CommerceAI Transistor (https://transistor.fm) https://feeds.transistor.fm/commerceai 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 774fae5b-432c-5379-b42a-7aa00a962913 yes Ian Jindal en Tue, 02 Jun 2026 13:50:02 +0100 Tue, 02 Jun 2026 13:50:14 +0100 https://img.transistorcdn.com/2jSeOXbUswGIzI1GoV1c3yaN2jC2hopICSNUPmmNwiw/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS82MmRl/ODliZTRlNjQ0OTVm/MGNkNmNjYjQ1YmM3/ZDE3YS5qcGc.jpg CommerceAI episodic Ian Jindal 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. CommerceAI explores how artificial intelligence is reshaping commerce in direct-to-consumer sectors - retail, grocery, hospitality, leisure, and entertainment. Ian Jindal No No "Customers can tell" - in conversation with Leticia Perez Muñoz of TOMS 6 6 "Customers can tell" - in conversation with Leticia Perez Muñoz of TOMS full 93afedb2-cd4f-42f2-a08f-fee795ff9b7e https://share.transistor.fm/s/0aa2cb6a 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 themes

  • AI 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 learn

  • Why 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 guest

Leticia 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 luxury fashion sector before joining TOMS. Her academic background spans a business degree and a master's in digital marketing and data science. She brings a measured, evidence-based perspective to AI adoption, shaped by the experience of managing a lean, multi-market team where selectivity and critical thinking matter as much as technical capability.


Quotes

"We want to appear every time someone's having a conversation with ChatGPT -- but we also want to be there as the impact-driven brand with the best espadrilles." 

"The consumer has been more prone to identify when something is not authentic. So that's something we actually want to move more into -- our authenticity, creating impact, going back to our roots." 

"We are not trying to jump into every trend. We try to be very critical: do we have the capacity, and is this actually solving a problem?" 

"Critical thinking is a muscle you need to be training. AI itself can help you be more comfortable asking the hard questions -- taking away the fear of being seen as challenging."

"We really want to take the lead on DTC, take those risks first, and once the learnings are capitalised, help our distributors and marketplaces with what we've learned." 

]]> 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 themes

  • AI 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 learn

  • Why 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 guest

Leticia 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 luxury fashion sector before joining TOMS. Her academic background spans a business degree and a master's in digital marketing and data science. She brings a measured, evidence-based perspective to AI adoption, shaped by the experience of managing a lean, multi-market team where selectivity and critical thinking matter as much as technical capability.


Quotes

"We want to appear every time someone's having a conversation with ChatGPT -- but we also want to be there as the impact-driven brand with the best espadrilles." 

"The consumer has been more prone to identify when something is not authentic. So that's something we actually want to move more into -- our authenticity, creating impact, going back to our roots." 

"We are not trying to jump into every trend. We try to be very critical: do we have the capacity, and is this actually solving a problem?" 

"Critical thinking is a muscle you need to be training. AI itself can help you be more comfortable asking the hard questions -- taking away the fear of being seen as challenging."

"We really want to take the lead on DTC, take those risks first, and once the learnings are capitalised, help our distributors and marketplaces with what we've learned." 

]]> Fri, 29 May 2026 13:37:34 +0100 Ian Jindal Ian Jindal 1567 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 themes

  • AI 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 learn

  • Why 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 guest

Leticia 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 luxury fashion sector before joining TOMS. Her academic background spans a business degree and a master's in digital marketing and data science. She brings a measured, evidence-based perspective to AI adoption, shaped by the experience of managing a lean, multi-market team where selectivity and critical thinking matter as much as technical capability.


Quotes

"We want to appear every time someone's having a conversation with ChatGPT -- but we also want to be there as the impact-driven brand with the best espadrilles." 

"The consumer has been more prone to identify when something is not authentic. So that's something we actually want to move more into -- our authenticity, creating impact, going back to our roots." 

"We are not trying to jump into every trend. We try to be very critical: do we have the capacity, and is this actually solving a problem?" 

"Critical thinking is a muscle you need to be training. AI itself can help you be more comfortable asking the hard questions -- taking away the fear of being seen as challenging."

"We really want to take the lead on DTC, take those risks first, and once the learnings are capitalised, help our distributors and marketplaces with what we've learned." 

]]> No Ian Jindal Leticia Perez Muños "Commerce is still personal" - in conversation with Simon Dyer, Mirakl 5 5 "Commerce is still personal" - in conversation with Simon Dyer, Mirakl full 595be027-13f5-48a3-8e02-d9bce19fcf2c https://share.transistor.fm/s/0cc9b845

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 themes

  • From 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 learn

  • Why 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 guest

Simon Dyer is Regional VP at Mirakl, responsible for the UK, Nordics, Middle East and Africa. Mirakl provides the platform infrastructure for retailers and brands to operate marketplace and drop-ship models, with a retail media layer built on top of the seller ecosystem. Simon's background spans enterprise software sales at Siebel and Oracle before moving to Mirakl, where he works with platform operators across retail, distribution, and digital commerce. His focus is on the commercial and structural implications of AI for marketplace economics and for the sales function specifically.


Quotes

"People aren't searching for white sneakers any more. They're saying: I've got a wedding in Italy, I'm wearing a blue suit, it'll be 35 degrees. Give me the top three and the pros and cons." 

"Retailers who aren't considering this yet will become invisible to the LLMs as they make their recommendations." 

"Community conversation -- that is where I really believe the difference will be. And if that data is structured in a way LLMs can find it, you've kind of won the recommendation competition." 

"AI is the expert executioner. The human is the strategic thinker."

"Your imagination is the only thing holding you back right now in how to use these tools."



]]>

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 themes

  • From 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 learn

  • Why 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 guest

Simon Dyer is Regional VP at Mirakl, responsible for the UK, Nordics, Middle East and Africa. Mirakl provides the platform infrastructure for retailers and brands to operate marketplace and drop-ship models, with a retail media layer built on top of the seller ecosystem. Simon's background spans enterprise software sales at Siebel and Oracle before moving to Mirakl, where he works with platform operators across retail, distribution, and digital commerce. His focus is on the commercial and structural implications of AI for marketplace economics and for the sales function specifically.


Quotes

"People aren't searching for white sneakers any more. They're saying: I've got a wedding in Italy, I'm wearing a blue suit, it'll be 35 degrees. Give me the top three and the pros and cons." 

"Retailers who aren't considering this yet will become invisible to the LLMs as they make their recommendations." 

"Community conversation -- that is where I really believe the difference will be. And if that data is structured in a way LLMs can find it, you've kind of won the recommendation competition." 

"AI is the expert executioner. The human is the strategic thinker."

"Your imagination is the only thing holding you back right now in how to use these tools."



]]> Thu, 28 May 2026 22:07:29 +0100 Ian Jindal Ian Jindal 2095

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 themes

  • From 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 learn

  • Why 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 guest

Simon Dyer is Regional VP at Mirakl, responsible for the UK, Nordics, Middle East and Africa. Mirakl provides the platform infrastructure for retailers and brands to operate marketplace and drop-ship models, with a retail media layer built on top of the seller ecosystem. Simon's background spans enterprise software sales at Siebel and Oracle before moving to Mirakl, where he works with platform operators across retail, distribution, and digital commerce. His focus is on the commercial and structural implications of AI for marketplace economics and for the sales function specifically.


