Salesforce VP: What Claudeforce Changes for Customers and Partners | Nia Samady

In this episode, recorded at Dreamforce 2026, Nia Samady, VP of Global Partner Success at Salesforce, joins Kyle to discuss what it takes for AI agent projects to succeed with customers. Nia has spent 22 years at Salesforce, starting as a college graduate on a developer support team, and now leads a technical team that works alongside consulting partners like Accenture and Deloitte across the full customer lifecycle. Nia explains how AIforce and Claudeforce are bringing Salesforce into the interfaces customers already use, and how Salesforce's network of 84 forward deployed engineering partner firms is tackling data readiness and customer readiness from the earliest stages of a deal. They also get into where AI is delivering real ROI today, from service use cases seeing 70%+ case deflection to regulated industries like health and financial services, the rise of AI native partners, and how hiring is changing as companies look for new grads who blend technical skills with business acumen. Key Topics Covered How Dreamforce 2026 announcements translate into partner success What Claudeforce and the Anthropic partnership mean for customers How Salesforce's FDE Partner Network supports Agentforce delivery Why data readiness is the foundation for AI agents Where AI agents are delivering ROI, including 70%+ case deflection in service Why regulated industries are a litmus test for AI The rise of AI native partners in the Salesforce ecosystem How AI is changing hiring for new grads and builders Mentorship, sponsorship and career growth in the AI era Episode Timestamps 00:00 - Introduction and Nia's 22 years at Salesforce 00:26 - What Nia didn't see coming in the agentic era 01:21 - From the Dreamforce keynote to partner success 02:52 - Claudeforce and the Anthropic partnership 04:15 - Early signals from customers using Salesforce in Claude 05:05 - How forward deployed engineers support Agentforce delivery 07:36 - What forward deployed engineers look at first 09:08 - Why partners value early engagement 09:53 - Where AI is delivering ROI today 11:32 - The rise of AI native partners 12:32 - Marc Benioff, the beginner's mind and a chance elevator ride 14:58 - How hiring has changed for new grads and builders 16:59 - How technical grads can build business skills 18:35 - How Salesforce keeps employees ahead of AI 20:09 - Mentorship, sponsorship and career growth 21:20 - What will be obvious to partners by Dreamforce 2027 Where to find Nia: LinkedIn - https://www.linkedin.com/in/nia-samady-48b99a46/ Salesforce - https://www.salesforce.com/ Partner Links Join The AI Report Pro: https://community.theaireport.ai/checkout/the-ai-report-pro Join our free live Claude session, every Tuesday: https://community.theaireport.ai/forms/497be10c Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, recorded at Dreamforce 2026, Denis Machuel, CEO of The Adecco Group, joins Kyle to discuss how AI agents are changing recruitment, workforce strategy, and the day-to-day work of recruiters. Denis explains how Adecco is moving from isolated AI pilots to scaled deployment across its business, with a focus on keeping people at the center of the transformation. They discuss Ada, Adecco's recruitment agent in the UK, why roughly half of its candidate conversations initially happened outside working hours, and how agents can take over repetitive outreach and first-level screening so recruiters can spend more time on higher-value human conversations. Denis also breaks down Adecco's work with Salesforce, the rollout of Agentforce Coworker to 27,000 sales professionals and recruiters, the importance of responsible AI and secure data, and why the next challenge is orchestrating multiple agents while bringing humans into the process at the moments where they add the most value. Key Topics Covered How AI is changing recruitment at The Adecco Group Why Adecco moved from broad AI pilots to scaling agents in core workflows How Ada supports candidate outreach and first-level screening Why recruiters can spend more time on higher-value human conversations Adecco's rollout of Agentforce Coworker Responsible AI, guardrails and secure candidate data How AI can make recruitment more proactive for candidates and clients Why agent orchestration and human-in-the-loop design matter Episode Timestamps 00:00 - Denis' background in computer science and AI 01:50 - The scale of Adecco and the future of work 03:33 - How Ada, Adecco's recruitment agent, works 05:22 - Scaling AI agents