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CommerceAI

CommerceAI

By: Ian Jindal
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CommerceAI explores how artificial intelligence is reshaping commerce in direct-to-consumer sectors - retail, grocery, hospitality, leisure, and entertainment. We take a board-level commercial view, evaluating AI's impact through the lenses of the customer's autonomy (CustomerAI), product development (ExperienceAI), efficiencies and capabilities (CapabilityAI) and the business-changing impact for shareholders (StrategicAI). In each episode, senior operators and builders share the experiments, decisions and trade‑offs and paths of progress... This podcasts keeps the human and retailcraft amidst the technological advancements.(c) 2026, RetailX Economics Management Management & Leadership
Episodes
  • "Customers can tell" - in conversation with Leticia Perez Muñoz of TOMS

    "Customers can tell" - in conversation with Leticia Perez Muñoz of TOMS
    May 29 2026
    For ecommerce leaders trying to work out where to place their bets as AI reshapes customer discovery, this episode offers something rarer than a technology playbook: a clear-eyed account of what is actually happening right now, at a real brand, with a small team. Leticia Pérez Muñoz of TOMS argues that the most important response to AI-generated content proliferation is not to produce more of it, but to go the other way -- doubling down on authenticity, real people, and genuine brand values. The conversation covers GEO, attribution, team culture, and the critical thinking skills that matter more than any individual tool.Key themesAI as a new channel to track, not yet to depend on. TOMS began tracking ChatGPT and Claude sessions in 2026. Traffic is minimal but real, and organic search is showing a small corresponding decline. Leticia's view is measured: meaningful revenue from AI discovery channels is still years away, but the monitoring infrastructure needs to be in place now.Authenticity as a competitive response to AI content. Leticia observes that consumers are increasingly able to identify AI-generated content, and that this is accelerating rather than stabilising. TOMS's response is to move toward real people, real environments (a recent campaign shot on the streets of London), and minimal AI involvement in content creation -- using the brand's own values as a quality filter.PDP enrichment as GEO preparation. TOMS is investing in richer product page content and editorial blogs to bring the in-store human conversation online -- providing the depth and context that AI agents need to recommend confidently. This is framed as serving both the human reader and the AI intermediary.Small team, high curiosity. The TOMS EMEA ecommerce team is small, young, and uses AI as additional capacity rather than a threat. Applications span email marketing, analytics, paid media, and content creation. The operating principle is selective: identify a genuine capacity problem first, then assess whether AI can address it.Critical thinking as the core skill. Leticia argues that the most important thing AI demands from practitioners is not technical fluency but stronger critical thinking -- the ability to interrogate outputs, apply brand context, and reject what is generic. She frames this as a muscle to train rather than a curriculum to follow, and suggests AI itself can help junior team members practise asking challenging questions safely.DTC as the risk-taking laboratory. TOMS's direct-to-consumer operation is positioned as the fastest-moving unit in the business -- the place to test new launches, new product lines, and new approaches before rolling learnings out to distributors and marketplace partners. Speed and risk appetite are the DTC team's distinctive contribution.⠀What you'll learnWhy tracking AI referral traffic matters now, even when the numbers are small.How a purpose-driven brand uses its values as a practical content filter when AI makes everything easier to produce.What a problem-first approach to AI adoption looks like inside a lean ecommerce team.Why critical thinking -- not prompt engineering -- is the skill worth developing in your team.How to position DTC as a structured learning engine for the wider business.Why consumers' growing ability to detect AI-generated content is a commercial consideration, not just a brand one.⠀Chapter structure~00:00 Introductions: Leticia Pérez Muñoz, TOMS, and the 20th anniversary~02:00 The One for One model: its origins, evolution, and the "Better Tomorrows" giving model~04:00 One-third of profits and $200m in grants: TOMS as a B-Corp with commercial purpose~05:00 AI in the hands of the customer: tracking ChatGPT and Claude as discovery channels~06:30 Authenticity as strategy: why TOMS is moving toward real people, not more AI content~08:00 PDP enrichment and GEO: adding depth, education, and in-store-quality content for AI-mediated discovery~09:30 New channel or accelerant: is AI genuinely new or does it raise the bar across everything?~11:00 Attribution and measurement: tracking session shifts from organic to AI referral~13:00 AI inside the TOMS team: Claude, email, analytics, paid media, and content~15:00 Cross-team alignment: early stage, building a shared approach across ecom, marketing, finance, and operations~17:00 Leticia's personal learning journey: prompting, source verification, spotting unreviewed AI output~19:00 Education and hiring: why critical thinking is the skill that matters most~22:00 Building critical thinking in the team: AI as a safe space to ask hard questions~24:00 The DTC-first strategy: testing, learning, and sharing with distributors and marketplace partners⠀About the guestLeticia Pérez Muñoz is EMEA eCommerce Manager at TOMS, based in Amsterdam, where she has worked for two and a half years leading direct-to-consumer and pure-play ecommerce across European markets. Originally from Mexico, she has worked across France and the ...
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    26 mins
  • "Commerce is still personal" - in conversation with Simon Dyer, Mirakl

