Podcasts about enterprise ai

This episode features an interview with Bruce Cleveland, author of the best-seller “Traversing the Traction Gap" and CEO of Traction Gap Partners, a Market Engineering advisory firm.In this episode, Bruce outlines why most startups fail and explains market engineering, a term he coined  to represent the ideas around category design. He shares insights into creating a category and what goes into startup success. Key Takeaways:Market engineering involves the ideas around category design or redefinition thought leadership to create a category.There are distinct advantages to being a category leader; the category leader generates about 76% of  all the profits from a category. While there is a first-mover advantage, there are also some associated challenges.Thought leadership is an essential component of creating a category. People want to be around peers they admire, so gathering the right people together leads to an eventual tipping point that makes it easier for a company to sell.Quote:  One of the reasons that you need to actively be involved in the thought leadership part of category creation is people wanna hang out with other people who they think are smart, who have some cool ideas. And that I think happens with companies as well. So eventually some companies kind of climb out of the morass, the cacophony of, fighting the marketing battle and begin to emerge as the thought leaders in those. And then they collectively gather more people and more people. And finally there's a tipping point where that company is perceived as the category leader. And so it becomes really easy for those companies to then sell more.Episode Timestamps: *(02:26) The Trust Tree: Traversing the Traction Gap *(07:31) The importance of category design*(26:05) Thought Leadership in category creation*(35:39) How to evaluate startupsSponsor:Pipeline Visionaries is brought to you by Qualified.com. Qualified helps you turn your website into a pipeline generation machine with PipelineAI. Engage and convert your most valuable website visitors with live chat, chatbots, meeting scheduling, intent data, and Piper, your AI SDR. Visit Qualified.com to learn more.Links:Connect with Ian on LinkedInConnect with Bruce on LinkedInLearn more about Traction Gap Partners or Traversing the Traction GapLearn more about Caspian Studios

The dollar will lose its status as the world's reserve currency & the greatest wealth transfer in history is already underway - warns the founder of one of India's fastest-growing unicorns!In this episode, Deepak Garg, founder of Rivigo and AnywhereJobs shares why Rivigo's iconic Relay model succeeded, and what ultimately limited it. He predicts Zomato's dominance, questions funding choices of startups and shares why India may miss the AI revolution without a radical energy shift.From Bitcoin vs. gold and Trump's potential Nobel Peace Prize to Tesla becoming a $30 trillion company, Deepak's predictions are bold and grounded in years of pattern recognition.If you're a founder, investor, or macro nerd, this is an episode you won't forget.0:00- Rivigo & Anywhere Jobs02:16 – When your business outgrows the market04:18 – Capital raising is a Double-edged sword05:02 – Which ideas truly need funding?07:33 – Build teams with Accuracy, not Kindness09:39 – How to know if you've chosen the right market?10:41 – Why Zomato is India's best Consumer tech bet16:00 – How the Power is shifting b/w nations today?20:18 – Will Dollar cease to be a Reserve currency?22:36 – Is Bitcoin better than Gold?26:59 – Who will be the Next global Superpower?31:56 – India in the Next 20 years34:22 – When 2 players control 80% of India's Private sector35:52 – Why China is far ahead of India in Nuclear Energy?40:17 – Will Trump win a Nobel Peace Prize in 2025?42:01 – How Tesla could become a $30 trillion company?47:15 – Wealth transfer from Wall Street to Main Street50:30 – Where India should focus in AI-------------​​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 your organisation struggling to scale AI beyond isolated experiments? In this episode, Rob Llewellyn unpacks what enterprise AI really means—and why it's not just about technology, but transformation. Discover the leadership mindset, scalable infrastructure, and cross-functional collaboration needed to drive AI at scale. Learn how cultural change plays a pivotal role in adoption and hear insights from real-world examples like Unilever. If you're a business leader, manager, or consultant navigating AI transformation, this episode offers the strategic clarity you need. 

The Big Themes:SAP's Flywheel Strategy: SAP introduced a compelling flywheel model that integrates applications, data, and AI to drive enterprise momentum. The idea is that integrated applications generate structured data, which then feeds a robust AI layer. As these layers build on one another, they create a self-reinforcing cycle of productivity, insight, and innovation—a flywheel effect. Unlike Microsoft and ServiceNow, which predict the collapse of applications in favor of agents, SAP asserts that AI agents will enhance, not replace, applications.The Business Data Cloud and Databricks Partnership: A highlight of the event was SAP's Business Data Cloud (BDC), launched in partnership with Databricks. This foundational layer brings together internal SAP data and external sources like Moody's or climate models, enabling richer decision-making. SAP showcased real-world use cases, such as tariff fluctuation impact analysis across supply chains, to demonstrate the power of combining enterprise and contextual data.Prompt Optimizer and the End of Prompt Engineering: SAP's introduction of a “Prompt Optimizer” signals a shift in the AI interface landscape. Instead of manual prompt engineering, users will soon rely on AI to manage and optimize prompts across multiple large language models (LLMs), including ChatGPT, Claude, Gemini, and Perplexity. CTO Philipp Herzig even declared we're at “the beginning of the end” of prompt engineering.The Big Quote: "[Customers are] not ready to deploy AI and have that completely eliminate the need for apps. The data is just not there. So, maybe five years from now, let's see what progress we've made. But what's in the here and now is that customers are looking for applications."

Aaron Levie, CEO & co-founder of Box, joins Azeem Azhar to explore how an “AI-first” mindset is reshaping every layer of Box – from product road-maps to pricing – and what that teaches the rest of us about building faster, smarter organisations.Timestamps:(00:00) Episode trailer(02:04) The "lump of labor fallacy" in sci-fi books(07:37) When individual productivity gains don't translate to teams(12:32) Box's Friday AI demos(21:23) How agents might redefine 100 years of management science(26:37) A lesson on AI innovation from the early days of Ford(29:52) Sundar Pichai, Satya Nadella, and Sergey Brin are coding again?(35:16) Pricing in a post-AI agent world(38:43) Cheaper tokens, heavier usage: AI's margin math(43:02) Solving AI's verifiability problem(48:24) How Aaron uses AI in his personal lifeAaron's links:Box: https://www.box.com/LinkedIn: https://www.linkedin.com/in/boxaaron/X/Twitter: https://x.com/levieAzeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharX/Twitter: https://x.com/azeemThis conversation was recorded for “Friday with Azeem Azhar”, live every Friday at 9 am PT / 12 pm ET. Catch it via Exponential View on Substack.Produced by supermix.io and EPIIPLUS1 Ltd

