State of the AI Union

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.