Quotes

"People aren't searching for white sneakers any more. They're saying: I've got a wedding in Italy, I'm wearing a blue suit, it'll be 35 degrees. Give me the top three and the pros and cons." 

"Retailers who aren't considering this yet will become invisible to the LLMs as they make their recommendations." 

"Community conversation -- that is where I really believe the difference will be. And if that data is structured in a way LLMs can find it, you've kind of won the recommendation competition." 

"AI is the expert executioner. The human is the strategic thinker."

"Your imagination is the only thing holding you back right now in how to use these tools."



]]> No Ian Jindal Simon Dyer "Worry, but not fear": in conversation with David Rose, Papa Johns 4 4 "Worry, but not fear": in conversation with David Rose, Papa Johns full 19ba3dac-f6d6-449b-bcdd-901ea7848981 https://share.transistor.fm/s/37a06026 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 themes

  • The 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 learn

  • How 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 guest

David Rose is VP of International Technology at Papa Johns, overseeing technology across approximately 2,500 stores in 50 international markets, and programme director for the company's group-wide AI initiatives. His background is in B2C commercial and marketing leadership -- a decade at Virgin Atlantic, regional roles at Starbucks, and a period leading digital transformation at NHS Blood and Transplant -- before pivoting deliberately into technology and AI. He retrained through an executive AI programme at Oxford Saïd Business School and now leads Papa Johns applied AI team, governance framework, and internal AI literacy programme.


Quotes

"My superpower is being able to influence change at scale."

"Nobody's done this before. I have no mentor to learn from. That's a fantastic place to be if you're looking to advance your career." 

"I turned poacher to gamekeeper. I'd created more problems than I solved, and my CTO asked me to build something that controlled this a little better." 

"Worry is good. Worry encourages planning, encourages thinking. Fear does not." 

"Often people are reticent to be the AI guy or girl. PJX just gives them a safe space to raise their hand." 



]]> 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 themes

  • The 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 learn

  • How 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 guest

David Rose is VP of International Technology at Papa Johns, overseeing technology across approximately 2,500 stores in 50 international markets, and programme director for the company's group-wide AI initiatives. His background is in B2C commercial and marketing leadership -- a decade at Virgin Atlantic, regional roles at Starbucks, and a period leading digital transformation at NHS Blood and Transplant -- before pivoting deliberately into technology and AI. He retrained through an executive AI programme at Oxford Saïd Business School and now leads Papa Johns applied AI team, governance framework, and internal AI literacy programme.


Quotes

"My superpower is being able to influence change at scale."

"Nobody's done this before. I have no mentor to learn from. That's a fantastic place to be if you're looking to advance your career." 

"I turned poacher to gamekeeper. I'd created more problems than I solved, and my CTO asked me to build something that controlled this a little better." 

"Worry is good. Worry encourages planning, encourages thinking. Fear does not." 

"Often people are reticent to be the AI guy or girl. PJX just gives them a safe space to raise their hand." 



]]> Thu, 28 May 2026 22:06:38 +0100 Ian Jindal Ian Jindal 2325 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 themes

  • The 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 learn

  • How 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 guest

David Rose is VP of International Technology at Papa Johns, overseeing technology across approximately 2,500 stores in 50 international markets, and programme director for the company's group-wide AI initiatives. His background is in B2C commercial and marketing leadership -- a decade at Virgin Atlantic, regional roles at Starbucks, and a period leading digital transformation at NHS Blood and Transplant -- before pivoting deliberately into technology and AI. He retrained through an executive AI programme at Oxford Saïd Business School and now leads Papa Johns applied AI team, governance framework, and internal AI literacy programme.


Quotes

"My superpower is being able to influence change at scale."

"Nobody's done this before. I have no mentor to learn from. That's a fantastic place to be if you're looking to advance your career." 

"I turned poacher to gamekeeper. I'd created more problems than I solved, and my CTO asked me to build something that controlled this a little better." 

"Worry is good. Worry encourages planning, encourages thinking. Fear does not." 

"Often people are reticent to be the AI guy or girl. PJX just gives them a safe space to raise their hand." 



]]> No Ian Jindal David Rose "Do this with me, not for me": in conversation with Emmanuelle Gounot, CommerceIQ 3 3 "Do this with me, not for me": in conversation with Emmanuelle Gounot, CommerceIQ full a9e1d721-b6d0-46c9-9e79-9cc886a8e27d https://share.transistor.fm/s/c20f6390 In this episode, Emmanuelle Gounot of CommerceIQ makes the case that the shift from rule-based systems to goal-oriented agents is a structural change, not a rebrand, and explains what that means for brands managing product content, media spend, availability, and pricing across dozens of retailers simultaneously. 


Key themes

  • The AI shelf is replacing the digital shelf. Rufus on Amazon, Sparky on Walmart, and similar assistants are changing the customer journey from search-click-buy to ask-answer-buy. Brands optimising for CARS metrics (content, availability, rating, search) now have to account for how AI agents, not humans, interpret and surface their products.
  • Rules to goals: what's actually changed. Emmanuelle draws a hard line between legacy rule-based automation and current agentic workflows. The distinction is context-awareness: agents can factor in macro environment, retailer-specific dynamics, brand constraints, and supply chain status simultaneously, in a way that rule engines cannot.
  • The operational arithmetic is compelling. A brand with 400 products, 30 variables each, changing three times a day faces roughly 25 decisions per minute. That number makes manual management structurally impossible and periodic weekly reviews operationally stale before they happen.
  • Trust is built incrementally, not assumed. CommerceIQ's first content agent launched with 30% customer approval of its recommendations. Within 45 days that reached 95%, at which point the customer switched to bulk approval for long-tail SKUs. The model: human in the loop by default, autonomy earned through demonstrated accuracy.
  • Orchestration is the unsolved problem. Many brands are experimenting with individual agents for content, shelf, and media, but risk replicating their existing silos in a new form. Emmanuelle argues the real value comes from a unified layer that ensures these workflows are aware of each other, so media spend is not pushed against out-of-stock or under-performing content.
  • Leaner teams, higher bar. Organisational structures are already flattening. The expectation is not that AI replaces skilled people but that the bar for asking the right question, spotting anomalies, and providing feedback to agents is now higher for everyone who remains.


⠀

What you'll learn

  • Why the 25-decisions-per-minute arithmetic makes periodic trading reviews structurally inadequate, not just inefficient.
  • How to separate genuine agentic capability from re-labelled automation when evaluating vendors.
  • What a trust-building cadence with agents looks like in practice, and at what point bulk delegation becomes safe.
  • Why coordinating agents across functions matters more than optimising any single agent in isolation.
  • How to think about developing your own skills as agents absorb routine analytical work, and where human judgement remains irreplaceable.
  • A concrete starting point for building personal AI workflows, based on picking one multi-step problem and building toward it.