    "Commerce is still personal" - in conversation with Simon Dyer, Mirakl
    May 28 2026
    For retailers and brands still treating product data as an SEO problem, this episode is a direct challenge. Simon Dyer of Mirakl argues that the structural shift underway is not faster search but a different kind of search entirely: AI agents acting on behalf of customers, asking intent-rich questions that current product catalogues simply cannot answer. The conversation moves from marketplace economics and retail media flywheels through to a specific and actionable claim -- that community-generated data is the one truly defensible moat -- and closes on the emerging trust battle between platform giants competing to own the protocol layer of agentic commerce.Key themesFrom SEO to GEO. As AI agents replace keyword search with conversational intent queries, product catalogue optimisation shifts from search engine optimisation to generative engine optimisation. Retailers who hold colour, size, and material data but not contextual, emotional, or situational attributes risk becoming invisible to the LLMs making recommendations on customers' behalf.Community data as the defensible moat. When brand product data is commoditised -- every retailer receives the same feed from New Balance -- the differentiator is proprietary community conversation: reviews, forum threads, and user-generated context that answer questions the manufacturer never thought to address. Simon's argument is that this data, structured so LLMs can find it, is where the recommendation competition will be won.The marketplace flywheel. Mirakl's model connects operators (retailers), sellers (brands and third parties), and customers in a self-reinforcing loop. Adding retail media to the mix creates a second revenue stream -- 70 to 80 per cent margin on promoted placements -- that scales as the seller ecosystem grows, solving the labour problem of managing hundreds of sellers through self-serve access.AI as the expert executioner. Simon's operating principle inside Mirakl is that AI executes faster, more completely, and more deeply than humans can, while humans define the process and make the strategic decisions. The balance he is watching for is the point at which the system has learnt his decision-making patterns well enough that he stops reviewing its choices.Sales reinvented: before and after. The two highest-value applications Simon describes are pre-meeting briefing (agents pulling from Salesforce, web, call recordings, and market data into a single brief) and post-meeting follow-up (summarised, multi-threaded, specific to each stakeholder). The drudgery of note-taking and CRM updating is automated; the relationship work is not.The protocol battle. Simon draws an explicit parallel between the current competition among Google, Apple, banks, and others to own agentic commerce infrastructure and the Betamax/VHS format war. The winner will define the data standards through which AI agents make purchases on customers' behalf. Trust -- specifically, willingness to open personal data -- is the unlock, and it remains unresolved.⠀What you'll learnWhy product data optimised for keyword search fails conversational AI agents, and what GEO requires instead.Which type of data is genuinely proprietary to retailers in a world where brand feeds are shared universally.How a marketplace retail media flywheel generates margin without proportional increases in headcount.What a working multi-agent sales pipeline looks like in a B2B software business today, end to end.Why the next sales hire should already be automating parts of their personal life as a proof point of AI fluency.Where the trust and data-standard battles of the next 18 months are likely to be fought, and by whom.⠀Chapter structure~00:00 Introductions: Simon Dyer and Mirakl's marketplace and drop-ship model~02:00 The department store analogy: extending range without tying up capital in stock~03:00 Commerce explosion: everything, everywhere, all the time as a genuine operating reality~05:00 Retail media as a natural extension of the marketplace flywheel; 70--80% margin on promoted placements~08:00 The structural AI shift: agents acting on customers' behalf, intent-based discovery replacing keyword search~11:00 GEO versus SEO: optimising product catalogues for LLM recommendation, not search ranking~14:00 Where the data value sits: brand feeds as commodity, community data as moat~18:00 Simon's career arc: Siebel, Oracle, enterprise software into Mirakl~19:00 AI inside Mirakl: agent-building at grassroots level, demand generation pipelines, automated CRM~22:00 Brokering the AI cacophony: summarisation as the most valuable daily use of AI~24:00 The expert executioner model: AI executes, human decides~26:00 The next sales hire: prep, follow-up, and evidence of personal AI fluency~29:00 The Betamax/VHS protocol battle: Google Universal Cart and the race to own agentic standards~32:00 Trust as the limiting factor: no-quibble reliability as the foundation for autonomous purchasing⠀About the guestSimon ...
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    35 mins
  • "Worry, but not fear": in conversation with David Rose, Papa Johns