Joel Christner, (@joelchristner, Founder/CEO at @viewyourdata) discusses the complexities of data management in AI, structured and unstructured data, the importance of RAG pipelines and vector databases. SHOW SUMMARY:  Aaron and Joel discusses the complexities of data management in AI, focusing on the concept of universal data representation. They explore the challenges organizations face with structured and unstructured data, the importance of RAG pipelines and vector databases, and the implications of data privacy in regulated industries. The conversation also touches on managing model versions and the emerging patterns in AI tooling that can help enterprises effectively utilize AI technologies.SHOW: 925SHOW TRANSCRIPT: The Cloudcast #925 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.[US CLOUD] Cut Enterprise IT Support Costs by 30-50% with US CloudSHOW NOTES:View.io websiteTopic 1 - Welcome to the show, Joel. Give everyone a quick introduction.Topic 2 - Our topic today is everything data and how to represent it and embed it into AI systems. First, what is the challenge with data, structured or unstructured, in organizations today and what is behind the concept of Universal Data RepresentationTopic 3 - Industry or customer specific data today is big challenge for organziations, especially in highly regulated industries such as healthcare, financial services, etc. The most prevalent solution I am seeing is taking an existing foundational model and then adding a RAG pipeline vs. the cost and time to fine tuning. What are you seeing?Topic 4 - Even when companies have good data, that doesn't mean that data makes it into the AI pipeline correctly, this is where the embedding problem and your concept of Universal Data Representation comes into play, correct?Topic 5 - But, once you get the first model out, then what? How should the data and models be handled over time? How do you create a platform and a continuous feedback loop to improve the results over time?Topic 6 - What are the most successful use cases you are seeing today with your customers?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Why do over 80% of AI projects in large organisation's fail? This episode explores how to build a high-impact AI strategy that delivers measurable business value. Discover the six essential components every leader must include, how to assess your organisation's AI readiness, and the common pitfalls to avoid. Whether you're leading transformation or shaping enterprise strategy. Stay tuned until the end. 

The buzz in Silicon Valley around AI agents has many asking: What's real and what's hype? Box's co-founder and CEO, Aaron Levie, joins Rapid Response to help decipher between fact and fiction, taking listeners inside the fast-paced evolution of agents and its impact on the future of enterprise AI. Plus, Levie unpacks how AI is really being adopted in the workplace, what it takes to legitimately build an AI-first organization, and what political leaders across both parties misunderstand about creating the best environment for technology to thrive. Visit the Rapid Response website here: https://www.rapidresponseshow.com/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The buzz in Silicon Valley around AI agents has many asking: What's real and what's hype? Box's co-founder and CEO, Aaron Levie, joins Rapid Response to help decipher between fact and fiction, taking listeners inside the fast-paced evolution of agents and its impact on the future of enterprise AI. Plus, Levie unpacks how AI is really being adopted in the workplace, what it takes to legitimately build an AI-first organization, and what political leaders across both parties misunderstand about creating the best environment for technology to thrive.Visit the Rapid Response website here: https://www.rapidresponseshow.com/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

SuperAnnotate is revolutionizing how companies manage their AI training data with a comprehensive infrastructure platform. Having raised over $53 million in funding, SuperAnnotate has evolved from a specialized algorithm for autonomous vehicles to a centralized data hub that enables enterprises to collaborate with multiple service providers and internal teams. In this episode of Category Visionaries, we spoke with Vahan Petrosyan, CEO and Co-Founder of SuperAnnotate, who shared his journey from PhD student to tech founder and unpacked his vision for creating what he describes as "a database for training data" - similar to Databricks but specialized for AI training data. Topics Discussed: SuperAnnotate's evolution from algorithm to comprehensive data labeling infrastructure The journey from academic research to founding a tech startup How an early contract with an autonomous driving company validated their solution The strategic pivot from competing with service providers to creating a collaborative ecosystem The transformation of their go-to-market strategy to create stickier enterprise relationships SuperAnnotate's focus on building a centralized training data platform for enterprise AI The importance of automation and "SuperAnnotate agents" for AI data operations How customizability has enabled SuperAnnotate to support diverse generative AI use cases GTM Lessons For B2B Founders: Recognize when to stop competing and start collaborating: Vahan's most important go-to-market decision was shifting from competing with service providers to creating an infrastructure that enables collaboration. "That's one of the mindset shifts... we are trying to build an ecosystem with our partners, not really trying to compete with them," he explains. B2B founders should consider whether creating an ecosystem platform might be more valuable than directly competing in fragmented service markets. Solution engineers are crucial for enterprise AI sales: Vahan emphasized that "solution engineering is super important because as you're touching enterprise AI, your solution engineers are more or less the core part of your team." Without proper technical enablement, enterprise customers won't be able to implement complex AI solutions. B2B founders selling sophisticated technology should invest heavily in solution engineering capabilities. Build for adaptability in rapidly evolving markets: SuperAnnotate achieved 3x growth by making their platform "fully customizable to any use case." Vahan noted, "If tomorrow there will be a new agentic workflow, then we'll be able to support it." Rather than offering point solutions, B2B founders in emerging technology spaces should build adaptable platforms that can evolve with changing market needs. Passive fundraising often yields better results than active campaigns: Vahan shared a counterintuitive fundraising insight: "Whenever I was actively fundraising, I was doing something wrong." His most successful raises came from casual coffees with investors who approached him, not from pitching dozens of VCs. B2B founders might benefit from focusing on building relationships and demonstrating value rather than running intensive fundraising campaigns. Enterprise AI is a long-term bet: Looking 3-5 years ahead, Vahan sees enterprise AI as the major opportunity. "Companies have datasets sitting in silos, but that dataset is gold," he explains. The ability to "transform that dataset to training data in a fast and accurate manner will define your moat moving forward." B2B founders should consider how their solutions can help enterprises unlock value from proprietary data.   //   Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe.  www.GlobalTalent.co  

Join us for this episode with Leigh Engel, Senior Product Manager for Enterprise AI at NVIDIA. Leigh's journey spans media, consulting, and big tech firms like Apple and WPP before landing in product leadership at one of the world's top AI companies. We explore her career pivots, how she balances innovation with user-centered design, and her take on evaluating AI models like GPT-4 and Llama for enterprise use. This is a great episode if you're curious about breaking into AI product management or understanding the future of generative AI in business. If you enjoy the episode, share it with a friend and leave us a comment!Please note: The views expressed in this episode are Leigh's alone and do not reflect those of her employer, NVIDIA.