⠀

Chapter structure

  • ~00:00 Introductions: Emmanuelle Gounot and CommerceIQ
  • ~02:00 What CommerceIQ does: e-commerce sales management, digital shelf, retail media as a unified platform
  • ~04:00 The AI shelf: how Rufus, Sparky, and retailer AI assistants are changing discovery
  • ~07:00 Rules vs goals: is agentic AI genuinely different from algorithmic automation?
  • ~11:00 Operational benefits: retail media, incremental sales, and the 24/7 trading floor
  • ~14:00 Breaking down silos: supply chain, media, and shelf data in one place
  • ~19:00 Organisational change: from top-100 SKUs with manual love to full catalogue coverage
  • ~22:00 Trust, governance, and the 30%-to-95% approval rate case study
  • ~25:00 Emmanuelle's career arc: BCG, Amazon, Alibaba, Uber, Flywheel, CommerceIQ
  • ~29:00 How CommerceIQ uses AI internally, including Claude across the organisation
  • ~33:00 The changing role of human judgement: "do this with me, not for me"
  • ~35:00 One hour of personal development: build your first multi-step workflow


⠀

About the guest

Emmanuelle Gounot is VP of Customer Success at CommerceIQ, where she works with enterprise brands to deploy AI-powered digital commerce operations spanning e-commerce sales management, digital shelf, and retail media. Her career spans strategy consulting at BCG, operational roles at Amazon and Lazada (later acquired by Alibaba), and commercial leadership at Uber and Flywheel, giving her direct experience of how both digital-native and legacy organisations manage data at scale. 


Quotes

"We're moving from a purely rule-based world to really looking at goals, and layering agents with what a human would do." 

"E-commerce never sleeps. The agent will not take a break while you're watching the football." 

"We need to change our relationship with AI. It's not 'do this for me'. It's 'do this with me. You're my analyst in my pocket.'" 

"At first, we had 30% approval on that content agent. Within 45 days, we were at 95%. At that point, the team manager said: just approve the long tail in bulk."


]]> In this episode, Emmanuelle Gounot of CommerceIQ makes the case that the shift from rule-based systems to goal-oriented agents is a structural change, not a rebrand, and explains what that means for brands managing product content, media spend, availability, and pricing across dozens of retailers simultaneously. 


Key themes

  • The AI shelf is replacing the digital shelf. Rufus on Amazon, Sparky on Walmart, and similar assistants are changing the customer journey from search-click-buy to ask-answer-buy. Brands optimising for CARS metrics (content, availability, rating, search) now have to account for how AI agents, not humans, interpret and surface their products.
  • Rules to goals: what's actually changed. Emmanuelle draws a hard line between legacy rule-based automation and current agentic workflows. The distinction is context-awareness: agents can factor in macro environment, retailer-specific dynamics, brand constraints, and supply chain status simultaneously, in a way that rule engines cannot.
  • The operational arithmetic is compelling. A brand with 400 products, 30 variables each, changing three times a day faces roughly 25 decisions per minute. That number makes manual management structurally impossible and periodic weekly reviews operationally stale before they happen.
  • Trust is built incrementally, not assumed. CommerceIQ's first content agent launched with 30% customer approval of its recommendations. Within 45 days that reached 95%, at which point the customer switched to bulk approval for long-tail SKUs. The model: human in the loop by default, autonomy earned through demonstrated accuracy.
  • Orchestration is the unsolved problem. Many brands are experimenting with individual agents for content, shelf, and media, but risk replicating their existing silos in a new form. Emmanuelle argues the real value comes from a unified layer that ensures these workflows are aware of each other, so media spend is not pushed against out-of-stock or under-performing content.
  • Leaner teams, higher bar. Organisational structures are already flattening. The expectation is not that AI replaces skilled people but that the bar for asking the right question, spotting anomalies, and providing feedback to agents is now higher for everyone who remains.


⠀

What you'll learn

  • Why the 25-decisions-per-minute arithmetic makes periodic trading reviews structurally inadequate, not just inefficient.
  • How to separate genuine agentic capability from re-labelled automation when evaluating vendors.
  • What a trust-building cadence with agents looks like in practice, and at what point bulk delegation becomes safe.
  • Why coordinating agents across functions matters more than optimising any single agent in isolation.
  • How to think about developing your own skills as agents absorb routine analytical work, and where human judgement remains irreplaceable.
  • A concrete starting point for building personal AI workflows, based on picking one multi-step problem and building toward it.


⠀

Chapter structure

  • ~00:00 Introductions: Emmanuelle Gounot and CommerceIQ
  • ~02:00 What CommerceIQ does: e-commerce sales management, digital shelf, retail media as a unified platform
  • ~04:00 The AI shelf: how Rufus, Sparky, and retailer AI assistants are changing discovery
  • ~07:00 Rules vs goals: is agentic AI genuinely different from algorithmic automation?
  • ~11:00 Operational benefits: retail media, incremental sales, and the 24/7 trading floor
  • ~14:00 Breaking down silos: supply chain, media, and shelf data in one place
  • ~19:00 Organisational change: from top-100 SKUs with manual love to full catalogue coverage
  • ~22:00 Trust, governance, and the 30%-to-95% approval rate case study
  • ~25:00 Emmanuelle's career arc: BCG, Amazon, Alibaba, Uber, Flywheel, CommerceIQ
  • ~29:00 How CommerceIQ uses AI internally, including Claude across the organisation
  • ~33:00 The changing role of human judgement: "do this with me, not for me"
  • ~35:00 One hour of personal development: build your first multi-step workflow


⠀

About the guest

Emmanuelle Gounot is VP of Customer Success at CommerceIQ, where she works with enterprise brands to deploy AI-powered digital commerce operations spanning e-commerce sales management, digital shelf, and retail media. Her career spans strategy consulting at BCG, operational roles at Amazon and Lazada (later acquired by Alibaba), and commercial leadership at Uber and Flywheel, giving her direct experience of how both digital-native and legacy organisations manage data at scale. 


Quotes

"We're moving from a purely rule-based world to really looking at goals, and layering agents with what a human would do." 

"E-commerce never sleeps. The agent will not take a break while you're watching the football." 

"We need to change our relationship with AI. It's not 'do this for me'. It's 'do this with me. You're my analyst in my pocket.'" 

"At first, we had 30% approval on that content agent. Within 45 days, we were at 95%. At that point, the team manager said: just approve the long tail in bulk."


]]> Thu, 28 May 2026 22:02:17 +0100 Ian Jindal Ian Jindal 2256 In this episode, Emmanuelle Gounot of CommerceIQ makes the case that the shift from rule-based systems to goal-oriented agents is a structural change, not a rebrand, and explains what that means for brands managing product content, media spend, availability, and pricing across dozens of retailers simultaneously. 