across languages and geographies 06:19 - Adecco's partnership with Salesforce and Agentforce 09:05 - Moving from individual AI use to enterprise deployment 10:54 - How a recruiter's workflow is changing with AI agents 13:34 - From reactive recruitment to proactive career support 15:18 - How Adecco measures the success of a hire 16:53 - AI adoption in Europe versus the US 19:15 - Regulation, responsibility and human-centric AI 20:12 - What drives Denis as CEO of The Adecco Group 22:02 - Why agent orchestration is the next big challenge Where to find Denis: LinkedIn - https://www.linkedin.com/in/denismachuel/ The Adecco Group - https://www.adeccogroup.com/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Ian Kahn, PwC partner and global leader for its Customer and Commercial Excellence platform, joins Liam to discuss what AI adoption actually looks like inside large organizations. Ian has spent 27 years at PwC and leads a global team of almost 10,000 practitioners focused on helping clients build stronger customer relationships and drive growth. Ian explains why the biggest challenge with AI is not access to the technology, but getting people and business models to change with it. He shares why PwC sees a small group of companies capturing most of the value from AI, how leaders can reduce resistance to change, and why the number of agents deployed matters less than how the business itself evolves. They also get into how Ian uses his own portfolio of AI agents, the shift toward voice-based interaction, and how agents can take over routine administrative work so teams can spend more time on higher-value customer relationships. Key Topics Covered Why Ian calls AI the biggest change of his 27-year career Why 20% of companies are capturing 75% of the value from AI deployment How leaders can overcome resistance to AI adoption How PwC is embedding AI into its business model How Ian uses his own portfolio of AI agents Why AI agents should free people for higher-value work The role of voice in the future of work Why leaders need to explain the "why" behind AI transformation Episode Timestamps 00:00 - Introduction and Ian's 27 years at PwC 02:15 - Why AI is the biggest change Ian has seen in his career 02:56 - Ian's role leading almost 10,000 practitioners at PwC 04:44 - Why continuous learning matters in a 175-year-old firm 06:08 - Why 20% of companies are capturing 75% of AI's value 07:57 - The role of culture and leadership in AI adoption 10:08 - Why Ian spends most of his time with clients and teams 11:06 - How PwC is embedding AI into its business model 12:55 - Using agents to free up higher-value capacity 14:17 - How Ian uses his own portfolio of AI agents 16:29 - Why leaders need to explain the "why" behind AI 18:28 - Career advice, mentorship and leading high-performing teams Where to find Ian: LinkedIn - https://www.linkedin.com/in/ianwkahn/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Mike Flynn joins Liam to break down how companies are moving from AI experimentation to measurable business value. Mike has spent more than 20 years in consulting, including nearly a decade at EY, nine years at PwC, and now leads the technology sector for EY's consulting business. They discuss why businesses need to redesign workflows around AI instead of simply adding AI to existing processes, how EY's "Design for Zero" approach works, and why AI agents are changing the economics of software. Mike also explains the real costs behind AI agents, what enterprises want from AI vendors, how AI is reshaping consulting, and why EY is investing in forward-deployed engineering. Key Topics Covered Moving from AI experiments to measurable ROI EY's "Design for Zero" approach to AI-first workflows Why per-seat software budgets break with AI agents The real cost of an AI agent task What enterprises want from AI vendors How AI is changing consulting and forward-deployed engineering Episode Timestamps 00:00 - Introduction and Mike's path from the Air Force to EY and PwC 01:58 - What Mike's role at EY looks like today 03:57 - Why point solutions create "trapped work" 04:29 - From AI experiments to end-to-end transformation 07:47 - Where AI-first redesign is working today 10:00 - Why AI is breaking the per-seat software model 13:01 - The real costs behind running an AI agent 16:00 - Measuring AI spend versus business value 18:28 - How much work will AI actually take over? 20:52 - Why companies need more granular AI cost controls 25:53 - Building automated control loops for AI 31:21 - Experimenting with enterprise AI tools at