Venture capital is much fancied today.Is this job which looks like cutting cheques for products and founders you like, for everyone?As for any work, there are traits you should have and some which won't help you on the job.We have with us Kushal Bhagia (All In Capital), Karthik Prabhakar (Peer Capital), and Rajan (Upekkha).Three people who interestingly all began as engineers and took different career paths to today become Fund managers of leading VC firms.All In Capital's $24M pre-seed fund backing early-stage founders,Peer Capital's $75M early-stage fund investing in tech-first Indian startups from seed to Series A,Upekkha's $40M Capital's accelerator-style fund supporting B2B SaaS startups.Tune in!01:53 – Why builders shouldn't become VCs?03:12 – Why best VCs sell well & stay curious05:12 – How the VC job is like Flying a plane07:48 – How parental instincts enable VCs?10:42 – Is fundraising harder than ever?14:12 – What makes people write VC cheques?18:03 – Where do India's rich family offices invest?20:30 – Why India still doesn't have its own YC?22:55 – The OG YC when startups weren't cool28:00 – Can YC's numbers ever be replicated?33:03 – David v/s Goliath of Small vs large funds39:02 – Zepto's first $50k cheque40:22 – Sectors VCs won't touch41:35 – Story-driven v/s numbers-driven Fundraising44:36 – How we missed Swiggy, Postman & Zepto?46:18 – The best VCs48:24 – What needs to change in Indian VC?49:47 – Founders we'd invest In (But not work for)51:25 – Unlearnings as an Investor-------------​​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

What makes AI trustworthy, ethical, and compliant in business? In this episode, we explore how Chief AI Officers lead governance efforts to align innovation with regulation. Learn how the CAIO bridges strategy, risk, and ethics to ensure responsible AI use across the enterprise. Ideal for executives, managers, and consultants navigating AI transformation. 

Is your AI helping—or quietly hurting—your business? In this episode, we uncover how hidden biases in large language models can quietly erode trust, derail decision-making, and expose companies to legal and reputational risk. You'll learn actionable strategies to detect, mitigate, and govern AI bias across high-stakes domains like hiring, finance, and healthcare. Perfect for corporate leaders and consultants navigating AI transformation, this episode offers practical insights for building ethical, accountable, and high-performing AI systems. 

Mastering the EU AI Act is no longer optional—it's a strategic necessity. In this episode, we unpack the critical compliance gaps that separate thriving companies from those falling behind. Learn how to categorise your AI systems, mitigate risk, and turn regulation into a competitive advantage. Perfect for business leaders, consultants, and transformation professionals navigating AI governance. 

Customer expectations have skyrocketed—people now demand instant, personalized, and seamless interactions across every touchpoint. But are companies truly meeting these expectations, or are they still stuck in reactive customer service models? What if AI could completely transform the customer experience into something proactive, predictive, and even empathetic? Joining me today is Vinod Muthukrishnan, VP & COO of Webex Customer Experience at Cisco. Vinod is a leader in the future of customer experience (CX), helping organizations use AI to anticipate customer needs, deliver seamless automation, and create personalized interactions at scale. Vinod Muthukrishnan is the VP & COO for the Webex Customer Experience Business Unit, overseeing Go To Market, Customer, and Business Operations. In this role he collaborates with Cisco field teams, partners, and customers to deliver innovative solutions. His passion lies in creating products that solve real customer pain points and providing a seamless customer experience. He also values building strong communities, teams, culture, and operating rhythms.Previously, Vinod spent three years in Enterprise AI at Uniphore, a Cisco Investments Portfolio Company, where he developed a product enabling Citizen Developers to create AI and automation solutions. He managed Uniphore's customer functions, including Delivery, Technical Support, Customer Success, and AI consulting, helping enterprises align their business goals with AI roadmaps.Vinod was also VP & COO at the Webex Contact Center Business Unit during a period of significant growth and innovation. During his tenure at the BU, the IMI CPaaS business was acquired, and Webex Contact Center was launched. These two initiatives now serve as the foundations of the Webex Customer Experience Business Unit. Vinod oversaw all GTM functions.He joined Cisco when his startup, CloudCherry, was acquired in 2019. As Co-Founder and CEO of CloudCherry, he and his team developed a Customer Experience Platform that became Webex Experience Management. They also built the foundations of the Customer Journey Data Service, essential to the Webex Portfolio today.Coming from a military family, Vinod began his career in the Merchant Marine at 18, becoming a certified First Officer with Maersk Line and sailing to over 60 countries. He later joined the founding team at MarketSimplified, which introduced mobile trading to major brokerages like TD Ameritrade and OptionsExpress. RESOURCESCisco: https://www.cisco.com Catch the future of e-commerce at eTail Boston, August 11-14, 2025. Register now: https://bit.ly/etailboston and use code PARTNER20 for 20% off for retailers and brandsOnline Scrum Master Summit is happening June 17-19. This 3-day virtual event is open for registration. Visit www.osms25.com and get a 25% discount off Premium All-Access Passes with the code osms25agilebrandDon't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brights minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.showCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnowThe Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company

 Is your organisation ready for AI success? In this episode, Rob Llewellyn unveils the essential steps to craft an AI Strategy Roadmap that delivers real business value. Discover how to align leadership, prioritise investments, drive enterprise-wide adoption, and gain competitive advantage. Whether you're a manager, leader, or consultant, learn how to turn AI from isolated experiments into strategic growth. Subscribe now to transform your AI ambitions into measurable success. 