Key themes

  • The AI shelf is replacing the digital shelf. Rufus on Amazon, Sparky on Walmart, and similar assistants are changing the customer journey from search-click-buy to ask-answer-buy. Brands optimising for CARS metrics (content, availability, rating, search) now have to account for how AI agents, not humans, interpret and surface their products.
  • Rules to goals: what's actually changed. Emmanuelle draws a hard line between legacy rule-based automation and current agentic workflows. The distinction is context-awareness: agents can factor in macro environment, retailer-specific dynamics, brand constraints, and supply chain status simultaneously, in a way that rule engines cannot.
  • The operational arithmetic is compelling. A brand with 400 products, 30 variables each, changing three times a day faces roughly 25 decisions per minute. That number makes manual management structurally impossible and periodic weekly reviews operationally stale before they happen.
  • Trust is built incrementally, not assumed. CommerceIQ's first content agent launched with 30% customer approval of its recommendations. Within 45 days that reached 95%, at which point the customer switched to bulk approval for long-tail SKUs. The model: human in the loop by default, autonomy earned through demonstrated accuracy.
  • Orchestration is the unsolved problem. Many brands are experimenting with individual agents for content, shelf, and media, but risk replicating their existing silos in a new form. Emmanuelle argues the real value comes from a unified layer that ensures these workflows are aware of each other, so media spend is not pushed against out-of-stock or under-performing content.
  • Leaner teams, higher bar. Organisational structures are already flattening. The expectation is not that AI replaces skilled people but that the bar for asking the right question, spotting anomalies, and providing feedback to agents is now higher for everyone who remains.


⠀

What you'll learn

  • Why the 25-decisions-per-minute arithmetic makes periodic trading reviews structurally inadequate, not just inefficient.
  • How to separate genuine agentic capability from re-labelled automation when evaluating vendors.
  • What a trust-building cadence with agents looks like in practice, and at what point bulk delegation becomes safe.
  • Why coordinating agents across functions matters more than optimising any single agent in isolation.
  • How to think about developing your own skills as agents absorb routine analytical work, and where human judgement remains irreplaceable.
  • A concrete starting point for building personal AI workflows, based on picking one multi-step problem and building toward it.


⠀

Chapter structure

  • ~00:00 Introductions: Emmanuelle Gounot and CommerceIQ
  • ~02:00 What CommerceIQ does: e-commerce sales management, digital shelf, retail media as a unified platform
  • ~04:00 The AI shelf: how Rufus, Sparky, and retailer AI assistants are changing discovery
  • ~07:00 Rules vs goals: is agentic AI genuinely different from algorithmic automation?
  • ~11:00 Operational benefits: retail media, incremental sales, and the 24/7 trading floor
  • ~14:00 Breaking down silos: supply chain, media, and shelf data in one place
  • ~19:00 Organisational change: from top-100 SKUs with manual love to full catalogue coverage
  • ~22:00 Trust, governance, and the 30%-to-95% approval rate case study
  • ~25:00 Emmanuelle's career arc: BCG, Amazon, Alibaba, Uber, Flywheel, CommerceIQ
  • ~29:00 How CommerceIQ uses AI internally, including Claude across the organisation
  • ~33:00 The changing role of human judgement: "do this with me, not for me"
  • ~35:00 One hour of personal development: build your first multi-step workflow


⠀

About the guest

Emmanuelle Gounot is VP of Customer Success at CommerceIQ, where she works with enterprise brands to deploy AI-powered digital commerce operations spanning e-commerce sales management, digital shelf, and retail media. Her career spans strategy consulting at BCG, operational roles at Amazon and Lazada (later acquired by Alibaba), and commercial leadership at Uber and Flywheel, giving her direct experience of how both digital-native and legacy organisations manage data at scale. 


Quotes

"We're moving from a purely rule-based world to really looking at goals, and layering agents with what a human would do." 

"E-commerce never sleeps. The agent will not take a break while you're watching the football." 

"We need to change our relationship with AI. It's not 'do this for me'. It's 'do this with me. You're my analyst in my pocket.'" 

"At first, we had 30% approval on that content agent. Within 45 days, we were at 95%. At that point, the team manager said: just approve the long tail in bulk."


]]> No Ian Jindal Emmanuelle Gounot "The product brain" : in conversation with Piotr Zaleski, Ingrid 2 2 "The product brain" : in conversation with Piotr Zaleski, Ingrid full 7ae9fa75-148a-4652-b7e7-004bd683292a https://share.transistor.fm/s/92d8e4ca In this episode we talk about how AI changes both customer experience and enterprise value.

Ian Jindal speaks with Piotr Zaleski, Founder & CPTO of Ingrid, about delivery as one of the last hard problems in digital retail: fragmented carriers, messy operational data, customer promises, conversion, margin and loyalty all bound together.

What makes the conversation distinctive is that Piotr is not only building an AI-enabled delivery intelligence business; he is also reshaping Ingrid internally around AI, centralised knowledge and a future “product brain”. Ingrid is listed as a delivery-first retail platform, and Piotr is also speaking at CommerceAI Summit 2026 in the “Strategic AI: The AI-Transformed Enterprise” session.  

Key themes

  • Delivery as a commercial proxy: Piotr argues that delivery is not a back-office function but a proxy for conversion, customer NPS, repeat purchase and profitability. Ingrid integrates across multiple delivery carriers and builds logic and customer interfaces to make delivery more precise, accurate and consumer-friendly.  
  • The “last big problem” in digital retail: The conversation revisits the messy reality behind ecommerce delivery: stock certainty, carrier choice, delivery cut-offs, in-flight changes and systems that were never designed to talk to each other. Piotr frames delivery as fragmented, complex and still not solved in a simple way.  
  • From checkout feature to whole-journey economics: Piotr challenges retailers to look beyond adding a carrier or running a checkout A/B test. Delivery choices affect AOV, cost to serve, customer acquisition cost, loyalty and the quality of the customer promise before conversion.
  • AI inside the enterprise, not just inside the product: Piotr describes AI as a “renaissance” for how Ingrid thinks about product. Rather than treating product as a bounded software surface, he sees every customer and end-customer interaction becoming part of product over time. 
  • The rise of the product brain: Ingrid is centralising knowledge and automation so that domain expertise can be used across support, sales, discovery, onboarding and eventually customer-facing product experiences. Piotr describes this as a future product increment: a “product brain” built from Ingrid’s domain knowledge, transactional data and operational logic.
  • Agentic shopping and the return of negotiation: Piotr suggests retail may move away from mass standardisation towards more contextual, flexible deals. Agents could evaluate convenience, returns, delivery capability, operational quality and price together — moving commerce closer to a personalised negotiation than a static buy button.

Topics

  • Why delivery should be evaluated as a driver of conversion, loyalty and margin, not just as a fulfilment cost.
  • How conflicting internal KPIs can distort delivery decisions unless retailers look at cost to serve across the whole customer journey.
  • Why AI adoption inside a SaaS business may start with knowledge management, workflow and team capability before it becomes a visible customer-facing feature.
  • How centralised knowledge and automation can help teams become more “T-shaped” without losing operational discipline.
  • Why agentic shopping may shift competitive advantage from product discovery towards operational superiority and fulfilment capability.
  • How to think about AI and enterprise value in the CapitalAI / StrategicAI sense: not just as productivity, but as product architecture, organisational design and future moat.

