Supply ChAInge https://supplychaingepodcast.com en Join supply chain expert Derek Aranda for a series investigating the challenges and opportunities of cognitive supply chains. We interview enterprise-level experts in AI, risk, change management, ethics, HR, and logistics to gain an understanding of the real-life implementation and impact behind intelligent supply chain management. Like all good investigations, our conclusion is not a foregone one, as complex systems require complex analysis, and the cutting edge of technology, well, it can be sharp.Join us on Supply ChAInge to ask the question behind the question: not just can a cognitive supply chain work, but what does it take at scale? https://megaphone.imgix.net/podcasts/2bbad6f4-5eb1-11f1-a8c8-7bc9d3585083/image/0e0e11236f1ddabef3f47d417221af0a.png?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress Supply ChAInge https://supplychaingepodcast.com episodic Derek Aranda Join supply chain expert Derek Aranda for a series investigating the challenges and opportunities of cognitive supply chains. We interview enterprise-level experts in AI, risk, change management, ethics, HR, and logistics to gain an understanding of the real-life implementation and impact behind intelligent supply chain management. Like all good investigations, our conclusion is not a foregone one, as complex systems require complex analysis, and the cutting edge of technology, well, it can be sharp.Join us on Supply ChAInge to ask the question behind the question: not just can a cognitive supply chain work, but what does it take at scale? Join supply chain expert Derek Aranda for a series investigating the challenges and opportunities of cognitive supply chains. We interview enterprise-level experts in AI, risk, change management, ethics, HR, and logistics to gain an understanding of the real-life implementation and impact behind intelligent supply chain management. Like all good investigations, our conclusion is not a foregone one, as complex systems require complex analysis, and the cutting edge of technology, well, it can be sharp.
Join us on Supply ChAInge to ask the question behind the question: not just can a cognitive supply chain work, but what does it take at scale?