You prolly missed this HUGE AI drop.Google quietly updated its NotebookLM behemoth to a thinking model and went FULL on multilingual. Millions of people are instantly getting a AI assistant overnight, but probably don't even know. So.... we're breaking it down. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the conversation.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:NotebookLM's Major 2024 AI Tool UpdatesGoogle's Gemini 2.5 Flash Multilingual FeaturesNotebookLM's Gemini Model Integration DetailsAI Reasoning Models in NotebookLM ExplainedAI Audio Overviews in 50+ LanguagesExploring NotebookLM's Mind Map FeatureDiscover Sources Function in NotebookLMUsing Deep Research with NotebookLMTimestamps:00:00 "Google Notebook AI Updates"06:27 ChatGPT-Controlled IBM Updates Demo08:48 Notebook LM Gains Global Attention13:18 Modeling Challenges and Learning Paths14:01 "Gemini 2.5 Flash: Powerful & Affordable"18:55 AI Struggle: Defining Chicago21:49 "Notebook LM Source Integration Guide"26:30 "Notebook LM: Studio and Mind Map"29:47 Watson x AI Updates Overview31:36 Mind Map: Chaos to Clarity36:39 "Adding Sources: Manual vs. Auto"39:02 Analyzing Watson x Updates Monthly41:08 IBM Watson x Trends Overview44:25 Evaluating John's Performance in Marketing48:05 "Leveraging Data with AI"Keywords:NotebookLM, Google Gemini, AI update, Gemini 2.5 flash model, Multilingual audio overviews, Large Language Model, Deep research tools, Google AI Studio, AI-powered deep dives, Gemini 2025, OpenAI, ChatGPT, AI-driven mind maps, IBM Watson x, Enterprise governance, AI reasoning model, Language support, AI-powered conversation, Audio overview features, AI flash model, Multimodal AI, Data protection, AI Studio integration, AI capabilities, Gemini reasoning, Machine learning advancements, AI feature updates, Enterprise AI solutions, Google Gemini thinking model, AI-driven insights, Language model updates, AI-driven research.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

OpenAI shared a short report with seven things they've seen work for companies using AI. These lessons come from real examples with firms like Morgan Stanley, Indeed, Klarna, BBVA, and Mercado Libre. The report reads like a blueprint for interested firms. Interested in sponsoring the show? nlw@breakdown.network Get Ad Free AI Daily Brief: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://patreon.com/AIDailyBrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kpmg.com/ai⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about how KPMG can help you drive value with our AI solutions.Blitzy.com - Go to ⁠⁠https://blitzy.com/⁠⁠ to build enterprise software in days, not months The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdown

AI is no longer experimental—it's foundational.In this explosive episode, PrimeVP Founder & Partner Shripati Acharya and Principal Gaurav Ranjan unpack how AI is reshaping B2B software—and what that means for evaluating and investing in early-stage startups.What you'll learn:⚙️ The rise of Enterprise AI and its real-world applications

Artie Intel and Micheline Learning report on Artificial Intelligence for the AI Report. Is the U.S. losing its AI lead? China is catching up fast. Enterprise AI has gone mainstream, with platforms that don’t just generate text, they reason, plan, and execute multi-step tasks. The buzzword? “Agentic AI.”  Large language models are now showing early signs of “reasoning”-they can solve logic puzzles, plan events, and even debug their own mistakes. The AI Report is brought to you by Sponsor: Pecan AI. From raw data to prediction – made effortless. Simply add your data and get the answers you need. Who’s going to churn? Who’s ready to buy more? We auto-build models to show you what matters – no setup, no hassle. Get started for FREE at https://www.pecan.ai/ The AI Report

AI projects often fail due to a lack of clear strategy and governance. In this episode, Rob Llewellyn introduces a proven framework for managing AI initiatives at scale, ensuring alignment from concept through to optimisation. Discover the five pillars of AI success, real-world case studies, and the leadership roles that drive transformation. Learn how to avoid common pitfalls and turn AI from speculative tech into a strategic business advantage. Tune in for actionable insights to lead successful AI projects. Don't forget to subscribe for more expert strategies. 

Many AI projects fail—not from poor tech, but from strategic unpreparedness. In this episode, we reveal how corporate leaders can assess and improve AI readiness before scaling initiatives. Explore a five-part framework that addresses leadership, culture, infrastructure, governance, and sustainability. Discover how to align AI with business goals, reduce implementation risks, and boost ROI. Whether you're advising clients or leading transformation internally, this checklist offers the clarity you need.

Artie Intel and Micheline Learning report on Artificial Intelligence and Robotics for The AI Report. Shipping giant UPS is in talks with Figure AI to deploy humanoid robots in its logistics operations. Figure’s robots, standing at 5’6”, are designed to sort packages and mimic human movements.  Trump administration has dismissed more than 200 AI experts hired under President Biden. Chinese tech giant Huawei is making waves with its new Ascend 910D AI chip. Electric vehicle powerhouse BYD is pushing the boundaries of automation. OpenAI’s latest model, GPT-4o, is under fire for being overly agreeable. Meta is on the hunt for an experienced Technical Program Manager to lead the setup and operations of a new robotics lab. The role involves managing research initiatives and collaborating with engineering teams to advance embodied AI. If you’re passionate about robotics and program management, check out it out: metacareers.com/jobs/. Meet “Archie,” the new AI agent from P-1 AI. Archie will help build things humanity can only dream of today Higgsfield AI has launched “Iconic Scenes,” a feature that lets users re-create legendary movie scenes with just a selfie. higgsfield.ai/scene Mode Mobile’s EarnPhone is turning smartphones into money-makers. Duolingo is accelerating its shift to AI, replacing more human tutors with advanced chatbots and voice models. This message comes from Sponsor, The AI and Big Data Expo! June 2025 in Santa Clara, California. showcasing Generative AI, Enterprise AI, Machine Learning, Security, Ethical AI, Deep Learning, Data Ecosystems, and more. Register for your free or gold pass to attend. Visit https://www.ai-expo.net/northamerica/ for more information. The AI Report

