State of the AI Union

Laura Fu interviews Abhay Rajaram, co-CEO of Sendoso, about how AI is transforming the world of direct mail and gifting. They explore how AI enhances personalization, scalability, and effectiveness in marketing and sales campaigns, emphasizing the importance of human touch alongside automation. key topicsAI-enhanced direct mail and giftingAgentic workflows and smart deliveryPersonalization and taste in marketingGuardrails and ethical considerations in AIThe evolving role of salespeople with AI key topicsAI-enhanced direct mail and giftingAgentic workflows and smart deliveryPersonalization and taste in marketingGuardrails and ethical considerations in AIThe evolving role of salespeople with AI

In this episode, Jared Robin, CEO of RevGenius, shares insights on community building, AI's role in marketing, and the future of revenue teams. Discover how AI can enhance engagement, scale human connection, and create competitive advantages in the evolving digital landscape. keywordscommunity building, AI in marketing, revenue teams, audience engagement, digital transformation, SaaS communities, founder insights key topicsAI's role in desiloing departmentsBuilding authentic communities at scaleUsing AI for audience signals and insightsThe importance of human touch in automationCommunity as a revenue and retention toolThe evolution of revenue teams and org structuresContent and signal gathering with AIThe future of community at scale

In this episode, Antoine from Qobra discusses how AI is transforming sales coaching and financial management by providing personalized, real-time support and motivation tools. keywordsAI, sales coaching, financial management, sales motivation, AI tools, revenue tracking key topicsAI as a sales coach and companionReal-time revenue and motivation triggersAutomation of financial calculationsPersonalized support for sales teamsImpact of AI on sales and finance workflows takeawaysAI can act as a personal sales coach, providing instant answers and motivation.Automated triggers based on revenue thresholds can boost sales motivation.AI tools simplify complex financial calculations and reporting.Personalized AI support can improve sales team performance.AI integration reduces manual back-and-forth with finance teams.Maroille Cheese: https://en.wikipedia.org/wiki/Maroilles_cheese

Rutger Katz, founder of Neon Triforce, shares insights on implementing AI in B2B SaaS companies, focusing on foundational elements like data, processes, and tech stack, and discusses common pitfalls and quick wins in AI adoption. keywordsAI, B2B SaaS, data strategy, process documentation, AI adoption, revenue operations, tech stack, organizational change key topicsAI foundations: data, processes, tech stackSiloed projects and their costsQuick wins: call transcription, content generation, account researchLeadership and sponsorship in AI initiativesEuropean vs. US AI adoption differencesresourcesNeon Triforce - https://neontriforce.comWinning by Design - Revenue Architecture - https://winningbydesign.com/revenue-architecture/ guest linksLinkedIn - https://linkedin.com/in/rutgerkatzTwitter - https://twitter.com/rutgerkatz

Matthew Volm discusses how AI is transforming revenue operations and go-to-market strategies, emphasizing practical use cases, challenges, and the importance of human-to-human interactions in sales. keywordsAI, revenue operations, go-to-market, sales, account research, trust, digital channels, in-person events, SaaS, CRM key topicsAI in revenue operationsAccount research and forecasting with AIChallenges of AI adoption in salesTrust and human connection in salesIn-person events vs digital outreachChapters00:00 Introduction to RevOps Co-op and AI in Sales02:02 Current Trends in Revenue Operations and AI Implementation06:57 Challenges of AI in Go-To-Market Strategies10:59 Understanding Productivity Changes with AI17:02 The Importance of Trust in Sales24:05 Leveraging AI for Human Connections30:06 Best Practices for Adopting SaaS Technology resourcesRevOps Co-op - https://revopscop.comEventful Software - https://www.heyeventful.com/Gong - https://gong.ioOpenAI - https://openai.com

In this episode, Tamar Gill, CEO of eCore, discusses the critical importance of data quality in go-to-market strategies, the role of AI in data validation and enrichment, and practical steps for organizations to improve their data processes. Tamar shares insights from her extensive experience in data management, emphasizing validation, real-time updates, and strategic use of AI. keywordsData Quality, AI in Data Validation, Go-to-Market Strategy, CRM Data, Data Enrichment, Data Validation Steps, Real-Time Data, B2B Data, Data Management Best Practices key topicsData validation and enrichment processesRole of AI in data managementBest practices for maintaining high-quality CRM dataChapters00:00 Introduction to eCore and Its Mission03:05 The Data Quality Crisis05:58 AI's Role in Data Validation08:48 The 80-Step Validation Process12:05 Real-Time Data Validation vs. Traditional Methods14:59 Best Practices for Go-to-Market Data17:52 Challenges in Data Accuracy21:11 Leveraging AI Effectively24:05 Advice for Data Buyers27:01 The Importance of Personalization30:05 Tamar's Journey and Entrepreneurial Advice resourceseCore Website - https://e-core.comZoominfo - https://www.zoominfo.com/Apollo Data Provider - https://apollo.ioTamar Gill - https://www.linkedin.com/in/tamargill/

In this episode, Mehmet Gonullu, head of Yassi Ventures, discusses the rapid evolution of AI in 2026, focusing on agentic AI, enterprise deployment, and future investment opportunities. He shares insights on how organizations can adopt AI responsibly and leverage it for competitive advantage. keywordsAI 2026, agentic AI, enterprise AI deployment, AI investment, GTM strategies, AI safety, vertical AI, data centers, space tech, AI education key topicsAgentic AI in 2026Enterprise AI deployment challenges and best practicesInvestment opportunities in AI supply chain and vertical AIAction itemsEducate your organization about AI safety and guardrailsStart AI deployment in sandbox environmentsFocus on one use case at a time for AI implementationStay updated with AI news and experiment with toolsChapters00:00 Introduction to AI and Yasi Ventures03:05 The Rise of Agentic AI06:11 AI Agents in Go-To-Market Strategies08:51 Enterprise Adoption of AI Agents12:09 Challenges in AI Deployment15:05 Education and Change Management in AI18:14 Capital Deployment in AI27:05 Future of Vertical AI and Potential Issues resourcesYassi Ventures - https://yassiventures.comThe CTO Show - https://www.mehmetcto.show/LinkedIn - https://www.linkedin.com/in/mgonullu/