]]> Supply ChAInge brands@speakerboxmedia.com Perfection Is the Wrong Benchmark Everyone says garbage in, garbage out. Almost no one acts on what that actually demands. In this episode, Derek Aranda sits down with Brian Reed, an executive sponsor at Oracle who has run supply chains, advised the C-suite, and built autonomous planning systems across 56 countries, to pull apart what "good data" really means on the road to AI. The conversation cuts against the reflex to hold AI to a standard of perfection no human has ever met, and reframes data quality as a question of timeliness, location, and fitness for purpose rather than flawless numbers. It is a clear-eyed look at why so many AI pilots get written off as failures when they are actually doing their job, and what it takes to move from hand-holding to trust to genuine autonomy. 🎧 Episode Highlights  [00:21] — Why AI's inaccuracies get all the scrutiny while human inaccuracy goes unquestioned. [12:31] — Why a "failed" AI pilot that exposes bad data was never really a failure. [17:41] — The "hands off the wheel" case study: 30 days of tracking that convinced a customer to trust the system over their own team. [24:11] — Who should own the data: resolving the IT vs. business tension and putting AI on the RACI chart. [35:24] — Teaching AI to "trust and verify": what it takes to move from human-in-the-loop to real autonomy. [52:42] — Brian's closing advice: experiment small, because "the quarters and the dimes" add up to millions. 🔑 Key Takeaways • Measure AI against humans, not perfection. The bar for a system is not flawlessness, it is whether it beats the fallible person it replaces on speed and accuracy. Brian's autonomous planning project proved the point: over 30 days of tracking, the humans consistently broke the system's answer. Perfection can stay the goal without being the benchmark you measure against to move forward. • A failed pilot usually isn't one. A program that reveals your data is awful just did something valuable, it found the problem you didn't know you had. The work isn't perfecting every field before you start; it's prioritizing which data actually needs bit-for-bit accuracy and which is good enough for the decision at hand. Eat the elephant one bite at a time instead of waiting for a fully stocked data lake. • Data ownership is joint, and the machine is now on the RACI. The business owns data creation and the value drawn from it; IT is the enabler in the middle keeping it centralized, clean, and usable. Those two groups belong in nearly every conversation together, and AI is now a third party on the chart, responsible for storage and processing, informed by inputs across the business. đź’¬ Notable Quotes "We talk all about the data and the inaccuracies of things about AI. We don't talk about the inaccuracy of humans." — Brian Reed "You didn't know your data was awful before you ran the program. Now you know your data's awful, so it has some value." — Brian Reed "Perfection can always be the goal, but it doesn't have to be the bar at which you measure to go forward." — Brian Reed "Small value adds up to big value. We call it the quarters and the dimes. We added up enough quarters and dimes, and they turned into millions of dollars." — Brian Reed 👤 About The Guest Brian Reed Brian Reed is a tenured supply chain expert with 20+ years of global experience spanning every aspect of supply chain, from customer service through final-mile fulfillment, across all industries and on both the business and IT sides of digital and physical supply chain solutions. Beyond technology and consulting organizations, he has led a high-growth supply chain organization focused on execution at a large U.S. CPG manufacturer. Brian has helped some of the world's largest technology and manufacturing companies transform their supply chains with technology as a tool for automation, insight, and resilience, including broad transformations delivering cost savings and revenue enablement measured in the hundreds of millions to billions of dollars. He was most recently the AWS Supply Chain and Pan-Amazon Supply Chain GTM Leader, working with Amazon's largest and most strategic customers and partners to develop and deliver new supply chain capabilities. He now advises the C-suite of Oracle's largest and most strategic customers, helping them deploy Oracle Applications and AI at scale. Outside of work, Brian and his family enjoy traveling internationally, food and wine, and hitting the mountain bike trails as often as possible, both at home in Southern California and while traveling. Stay Connected: • https://supplychaingepodcast.com • https://april12advisors.com Produced by Speakerbox Media. Thu, 16 Jul 2026 13:30:00 -0000 full Derek Aranda Everyone says garbage in, garbage out. Almost no one acts on what that actually demands. In this episode, Derek Aranda sits down with Brian Reed, an executive sponsor at Oracle who has run supply chains, advised the C-suite, and built autonomous planning systems across 56 countries, to pull apart what "good data" really means on the road to AI. The conversation cuts against the reflex to hold AI to a standard of perfection no human has ever met, and reframes data quality as a question of timeliness, location, and fitness for purpose rather than flawless numbers. It is a clear-eyed look at why so many AI pilots get written off as failures when they are actually doing their job, and what it takes to move from hand-holding to trust to genuine autonomy. 🎧 Episode Highlights  [00:21] — Why AI's inaccuracies get all the scrutiny while human inaccuracy goes unquestioned. [12:31] — Why a "failed" AI pilot that exposes bad data was never really a failure. [17:41] — The "hands off the wheel" case study: 30 days of tracking that convinced a customer to trust the system over their own team. [24:11] — Who should own the data: resolving the IT vs. business tension and putting AI on the RACI chart. [35:24] — Teaching AI to "trust and verify": what it takes to move from human-in-the-loop to real autonomy. [52:42] — Brian's closing advice: experiment small, because "the quarters and the dimes" add up to millions. 🔑 Key Takeaways • Measure AI against humans, not perfection. The bar for a system is not flawlessness, it is whether it beats the fallible person it replaces on speed and accuracy. Brian's autonomous planning project proved the point: over 30 days of tracking, the humans consistently broke the system's answer. Perfection can stay the goal without being the benchmark you measure against to move forward. • A failed pilot usually isn't one. A program that reveals your data is awful just did something valuable, it found the problem you didn't know you had. The work isn't perfecting every field before you start; it's prioritizing which data actually needs bit-for-bit accuracy and which is good enough for the decision at hand. Eat the elephant one bite at a time instead of waiting for a fully stocked data lake. • Data ownership is joint, and the machine is now on the RACI. The business owns data creation and the value drawn from it; IT is the enabler in the middle keeping it centralized, clean, and usable. Those two groups belong in nearly every conversation together, and AI is now a third party on the chart, responsible for storage and processing, informed by inputs across the business. đź’¬ Notable Quotes "We talk all about the data and the inaccuracies of things about AI. We don't talk about the inaccuracy of humans." — Brian Reed "You didn't know your data was awful before you ran the program. Now you know your data's awful, so it has some value." — Brian Reed "Perfection can always be the goal, but it doesn't have to be the bar at which you measure to go forward." — Brian Reed "Small value adds up to big value. We call it the quarters and the dimes. We added up enough quarters and dimes, and they turned into millions of dollars." — Brian Reed 👤 About The Guest Brian Reed Brian Reed is a tenured supply chain expert with 20+ years of global experience spanning every aspect of supply chain, from customer service through final-mile fulfillment, across all industries and on both the business and IT sides of digital and physical supply chain solutions. Beyond technology and consulting organizations, he has led a high-growth supply chain organization focused on execution at a large U.S. CPG manufacturer. Brian has helped some of the world's largest technology and manufacturing companies transform their supply chains with technology as a tool for automation, insight, and resilience, including broad transformations delivering cost savings and revenue enablement measured in the hundreds of millions to billions of dollars. He was most recently the AWS Supply Chain and Pan-Amazon Supply Chain GTM Leader, working with Amazon's largest and most strategic customers and partners to develop and deliver new supply chain capabilities. He now advises the C-suite of Oracle's largest and most strategic customers, helping them deploy Oracle Applications and AI at scale. Outside of work, Brian and his family enjoy traveling internationally, food and wine, and hitting the mountain bike trails as often as possible, both at home in Southern California and while traveling. Stay Connected: • https://supplychaingepodcast.com • https://april12advisors.com Produced by Speakerbox Media. Everyone says garbage in, garbage out. Almost no one acts on what that actually demands. In this episode, Derek Aranda sits down with Brian Reed, an executive sponsor at Oracle who has run supply chains, advised the C-suite, and built autonomous planning systems across 56 countries, to pull apart what "good data" really means on the road to AI. The conversation cuts against the reflex to hold AI to a standard of perfection no human has ever met, and reframes data quality as a question of timeliness, location, and fitness for purpose rather than flawless numbers. It is a clear-eyed look at why so many AI pilots get written off as failures when they are actually doing their job, and what it takes to move from hand-holding to trust to genuine autonomy.