Highlights

  • 00:01 – CommerceAI framing: Ian introduces the CommerceAI lens: AI across customer, experience, operations and capital value chains.
  • 01:27 – What Ingrid does: Piotr introduces Ingrid as a delivery intelligence platform integrating across carriers and customer interfaces.
  • 03:53 – Why delivery was the problem to solve: Piotr explains how fragmented delivery information created cognitive load for consumers and an underserved operational challenge for retailers.
  • 07:42 – Bridging carriers and retailers: Ingrid has to build trust and integration with carriers while persuading retailers to rethink hard-won internal delivery logic.
  • 12:58 – The KPI tension: Piotr unpacks the conflict between top-line teams seeking lower delivery fees and operations teams managing cost to serve.
  • 17:58 – AI as a renaissance: Piotr explains how AI has changed his philosophy of product, turning internal knowledge, customer support and sales enablement into part of the product surface.
  • 24:17 – The Ingrid way of AI: Piotr describes centralising information, automation and cross-functional AI enablement rather than allowing every team to build in isolation.
  • 29:42 – From standardised retail to contextual retail: The conversation turns to agentic shopping, personalisation from both ends of the transaction and the possibility of a more negotiated form of commerce.
  • 35:57 – Direction of travel: Piotr argues that discovery may become more horizontal through agents and LLMs, while fulfilment remains a powerful source of retailer differentiation.

About the guest

Piotr Zaleski is Founder and CPTO of Ingrid, a delivery intelligence platform founded in 2017. Ingrid connects retailers, carriers and consumers, helping retailers create more accurate, flexible and customer-friendly delivery experiences.  

Piotr brings the perspective of a technical founder who has built in ecommerce both before and during the current AI wave. In this conversation, his relevance for CommerceAI is twofold: he is building an AI-enabled business around delivery intelligence, and he is also redesigning the way Ingrid itself uses AI internally across knowledge, automation, product and customer-facing workflows.


Quotes

“Delivery is such a proxy for everything that has to do with retail — conversion rate, customer NPS, repeat-buy patterns.”

“It’s probably the last big problem of digital retail that hasn’t been solved in a simple way yet.”

“The only real way to solve it is to look at data that actually spans across the entire customer journey.”

“AI has changed my philosophy around what product is.”

“You can make people really T-shaped. From being a specialist, you can become a generalist quite quickly.”

References
Piotr Zeleski: https://www.linkedin.com/in/piotrzaleski/
Ian Jindal: https://www.linkedin.com/in/ianjindal

]]> In this episode we talk about how AI changes both customer experience and enterprise value.

Ian Jindal speaks with Piotr Zaleski, Founder & CPTO of Ingrid, about delivery as one of the last hard problems in digital retail: fragmented carriers, messy operational data, customer promises, conversion, margin and loyalty all bound together.

What makes the conversation distinctive is that Piotr is not only building an AI-enabled delivery intelligence business; he is also reshaping Ingrid internally around AI, centralised knowledge and a future “product brain”. Ingrid is listed as a delivery-first retail platform, and Piotr is also speaking at CommerceAI Summit 2026 in the “Strategic AI: The AI-Transformed Enterprise” session.  

Key themes

  • Delivery as a commercial proxy: Piotr argues that delivery is not a back-office function but a proxy for conversion, customer NPS, repeat purchase and profitability. Ingrid integrates across multiple delivery carriers and builds logic and customer interfaces to make delivery more precise, accurate and consumer-friendly.  
  • The “last big problem” in digital retail: The conversation revisits the messy reality behind ecommerce delivery: stock certainty, carrier choice, delivery cut-offs, in-flight changes and systems that were never designed to talk to each other. Piotr frames delivery as fragmented, complex and still not solved in a simple way.  
  • From checkout feature to whole-journey economics: Piotr challenges retailers to look beyond adding a carrier or running a checkout A/B test. Delivery choices affect AOV, cost to serve, customer acquisition cost, loyalty and the quality of the customer promise before conversion.
  • AI inside the enterprise, not just inside the product: Piotr describes AI as a “renaissance” for how Ingrid thinks about product. Rather than treating product as a bounded software surface, he sees every customer and end-customer interaction becoming part of product over time. 
  • The rise of the product brain: Ingrid is centralising knowledge and automation so that domain expertise can be used across support, sales, discovery, onboarding and eventually customer-facing product experiences. Piotr describes this as a future product increment: a “product brain” built from Ingrid’s domain knowledge, transactional data and operational logic.
  • Agentic shopping and the return of negotiation: Piotr suggests retail may move away from mass standardisation towards more contextual, flexible deals. Agents could evaluate convenience, returns, delivery capability, operational quality and price together — moving commerce closer to a personalised negotiation than a static buy button.

Topics

  • Why delivery should be evaluated as a driver of conversion, loyalty and margin, not just as a fulfilment cost.
  • How conflicting internal KPIs can distort delivery decisions unless retailers look at cost to serve across the whole customer journey.
  • Why AI adoption inside a SaaS business may start with knowledge management, workflow and team capability before it becomes a visible customer-facing feature.
  • How centralised knowledge and automation can help teams become more “T-shaped” without losing operational discipline.
  • Why agentic shopping may shift competitive advantage from product discovery towards operational superiority and fulfilment capability.
  • How to think about AI and enterprise value in the CapitalAI / StrategicAI sense: not just as productivity, but as product architecture, organisational design and future moat.

Highlights

  • 00:01 – CommerceAI framing: Ian introduces the CommerceAI lens: AI across customer, experience, operations and capital value chains.
  • 01:27 – What Ingrid does: Piotr introduces Ingrid as a delivery intelligence platform integrating across carriers and customer interfaces.
  • 03:53 – Why delivery was the problem to solve: Piotr explains how fragmented delivery information created cognitive load for consumers and an underserved operational challenge for retailers.
  • 07:42 – Bridging carriers and retailers: Ingrid has to build trust and integration with carriers while persuading retailers to rethink hard-won internal delivery logic.
  • 12:58 – The KPI tension: Piotr unpacks the conflict between top-line teams seeking lower delivery fees and operations teams managing cost to serve.
  • 17:58 – AI as a renaissance: Piotr explains how AI has changed his philosophy of product, turning internal knowledge, customer support and sales enablement into part of the product surface.
  • 24:17 – The Ingrid way of AI: Piotr describes centralising information, automation and cross-functional AI enablement rather than allowing every team to build in isolation.
  • 29:42 – From standardised retail to contextual retail: The conversation turns to agentic shopping, personalisation from both ends of the transaction and the possibility of a more negotiated form of commerce.
  • 35:57 – Direction of travel: Piotr argues that discovery may become more horizontal through agents and LLMs, while fulfilment remains a powerful source of retailer differentiation.

About the guest

Piotr Zaleski is Founder and CPTO of Ingrid, a delivery intelligence platform founded in 2017. Ingrid connects retailers, carriers and consumers, helping retailers create more accurate, flexible and customer-friendly delivery experiences.  

Piotr brings the perspective of a technical founder who has built in ecommerce both before and during the current AI wave. In this conversation, his relevance for CommerceAI is twofold: he is building an AI-enabled business around delivery intelligence, and he is also redesigning the way Ingrid itself uses AI internally across knowledge, automation, product and customer-facing workflows.


Quotes

“Delivery is such a proxy for everything that has to do with retail — conversion rate, customer NPS, repeat-buy patterns.”

“It’s probably the last big problem of digital retail that hasn’t been solved in a simple way yet.”

“The only real way to solve it is to look at data that actually spans across the entire customer journey.”

“AI has changed my philosophy around what product is.”