In this episode, Michelle Lim, CEO of Flint, discusses how AI is transforming marketing strategies, personalization expectations, and the importance of systematizing marketing processes for faster, more authentic customer engagement. keywordsAI marketing, personalization, marketing automation, Flint, customer experience, marketing strategy, AI tools, systematization, branding, customer expectations key topicsAI's impact on personalization expectationsSystematizing marketing processes for speedBalancing authenticity and AI-generated contentChapters00:00 Introduction to AI in Marketing03:01 Changing Buyer Expectations05:48 Personalization vs. Automation08:49 The Role of Strategy in Marketing12:03 Leveraging AI for Marketing Insights14:59 The Importance of Feedback Loops18:01 Human Involvement in AI Processes21:00 Choosing the Right AI Tools23:53 Michelle's Journey as a Founder26:51 Cultural Influences on Entrepreneurship29:55 Favorite Singaporean Cuisine resourcesFlint - AI Marketing Platform - https://www.flint.com/LinkedIn - https://www.linkedin.com/in/michlimlim/

In this episode, Stephen Steers, author of Superpower Storytelling, shares insights on how storytelling and AI are transforming sales. Discover how top salespeople leverage storytelling, the role of curiosity, and practical AI applications to enhance customer connections and decision-making. keywordssales, storytelling, AI, customer connection, sales tips, AI tools, sales strategy, storytelling framework, sales training, AI in sales key topicsThe role of storytelling in sales successUsing AI for pre-call preparation and pattern recognitionThe importance of curiosity in sales and AI's supportMitigating risks of AI tools in sales and hiringBuilding trust, judgment, and taste through AIChapters00:00 Introduction to Stephen Steers and his expertise01:20 Why great salespeople focus on helping, not selling02:25 Understanding customer outcomes and AI's role03:21 AI in pre-call prep and pattern recognition04:46 Using AI to identify messaging patterns06:01 The importance of fundamentals and curiosity in AI use07:02 How AI can scale poor sales practices08:23 Creating multiple messaging versions for testing09:36 Using AI to optimize outreach and engagement11:11 The impact of AI on storytelling and human connection13:06 The importance of being real and authentic in sales13:29 Assessing judgment, taste, and trust with AI15:37 Guardrails for AI in hiring and sales16:25 Challenging AI outputs and avoiding bias17:16 Using AI to identify blind spots in decision-making17:57 Starting an AI-native sales organization20:16 Stephen's book: Superpower Storytelling23:23 Upcoming book: Seven Minute Sales24:15 Final advice on using AI wisely resourcesStephen Steers on LinkedIn

In this episode, sales expert David Weiss shares insights on how AI can enhance or hinder sales processes, emphasizing the importance of critical thinking and expertise before scaling with AI. He discusses practical strategies for integrating AI into sales training, deal management, and leadership to improve accuracy and decision-making. key topicsAI's impact on sales and deal managementThe importance of critical thinking in AI adoptionStrategies for training sales teams with AIRisks of scaling bad sales behaviors with AILeadership's role in data and AI oversight keywordsAI in sales, deal management, sales training, critical thinking, AI tools, sales leadership, deal inspection, AI risks, sales efficiency, AI adoption resources & guest linksDealDocDavid Weiss

In this episode, CT Leong shares insights on go-to-market strategies, the evolution of outbound marketing, and the role of AI in revenue operations. Discover practical frameworks, the importance of data, and how to leverage AI agents for scalable growth. key topicsGTM architect role and responsibilitiesEvolution of outbound marketing strategiesAI-driven revenue operations and revopsFramework for optimizing current GTM effortsChallenges and opportunities in Southeast Asian markets keywordsGTM, outbound marketing, AI in revops, SaaS growth, GTM architecture, outbound strategy, AI agents, revenue operations, startup advice resourcesGo-To-Market Architects - https://go-to-market.comCT Leong on LinkedIn - https://linkedin.com/in/ctleong

Ben Orthlieb from Blue Moon Ventures discusses how AI is transforming venture capital by automating sourcing, screening, and analysis of startups. He shares insights on leveraging AI for faster, better investment decisions, and how founders can prepare for VC meetings in an AI-driven landscape. key topicsAI in venture capitalAutomated sourcing and screeningImpact of AI on founder selectionDiversity in startup portfoliosFuture of VC technology keywordsAI, Venture Capital, Startup Funding, Machine Learning, Investment Strategies, Founders, VC Tech, Portfolio Diversification resourcesDevRev for Startups - https://devrev.ai/startupsBlue Moon Ventures - https://bluemoon.vc guest linksLinkedIn - https://linkedin.com/in/benorthlieb

In this episode, Tiffany Benitez shares her insights on the rapid evolution of AI governance, the importance of data quality, and the role of women in shaping the future of AI. She discusses how organizations can navigate the fast-moving AI landscape, evaluate vendors, and foster diversity in tech leadership. key topicsAI governance and data qualityChallenges of rapid AI transformationVendor evaluation and trust-buildingThe role of women in AI leadership keywordsAI governance, data quality, women in tech, AI transformation, vendor evaluation, AI ethics, leadership in AI, enterprise AI, AI ROI, diversity in AI resourcesWomen in Tech of Northwest Arkansas - https://www.womenintechnwarkansas.org/Trailblazers Podcast - https://trailblazers-podcast.com/guest linksLinkedIn - https://linkedin.com/in/tiffanybenitezTwitter - https://twitter.com/tiffanybenitez

In this insightful interview, Mark Azad, CRO of Across AI, discusses the transformative potential of reasoning graphs and long-term memory in enterprise AI. He explains how these technologies surpass traditional RAG systems, enabling more reliable, scalable, and context-aware AI applications across sales, marketing, and operations. key topicsThe difference between reasoning graphs and RAG systemsThe importance of memory in enterprise AIHow reasoning graphs enable reliable, scalable actionsEvaluating AI vendors: ROI and extensibilityChange management in AI adoption keywordsAI, reasoning graph, long-term memory, enterprise AI, RAG systems, AI deployment, ROI, change management, AI buyer guide