🎧 Episode Highlights 

[00:21] — Why AI's inaccuracies get all the scrutiny while human inaccuracy goes unquestioned.

[12:31] — Why a "failed" AI pilot that exposes bad data was never really a failure.

[17:41] — The "hands off the wheel" case study: 30 days of tracking that convinced a customer to trust the system over their own team.

[24:11] — Who should own the data: resolving the IT vs. business tension and putting AI on the RACI chart.

[35:24] — Teaching AI to "trust and verify": what it takes to move from human-in-the-loop to real autonomy.

[52:42] — Brian's closing advice: experiment small, because "the quarters and the dimes" add up to millions.



🔑 Key Takeaways

• Measure AI against humans, not perfection. The bar for a system is not flawlessness, it is whether it beats the fallible person it replaces on speed and accuracy. Brian's autonomous planning project proved the point: over 30 days of tracking, the humans consistently broke the system's answer. Perfection can stay the goal without being the benchmark you measure against to move forward.


• A failed pilot usually isn't one. A program that reveals your data is awful just did something valuable, it found the problem you didn't know you had. The work isn't perfecting every field before you start; it's prioritizing which data actually needs bit-for-bit accuracy and which is good enough for the decision at hand. Eat the elephant one bite at a time instead of waiting for a fully stocked data lake.


• Data ownership is joint, and the machine is now on the RACI. The business owns data creation and the value drawn from it; IT is the enabler in the middle keeping it centralized, clean, and usable. Those two groups belong in nearly every conversation together, and AI is now a third party on the chart, responsible for storage and processing, informed by inputs across the business.