“You can make people really T-shaped. From being a specialist, you can become a generalist quite quickly.”

References
Piotr Zeleski: https://www.linkedin.com/in/piotrzaleski/
Ian Jindal: https://www.linkedin.com/in/ianjindal

]]> Sun, 26 Apr 2026 16:25:03 +0100 Ian Jindal Ian Jindal 2516 In this episode we talk about how AI changes both customer experience and enterprise value.

Ian Jindal speaks with Piotr Zaleski, Founder & CPTO of Ingrid, about delivery as one of the last hard problems in digital retail: fragmented carriers, messy operational data, customer promises, conversion, margin and loyalty all bound together.

What makes the conversation distinctive is that Piotr is not only building an AI-enabled delivery intelligence business; he is also reshaping Ingrid internally around AI, centralised knowledge and a future “product brain”. Ingrid is listed as a delivery-first retail platform, and Piotr is also speaking at CommerceAI Summit 2026 in the “Strategic AI: The AI-Transformed Enterprise” session.  

Key themes

  • Delivery as a commercial proxy: Piotr argues that delivery is not a back-office function but a proxy for conversion, customer NPS, repeat purchase and profitability. Ingrid integrates across multiple delivery carriers and builds logic and customer interfaces to make delivery more precise, accurate and consumer-friendly.  
  • The “last big problem” in digital retail: The conversation revisits the messy reality behind ecommerce delivery: stock certainty, carrier choice, delivery cut-offs, in-flight changes and systems that were never designed to talk to each other. Piotr frames delivery as fragmented, complex and still not solved in a simple way.  
  • From checkout feature to whole-journey economics: Piotr challenges retailers to look beyond adding a carrier or running a checkout A/B test. Delivery choices affect AOV, cost to serve, customer acquisition cost, loyalty and the quality of the customer promise before conversion.
  • AI inside the enterprise, not just inside the product: Piotr describes AI as a “renaissance” for how Ingrid thinks about product. Rather than treating product as a bounded software surface, he sees every customer and end-customer interaction becoming part of product over time. 
  • The rise of the product brain: Ingrid is centralising knowledge and automation so that domain expertise can be used across support, sales, discovery, onboarding and eventually customer-facing product experiences. Piotr describes this as a future product increment: a “product brain” built from Ingrid’s domain knowledge, transactional data and operational logic.
  • Agentic shopping and the return of negotiation: Piotr suggests retail may move away from mass standardisation towards more contextual, flexible deals. Agents could evaluate convenience, returns, delivery capability, operational quality and price together — moving commerce closer to a personalised negotiation than a static buy button.

Topics

  • Why delivery should be evaluated as a driver of conversion, loyalty and margin, not just as a fulfilment cost.
  • How conflicting internal KPIs can distort delivery decisions unless retailers look at cost to serve across the whole customer journey.
  • Why AI adoption inside a SaaS business may start with knowledge management, workflow and team capability before it becomes a visible customer-facing feature.
  • How centralised knowledge and automation can help teams become more “T-shaped” without losing operational discipline.
  • Why agentic shopping may shift competitive advantage from product discovery towards operational superiority and fulfilment capability.
  • How to think about AI and enterprise value in the CapitalAI / StrategicAI sense: not just as productivity, but as product architecture, organisational design and future moat.

Highlights

  • 00:01 – CommerceAI framing: Ian introduces the CommerceAI lens: AI across customer, experience, operations and capital value chains.
  • 01:27 – What Ingrid does: Piotr introduces Ingrid as a delivery intelligence platform integrating across carriers and customer interfaces.
  • 03:53 – Why delivery was the problem to solve: Piotr explains how fragmented delivery information created cognitive load for consumers and an underserved operational challenge for retailers.
  • 07:42 – Bridging carriers and retailers: Ingrid has to build trust and integration with carriers while persuading retailers to rethink hard-won internal delivery logic.
  • 12:58 – The KPI tension: Piotr unpacks the conflict between top-line teams seeking lower delivery fees and operations teams managing cost to serve.
  • 17:58 – AI as a renaissance: Piotr explains how AI has changed his philosophy of product, turning internal knowledge, customer support and sales enablement into part of the product surface.
  • 24:17 – The Ingrid way of AI: Piotr describes centralising information, automation and cross-functional AI enablement rather than allowing every team to build in isolation.
  • 29:42 – From standardised retail to contextual retail: The conversation turns to agentic shopping, personalisation from both ends of the transaction and the possibility of a more negotiated form of commerce.
  • 35:57 – Direction of travel: Piotr argues that discovery may become more horizontal through agents and LLMs, while fulfilment remains a powerful source of retailer differentiation.

About the guest

Piotr Zaleski is Founder and CPTO of Ingrid, a delivery intelligence platform founded in 2017. Ingrid connects retailers, carriers and consumers, helping retailers create more accurate, flexible and customer-friendly delivery experiences.  

Piotr brings the perspective of a technical founder who has built in ecommerce both before and during the current AI wave. In this conversation, his relevance for CommerceAI is twofold: he is building an AI-enabled business around delivery intelligence, and he is also redesigning the way Ingrid itself uses AI internally across knowledge, automation, product and customer-facing workflows.


Quotes

“Delivery is such a proxy for everything that has to do with retail — conversion rate, customer NPS, repeat-buy patterns.”

“It’s probably the last big problem of digital retail that hasn’t been solved in a simple way yet.”

“The only real way to solve it is to look at data that actually spans across the entire customer journey.”

“AI has changed my philosophy around what product is.”

“You can make people really T-shaped. From being a specialist, you can become a generalist quite quickly.”

References
Piotr Zeleski: https://www.linkedin.com/in/piotrzaleski/
Ian Jindal: https://www.linkedin.com/in/ianjindal

]]> No Ian Jindal Piotr Zaleski "Democratising Intelligence" - Stephen Dewar of Blackcircles.com 1 1 "Democratising Intelligence" - Stephen Dewar of Blackcircles.com full e2478209-21ca-4c36-b879-c3568e6b149a https://share.transistor.fm/s/887b07b0 In this episode of CommerceAI, Ian Jindal speaks with Stephen Dewar, Director of Marketing and AI Transformation at Blackcircles.com, the UK’s leading online tyre retailer and part of the Michelin Group. They explore how a business that turns an anxious, “your money, your life” purchase into a simple, people‑centred journey is weaving AI through every layer of its operations – from customer research and tyre merchandising to internal tooling and board‑level decision‑making. Stephen shares how Blackcircles built its own Nexus platform with 30–40 AI agents, set out a people‑first AI policy, and uses AI to democratise intelligence without compromising privacy or safety. This is a grounded, practitioner’s view of AI transformation in a very real, very physical commerce business. 