In this episode, Laura Fu interviews Elon Salfati, CEO of Salfati Agency, discussing the rapid evolution of AI and its impact on business adaptation. They explore how organizations can build adaptive infrastructures, the changing landscape of buyer decisions, and the importance of focusing on outcomes rather than mere experimentation. Elon emphasizes the need for human creativity in an AI-driven world and shares insights on the European startup ecosystem, highlighting the balance between AI efficiency and human control.TakeawaysAI is transforming business models and requires organizations to adapt quickly.Building an infrastructure that changes every three months is essential for success.Focus on outcomes rather than just optimizing existing processes with AI.Human creativity is irreplaceable and should be prioritized in AI implementations.Trust is built through human connection, even in an AI-driven environment.The future of work will emphasize learning and creativity over traditional certifications.Interviewing for problem-solving skills is more valuable than technical knowledge alone.AI can enhance human connection by automating mundane tasks.The European startup ecosystem is evolving with a focus on trust and collaboration.Organizations must empower individuals to adapt and thrive in a changing landscape.KeywordsAI, business adaptation, adaptive organizations, infrastructure, buyer decisions, AI optimization, human control, creativity, trust, European startup ecosystem

In this episode, Laura Fu interviews Mala Ramakrishnan, a seasoned investor and founder, discussing the evolving landscape of AI companies. They explore the key signals of sustainability in AI startups, the importance of go-to-market strategies, and the common pitfalls founders face in sales. Mala emphasizes the need for domain expertise, resilience, and the ability to articulate value when pitching AI solutions. The conversation also touches on the unique strengths women bring to the table in tech and the impact of parenting on risk management.TakeawaysGo-to-market strategies are crucial for AI companies.AI should be viewed as a tool to solve bigger problems.Founders need domain expertise to differentiate their products.Sales pitches should focus on value, not just technology.Understanding customer metrics is key to selling AI solutions.Common mistakes include using investment decks for sales pitches.Identifying scalable problems is essential for startup success.Resilience is a vital trait for founders in tech.Women bring unique strengths to the tech industry.Parenting can influence risk management and decision-making.KeywordsAI investment, sustainable companies, go-to-market strategies, sales mistakes, women in tech, parenting, resilience, founder advice, technology exposure

In this episode, Laura Fu interviews Imran Syed, CEO of HatchProof, discussing the evolving landscape of performance coaching in the age of AI. They explore the importance of hiring for capability over ability, the role of AI in assessing performance, and the need for continuous feedback in organizations. Imran emphasizes the significance of lived experiences and emotional intelligence in the workplace, while also addressing the challenges of giving and receiving feedback. The conversation concludes with insights on the future of human skills in an AI-driven world and the importance of taking action and learning from mistakes.Key TakeawaysPerformance management needs to evolve beyond traditional methods.Hiring for capability allows employees to grow within organizations.AI can enhance performance assessment but should not replace human judgment.Continuous feedback is more effective than annual performance reviews.Lived experiences shape our skills and work ethic.Emotional intelligence is crucial in giving and receiving feedback.AI can help in providing constructive feedback but should not replace human interaction.Organizations must adapt to the changing landscape of work due to AI.The journey of learning is as important as the destination.Taking action and embracing mistakes is essential for growth.keywordsAI, performance coaching, hiring, capability, feedback, communication, workplace, human skills, lived experience, technology

In this episode, Laura Fu interviews Steve Baker, co-founder and CEO of VendorSage, discussing the challenges and strategies surrounding software adoption, particularly in the context of AI integration. They explore the importance of understanding business goals, the need for effective evaluation of AI tools, and how to measure success in a rapidly evolving tech landscape. Steve shares insights on the cultural differences in business practices between New Zealand and Silicon Valley, and offers advice for those looking to sell AI solutions effectively.TakeawaysVendorSage acts as the execution layer between business intent and software reality.Businesses struggle with effectively managing and adopting software, especially AI tools.AI tools increase the number of decisions businesses must make, complicating strategy execution.Successful AI implementation requires careful evaluation and proof of concept.Measuring AI success should focus on business outcomes rather than just adoption rates.AI should be treated as a team member, requiring constant adjustments and management.Finance and IT teams must collaborate more closely to evaluate AI tools effectively.Cultural differences influence leadership styles and business practices in New Zealand.Tall poppy syndrome can impact entrepreneurial ambition but is changing in New Zealand's tech ecosystem.Customer success should be the primary focus for sales reps in the AI space.KeywordsVendorSage, AI integration, software adoption, tech strategy, business outcomes, finance evaluation, SaaS management, New Zealand culture, customer success, AI sales

In this episode of the State of the AI Union, host Laura Fu interviews Nitya Arora, co-founder of Pepper AI, a platform designed to assist sellers in real-time during customer interactions. Nitya discusses the evolution of Pepper AI from its origins in engineering to its current focus on sales enablement, emphasizing the importance of privacy, technical innovations, and the challenges of building a tech startup. She shares insights from her entrepreneurial journey, the impact metrics for sales enablement, and the significance of pre-call preparation for sellers.TakeawaysPepper AI serves as a real-time co-pilot for sellers.The platform originated from addressing knowledge silos in engineering teams.Real-time assistance enhances seller-customer interactions during calls.Privacy and consent are prioritized in call management.Technical innovations focus on reducing latency and improving user experience.Building infrastructure for Pepper AI took significant time and effort.Nitya's early experiences shaped her entrepreneurial journey.Sales enablement metrics include time to productivity and reduced call dependencies.Pre-call preparation tools enhance seller readiness and effectiveness.Nitya's background in Singapore influenced her entrepreneurial mindset.KeywordsAI, Pepper AI, sales enablement, real-time assistance, data privacy, entrepreneurship, Nitya Arora, technology innovation, sales productivity, customer engagement

In this episode, Laura Fu interviews Mike Groeneveld from Everstage, discussing the intricacies of compensation management and the transformative role of AI in sales. They explore the challenges of deploying AI, the importance of training sales reps, and how AI can enhance compensation plans in real-time. Mike shares insights on the behaviors of top sales reps and the pitfalls of poorly designed compensation plans, emphasizing the need for thoughtful implementation of AI in revenue operations.TakeawaysCompensation is a critical topic in revenue operations.AI has shifted the landscape of sales and compensation management.Security concerns are a major barrier to AI deployment.Sales reps need training to handle AI-related conversations.Top reps leverage AI to enhance their strategic approach.Real-time adjustments to compensation plans are now possible with AI.Understanding compensation plans is crucial for sales reps.Clawbacks from consumption-based models can create significant issues.AI can help identify effective compensation behaviors.Embracing AI can secure a successful career in sales.