đź’¬ Notable Quotes

"We talk all about the data and the inaccuracies of things about AI. We don't talk about the inaccuracy of humans." — Brian Reed


"You didn't know your data was awful before you ran the program. Now you know your data's awful, so it has some value." — Brian Reed


"Perfection can always be the goal, but it doesn't have to be the bar at which you measure to go forward." — Brian Reed


"Small value adds up to big value. We call it the quarters and the dimes. We added up enough quarters and dimes, and they turned into millions of dollars." — Brian Reed



👤 About The Guest

Brian Reed

Brian Reed is a tenured supply chain expert with 20+ years of global experience spanning every aspect of supply chain, from customer service through final-mile fulfillment, across all industries and on both the business and IT sides of digital and physical supply chain solutions. Beyond technology and consulting organizations, he has led a high-growth supply chain organization focused on execution at a large U.S. CPG manufacturer. Brian has helped some of the world's largest technology and manufacturing companies transform their supply chains with technology as a tool for automation, insight, and resilience, including broad transformations delivering cost savings and revenue enablement measured in the hundreds of millions to billions of dollars.


He was most recently the AWS Supply Chain and Pan-Amazon Supply Chain GTM Leader, working with Amazon's largest and most strategic customers and partners to develop and deliver new supply chain capabilities. He now advises the C-suite of Oracle's largest and most strategic customers, helping them deploy Oracle Applications and AI at scale.


Outside of work, Brian and his family enjoy traveling internationally, food and wine, and hitting the mountain bike trails as often as possible, both at home in Southern California and while traveling.



Stay Connected:

• https://supplychaingepodcast.com

• https://april12advisors.com


Produced by Speakerbox Media.