Key themes


  • Making a stressful purchase simple: Blackcircles was founded to take the complexity and anxiety out of buying tyres by guiding customers from number plate to the right product, backed by strong data and people‑centred service.
  • AI lands with employees before customers: A turning point came when an internal presentation, powered quietly by an AI tool, made the leadership realise AI would reshape how staff, stakeholders and partners work long before it showed up in marketing decks.
  • Customers arrive better briefed: Search behaviour has shifted from “buy tyres” to natural‑language questions like “what are the best tyres for my Volvo,” with customers arriving more informed and conducting research through LLMs and agents before they ever hit the site.
  • A cyborg approach to merchandising: Product selection combines internal tools and AI with human specialists who still override and fine‑tune recommendations for safety‑critical “your money, your life” purchases, boosting both conversion and customer satisfaction.
  • Nexus: internal AI for everyone: Blackcircles built Nexus, a browser‑based internal hub of 30–40 AI modules that any employee can use for tasks ranging from email drafting to legal analysis and financial modelling, sitting safely behind existing security and log‑ins.
  • People, privacy, performance: Their AI adoption is anchored in three pillars – improving people’s work and satisfaction, protecting privacy by keeping PII out of AI systems, and only deploying AI where it genuinely improves performance rather than adding novelty.
  • Boards in an AI age: Stephen argues that boards don’t need to own AI as a function; they need the humility to listen, communicate well, and equip teams with tools and skills, treating AI as a societal shift rather than a niche IT project.
  • Skills for the AI generation: For new joiners and first‑line managers alike, communication (including with AI agents) and critical thinking are the two universal skills that will matter across industries, geographies and roles.


Highlights


  • ~00:01 – Stephen introduces Blackcircles.com, its Michelin ownership, and how the brand turns a stressful, complex tyre purchase into a simple people‑centred journey.
  • ~04:05 – The internal “AI moment”: a staff presentation built with an AI tool triggers the realisation that AI will reshape how employees, not just customers, work.
  • ~06:48 – Changing search behaviour and customer research: from “buy tyres” to conversational queries and deeper exploration of product and brand pages.
  • ~09:41 – Launching an on‑site conversational agent to both guide customers and learn how they actually ask questions about tyres.
  • ~11:34 – A “cyborg” approach to merchandising: blending algorithms with tyre specialists to protect safety and improve accessibility and satisfaction.
  • ~16:06 – The three‑pillar AI policy (people, privacy, performance) and why they deliberately keep PII out of AI systems.
  • ~18:02 – Introducing Nexus: an internal, browser‑based home for 30–40 AI modules used across departments for writing, research, legal and financial tasks.
  • ~23:31 – “The AI age took about 40 minutes”: Stephen on the speed of adoption and why cross‑functional communication matters more than ownership.
  • ~29:48 – How leadership and frontline teams are encouraged to “go and play” with AI, leading to a complete redesign of management meetings using Notebook LM.
  • ~32:25 – Stephen’s three buckets for AI value – efficiencies, opportunities and innovation – and how AI lets creative people build what they imagine.


About the guest


Stephen Dewar is Director of Marketing and AI Transformation at Blackcircles.com, the UK’s leading online tyre retailer and part of the Michelin Group. He leads all marketing activity – from paid and owned media to how the brand shows up inside large language models – while steering Blackcircles’ AI transformation across departments. With a deep focus on customer data and journey design, Stephen has helped the business blend algorithmic tools with human tyre specialists to improve safety, satisfaction and conversion. He also designs internal AI capabilities like the Nexus platform, aimed at democratising intelligence and augmenting teams rather than replacing them. 


Quotes


  • “AI is not a new performance channel or just a coding tool. It’s a paradigm shift in society more than anything else.”
  • “We didn’t want a toaster with AI for the sake of novelty. We wanted AI where it genuinely makes a difference to how people work and how we deliver for customers.”
  • “Tyres are a ‘your money, your life’ product. We had to be 100% certain the products we show will keep people safe on the road and still be accessible.”
  • “The AI age took about 40 minutes. The agricultural age took 2,000 years, the industrial age 100, the internet age 10; AI has moved faster than all of them.”
  • “Communication and critical thinking are the two skills that will matter regardless of industry, sector or geography in an AI age.”

Links and references
Stephen Dewar on Linkedin: https://www.linkedin.com/in/stepheniaindewar/
Ian Jindal on Linkedin: https://www.linkedin.com/in/ianjindal
CommerceAI event on 3 June, 2026: https://retailx.events/commerceai-summit-2026

]]> In this episode of CommerceAI, Ian Jindal speaks with Stephen Dewar, Director of Marketing and AI Transformation at Blackcircles.com, the UK’s leading online tyre retailer and part of the Michelin Group. They explore how a business that turns an anxious, “your money, your life” purchase into a simple, people‑centred journey is weaving AI through every layer of its operations – from customer research and tyre merchandising to internal tooling and board‑level decision‑making. Stephen shares how Blackcircles built its own Nexus platform with 30–40 AI agents, set out a people‑first AI policy, and uses AI to democratise intelligence without compromising privacy or safety. This is a grounded, practitioner’s view of AI transformation in a very real, very physical commerce business. 


Key themes


  • Making a stressful purchase simple: Blackcircles was founded to take the complexity and anxiety out of buying tyres by guiding customers from number plate to the right product, backed by strong data and people‑centred service.
  • AI lands with employees before customers: A turning point came when an internal presentation, powered quietly by an AI tool, made the leadership realise AI would reshape how staff, stakeholders and partners work long before it showed up in marketing decks.
  • Customers arrive better briefed: Search behaviour has shifted from “buy tyres” to natural‑language questions like “what are the best tyres for my Volvo,” with customers arriving more informed and conducting research through LLMs and agents before they ever hit the site.
  • A cyborg approach to merchandising: Product selection combines internal tools and AI with human specialists who still override and fine‑tune recommendations for safety‑critical “your money, your life” purchases, boosting both conversion and customer satisfaction.
  • Nexus: internal AI for everyone: Blackcircles built Nexus, a browser‑based internal hub of 30–40 AI modules that any employee can use for tasks ranging from email drafting to legal analysis and financial modelling, sitting safely behind existing security and log‑ins.
  • People, privacy, performance: Their AI adoption is anchored in three pillars – improving people’s work and satisfaction, protecting privacy by keeping PII out of AI systems, and only deploying AI where it genuinely improves performance rather than adding novelty.
  • Boards in an AI age: Stephen argues that boards don’t need to own AI as a function; they need the humility to listen, communicate well, and equip teams with tools and skills, treating AI as a societal shift rather than a niche IT project.
  • Skills for the AI generation: For new joiners and first‑line managers alike, communication (including with AI agents) and critical thinking are the two universal skills that will matter across industries, geographies and roles.


Highlights


  • ~00:01 – Stephen introduces Blackcircles.com, its Michelin ownership, and how the brand turns a stressful, complex tyre purchase into a simple people‑centred journey.
  • ~04:05 – The internal “AI moment”: a staff presentation built with an AI tool triggers the realisation that AI will reshape how employees, not just customers, work.
  • ~06:48 – Changing search behaviour and customer research: from “buy tyres” to conversational queries and deeper exploration of product and brand pages.
  • ~09:41 – Launching an on‑site conversational agent to both guide customers and learn how they actually ask questions about tyres.
  • ~11:34 – A “cyborg” approach to merchandising: blending algorithms with tyre specialists to protect safety and improve accessibility and satisfaction.
  • ~16:06 – The three‑pillar AI policy (people, privacy, performance) and why they deliberately keep PII out of AI systems.
  • ~18:02 – Introducing Nexus: an internal, browser‑based home for 30–40 AI modules used across departments for writing, research, legal and financial tasks.
  • ~23:31 – “The AI age took about 40 minutes”: Stephen on the speed of adoption and why cross‑functional communication matters more than ownership.
  • ~29:48 – How leadership and frontline teams are encouraged to “go and play” with AI, leading to a complete redesign of management meetings using Notebook LM.
  • ~32:25 – Stephen’s three buckets for AI value – efficiencies, opportunities and innovation – and how AI lets creative people build what they imagine.


About the guest


Stephen Dewar is Director of Marketing and AI Transformation at Blackcircles.com, the UK’s leading online tyre retailer and part of the Michelin Group. He leads all marketing activity – from paid and owned media to how the brand shows up inside large language models – while steering Blackcircles’ AI transformation across departments. With a deep focus on customer data and journey design, Stephen has helped the business blend algorithmic tools with human tyre specialists to improve safety, satisfaction and conversion. He also designs internal AI capabilities like the Nexus platform, aimed at democratising intelligence and augmenting teams rather than replacing them. 


Quotes


  • “AI is not a new performance channel or just a coding tool. It’s a paradigm shift in society more than anything else.”
  • “We didn’t want a toaster with AI for the sake of novelty. We wanted AI where it genuinely makes a difference to how people work and how we deliver for customers.”
  • “Tyres are a ‘your money, your life’ product. We had to be 100% certain the products we show will keep people safe on the road and still be accessible.”
  • “The AI age took about 40 minutes. The agricultural age took 2,000 years, the industrial age 100, the internet age 10; AI has moved faster than all of them.”
  • “Communication and critical thinking are the two skills that will matter regardless of industry, sector or geography in an AI age.”

Links and references
Stephen Dewar on Linkedin: https://www.linkedin.com/in/stepheniaindewar/
Ian Jindal on Linkedin: https://www.linkedin.com/in/ianjindal
CommerceAI event on 3 June, 2026: https://retailx.events/commerceai-summit-2026

]]> Sun, 19 Apr 2026 16:50:32 +0100 Ian Jindal Ian Jindal 2113 In this episode of CommerceAI, Ian Jindal speaks with Stephen Dewar, Director of Marketing and AI Transformation at Blackcircles.com, the UK’s leading online tyre retailer and part of the Michelin Group. They explore how a business that turns an anxious, “your money, your life” purchase into a simple, people‑centred journey is weaving AI through every layer of its operations – from customer research and tyre merchandising to internal tooling and board‑level decision‑making. Stephen shares how Blackcircles built its own Nexus platform with 30–40 AI agents, set out a people‑first AI policy, and uses AI to democratise intelligence without compromising privacy or safety. This is a grounded, practitioner’s view of AI transformation in a very real, very physical commerce business. 


Key themes


  • Making a stressful purchase simple: Blackcircles was founded to take the complexity and anxiety out of buying tyres by guiding customers from number plate to the right product, backed by strong data and people‑centred service.
  • AI lands with employees before customers: A turning point came when an internal presentation, powered quietly by an AI tool, made the leadership realise AI would reshape how staff, stakeholders and partners work long before it showed up in marketing decks.
  • Customers arrive better briefed: Search behaviour has shifted from “buy tyres” to natural‑language questions like “what are the best tyres for my Volvo,” with customers arriving more informed and conducting research through LLMs and agents before they ever hit the site.
  • A cyborg approach to merchandising: Product selection combines internal tools and AI with human specialists who still override and fine‑tune recommendations for safety‑critical “your money, your life” purchases, boosting both conversion and customer satisfaction.
  • Nexus: internal AI for everyone: Blackcircles built Nexus, a browser‑based internal hub of 30–40 AI modules that any employee can use for tasks ranging from email drafting to legal analysis and financial modelling, sitting safely behind existing security and log‑ins.
  • People, privacy, performance: Their AI adoption is anchored in three pillars – improving people’s work and satisfaction, protecting privacy by keeping PII out of AI systems, and only deploying AI where it genuinely improves performance rather than adding novelty.
  • Boards in an AI age: Stephen argues that boards don’t need to own AI as a function; they need the humility to listen, communicate well, and equip teams with tools and skills, treating AI as a societal shift rather than a niche IT project.
  • Skills for the AI generation: For new joiners and first‑line managers alike, communication (including with AI agents) and critical thinking are the two universal skills that will matter across industries, geographies and roles.


Highlights


  • ~00:01 – Stephen introduces Blackcircles.com, its Michelin ownership, and how the brand turns a stressful, complex tyre purchase into a simple people‑centred journey.
  • ~04:05 – The internal “AI moment”: a staff presentation built with an AI tool triggers the realisation that AI will reshape how employees, not just customers, work.
  • ~06:48 – Changing search behaviour and customer research: from “buy tyres” to conversational queries and deeper exploration of product and brand pages.
  • ~09:41 – Launching an on‑site conversational agent to both guide customers and learn how they actually ask questions about tyres.
  • ~11:34 – A “cyborg” approach to merchandising: blending algorithms with tyre specialists to protect safety and improve accessibility and satisfaction.
  • ~16:06 – The three‑pillar AI policy (people, privacy, performance) and why they deliberately keep PII out of AI systems.
  • ~18:02 – Introducing Nexus: an internal, browser‑based home for 30–40 AI modules used across departments for writing, research, legal and financial tasks.
  • ~23:31 – “The AI age took about 40 minutes”: Stephen on the speed of adoption and why cross‑functional communication matters more than ownership.
  • ~29:48 – How leadership and frontline teams are encouraged to “go and play” with AI, leading to a complete redesign of management meetings using Notebook LM.
  • ~32:25 – Stephen’s three buckets for AI value – efficiencies, opportunities and innovation – and how AI lets creative people build what they imagine.


About the guest


Stephen Dewar is Director of Marketing and AI Transformation at Blackcircles.com, the UK’s leading online tyre retailer and part of the Michelin Group. He leads all marketing activity – from paid and owned media to how the brand shows up inside large language models – while steering Blackcircles’ AI transformation across departments. With a deep focus on customer data and journey design, Stephen has helped the business blend algorithmic tools with human tyre specialists to improve safety, satisfaction and conversion. He also designs internal AI capabilities like the Nexus platform, aimed at democratising intelligence and augmenting teams rather than replacing them. 


Quotes


  • “AI is not a new performance channel or just a coding tool. It’s a paradigm shift in society more than anything else.”
  • “We didn’t want a toaster with AI for the sake of novelty. We wanted AI where it genuinely makes a difference to how people work and how we deliver for customers.”
  • “Tyres are a ‘your money, your life’ product. We had to be 100% certain the products we show will keep people safe on the road and still be accessible.”
  • “The AI age took about 40 minutes. The agricultural age took 2,000 years, the industrial age 100, the internet age 10; AI has moved faster than all of them.”
  • “Communication and critical thinking are the two skills that will matter regardless of industry, sector or geography in an AI age.”

Links and references
Stephen Dewar on Linkedin: https://www.linkedin.com/in/stepheniaindewar/
Ian Jindal on Linkedin: https://www.linkedin.com/in/ianjindal
CommerceAI event on 3 June, 2026: https://retailx.events/commerceai-summit-2026

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