scale 36:02 - Why enterprise AI adoption moves slower than personal AI 39:37 - CIOs shift from adoption to process redesign 41:29 - What enterprises want from AI vendors 45:24 - Consulting versus AI deployment at EY 46:36 - Why AI may create more consulting work, not less 49:00 - EY's forward-deployed engineering strategy 52:16 - The two qualities Mike looks for when hiring 55:33 - Why Mike does what he does Where to find Mike: LinkedIn - https://www.linkedin.com/in/michaelbernardflynn/ EY - https://www.ey.com/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Cillian Kieran, CEO and founder of Ethyca, joins Liam to talk about why data privacy and AI governance need to be treated as engineering problems, not just legal and compliance challenges. Cillian explains how a consulting project for Heineken ahead of the GDPR pushed him to rethink privacy from an engineer's perspective and eventually build Ethyca. He breaks down how the company's product suite works, why Fides has become a widely adopted open-source privacy standard, and what changes when AI agents are given read and write access to tools like Stripe, QuickBooks or a CRM. They also get into what foundation model providers may be missing on governance, why AI needs a harness that directs its capabilities without slowing it down, the responsibility engineers have when building AI systems, and why he thinks much of what we call AI is still statistical math wrapped in a marketing label. Later, Cillian and Liam discuss where AI startups may consolidate, Yann LeCun's work on world models, the human impact of increasingly agent-driven work, and how growing up around art shaped Cillian's view of software as a creative pursuit. Key Topics Covered Why privacy and AI governance are engineering problems How Fides became a widely adopted open-source privacy standard The risk of giving AI agents read and write access to business systems Why AI needs a harness that directs its capabilities without slowing it down What foundation model providers may be missing on governance Why engineers, not just users, carry responsibility for AI safety Why Cillian calls AI "statistical math" with a marketing label World models, startup consolidation and the future of AI infrastructure Episode Timestamps 00:00 - Introduction and welcome 00:58 - From a physics dropout to a data consultancy for Fortune 500 brands 03:17 - The Six Problems of Privacy and becoming the world's most trusted technology company 07:20 - Inside the product suite: Fides, Helios, Janus, Lethe and Astralis 11:29 - How Fides became a widely adopted open-source privacy standard 14:20 - What Cillian believes foundation model providers are missing on governance 16:08 - Why AI needs a harness, not just guardrails 18:19 - Ethyca's business model and the rise in demand for consulting 23:07 - Why Ethyca went after enterprise customers first 24:50 - Testing Grok's agent tools 27:34 - MCP proliferation, OpenClaw and the risk of "permissive access" 31:11 - The seatbelt analogy for building safe AI systems 34:28 - Why AI governance isn't getting the coverage it deserves 36:26 - AI as "statistical math" and Ted Chiang's take on the label 38:43 - Foundation model economics and the future of AI startups 39:49 - Yann LeCun, world models and where Cillian would place his next bet 50:12 - Zen and the Art of Motorcycle Maintenance: classical versus romantic thinking 56:05 - Why Cillian still does what he does 01:03:34 - Where to find Cillian and closing thoughts Where to find Cillian: LinkedIn - https://www.linkedin.com/in/cilliankieran Ethyca - https://ethyca.com Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

Brad Axen, Block's Head of AI Capabilities and the original author of Goose, joins Liam to talk about what actually makes AI useful at work. They cover how Block went from building an early open-source AI agent to MoneyBot, ManagerBot, BuilderBot and Buzz, and why Brad thinks the hardest problems now are memory, access and interface, not just model intelligence. Brad also explains why AI memory should belong to the business rather than a single bot, what ants can teach us about shared memory systems, why "meat proxy" is becoming a new office problem, and how Buzz is testing a multiplayer model where humans and AI agents work in the same space. Key Topics Covered Goose, Block's open-source AI agent, and the Agentic AI Foundation Agents vs. harnesses vs. interfaces MoneyBot, ManagerBot and BuilderBot Why AI memory should belong to the business, not the bot Stigmergy and ants as a model for shared memory