]]> 3218 Why Trust Is the Real AI Strategy, with Chris Pitre Episode Summary: In this episode, Chris Pitre of Softway makes the case that AI transformation is a leadership and culture journey, not a tools journey. Fear, loss of control, and concerns about being replaced only get resolved when leaders lead with vulnerability, curiosity, and psychological safety. Before AI can take root and scale, Chris argues, you have to till the soil of culture: trust, inclusion, and honest dialogue. The challenge he leaves leaders with is direct: be brave enough to admit you do not have all the answers, and run your AI journey as an ongoing, human-centered transformation. 🎧 Episode Highlights  03:21]: AI as a transformation, not just a tool, and why many pilots disappoint [05:43]: Psychological safety and trust: why people won’t speak up about AI fears [10:30]: Soft skills become critical as AI takes over repetitive, mechanical work [21:20]: “Everything moves at the speed of trust”: designing safe-to-fail AI experiments [29:10]: The IKEA case: retraining 8,500 agents into consultants and unlocking $1.4B in new revenue 🔑 Key Takeaways: AI transformation is primarily a leadership and culture challenge, not a technology rollout. The episode argues that fear of job loss, loss of control, and being “devalued” will derail AI efforts if leaders don’t first build trust, inclusion, and psychological safety. Embracing vulnerability, openly saying “I don’t know,” naming fears, and inviting help becomes a core leadership behavior that enables honest conversations about what AI really means for people and the business.Leaders must shift from cost-cutting and control to curiosity, experimentation, and empowerment. Instead of “go do AI” or chasing shiny tools, they should educate everyone on what AI actually is, surface real “migraines” from the front line, and run small, fast, safe-to-fail experiments. Failure is reframed as learning, with accountability separated from punishment, so teams feel safe to try, break small things, retrain the models, and iterate quickly.AI’s real promise is to unlock human potential and more joyful work, not just replace headcount. By offloading repetitive, mechanical tasks to AI, organizations can reskill people into higher-value, more creative roles as illustrated by IKEA retraining 8,500 call center agents into interior design consultants and unlocking $1.4B in new revenue. This requires leaders to rethink org design, role definitions, and the employee value proposition, placing “love as a business strategy” at the center of how AI is adopted and scaled. đź’¬ Notable Quotes: “AI is a tool, not a teammate.”“Everything moves at the speed of trust.”“Silence is the biggest cost, never on a balance sheet.”“Curiosity is above knowledge in the age of AI.”“If you can forgive a human, why can’t you forgive technology?” 👤 About The Guest: Chris Pitre Chris Pitre is the Vice President at Softway and co-author of the best-selling books Love as a Business Strategy and Love as a Change Strategy. He helps organizations lead AI and digital transformations by centering on culture, using trust, vulnerability, and psychological safety to unlock sustainable performance. Chris works with executives and frontline teams alike to redesign how work gets done so AI augments people, rather than replaces them. Learn more about Softway and Culture+: https://www.softway.com https://www.culture-plus.com Stay Connected: https://supplychaingepodcast.com https://april12advisors.com Produced by Speakerbox Media. Thu, 02 Jul 2026 12:32:00 -0000 full Derek Aranda Episode Summary: In this episode, Chris Pitre of Softway makes the case that AI transformation is a leadership and culture journey, not a tools journey. Fear, loss of control, and concerns about being replaced only get resolved when leaders lead with vulnerability, curiosity, and psychological safety. Before AI can take root and scale, Chris argues, you have to till the soil of culture: trust, inclusion, and honest dialogue. The challenge he leaves leaders with is direct: be brave enough to admit you do not have all the answers, and run your AI journey as an ongoing, human-centered transformation. 🎧 Episode Highlights  03:21]: AI as a transformation, not just a tool, and why many pilots disappoint [05:43]: Psychological safety and trust: why people won’t speak up about AI fears [10:30]: Soft skills become critical as AI takes over repetitive, mechanical work [21:20]: “Everything moves at the speed of trust”: designing safe-to-fail AI experiments [29:10]: The IKEA case: retraining 8,500 agents into consultants and unlocking $1.4B in new revenue 🔑 Key Takeaways: AI transformation is primarily a leadership and culture challenge, not a technology rollout. The episode argues that fear of job loss, loss of control, and being “devalued” will derail AI efforts if leaders don’t first build trust, inclusion, and psychological safety. Embracing vulnerability, openly saying “I don’t know,” naming fears, and inviting help becomes a core leadership behavior that enables honest conversations about what AI really means for people and the business.Leaders must shift from cost-cutting and control to curiosity, experimentation, and empowerment. Instead of “go do AI” or chasing shiny tools, they should educate everyone on what AI actually is, surface real “migraines” from the front line, and run small, fast, safe-to-fail experiments. Failure is reframed as learning, with accountability separated from punishment, so teams feel safe to try, break small things, retrain the models, and iterate quickly.AI’s real promise is to unlock human potential and more joyful work, not just replace headcount. By offloading repetitive, mechanical tasks to AI, organizations can reskill people into higher-value, more creative roles as illustrated by IKEA retraining 8,500 call center agents into interior design consultants and unlocking $1.4B in new revenue. This requires leaders to rethink org design, role definitions, and the employee value proposition, placing “love as a business strategy” at the center of how AI is adopted and scaled. đź’¬ Notable Quotes: “AI is a tool, not a teammate.”“Everything moves at the speed of trust.”“Silence is the biggest cost, never on a balance sheet.”“Curiosity is above knowledge in the age of AI.”“If you can forgive a human, why can’t you forgive technology?” 👤 About The Guest: Chris Pitre Chris Pitre is the Vice President at Softway and co-author of the best-selling books Love as a Business Strategy and Love as a Change Strategy. He helps organizations lead AI and digital transformations by centering on culture, using trust, vulnerability, and psychological safety to unlock sustainable performance. Chris works with executives and frontline teams alike to redesign how work gets done so AI augments people, rather than replaces them. Learn more about Softway and Culture+: https://www.softway.com https://www.culture-plus.com Stay Connected: https://supplychaingepodcast.com https://april12advisors.com Produced by Speakerbox Media. Episode Summary:

In this episode, Chris Pitre of Softway makes the case that AI transformation is a leadership and culture journey, not a tools journey. Fear, loss of control, and concerns about being replaced only get resolved when leaders lead with vulnerability, curiosity, and psychological safety. Before AI can take root and scale, Chris argues, you have to till the soil of culture: trust, inclusion, and honest dialogue. The challenge he leaves leaders with is direct: be brave enough to admit you do not have all the answers, and run your AI journey as an ongoing, human-centered transformation.