Gloria Osardu urges researchers to slow down, understand the business, and partner with intention—sharing why empathy, alignment, and asking the right questions are the foundation for research that matters. Highlights include: 05:02 – How did your parents shape your worldview and curiosity? 15:22 – Why is success as a research leader about facilitating others? 24:26 – What does “understanding the business” really mean? 30:43 – What does “skin in the game” mean for stakeholders? 45:24 – How has embracing slowness changed your approach to leadership? === Who is Gloria Osardu? Gloria is a Senior Director and Global Head of UX Research, Brand, and Consumer Insights at Yahoo!, where she leads initiatives to deepen Yahoo's understanding of consumer behaviour, aligning the company's brand, product, and marketing strategies with these insights. Before joining Yahoo!, Gloria was Senior Director and Head of UX Research and Insights at Gusto, where she built and led Gusto's Global Product Experience Research Team, overseeing both research and research operations professionals. She has also held senior research roles at Amazon Web Services, where she led UX Research Operations and Programs, and at Capital One, where she headed UX Research for their Enterprise AI and ML Platforms. With extensive commercial experience in customer insights and analytics, Gloria also brings an impressive academic background, holding a PhD in Human-Computer Interaction, a Master of Science in Applied Health Informatics, and a Bachelor of Science in Computer Science. A committed contributor to our field, Gloria is a long-standing mentor with the Computing Research Association and has been featured in User Weekly, as well as on podcasts like Black Stories and Saving UX. === Find Gloria here: LinkedIn: https://www.linkedin.com/in/gloriaosardu/ ====== Subscribe to Brave UX Liked what you heard and want to hear more? Subscribe and support the show by leaving a review on Apple Podcasts (or wherever you listen). Apple Podcast Spotify YouTube Podbean Follow us on our other social channels for more great Brave UX content! LinkedIn TikTok Instagram Brendan Jarvis hosts the Show, and you can find him here: Brendan Jarvis on LinkedIn The Space InBetween Website

Erwan Menard is the director of product management for Google Cloud's Cloud AI division, where he helps lead innovation at the intersection of AI agents, enterprise systems, and business outcomes. In part two of our series, Google Cloud and the AI Revolution, Erwan joins Bob Evans to discuss how governance, intentionality, and rapid scaling are critical to AI agent success, share insights on Google Cloud's Agentspace and Agent Builder tools, and explore how multi-agent collaboration is reshaping the future of enterprise technology.Purpose Driven AI InnovationThe Big Themes:Intentionality Drives Impact: Menard advises organizations not to jump into AI agent development for novelty's sake, but to begin with a clearly defined problem and desired business outcome. However, once value is proven, it's crucial to scale intentionally. He shares the example of a customer rolling out 40,000 licenses of Agentspace only after deeply considering what kind of first experience they wanted their employees to have.Organizational Culture Shapes AI Adoption: There's no universal model for who should “own” AI governance. It depends on the company's culture. Some companies may create centralized AI governance teams; others may embed responsibilities within existing business units or IT teams. The key is cultural acknowledgment: governance must be understood as a shared responsibility, not just an operational afterthought.Anchor in Business Value: With so many tools, models, and frameworks emerging, it's easy for companies to fall into what he calls “optionality evaluation.” That is, spending so much time chasing the latest innovations that they lose sight of why they started exploring AI in the first place. Instead, he urges leaders to ask: What are we trying to improve? Whether it's speeding up contract workflows, freeing up data scientists from routine tasks, or enhancing customer service, the goal should be clear.The Big Quote: "If you find yourself in a constant evaluation loop for the new shiny object, maybe it's worth taking a pause and saying, 'Why are we doing this again?'"

In this episode, Dr. Keith Morse, Clinical Associate Professor of Pediatrics & Medical Director of Clinical Informatics – Enterprise AI at Stanford Children's Health, shares real-world applications and future visions for generative AI (GenAI) in pediatric care. The discussion highlights how LLMs are being practically integrated into clinical workflows, reducing clinician burden and enhancing hospital operations. Dr. Morse emphasizes the importance of upskilling the workforce to fully leverage AI's potential. With limited prior exposure to tools like LLMs, clinicians and administrative staff need hands-on training. Stanford has launched initiatives including a PHI-compliant internal chatbot, prompt engineering workshops, and engaging frontline staff in pilot projects to build confidence and competence across roles. Dr. Morse sees immense promise in technologies like ambient listening and agentic AI but stresses the need for cautious adoption. In the absence of comprehensive regulation, healthcare systems must take ownership of AI oversight to ensure safety and mitigate risk. He emphasizes the importance of balancing innovation with responsibility, especially in the sensitive context of pediatric care. Take a listen.

 Are your AI initiatives stuck in pilot mode? In this episode, we reveal a proven AI Strategy Roadmap used by top firms to move from experimentation to enterprise-scale transformation. Learn how to align AI with strategic goals, govern responsibly, build capability, and prioritise investments for real business value. Ideal for corporate leaders, consultants, and transformation professionals. 

The construction industry stands at a critical crossroads. In this episode, we explore how forward-thinking leaders are bridging the digital divide—not just by adopting new technologies, but by aligning digital initiatives with long-term business strategy. Discover how frameworks like BTM² enable scalable, structured transformation, and learn how leadership vision, digital maturity, and data-driven decision-making drive real value. Whether you're navigating your first AI-driven project or scaling enterprise-wide change, this episode offers actionable insight into transforming construction through strategic digital leadership. 

In this Tech Barometer podcast, MLCommons Co-founder David Kanter talks about creating the MLPerf benchmark to help enterprises understand AI...[…]

In this interview, we're excited to speak with Pravi Devineni, who was into AI before it was insane. Pravi has a PhD in AI and remembers the days when machine learning (ML) and AI were synonymous. This is where we'll start our conversation: trying to get some perspective around how generative AI has changed the overall landscape of AI in the enterprise. Then, we move on to the topic of AI safety and whether that should be the CISO's job, or someone else's. Finally, we'll discuss the future of AI and try to end on a positive or hopeful note! What a time to have this conversation! Mere days from the certain destruction of CVE, averted only in the 11th hour, we have a chat about vulnerability management lifecycles. CVEs are definitely part of them. Vulnerability management is very much a hot mess at the moment for many reasons. Even with perfectly stable support from the institutions that catalog and label vulnerabilities from vendors, we'd still have some serious issues to address, like: disconnects between vulnerability analysts and asset owners gaps and issues in vulnerability discovery and asset management different options for workflows between security and IT: which is best? patching it like you stole it Oh, did we mention Matt built an open source vuln scanner? https://sirius.publickey.io/ In the enterprise security news, lots of funding, but no acquisitions? New companies new tools including a SecOps chrome plugin and a chrome plugin that tells you the price of enterprise software prompt engineering tips from google being an Innovation Sandbox finalist will cost you Security brutalism CVE dumpster fires and a heartwarming story about a dog, because we need to end on something happy! All that and more, on this episode of Enterprise Security Weekly. Visit https://www.securityweekly.com/esw for all the latest episodes! Show Notes: https://securityweekly.com/esw-403