In this episode of the State of the AI Union, host Laura Fu speaks with Warren Kucker, CRO at Basic, about the evolving landscape of conversational intelligence and its implications for sales and business operations. They discuss the transition from founder-led sales to structured sales processes, the importance of leveraging conversations in sales, and the impact of AI and LLMs on meeting efficiency and workflow automation. Warren shares insights on the challenges of information overload in the workplace, the need for effective conversation orchestration, and the future of conversational intelligence in various industries, including shipping. The conversation concludes with Warren's reflections on his entrepreneurial journey and aspirations beyond SaaS.TakeawaysThe shift from founder-led sales to structured sales processes can be challenging.Conversations are central to sales success and should be leveraged effectively.AI can help bridge the gap between meetings and actionable workflows.The transition to LLMs has changed how we process meeting information.Grind mode vs. vibe mode highlights the balance between information overload and effective work.Conversation orchestration is essential for maximizing the benefits of AI in sales.The future of conversational intelligence will focus on improving human interactions.Proactive communication in industries like shipping can be enhanced with AI.Sales managers can use AI to improve their coaching effectiveness.The need for software that surfaces relevant information at the right time is critical.

Buy the book here: Amazon Barnes & Noble BookshopAmplifyIn this episode, Laura Fu discusses her new book, 'Designing for Excellence,' which focuses on sales enablement in an AI native world. The conversation explores the principles of effective sales enablement, the importance of metrics, and how AI can transform the sales process. Laura emphasizes the need for organizations to be agile and ready for change as they integrate AI into their systems. The discussion also covers the significance of real-time feedback and the role of leadership in driving successful enablement strategies.TakeawaysLaura Fu's book is a blueprint for AI native enablement.AI native companies integrate AI into their systems fundamentally.Sales enablement requires motivation and reinforcement.Clarity in communication boosts rep confidence.Pipeline creation is a leading indicator of success.The enablement flywheel consists of content, programs, and tools.Real-time feedback is essential for effective enablement.Organizations must be agile to adapt to AI changes.Identifying impactful metrics is crucial for success.Change management is key to implementing new processes.KeywordsAI native, sales enablement, revenue enablement, metrics, feedback loop, change management, organizational readiness, enablement flywheel, Laura Fu, Designing for Excellence

In this episode, Laura Fu interviews Habib Basiri, a leader in AI product management, discussing the critical role of data governance and knowledge graphs in building trustworthy AI systems. They explore how enterprises can evolve their data strategies to enhance AI adoption, the importance of transparency in AI models, and the future of AI with verticalized graphs. Habib emphasizes the need for proactive governance and the integration of data management as a core feature of AI products.TakeawaysKnowledge graphs play a crucial role in understanding data semantics.Trust in AI is built through transparency and explainability.Data governance should be proactive, not reactive.Companies need to automate governance to avoid bottlenecks.The maturity of data governance directly impacts AI adoption.Verticalized graphs will enhance the accuracy of AI models.Real-time data access is essential for effective AI.Zero copy data management reduces compliance risks.Investing in governance platforms is crucial for long-term success.AI governance is a product feature, not just a compliance requirement.KeywordsAI, data governance, knowledge graph, trust in AI, enterprise AI, data transparency, AI adoption, verticalized AI, real-time data, AI strategy

Laura Fu interviews Michael Hoy, CEO of Atlas, discussing the evolving landscape of AI monetization. They explore the challenges founders face in transitioning from innovation to monetization, the complexities of pricing models in AI, and the importance of simplifying pricing and packaging for startups. Michael shares insights on leveraging usage data to inform pricing strategies and how Atlas serves as a solution for monetization and billing control. The conversation also touches on differentiating in a crowded AI marketplace, lessons learned from building AI native companies, and the future of AI monetization.TakeawaysAI monetization is a critical focus for founders today.Traditional SaaS pricing models often do not work for AI products.Startups should keep pricing and packaging simple initially.Usage data is essential for understanding product value.Atlas helps companies manage monetization and billing effectively.Differentiation in the AI space requires unique experiences and messaging.Building AI companies requires a shift in traditional growth strategies.The future of AI monetization will involve more automation.Sales processes will still require human interaction despite automation.Understanding ROI is crucial for both vendors and customers.KeywordsAI monetization, pricing models, Atlas, startup strategies, AI innovation, consumption-based pricing, value-based pricing, SaaS, business models, customer insights

In this episode, Janis Zech, CEO and co-founder of Weflow, discusses the evolving role of AI in go-to-market strategies, the challenges of AI deployment, and the potential for AI to enhance sales efficiency. He shares insights on current capabilities, the future of sales roles, and innovative use cases of AI in sales processes, emphasizing the importance of data-driven approaches and the convergence of sales roles.TakeawaysWeflow offers a cost-effective alternative to Gong for sales intelligence.AI deployment in sales is still in its early stages despite high expectations.AI is transforming outbound sales strategies by personalizing outreach at scale.Sales efficiency is being enhanced through automation of administrative tasks.The convergence of sales roles is expected as AI takes over routine tasks.AI can improve data capture and mapping against custom data structures.The future of sales will see a reduction in the ratio of AEs to SEs.AI is becoming essential in sales processes, moving from a nice-to-have to a must-have.Innovative use cases of AI are emerging in conversation intelligence and data structuring.Community engagement is vital for sharing insights and best practices in revenue operations.KeywordsAI, Go-to-Market, Sales Efficiency, Weflow, Janis Zech, AI Deployment, Sales Roles, Conversation Intelligence, Sales Automation, Revenue Operations