Buzz and Block's "multiplayer" approach to AI at work "Meat proxy" and the new office busywork AI can create Why the bottleneck is shifting from writing code to deciding what to build How AI is changing hiring, interviews and day-to-day work The human cost of spending all day working with AI Episode Timestamps 00:00 Intro 00:07 What Block actually is 02:40 From CERN to Block 05:19 Building Goose and taking it open source 06:29 Agents vs. harnesses vs. interfaces 10:28 MoneyBot, ManagerBot and BuilderBot 15:56 Memory, access and learning over time 22:53 What ants can teach us about AI memory 26:48 Two versions of where AI could go 28:26 Buzz and the idea of multiplayer AI 29:56 "Meat proxy": the new office problem 35:09 The Buzz case study and a 50% productivity jump 37:06 The new bottleneck now that AI can write the code 49:11 Rebuilding institutional knowledge after team restructuring 52:00 How AI is changing hiring and interviews 57:35 The loneliness of working with AI all day 59:13 Why Brad does what he does Connect with Brad on LinkedIn: https://www.linkedin.com/in/bradleyaxen/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Loni Stark, VP of Strategy and Product at Adobe, joins Liam to talk about what happens when a 25 year tech career runs alongside a full creative practice in painting, sculpture and writing, and what that split brain teaches her about building for the AI era. Loni explains why she thinks brands may already be invisible, or worse, misrepresented, inside AI answers, why every company needs to start treating AI as a new kind of audience, and how she is running her own home AI lab, complete with a self built server and a personal agent that has now run continuously for over 150 days, to understand what actually gives an AI agent an identity. Along the way, Loni and Liam get into her Harvard Extension School research into "orphan values," the personal values people can't express in any of their current life roles, and what happens to that alignment as AI reshapes the roles themselves. She also breaks down how she balances Adobe's biggest enterprise bets, including Experience Manager, Commerce, Brand Concierge and LLM Optimizer, against the need to experiment without limits in her own time. Key Topics Covered Why Loni keeps a full art practice, painting, sculpture and writing, alongside her tech career Growing up with parents who didn't understand the arts, and using creativity as a form of rebellion Whether humans are innately creative, and why AI makes protecting your own voice more important Why Loni has stayed at Adobe for 25 years, and how she thinks about "growing the aquarium" Building a personal AI server at home instead of a garden, and what that setup actually involves Swapping the underlying model and the agent harness to test what gives an AI agent a persistent identity Her Harvard Extension School research into "orphan values" and how AI is reshaping the roles we express them through The shift from human mediated to AI mediated experiences, and why that changes what "traffic" even means Why being invisible to AI isn't the worst case, being misrepresented by it is How brands should start preparing their content and catalogs to be "agent ready" The placebo effect of working with agents, and how that belief shapes performance and creativity How Loni balances limitless experimentation with the governance enterprise AI actually requires Why she does what she does: an insatiable need to grow, create and become more than she currently is Episode Timestamps 00:00 - Introduction and welcome 00:05 - Balancing a full art practice with a 25 year tech career 05:38 - Why she's stayed at Adobe for 25 years 08:30 - AI as the biggest creativity enabler she's seen 12:50 - Inside her home AI lab: hardware, memory, and swapping the agent harness 18:11 - Studying psychology at Harvard, and what "orphan values" mean 26:28 - The shift to AI mediated business, and why invisible isn't the worst case 31:46 - How brands become "agent ready" for the AI agent economy 36:23 - The placebo effect of working with AI agents 37:35 - Balancing limitless experimentation with enterprise governance 47:05 - Why Loni does what she does 49:14 - Where to find Loni and closing thoughts Loni's Socials: LinkedIn - https://www.linkedin.com/in/lonistark/ Loni’s Art Gallery: https://atelierstark.com/work/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Braden Warwick, Financial Planning Product Architect at PWL Capital, breaks down why so much of the financial advice sold at big banks is a sales pitch dressed