In this interview, we're excited to speak with Pravi Devineni, who was into AI before it was insane. Pravi has a PhD in AI and remembers the days when machine learning (ML) and AI were synonymous. This is where we'll start our conversation: trying to get some perspective around how generative AI has changed the overall landscape of AI in the enterprise. Then, we move on to the topic of AI safety and whether that should be the CISO's job, or someone else's. Finally, we'll discuss the future of AI and try to end on a positive or hopeful note! What a time to have this conversation! Mere days from the certain destruction of CVE, averted only in the 11th hour, we have a chat about vulnerability management lifecycles. CVEs are definitely part of them. Vulnerability management is very much a hot mess at the moment for many reasons. Even with perfectly stable support from the institutions that catalog and label vulnerabilities from vendors, we'd still have some serious issues to address, like: disconnects between vulnerability analysts and asset owners gaps and issues in vulnerability discovery and asset management different options for workflows between security and IT: which is best? patching it like you stole it Oh, did we mention Matt built an open source vuln scanner? https://sirius.publickey.io/ In the enterprise security news, lots of funding, but no acquisitions? New companies new tools including a SecOps chrome plugin and a chrome plugin that tells you the price of enterprise software prompt engineering tips from google being an Innovation Sandbox finalist will cost you Security brutalism CVE dumpster fires and a heartwarming story about a dog, because we need to end on something happy! All that and more, on this episode of Enterprise Security Weekly. Visit https://www.securityweekly.com/esw for all the latest episodes! Show Notes: https://securityweekly.com/esw-403

In this interview, we're excited to speak with Pravi Devineni, who was into AI before it was insane. Pravi has a PhD in AI and remembers the days when machine learning (ML) and AI were synonymous. This is where we'll start our conversation: trying to get some perspective around how generative AI has changed the overall landscape of AI in the enterprise. Then, we move on to the topic of AI safety and whether that should be the CISO's job, or someone else's. Finally, we'll discuss the future of AI and try to end on a positive or hopeful note! Show Notes: https://securityweekly.com/esw-403

In this interview, we're excited to speak with Pravi Devineni, who was into AI before it was insane. Pravi has a PhD in AI and remembers the days when machine learning (ML) and AI were synonymous. This is where we'll start our conversation: trying to get some perspective around how generative AI has changed the overall landscape of AI in the enterprise. Then, we move on to the topic of AI safety and whether that should be the CISO's job, or someone else's. Finally, we'll discuss the future of AI and try to end on a positive or hopeful note! Show Notes: https://securityweekly.com/esw-403

Is the AI race moving too fast for its own good? In this episode of Leveraging AI, we unpack Altman's revealing TED Talk, OpenAI's silent safety rollback, and why ChatGPT just went all-in on vibe coding, enterprise disruption, and social media dominance.This week's AI news is anything but boring — and has massive implications for business leaders. In this session, you'll discover:Why Sam Altman believes ChatGPT will become your lifelong digital companion — and why that's both exciting and terrifyingWhat Altman didn't say about AI safety and the future of AGIHow OpenAI is aggressively chasing developer dominance with its new GPT-4.1 familyThe $3B coding acquisition OpenAI is reportedly chasing — and what that means for the future of softwareWhy OpenAI skipped publishing a safety report for their newest models (yep, really)How ChatGPT is crushing TikTok in app downloads — and quietly testing a social networkThe enterprise AI gold rush: from supply chains and email to avatars and customer service

Send us a textEnterprise AI is having a moment—but it's not the one you think. In this episode, Jeremy sits down with Inna Tokarev Sela, founder of Illumex, to talk about what it really takes to build reliable, governed AI systems at scale. With decades of experience at Sisense, SAP, and now at the helm of her own company, Inna lays out why explainability, trust, and diversity in leadership are the keys to unlocking AI's potential inside the enterprise. From hallucination hazards to governance gaps and the ethics of AI design, this episode is a must-listen for anyone building the future of intelligent systems.Here's what you'll learn:Why the biggest obstacle to enterprise AI isn't ethics—it's infrastructureThe three pillars of AI trust: explainability, data access, and governanceHow diverse teams lead to more ethical and effective AIWhat hallucinations in AI outputs reveal about your data pipelineWhy unifying context matters more than ever for AI accuracyHow leaders can future-proof their companies by fixing their foundationsWhy being “application-free” might be the future of business workflows

Aidan joined this week's Unsupervised Learning for a wide-ranging conversation on model architectures, enterprise adoption, and what's breaking in the foundation model stack. If you're building or investing in AI infrastructure, Aidan is worth listening to. He co-authored the original Transformer paper, leads one of the most advanced model labs outside of the hyperscalers, and is now building for real-world enterprise deployment with Cohere's agent platform, North. Cohere serves thousands of customers across sectors like finance, telco, and healthcare — and they've made a name for themselves by staying model-agnostic, privacy-forward, and deeply international (with major bets in Japan and Korea) (0:00) Intro(0:32) Enterprise AI(3:23) Custom Integrations and Future of AI Agents(4:33) Enterprise Use Cases for Gen AI(7:02) The Importance of Reasoning in AI Models(10:38) Custom Models and Synthetic Data(17:48) Cohere's Approach to AI Applications(23:24) Future Use Cases and Market Fit(27:11) Building a Unified Automation Platform(27:34) Strategic Decisions in the AI Journey(29:19) International Partnerships and Language Models(31:05) Future of Foundation Models(32:27) AI in Specialized Domains(34:40) Challenges in Data Integration(35:06) Emerging Foundation Model Companies(35:31) Technological Frontiers and Architectures(37:29) Scaling Hypothesis and Model Capabilities(42:26) AI Research Culture and Team Building(44:39) Future of AI and Societal Impact(48:31) Addressing AI Risks With your co-hosts:  @jacobeffron  - Partner at Redpoint, Former PM Flatiron Health  @patrickachase  - Partner at Redpoint, Former ML Engineer LinkedIn  @ericabrescia  - Former COO Github, Founder Bitnami (acq'd by VMWare)  @jordan_segall  - Partner at Redpoint