In this episode, Laura Fu interviews Dot Koh, co-founder and CEO of BotMD, a health tech startup focused on integrating AI into healthcare workflows. Dot shares her journey from recognizing the challenges faced by doctors in remote areas to building a solution that enhances user experience in healthcare technology. The conversation explores the adoption of AI in Southeast Asia, the role of technology in alleviating the burden on healthcare professionals, and the future of AI in the industry. Dot also reflects on her educational background and leadership style, emphasizing the importance of creating technology that doctors love to use and the potential for significant impact in the region.TakeawaysBotMD started as a hobby in 2018.Doctors often rely on technology but dislike healthcare tech due to its complexity.The goal is to design technology that fits seamlessly into doctors' workflows.AI can significantly improve healthcare efficiency and patient care.Southeast Asia has a high demand for tech solutions in healthcare due to patient volume.Trust is crucial for AI adoption in healthcare.The future of healthcare involves eliminating manual workflows.AI should enhance human roles, not replace them.Home is defined by emotional connections and community.Asia presents unique opportunities for impactful healthcare technology. KeywordsAI, healthcare, BotMD, technology, Southeast Asia, medical technology, healthcare innovation, Dot Koh, patient care, digital transformation

In this episode, Laura Fu interviews Nirman Dave from Zams, an AI command center designed for B2B sales teams. They discuss the evolution of revenue operations (RevOps) teams, emphasizing the need for strategic alignment with the Chief Revenue Officer (CRO) and the importance of leveraging AI to streamline processes. Nirman shares insights on the functional responsibilities of RevOps, the limitations of current AI applications, and how Zams aims to revolutionize the RevOps landscape by automating heavy lifting and providing actionable insights. The conversation also touches on the future of RevOps jobs, the technical aspects of Zams, and the challenges of data access in custom CRM environments. Nirman concludes with a vision for the future of RevOps, where the focus shifts from manual tasks to strategic orchestration.TakeawaysZams is an AI command center for B2B sales teams.RevOps teams should focus on strategy rather than functional tasks.The best RevOps teams are aligned with the CRO's vision.AI can streamline lead routing and data analysis.Limitations of AI include context handling and personalization.XAMPS automates heavy lifting in sales processes.Technical optimization is key for effective AI integration.Data access challenges arise from custom CRM setups.Future RevOps roles will focus on orchestration and strategy.Building Zams was driven by personal sales challenges.KeywordsZams, RevOps, AI, automation, sales, revenue operations, CRM, technology, efficiency, business strategyChapters00:00 Introduction to Zams and RevOps02:14 The Role of RevOps in Sales Strategy06:27 Functional Responsibilities of RevOps Teams08:44 AI in RevOps: Current Use Cases and Limitations13:51 The Future of RevOps with AI18:54 Technical Insights on XAMPPs22:19 The Evolution of RevOps Roles26:55 Building Zams: Challenges and Decisions30:58 The Human Element in Sales34:02 The Future of RevOps Dashboards35:08 NEWCHAPTER

In this episode, Laura Fu interviews Will Szamosszegi, founder of MyWorkerAI and Sazmining, discussing the intersection of AI and Bitcoin mining. They explore the similarities and differences between the two technologies, the importance of trust in AI, and the future of work with AI integration. Will shares insights on how Bitcoin mining has influenced his approach to building AI solutions and the potential for AI to enhance individual creativity and productivity.TakeawaysAI and Bitcoin mining share infrastructure and energy needs.The confusion around use cases is common in both AI and Bitcoin.AI has the potential to solve real-world problems more effectively than Bitcoin.The capital influx in AI is significantly larger than in early crypto projects.AI can enhance user experience and drive revenue for companies.Trust in AI is centralized with a few major companies, unlike Bitcoin's decentralized trust.The future of work will be transformed by AI, allowing for more individual creativity.AI tools can empower individuals to monetize their passions.The integration of AI into society is happening rapidly and deeply.Conversations about AI must address safety and ethical considerations.KeywordsAI, Bitcoin, mining, trust, future of work, technology, startups, innovation, data centers, renewable energy

In this episode of the State of the AI Union, host Laura Fu interviews Drew Munro, CEO of Up Meals, about the intersection of AI and the food service industry. Drew shares his journey from being a chef to leading a tech startup that automates various kitchen tasks, helping food service operators save time and improve efficiency. The conversation explores the challenges of implementing AI in kitchens, the importance of vertical AI tailored to specific industries, and the potential future of AI in enhancing guest experiences. Drew emphasizes the need for skilled labor in the culinary field and the role of AI in supporting rather than replacing kitchen staff. The episode concludes with insights on the importance of local restaurants and the vibrant food scene.TakeawaysDrew Munro transitioned from chef to tech entrepreneur to impact the food service industry.Up Meals automates manual tasks in food service to save time and improve efficiency.AI helps food operators manage fluctuating ingredient costs effectively.The food service industry faces a skilled labor shortage, impacting operations.AI enhances kitchen jobs by reducing administrative burdens on chefs.Vertical AI is crucial for addressing specific industry needs, like food service compliance.Drew believes chefs possess valuable skills that translate well into entrepreneurship.The future of AI in food service includes predictive analytics for ingredient management.Supporting local restaurants is vital for a thriving food economy.AI will allow chefs to focus more on creativity and guest experiences. KeywordsAI, food service, automation, Up Meals, technology, kitchen management, vertical AI, culinary innovation, restaurant industry, guest experience

In this episode, Laura Fu and Chad McAllister discuss the evolving role of CFOs in the context of AI adoption within organizations. They explore the disconnect between the boardroom discussions about AI and the actual implementation challenges faced by CFOs. The conversation highlights the importance of data infrastructure, the need for a multi-year commitment to AI initiatives, and the necessity of aligning AI strategies with overall business goals. They also touch on the financial implications of AI investments and the role of advisors in shaping effective AI strategies.TakeawaysCFOs are often disconnected from the practical applications of AI.There is a significant divide between what CFOs say about AI and what they actually implement.AI mandates are prevalent in boardrooms, but practical use cases are lacking.Data infrastructure is crucial for successful AI implementation.CFOs need to engage with CIOs about long-term data strategies.AI should be viewed as a multi-year journey, not a quick fix.Forecasting AI's value creation is essential for CFOs.Investments in AI should be treated with the same diligence as M&A activities.Advisors can play a key role in guiding AI strategy for organizations.Understanding the difference between CapEx and OpEx is important for AI budgeting.KeywordsCFO, AI, finance, data infrastructure, value creation, boardroom, investment, strategy, technology, business growth