up as a plan, and what a real financial plan actually requires. Braden traded a PhD in aerospace engineering for a career rebuilding how Canadians plan their money, and he brings that same engineering mindset to financial planning: define your objectives, map your constraints, then solve for the outcome that actually improves your life. Braden also walks Liam through the AI infrastructure PWL has built in house, from a proprietary data lake to an AI powered meeting note tool and planning summaries, and explains why they chose to build their own tools instead of buying off the shelf software. They get into Monte Carlo simulations, why financial planning is really about the distribution of outcomes rather than one predicted path, and what a financial planning engagement might look like in 2031. Key Topics Covered How Braden went from a PhD in aerospace acoustics to building financial planning tools at PWL Capital Why PWL's advisors are paid for advice, not for selling products, and how that changes the plan you get The six areas of a real financial plan: investing, cash flow, tax, insurance, retirement, and estate Treating a financial plan like an engineering problem: objectives, variables, and constraints Why Monte Carlo simulations model financial planning as a distribution of outcomes, not one fixed path What forms of uncertainty most financial software still misses, from real estate values to life expectancy Why PWL built its own AI meeting note tool and data lake instead of buying an off the shelf solution How AI is helping PWL's advisors scale personalized, evidence based financial plans PWL's acquisition by One Digital and what it changed, and did not change, about how Braden works What a financial planning engagement could look like by 2031 Episode Timestamps 00:00 - Introduction 00:40 - From aerospace engineering to financial planning 03:54 - Why PWL approaches financial advice differently 07:31 - The six areas of a real financial plan 11:48 - Financial planning as an engineering problem 17:56 - The psychology behind financial planning 23:14 - Objectives, constraints, and uncertainty 28:10 - How Monte Carlo simulations work 33:21 - What financial planning software still misses 39:11 - Building financial planning tools at PWL 44:16 - Inside PWL's financial planning system 51:38 - How AI is changing the advisor workflow 57:20 - Why PWL built its own AI tools and data infrastructure 1:03:41 - What changed after the OneDigital acquisition 1:06:34 - The future of financial planning 1:11:47 - Why Braden does what he does Braden's Socials: LinkedIn - https://www.linkedin.com/in/braden-warwick-a40b48a3/ Resources Mentioned: Braden’s article, The Optimal Financial Plan - https://pwlcapital.com/the-optimal-financial-plan/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Andrew McNamara, VP of Applied ML at Shopify, returns to unpack how much has changed in agentic commerce since his last episode. Andrew and Liam dig into why agents are becoming "the new front door to commerce," why orders coming to Shopify stores from AI are up 13x, and what's actually happening inside Shopify's personalized shopping agent in the Shop app. They also get into the Universal Commerce Protocol (UCP) and why AI commerce is growing 9x faster than social commerce did at the same stage, how Sidekick's architecture and app extensions work, and SimGym, Shopify's system for training AI shoppers to A/B test store changes before they ever reach a real customer. Key Topics Covered How shopping is shifting from stores and desktops toward agents as "the new front door to commerce" Why orders coming to Shopify stores from AI are up 13x, and why catalog-powered AI search converts twice as well as general AI search Inside Shop app's personalized shopping agent, and how it learns different shopping personas (like shopping for a pet versus a child) Why customers are shifting from keyword searches to natural language queries, and the higher conversion rates that come with it Why Shopify keeps shopping data personalized to the individual user rather than training it into a larger internal model What the Universal Commerce Protocol (UCP) is, and why AI commerce is growing 9x faster than social commerce and 3x faster than mobile did at the same stage The story of Shopify's CEO giving his own Hermes agent a budget so it can send him gifts in the mail Sidekick's app extensions, and how partners like Klaviyo and Loop plugged in at launch Campaign Autopilot's "auto research loop," and its parallels to reinforcement learning SimGym, and