    "Worry, but not fear": in conversation with David Rose, Papa Johns
    May 28 2026
    For commercial and technology leaders trying to move fast with AI without breaking things that cannot be broken, this episode offers a tested playbook rather than aspirations. David Rose arrived at Papa Johns from a marketing career spanning Virgin Atlantic, Starbucks and the NHS, retrained himself on AI through deliberate study, and now leads both international technology and the company's group-wide AI programme across 6,000 stores in 50 markets. The conversation covers governance design, building internal AI capability from existing talent, and why AI literacy is not just an HR box but a social obligation -- all grounded in what actually happened over two years of experimentation, failure and recovery.Key themesThe challenger brand advantage. Papa Johns is large enough to have genuine enterprise constraints but not so large that it cannot move. David argues challenger brands earn a little more licence from customers and boards to take risks and iterate, which creates a structural opening for AI adoption that market leaders often lack.Poacher turned gamekeeper. David's first two years at Papa Johns involved launching pilots without adequate governance, creating problems his CTO then asked him to fix. The resulting four stage-gate process -- intake, pilot approval, pilot evaluation, production sign-off -- sits upstream of standard processes and was deliberately built fast and light so it enables rather than obstructs innovation.Three pillars, in order. David's framework for AI transformation in any enterprise: governance first, then a structured innovation programme aligned to actual strategy (not just interesting pilots), then AI literacy for the whole organisation. He is candid that early pilots pulled the best talent off-strategy and frustrated executives.AI literacy as a social imperative. Beyond internal training, David frames AI literacy as a genuine social responsibility for businesses. He distinguishes worry from fear: worry prompts planning, fear prompts paralysis. He believes boards and investors now expect companies to prepare their workforces, and that this obligation extends to both current employees and new entrants.Surfacing hidden talent. PJX, Papa Johns informal internal AI community, exists to give permission to people who are already experimenting on the side but feel exposed doing so openly. The cultural insight: talent is there, it just needs a safe space and a signal from leadership that curiosity is valued.Nobody has done this before. David's consistent refrain is that AI is the first technology shift where no one has a twenty-year head start. That democratises learning and creates the unusual situation where a diligent self-taught practitioner can genuinely be at the front line alongside specialists.⠀What you'll learnHow to construct a lightweight four stage-gate AI governance process that enables rather than blocks experimentation.Why aligning pilots to existing strategy before chasing interesting technology is the difference between progress and wasted executive goodwill.How to surface the AI talent already inside your organisation before hiring externally.What a two-year arc from early chaos to structured internal capability actually looks like in a large QSR business.Why the worry/fear distinction matters when communicating about AI disruption to boards, teams, and new workforce entrants.How a commercial and marketing background -- with no deep technical formation -- can be a genuine asset in leading AI transformation at scale.⠀Chapter structure~00:00 Introductions: David Rose and Papa Johns -- 6,000 stores, 50 markets, Kentucky origins~02:00 Menu consistency vs local innovation: croissant pizza, sourdough, deep-fried prawn~04:00 Consistency and innovation as parallel disciplines, not opposites~07:00 Papa Johns as an e-commerce brand: 80% of revenue through digital channels~08:00 Career arc: British Army, Virgin Atlantic, Starbucks AMEA, NHS blood donation, Papa Johns~12:00 The cartilage between the customer and the infrastructure -- David's self-described superpower~14:00 From marketing to technology: product thinking as the bridge; the Oxford AI course~17:00 "Nobody's done this before" -- the democratisation of AI learning~19:00 Balancing pace and governance: the classic enterprise tension~21:00 The three pillars: governance, structured innovation, AI literacy~24:00 The poacher-to-gamekeeper story: early fires, CTO intervention, AI committee~27:00 Pilot design: customer-facing, internal operations, personal tools~29:00 Building internal AI capability: data science roots, PJX community, external vendors as primers~32:00 Voice AI ordering product launched in the US via Google Cloud~33:00 Junior talent, the social worry, and the case for AI academies~37:00 Closing: worry encourages planning; fear does not⠀About the guestDavid Rose is VP of International Technology at Papa Johns, overseeing technology across approximately 2,500 stores in 50 international markets, and ...
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    39 mins
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