Enterprise AI is rapidly evolving from a decision-support tool to an autonomous decision-maker, fundamentally changing how businesses operate. Just as human metabolism regulates energy for sustainable growth, AI has its own metabolism of knowledge—continuously consuming, processing, and acting on data at unprecedented speed and scale. But without governance, this self-directed AI evolution can reinforce biases, create decision loops beyond human oversight, and introduce systemic risks—leading to operational inefficiencies, compliance failures, and reputational damage. How do enterprise leaders ensure AI remains explainable, aligned, and controllable—before it starts making critical business decisions on its own? Guest: Vince Kellen, Chief Information Officer, University of California, San Diego. Guest Bio: Vince Kellen Expert Insights and Contribution More on this Episode: The Metabolism of Knowledge in Agentic AI: Governing AI Before It Governs US More on Healthcare:  https://www.ciotalknetwork.com/extracting-value-from-genai/ Visit CIO Talk Network Website: https://www.ciotalknetwork.com/

In this episode, Dipendra Kumar, Staff Research Scientist, and Alnur Ali, Staff Software Engineer at Databricks, discuss the challenges of applying AI in enterprise environments and the tools being developed to bridge the gap between research and real-world deployment.Highlights include:- The challenges of real-world AI—messy data, security, and scalability.- Why enterprises need high-accuracy, fine-tuned models over generic AI APIs.- How QuickFix learns from user edits to improve AI-driven coding assistance.- The collaboration between research & engineering in building AI-powered tools.- The evolving role of developers in the age of generative AI.

Jim Rowan goes inside the epicenter of AI innovation with Kevin Laughridge, lead partner for Deloitte's Google alliance, to learn how recent advancements are accelerating the information-insight-action pipeline to unlock enterprise ROI.

The world is completely different than it was before ChatGPT.

This episode is sponsored by Thuma. Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details.   To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai     What happens when AI agents start negotiating, automating workflows, and rewriting how the enterprise world operates?   In this episode of the Eye on AI podcast, Will Grannis, CTO of Google Cloud, reveals how Google is leading the charge into the next frontier of artificial intelligence: agentic AI. From multi-agent systems that can file your expenses to futuristic R2-D2-style assistants in real-time race strategy, this episode dives deep into how AI is no longer just about models—it's about autonomous action. In this episode, we explore: How AgentSpace is transforming how enterprises build AI agents The evolution from rule-based workflows to intelligent orchestration Real-world use cases: expense automation, content creation, code generation Trust, sovereignty, and securing agentic systems at scale The future of multi-agent ecosystems and AI-driven scientific discovery How large enterprises can match startup agility using their data advantage   Whether you're a founder, engineer, or enterprise leader—this episode will shift how you think about deploying AI in the real world.   Subscribe for more deep dives with tech leaders and AI visionaries. Drop a comment with your thoughts on where agentic AI is headed!     (00:00) Preview and Intro (02:34) Will Grannis' Role at Google Cloud (05:14) Origins of Agentic Workflows at Google (09:10) How Generative AI Changed the Agent Game (12:29) Agents, Tool Access & Trust Infrastructure (14:01) What is Agent Space? (16:30) Creative & Marketing Agents in Action (23:29) Core Components of Building Agents (25:29) Introducing the Agent Garden (28:06) The “Cloud of Connected Agents” Concept (33:53) Solving Agent Quality & Self-Evaluation (37:19) The Future of Autonomous Finance Agents (40:55) How Enterprises Choose Cloud Partners for Agents (43:50) Google Cloud's Principles in Practice (46:27) Gemini's Context Power in Cybersecurity (49:50) Robotics and R2D2-Inspired AI Projects (52:39) How to Try Agent Space Yourself  

In this video interview with The Forecast, Simon Robinson, principal analyst at Enterprise Strategy Group, discusses the complexities of managing...[…]

Analyst Jean S. Bozman, president of Cloud Architect Advisors, explains how evolving traditional IT to hybrid cloud and enterprise AI...[…]

" We did a survey with The Economist globally which obviously included Europe and APAC as well. And we asked the question, 'Does my organization's current architecture supports the unique demands of AI workloads.' Basically 85% said, 'No. We don't have the architecture to support it.' Some partially does, but it needs lots of modifications. So we can still feel a lot of people are still in the early stages and that data point ties back to: 85% of GenAI [proof of concepts] has not gone into production. I think that another interesting point is, 'Does your architecture connect AI application? -your relevant business data.' which is probably nearly even more important for me. Again, it was still about 80%- 'We don't have that.' Because that business data is all over the place. Without the clean data, you cannot get good AI." - Patrick Kelly Fresh out of the studio, Patrick Kelly, Senior Director for Digital Natives, Startups & Enterprise and Commercial Sales in Southeast Asia at Databricks, joins us to discuss how data intelligence is powering enterprise AI applications in ASEAN. Beginning with his career journey from network engineering to tech leadership across Asian markets, Patrick explained how Databricks pioneered the Lakehouse architecture and integrated generative AI into enterprise workloads. Emphasizing the critical role of data quality in AI success, he showcased compelling customer case studies from across ASEAN and revealed striking generative AI trends in Asia - notably that 85% of organizations lack proper architecture to support AI workloads, reinforcing that clean data remains foundational for effective AI implementation. Patrick concluded by sharing his vision of what success looks like for Databricks in Southeast Asia. Audio Episode Highlights: [00:46] Quote of the Day #QOTD [01:49] Introduction: Patrick Kelly from Databricks [02:28] Career journey from network engineering to technology sales leadership [06:43] Lessons from Patrick's career journey [09:10] The Data & AI total market opportunity in Southeast Asia and How Databricks is poised to capture the market. [10:08] How Databricks pioneered the Lakehouse concept, combining data lake and data warehouse capabilities. [15:24] The One Thing that Patrick know about Databricks that very few do. [17:52] Customer success stories from Grab, GetGo, GovTech to Siam Commercial Bank [22:50] How Mosaic AI positioned Databricks to develop enterprise-quality AI solutions with customers. [27:29] Key Trends in Asia Pacific on Generative AI. [32:23] The Importance of Data Architecture for Enterprises adopting AI. [35:13] Advice for Businesses on Implementation of AI [37:25] What does great look like for Databricks? [41:40] Closing remarks and invitation to the Data and AI Summit. Profile: Patrick Kelly, Senior Director for Digital Natives, Startups & Enterprise and Commercial Sales in Southeast Asia at Databricks. LinkedIn: https://www.linkedin.com/in/patrick-kelly-aab6168/ Podcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format. Here are the links to watch or listen to our podcast. Analyse Asia Main Site: https://analyse.asia Analyse Asia Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Asia Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Asia YouTube: https://www.youtube.com/@AnalyseAsia Analyse Asia LinkedIn: https://www.linkedin.com/company/analyse-asia/ Analyse Asia X (formerly known as Twitter): https://twitter.com/analyseasia Analyse Asia Threads: https://www.threads.net/@analyseasia Sign Up for Our This Week in Asia Newsletter: https://www.analyse.asia/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288

Ishan Mukherjee is the Co-Founder/CEO of Rox, a Sequoia-backed AI-powered sales productivity platform. Before Rox, he was the Chief Growth Officer at New Relic where he scaled the self-serve business from $0-$100M in ARR. Prior to New Relic, Ishan founded Pixie Labs (acq by New Relic). Before that he led product at Siri Knowledge Graph at Apple, Lattice Data (acquired by Apple), Premise Data, and Amazon Robotics. Ishan was also an early engineer in Kiva (acquired by Amazon) where he joined after graduating from MIT. In Today's Episode We Discuss: 04:50 Biggest Lessons Scaling New Relic's PLG to $100M in ARR 05:59 How to Do PLG and Enterprise at the Same Time 07:00 How to do Content in a PLG World 08:50 Performance Marketing or Organic Content: What Works for PLG 10:27 Why You Should Stop Marketing at Events 11:47 Why SEM is a Cartel 14:15 Why Unpaid Design Partners are BS 17:17 How AI Changes the World of Enterprise Sales: Commit-Based vs. Usage-Based  20:49 How to do Sales Compensation Plans 24:44 How to Ramp New Sales Reps 25:03 The Impact of AI on Sales Research 29:18 How to do Deep Customer Research in an AI World 35:56 Changing Spending Patterns in SaaS 41:41 Retention and Churn in Enterprise AI 43:31 The Future of Sales Teams with AI 44:45 Hiring and Scaling Sales Teams 54:28 Quickfire    

Here's the thing: we all heard Sundar Pichai say that the easy wins in AI have faded and that we may see fewer headline‑grabbing releases from the big players over the next year. That comment feels like a red flag for momentum, but I see it as a green light for action.  In this episode, I chatted with Rahul Pradhan, VP of Product and Strategy at Couchbase, about how teams can take advantage of this pause to move projects from simple experiments into solid, production‑ready services. I ask why many organizations hesitate to send their data to public AI endpoints. Rahul explains that when you've invested years building data platforms, handing over your proprietary information—even in encrypted form—can feel like handing over the keys to your kingdom. He walks us through how running models inside your security perimeter keeps private data safe and brings up model accuracy since you can tailor inputs and scrub out noise before it ever reaches the inference engine. Next, we tackle the question of stability. Companies often assume that the path to a live service is straightforward once a pilot works. Rahul warns that managing GPUs, orchestrating models, and serving them at low latency all require skill sets that live at the crossroads of ML engineering and traditional software development. We round out our conversation by shifting focus from tools to teams. Technology alone cannot carry an AI initiative. We need leaders who set a clear vision, data stewards who govern every data flow, and developers who feel as comfortable writing database queries as they define training pipelines. Rahul offers thoughtful advice on building that culture and shares examples of industries—healthcare, financial services, and retail—where the most far‑reaching uses of AI are taking root. If you're wondering how to push your proof of concept into a robust service that customers depend on, this episode is for you. I promise you'll come away with ideas you can apply tomorrow and a fresh view of why a little breathing room in AI releases can become the launch pad for your subsequent big success.    

Join us for this episode with Claire Lecarpentier, Director of Product Strategy & Operations for Platform AI at ServiceNow. After experiences in consulting and corporate venture, Claire transitioned to Product Strategy at leading enterprise service provider ServiceNow. This is a great episode if you are interested in learning how to transition from consulting or banking into the technology industry, especially at AI-driven firms. If you like the show, make sure to share it with a friend, and feel free to leave a comment, too!

Yashodha Bhavnani, Head of AI at Box, reveals Box's vision for intelligent content management that transforms unstructured data into actionable insights. Topics Include:Yashodha Bhavnani leads AI products at Box.Box's mission: power how the world works together.Box serves customers globally across various industries.Works with majority of Fortune 500 companies.AI agents will join workforce for repetitive tasks.Workflows like hiring will become easily automated with AI.Content will work for users, not vice versa.Customers demand better experiences with generative AI.Box calls this shift "intelligent content management."90% of enterprise content is unstructured data.AI thrives on unstructured data.Current content systems are unproductive and unsecured.AI can generate insights from scattered company knowledge.AI extracts metadata automatically from documents like contracts.Automated workflows triggered by AI-extracted data.Box provides enterprise-grade AI connected to your content.AI follows same permissions as the content itself.Customer data never used to train AI models.AI helps classify sensitive data to prevent leaks.Box offers choice of AI models to customers.AI is seamlessly connected with customer content.Administrators control AI deployment across their organization.Partnership with AWS Bedrock brings frontier models to Box.Box supports customers using their own custom models.Box preparing for AI agents to join workforce.Introduced "AI Units" for flexible pricing.Basic AI included free with Business Plus tiers.Both horizontal and vertical multi-agent architectures planned.Working toward agent-to-agent communication protocols.Participants:Yashodha Bhavnani - VP of Product Management, AI products, BoxSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Box CEO Aaron Levie joined the show to share his perspective on how AI is reshaping the enterprise landscape. He shared what his customers are actually thinking about when it comes to AI, the shift from closed to open-source models, and why the biggest opportunities might not be in flashy consumer tools but in workflow automation and data-rich enterprise applications. Aaron also shares his take on the changing business model dynamics in B2B, including the rise of usage-based pricing, and what it means for the next wave of software companies. 00:00 Introduction to AI in B2B and Consumer Worlds 00:32 Conversation with Aaron Levy: AI in the Enterprise 01:10 Defining a Model Company and AI's Role 02:19 Challenges and Opportunities in AI for Enterprises 03:44 Open Source vs. Commercial AI Models 05:40 The Future of AI Agents 11:42 AI's Impact on Business Models and Pricing 15:16 AI's Potential to Transform Industries 27:14 Comparing AI and Cloud Adoption 30:05 Strategizing AI Implementation 31:08 Customer Adoption and Resistance 32:59 Practical Considerations and Challenges 34:58 Building Flexible AI Architectures 37:57 Exploring Agentic Workflows 41:57 Future of AI in Enterprises 43:36 Rapid Fire Questions on AI Impact 53:20 Exciting AI Features at Box 55:53 Conclusion and Final Thoughts