how Shopify trains AI shoppers to A/B test store changes before running them on real customers Why Sidekick runs on Anthropic's Sonnet model hosted on Google Cloud, and why that choice is model agnostic Andrew's own habit of shopping by taking pictures throughout the week and searching by image through UCP-connected agents Episode Timestamps: 00:00 - Introduction and welcome 00:29 - What's changed in AI and shopping since their last conversation 01:47 - Agents becoming "the new front door to commerce" 04:16 - Inside Shop app's personalized shopping agent 07:32 - Why data stays personalized to each shopper instead of training a larger model 11:53 - What the Universal Commerce Protocol (UCP) is, and orders from AI up 13x 14:58 - Merchant tooling for tracking AI-driven traffic and conversions 15:55 - The story of Tobi's Hermes agent sending him gifts in the mail 20:48 - Andrew's own habit of shopping by taking pictures and searching by image 26:59 - Sidekick's app extensions and partner integrations 33:02 - Inside Sidekick's architecture: the Sonnet model and knowledge base 35:18 - Campaign Autopilot's auto research loop 38:58 - SimGym: training AI shoppers to test store changes 42:23 - What's next for Shopify's agentic commerce features 44:17 - Where to find Andrew Andrew's Socials: Twitter (X) - https://x.com/DrewCH LinkedIn: https://www.linkedin.com/in/andrewmcnamara1/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

David McIntosh, Chief Connected Stores Officer at Instacart, joins Liam to explain why the company is betting on smart shopping carts instead of rewiring stores with ceiling cameras. David walks through the $350 million acquisition of Caper, how Instacart is now live in more than 100 cities with thousands of connected carts, and why the screen on the cart, not the checkout speed, turned out to be the real driver of sales lift for retailers. David also gets into the surprisingly hard engineering problems behind a smart cart, like figuring out whether a basket is actually empty, fusing camera and scale data in real time, and building recommendations that know exactly where a shopper is standing in the store. He and Liam talk about who owns all that shopping data, what agentic AI looks like when it moves from chat into the aisle with tools like Cart Assistant, and why grocery budgets and meal planning are becoming one of the most requested AI features in the store. Key Topics Covered Why David left Tenor, the GIF search engine used by billions, to build Instacart's Connected Store business The strategic bet behind unifying online and in-store grocery shopping Why Instacart acquired Caper for $350 million instead of building smart carts in-house The reason Instacart chose carts over ceiling cameras for in-store AI How a simple running total and real-time coupons drive measurable sales lift The NVIDIA Jetson hardware and multimodal sensor fusion that let the cart "see" what's in the basket The strange edge cases in physical AI, like why "is this cart empty" is a genuinely hard question Who owns retailer and shopper data, and how it's used to improve recommendations Cart Assistant: how Instacart lets customers shop inside ChatGPT and directly on retailer websites Using agentic AI to fix store operations like out-of-stock items and supplier issues How budget-conscious meal planning became one of the most requested AI features in the store David's answer to Liam's closing question: why he does what he does Episode Timestamps 00:00 - Introduction and welcome 00:14 - David's path from Tenor to Instacart's Connected Store 02:01 - The bigger bet behind bringing online and in-store shopping together 05:29 - Entering the smart cart market and acquiring Caper 08:07 - Caper's scale today: 100+ cities and millions of daily sensor inputs 10:28 - How the smart cart actually drives sales lift 12:49 - Why Instacart bet on carts instead of ceiling cameras 17:33 - The unglamorous detail that makes or breaks adoption: charging 19:26 - What makes the experience sticky enough to keep customers coming back 24:41 - Inside the hardware: NVIDIA Jetson and multimodal sensor fusion 28:43 - The strange edge case behind a seemingly simple question 35:14 - Who owns the shopping data, and how retailers use it 37:30 - Agentic shopping: Cart Assistant and buying inside ChatGPT 42:16 - Using agentic AI to fix store operations, not just shopping 46:17 - Why David does what he does Connect with David on LinkedIn: LinkedIn: https://www.linkedin.com/in/mcintoshdavid/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices