gayle Gorvett, we tackle the ultimate balancing act facing every organization: creating robust AI oversight that moves at the speed of business. From shocking federal court rulings that could force AI companies to retain all user data indefinitely, to the Trump administration's potential overhaul of copyright law, this episode reveals how rapidly the legal landscape is shifting beneath our feet. Gayle breaks down practical frameworks from NIST and Duke University that adapt to your specific business needs while avoiding the dreaded legal bottleneck. Whether you're protecting customer data or designing the future of work, this customer success playbook episode provides the roadmap for scaling governance without sacrificing innovation velocity.Detailed AnalysisThe tension between governance speed and innovation velocity represents one of the most critical challenges facing modern businesses implementing AI at scale. Gayle Gorvett's insights into adaptive risk frameworks offer a compelling alternative to the traditional "slow and thorough" legal approach that often strangles innovation in bureaucratic red tape.The revelation about the OpenAI versus New York Times case demonstrates how quickly the legal landscape can shift with far-reaching implications. A single magistrate judge's ruling requiring OpenAI to retain all user data—regardless of contracts, enterprise agreements, or international privacy laws—illustrates the unpredictable nature of AI regulation. For customer success professionals, this uncertainty demands governance frameworks that can rapidly adapt to new legal realities without completely derailing operational efficiency.The discussion of NIST and Duke University frameworks reveals the democratization of enterprise-level governance tools. These resources make sophisticated risk assessment accessible to organizations of all sizes, eliminating the excuse that "we're too small for proper AI governance." This democratization aligns perfectly with the customer success playbook philosophy of scalable, repeatable processes that deliver consistent outcomes regardless of organizational size.Perhaps most intriguingly, the conversation touches on fundamental questions about intellectual property and compensation models in an AI-driven economy. Kevin's observation about automating human-designed workflows raises profound questions about fair compensation when human knowledge gets embedded into perpetual AI systems. This shift from time-based to value-based compensation models reflects broader changes in how customer success teams will need to demonstrate and capture value in an increasingly automated world.The technical discussion about local versus hosted AI models becomes particularly relevant for customer success teams handling sensitive customer data. The ability to contain AI processing within controlled environments versus leveraging cloud-based solutions represents a strategic decision that balances capability, cost, and compliance considerations.Gayle's emphasis on human oversight—Kevin's offeringPlease Like, Comment, Share and Subscribe. You can also find the CS Playbook Podcast:YouTube - @CustomerSuccessPlaybookPodcastTwitter - @CS_PlaybookYou can find Kevin at:Metzgerbusiness.com - Kevin's person web siteKevin Metzger on Linked In.You can find Roman at:Roman Trebon on Linked In.

Why do so many enterprise AI initiatives stall? In this episode, we unpack the leadership gap most organisations overlook. Discover how Meta-Leadership drives real-time oversight, system-wide execution, and strategic fluency across silos. Learn the 7 disciplines transformation leaders use to scale AI effectively. Tune in to rethink leadership for the AI era—and lead beyond your team. 

AGNTCY - Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support. What if the most important AI conference in the world wasn't built by academics or hype merchants, but by operators who actually understand what businesses need? In this episode, Craig sits down with Andrew Blum, Co-Founder and COO of HumanX, the breakout AI conference that has quickly become the go-to gathering for enterprise leaders, AI builders, and government policymakers. Andrew shares the inside story of how HumanX went from an idea born in a VC incubator to hosting 3,300+ attendees, 350 speakers, and leaders from OpenAI, Anthropic, Mistral, Snowflake, and more, all within 18 months. You'll hear how HumanX is different from other conferences, why face-to-face connection matters more than ever, and how HumanX is creating the bridge between AI innovation and real-world business transformation. This is a behind-the-scenes look you won't want to miss. Like and subscribe for more! Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI

This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload – where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI…and saved.    Try OCI for free at http://oracle.com/eyeonai   What if the future of enterprise wasn't human-driven, but agent-driven? In this groundbreaking episode, Steve Lucas, CEO of Boomi, unveils a radical vision for the next era of business: one where AI agents will power 75% of enterprise operations by 2026. From eliminating traditional user interfaces to transforming legacy systems with no-code automation, Steve walks us through how Boomi is building the infrastructure for a self-driving enterprise, and why businesses that fail to prepare will be left behind.   This episode will shift your perspective on where the enterprise is headed and who (or what) will be running it. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI

Is generative AI just another tool in the belt, or is it a fundamental transformation of the developer profession? We kick off a two-part special to get to the bottom of how AI is impacting the enterprise. SADA's Associate CTO of AI & ML, Simon Margolis, sits down with Ameer Abbas, Senior Product Manager at Google Cloud, for an insider's look at the future of software development. They cut through the noise to discuss how tools like Gemini Code Assist are moving beyond simple code completion to augment the entire software delivery lifecycle, solving real-world challenges and changing the way we think about productivity, quality, and automation. In this episode, you'll learn: What Gemini Code Assist is and the broad range of developer personas it serves. The critical debate: Is AI augmenting developer skills or automating their jobs? How to leverage AI for practical enterprise challenges like application modernization, improving test coverage, and tackling technical debt. Why the focus is shifting from developer productivity to overall software delivery performance. Ameer's perspective on the future of development careers and why students should lean into AI, not fear it. The limitations of "vibe coding" and the need for intentional, high-quality AI prompting in a corporate environment.   Join us for more content by liking, sharing, and subscribing!

Join host George Firican on the Lights On Data Show as he interviews John Kucera, Senior Vice President of Salesforce AI, to explore the transformative power of Agentforce. Learn how this technology is reshaping enterprise AI by automating digital labor, offering powerful new capabilities like observability and interoperability, and seamlessly integrating with the broader Salesforce ecosystem. Discover real-world success stories, best practices for implementation, and essential insights for tech and business leaders looking to leverage AI effectively.