In this episode, Laura Fu interviews Josh Garbuio, co-founder of Leo, about the transformative impact of AI on marketing. They discuss the evolution of marketing budgets, the role of AI in automating marketing operations, and the importance of maintaining authenticity in AI-driven campaigns. Josh shares insights on the challenges of building an AI startup and the future trends in marketing AI, emphasizing the need for a human touch in the marketing process. The conversation highlights the potential of AI to enhance productivity and efficiency in marketing while also addressing the evolving role of digital marketers.TakeawaysAI has the potential to boost the US economy.Productivity means doing more with less, especially in marketing.Marketing budgets are expanding, but ROI is a concern.AI can automate ad creation and management.The role of digital marketers is evolving with AI.Authenticity in marketing is crucial, even with AI.AI tools can save marketers time and enhance efficiency.Building an AI startup comes with significant challenges.Companies are transitioning from agencies to AI solutions.Integrating AI into marketing requires a strategic approach.KeywordsAI, marketing, productivity, startups, digital marketing, automation, advertising, technology, business, innovation

In this episode, Laura Fu interviews Pujun Bhatnagar and Jeff Gibson, co-founders of Kintsugi, an AI-native company focused on revolutionizing sales tax compliance. They discuss their backgrounds, the vision behind Kintsugi, and the unique challenges of building an AI-first company. The conversation explores the importance of understanding customer needs, the evolution of pricing strategies in AI, and the future of AI in enterprises. They also share insights on the changing perspectives of buyers and the significance of having the right team in a startup environment.TakeawaysKintsugi aims to empower local businesses by simplifying sales tax compliance.AI can automate tedious tasks, allowing humans to focus on more complex problems.Building an AI-native company requires a different approach to hiring and team dynamics.Pricing strategies in AI must reflect the value delivered to customers, not just costs.Buyers should evaluate AI tools based on their ability to solve real business problems.The future of AI will involve cracking the code of symbolic manipulation for better reasoning.Startups must focus on accountability and clear roles to succeed.Investors are increasingly concerned with compliance during due diligence.The right team can make the difference between startup success and failure.Understanding and leveraging existing tools can maximize their value. KeywordsAI, Kintsugi, startups, sales tax, compliance, machine learning, pricing strategies, enterprise AI, buyer perspectives, entrepreneurship

We’re bringing together founders, operators, investors, and builders to make sense of the AI moment we’re all living through. From how enterprises are actually buying AI, to what happens when companies try to build it in-house, to why new standards like MCP matter — we’ll cut through the noise and give sales teams (and anyone selling into this space) the clarity they need.It’s fast, it’s practical, and it’s the state of the union on AI — straight from the people shaping it.

SummaryIn this episode, Laura Fu and Anirudh Shenoy discuss the evolving role of AI in the workplace, emphasizing the importance of human oversight in AI systems. They explore the concept of 'humans in the loop', the challenges of AI adoption, and the need for a shift in mindset when managing AI agents. The conversation highlights design principles for effective human-AI interaction and best practices for deploying AI systems, ultimately advocating for a collaborative approach between humans and AI to enhance productivity and efficiency.TakeawaysThe concept of 'humans in the loop' emphasizes the need for human oversight in AI systems.AI adoption faces challenges related to trust and expectations.Managing AI agents requires a different approach than managing human workers.Shifting mindsets in workflows is essential for leveraging AI effectively.Design principles for human-AI interaction are still being developed.Buyers of AI systems need to understand the probabilistic nature of AI.AI systems should have escape mechanisms for when they cannot handle tasks.The interaction interface for AI should be tailored to the use case.Expectations of AI should be realistic and aligned with its capabilities.The future of work involves collaboration between humans and AI.KeywordsAI, Human in the Loop, Generative AI, Trust in AI, AI Adoption, AI Management, AI Design Principles, AI Deployment, AI Buyers, Human-AI Collaboration

SummaryIn this episode, Laura Fu and Ribhu Chawla discuss the Model Context Protocol (MCP), its significance in the AI landscape, and how it transforms the way AI agents interact with various tools and APIs. They explore the differences between MCP and traditional APIs, the importance of implementing MCP in organizations, and how it can enhance efficiency and data utilization through the Knowledge Graph. The conversation emphasizes the need for organizations to adapt to this new technology to remain competitive in the evolving AI ecosystem.TakeawaysMCP stands for Model Context Protocol, an open-source protocol.MCP acts as a connector for AI tools, similar to USB-C.MCP enhances interoperability between different AI applications.Organizations need to consider MCP to stay relevant in AI.MCP is not a replacement for APIs but builds on top of them.Implementing MCP requires understanding use cases and tools to expose.MCP can significantly improve organizational efficiency and productivity.The Knowledge Graph enriches data for better AI performance.MCP helps automate decision-making processes in organizations.MCP is a critical step towards becoming AI-ready. KeywordsMCP, Model Context Protocol, AI agents, APIs, organizational efficiency, Knowledge Graph, AI readiness, data management, automation, integration

In this episode, Laura Fu interviews Itamar Novick from Recursive Ventures, discussing his extensive background in technology and venture capital, particularly focusing on data and AI. They explore the evaluation of AI companies, the importance of defensibility and moats, challenges in acquiring enterprise customers, and the critical role of human-machine interaction in AI adoption. The conversation also touches on market saturation, valuations, and predictions for the future of AI and technology.TakeawaysItamar Novik has 25 years of experience in tech and venture capital.The relationship between data and AI is crucial for company success.Investors look for strong teams, technology, and market potential.Defensibility in AI companies is essential to avoid competition.Proprietary data can create a significant competitive advantage.Human-machine interaction is key for AI adoption.The market for AI startups is growing but not oversaturated.Valuations for AI applications remain stable compared to previous years.The future of AI will see new companies emerge as leaders.Jumping on new technology waves is vital for professional growth.KeywordsAI, Data, Startups, Venture Capital, Technology, Generative AI, Market Trends, Investment Strategies, Enterprise Solutions, Human-Machine Interaction

In this episode, Laura Fu hosts Griffin Churich and Juan Furcada to discuss a remarkable deal closed in record time. They delve into the strategies employed to understand customer needs, engage technical teams, navigate stakeholder priorities, and validate their product. The conversation highlights the importance of AI in solving business problems, efficient data migration, and the competitive advantage gained during the POC process. The guests share valuable lessons learned and advice for future deals, emphasizing the significance of authenticity and proactivity in customer relationships.takeawaysThe deal was closed in record time, showcasing effective strategies.Understanding customer needs is crucial for successful engagement.Engaging technical teams early can enhance the sales process.Navigating stakeholder priorities helps in focusing on impactful solutions.AI should be positioned as a solution to real business problems.Product validation through real data is essential for customer confidence.Efficient data migration can significantly impress customers.A competitive advantage can be gained through effective POC execution.Building strong relationships with customers fosters trust and collaboration.Authenticity and proactivity are key in sales, especially in AI.keywords: AI, sales, customer engagement, product validation, data migration, competitive advantage, business solutions, stakeholder management, deal closure, technology

In this episode, Laura Fu and Jeff Ignacio discuss the integration of AI in Revenue Operations (RevOps). They explore current use cases, implementation strategies, and the challenges faced by RevOps professionals in adopting AI technologies. Jeff shares insights on ideal AI tools, the importance of data hygiene, and offers advice for both AI vendors and buyers in the RevOps space.takeawaysAI is being used for data enrichment and meeting recaps.Many RevOps professionals are still in the dabbling phase with AI.Zapier can streamline workflows and data integration.Data hygiene is a significant challenge in RevOps.RevOps leaders need to think strategically about data and insights.AI tools should integrate seamlessly with existing systems.Overcoming IT policies is crucial for AI adoption.RevOps professionals are eager to adopt AI technologies.Understanding the business problem is key when evaluating AI tools.Total cost of ownership should be considered when buying tools.keywords:RevOps, AI, data enrichment, Zapier, AI adoption, revenue operations, AI tools, data hygiene, AI vendors, business insights

In this episode, Laura Fu and Thomas Hill discuss the current landscape of AI in business, focusing on the challenges and opportunities that arise when companies consider building their own AI solutions versus purchasing existing ones. They explore the importance of data management, the role of customer support, and how DevRev's unique approach with a knowledge graph can help organizations effectively leverage AI. The conversation highlights the complexities of implementing AI, the necessity of data preparation, and the future potential of AI in various business contexts.takeawaysAI is becoming integral to various business use cases.Many companies are leveraging OpenAI for internal solutions.Building custom AI solutions can be complex and challenging.Data context is crucial for effective AI implementation.Customer support chatbots often fail to meet expectations.AI's effectiveness is tied to the quality of data preparation.DevRev's knowledge graph provides a unified data context.Data preparation can account for a significant portion of AI project costs.Identifying gaps in processes can lead to better AI integration.AI technology will continue to evolve and improve.

In this episode, Laura Fu and Ahmed discuss the essential infrastructure needed for AI, the challenges faced by traditional software in adapting to AI, and the importance of a data-driven strategy. They explore the balance between horizontal and vertical AI strategies, the significance of intentionality in AI development, and the engineering challenges that arise in building AI solutions. The conversation also touches on safety, governance, and the future of AI models, emphasizing the need for companies to meet customers where they are and to be mindful of the risks associated with AI implementation.TakeawaysAI infrastructure is crucial for actionable intelligence.Traditional software isn't ready for scalable AI.Data is fundamentally important for AI strategy.Intentionality is key in building AI solutions.Horizontal AI strategies can interoperate across systems.Meet customers where they are with AI.Domain-specific models will shape the future of AI.Vibe coding simplifies app development for everyone.Safety and governance are top priorities for AI buyers.Understand the risks and guardrails of AI systems.

summaryIn this episode, Vishal and Jason discuss effective strategies for closing large AI deals, emphasizing the importance of identifying real business problems, demonstrating AI value through proof of concepts (POCs), and addressing the critical role of data in AI solutions. They share insights on navigating buyer skepticism and the significance of platform thinking in AI sales. The conversation culminates in lessons learned from closing significant deals and the ongoing innovation in the AI space.TakeawaysAI can solve business problems, not just AI problems.Demonstrating AI value is key to closing deals.Data is the biggest problem for companies today.Focus on the pain points, which is in the data.Get the tech in the hands of the customer.AI can do anything, but it can't do everything.The data problem exists, don't perpetuate it.Innovation in AI is still ongoing, keep learning.Every business problem has a corresponding AI solution.AI solutions must be tied to real business pain.

SummaryIn this episode, Laura Fu interviews Neel Kamal, CEO of AdamX, discussing the challenges sales teams face despite the abundance of AI tools. Neel introduces the concepts of 'grind mode' and 'vibe mode' in sales technology, emphasizing the need for tools that reduce mental load and enhance decision-making. He explores the Vibe Stack, a collection of technologies that streamline sales processes, and critiques traditional sales enablement methods. The conversation highlights the importance of understanding the buyer journey and how AI can assist in this process, ultimately advocating for a shift towards vibe-oriented technologies that empower sales representatives.takeawaysNeel Kamal is the CEO of AdamX, focusing on sales enablement.Sales teams struggle despite having numerous AI tools.'Grind mode' requires reps to do all the mental work.'Vibe mode' allows technology to make decisions for reps.Most sales tools currently operate in grind mode.The Vibe Stack is essential for effective sales enablement.Sales enablement should focus on both rep and sales mastery.AI can enhance salesmanship by understanding buyer needs.Understanding the buyer journey is crucial for sales success.Evaluating sales technology should focus on decision-making capabilities.

In this episode, Ribhu Chawla discusses the principles of building effective AI agents, contrasting traditional automation with the flexibility of AI agents. He emphasizes the importance of outcome orientation, context, and memory in agent development, as well as the need for collaboration and inspection capabilities to ensure trust and adaptability in AI systems. Ribhu also provides insights on how to choose the right agent builder and the future of agent building in a rapidly evolving technological landscape.TakeawaysAI agents differ from traditional automation in their flexibility.Building agents requires an outcome-oriented approach.Agents must evolve with changing goals and contexts.Clear communication is essential in agent development.Collaboration among team members enhances agent effectiveness.Guardrails are necessary to ensure safe AI operations.Inspection capabilities help build trust in AI agents.Understanding internal expectations is crucial for effective AI implementation.Agent builders should facilitate continuous innovation and adaptation.Thinking in terms of systems and processes can improve efficiency.

summaryIn this episode of the State of the AI Union podcast, Laura Fu and Chris Connelly discuss the complexities of defining work in the context of AI collaboration. They explore how AI can assist in tasks but emphasize the importance of human judgment and experience. The conversation delves into generational differences in adapting to AI technologies, the need for clear communication skills, and the significance of designing AI systems that prioritize human needs and connections. Chris highlights the evolving role of AI in the workplace and the importance of empathy and patience as we navigate this technological landscape.takeaways​Work is a complicated idea that varies by role.​AI is improving in understanding quality outputs.​Younger generations will need new skills for AI collaboration.​Clarity in communication is essential for effective teamwork.​Patience is necessary as we adapt to AI technologies.​AI can enhance human connections in the workplace.​Designing AI should prioritize human-centered principles.​Communication skills are crucial in the AI era.​We must be empathetic to those without access to AI tools.​The journey with AI is just beginning, and we are all learning.

SummaryIn this episode, Laura Fu and Rizurekh discuss the evolving landscape of AI agents, differentiating between true AI agents and deterministic workflows. They explore the nuances of agentic workflows, the challenges in building effective AI agents, and the importance of setting user expectations. The conversation also delves into technical considerations for AI agents and the distinction between agents and other AI tools like co-pilots. Real-world applications of AI agents are highlighted, showcasing their potential to enhance productivity and collaboration.takeawaysAI agents can perform tasks autonomously and reason through complex situations.Deterministic workflows often fail to account for all possible scenarios.Agentic workflows involve collaboration and decision-making with AI.Not all AI tools labeled as agents are truly autonomous.User expectations must be managed when deploying AI agents.Designing the user experience for AI agents is critical for success.Technical preparation and context management are essential for effective AI agents.AI agents should be able to communicate their capabilities to users.Search relevance and data preparation are key metrics for AI performance.Co-pilots can be agentic but are not necessarily agents.

SummaryIn this episode, Laura Fu and Kevin Su discuss the evolving landscape of AI funding, the challenges and opportunities in the market, and the importance of evaluating AI vendors for long-term sustainability. They explore the shifts in investment strategies, the definition of AI platforms versus point solutions, and the valuation trends in AI companies. The conversation highlights the growing demand for AI solutions and the need for enterprise buyers to assess the viability of AI vendors beyond just features.takeawaysAI funding is booming, but early-stage funding is declining.2023 marked a significant shift towards LLMs and infrastructure.Investors are increasingly focused on long-term viability of AI companies.Valuations in AI are often based on high revenue multiples.The demand for AI tools is driven by both market needs and product improvements.AI companies are more fragile due to increased competition and funding challenges.Evaluating existing customer satisfaction is crucial for buyers.Buyers should assess the runway and financial health of AI vendors.Long-term moats are essential for AI companies to differentiate themselves.The AI market is expected to continue growing and evolving rapidly.

SummaryIn this episode of the State of the AI Union, host Laura Fu and guest Tim discuss the intersection of AI and design, exploring how architecture can enhance human experiences. They delve into the evolving role of designers as AI systems become more integrated into workflows, emphasizing the importance of user experience and trust. The conversation covers the limitations and potential of conversational interfaces, the balance between speed and logic in AI interactions, and practical applications of AI in design. Tim shares insights on how designers can leverage AI tools while maintaining intentionality in their work.takeawaysAs AI evolves, designers must adapt their roles.User experience is paramount in AI design.Conversational interfaces are just the beginning.Trust in AI systems is built through effective solutions.Speed and quality are both important to users.AI can significantly enhance productivity.Designers should focus on intentionality in their work.Not all tasks are suited for conversational interfaces.Prototyping with AI should solve real user problems.Creating without purpose can lead to wasted effort.

SummaryIn this conversation, Andrew Lockman discusses the evolving landscape of AI in enterprises, focusing on buyer concerns, the importance of use cases, and the role of sales professionals in navigating this complex environment. He emphasizes the need for predictability, security, and effective user experiences while addressing the hesitations and urgency surrounding AI adoption. The conversation also highlights the transformative power of AI and the necessity for sales teams to adapt to changing customer expectations.takeawaysBuyers want predictability and security in AI solutions.Use cases should drive AI discussions with enterprises.Hesitation exists among buyers regarding AI adoption.Successful AI implementation requires a focus on user experience.Sales professionals must be curious and humble in their approach.AI's impact will be felt across all levels of an organization.Prescriptive use cases are essential for effective AI solutions.The role of enterprise sellers is evolving with AI advancements.Customer expectations are rising with AI capabilities.AI can significantly improve efficiency in sales processes.

summaryIn this episode, Laura Fu and Chad McAllister discuss the current state of AI, particularly focusing on its perception in Silicon Valley versus outside it. They explore the adoption cycles of technology, the dynamics of enterprise AI in boardrooms, and how AI is integrated into daily life. The conversation also touches on the comparison between AI and cryptocurrency, emphasizing the importance of user experience and the invisible nature of AI in everyday tasks.takeawaysAI is perceived differently in Silicon Valley compared to other regions.Adoption cycles for technology often take longer than expected.User behavior is crucial in the adoption of AI technologies.Enterprise AI discussions are prevalent, but implementation is challenging.People may use AI without realizing it in their daily lives.The comparison between AI and crypto highlights different value propositions.Invisible technology can enhance user experience without being intrusive.Adoption is often driven by experience rather than awareness.The integration of AI into daily tasks is becoming more seamless.Future AI applications will likely be more connected and user-friendly.

In this episode of the State of the AI Union, Laura Fu and Katya discuss the integration of AI into workflows, particularly in finance and coding. They explore the concept of invisible AI, its role in enhancing productivity, and the importance of organizational structures for successful AI implementation. The conversation highlights the need for clear instructions and data organization to maximize AI's potential while addressing the challenges of counter productivity and the necessary behavioral changes for effective AI use.takeawaysAI is already delivering value in various workflows.Invisible AI automates repetitive tasks to enhance productivity.AI boosts critical thinking by encouraging improvement.Intelligence involves understanding overarching goals and processes.Combining AI insights with personal knowledge is essential.Organizational structure is crucial for effective AI integration.Data organization is key for AI to provide meaningful insights.AI can be counterproductive if prompts are not specific.Long-term behavior changes are needed for successful AI adoption.Accessible dashboards are vital for understanding metrics.