🎧 Episode Highlights 

03:21]: AI as a transformation, not just a tool, and why many pilots disappoint [05:43]: Psychological safety and trust: why people won’t speak up about AI fears [10:30]: Soft skills become critical as AI takes over repetitive, mechanical work [21:20]: “Everything moves at the speed of trust”: designing safe-to-fail AI experiments [29:10]: The IKEA case: retraining 8,500 agents into consultants and unlocking $1.4B in new revenue


🔑 Key Takeaways:

AI transformation is primarily a leadership and culture challenge, not a technology rollout. The episode argues that fear of job loss, loss of control, and being “devalued” will derail AI efforts if leaders don’t first build trust, inclusion, and psychological safety. Embracing vulnerability, openly saying “I don’t know,” naming fears, and inviting help becomes a core leadership behavior that enables honest conversations about what AI really means for people and the business.Leaders must shift from cost-cutting and control to curiosity, experimentation, and empowerment. Instead of “go do AI” or chasing shiny tools, they should educate everyone on what AI actually is, surface real “migraines” from the front line, and run small, fast, safe-to-fail experiments. Failure is reframed as learning, with accountability separated from punishment, so teams feel safe to try, break small things, retrain the models, and iterate quickly.AI’s real promise is to unlock human potential and more joyful work, not just replace headcount. By offloading repetitive, mechanical tasks to AI, organizations can reskill people into higher-value, more creative roles as illustrated by IKEA retraining 8,500 call center agents into interior design consultants and unlocking $1.4B in new revenue. This requires leaders to rethink org design, role definitions, and the employee value proposition, placing “love as a business strategy” at the center of how AI is adopted and scaled.



đź’¬ Notable Quotes:

“AI is a tool, not a teammate.”“Everything moves at the speed of trust.”“Silence is the biggest cost, never on a balance sheet.”“Curiosity is above knowledge in the age of AI.”“If you can forgive a human, why can’t you forgive technology?”


👤 About The Guest:

Chris Pitre

Chris Pitre is the Vice President at Softway and co-author of the best-selling books Love as a Business Strategy and Love as a Change Strategy. He helps organizations lead AI and digital transformations by centering on culture, using trust, vulnerability, and psychological safety to unlock sustainable performance. Chris works with executives and frontline teams alike to redesign how work gets done so AI augments people, rather than replaces them.


Learn more about Softway and Culture+:

https://www.softway.com

https://www.culture-plus.com


Stay Connected:

https://supplychaingepodcast.com

https://april12advisors.com



Produced by Speakerbox Media.

]]> 3132 Don't Lead AI Like a Project: A Leadership Guide to AI and Autonomy in the Supply Chain AI will not fix a broken supply chain and it requires committed leadership. In this solo episode, Derek Aranda argues that autonomy demands a fundamentally different kind of leader, one who treats AI as a continuous journey rather than a project with a finish line. Using lean as both a blueprint and a warning, he lays out the two strategic choices every leader has to make, the four things AI forces you to rethink, and why many failed AI pilots are leadership failures in disguise. The takeaway: get out of the boardroom, pressure-test what your dashboards claim, and build the conditions in which AI can actually succeed. 🎧 Episode Highlights [00:00]: Why today’s geopolitical shocks expose the need for AI in supply chains. [02:45]: Two strategic AI choices every supply chain leader must make.[08:40]: Lean as the blueprint (and warning) for your AI and autonomy. journey. [15:20]: Why many AI “failures” are actually leadership and culture failure [23:55]: Getting out of the boardroom: how real leaders make AI work on the front line. 🧭 Frameworks Worth SavingThe two strategic choices that come before any technology decision: • How existential is AI and autonomy to your operations? • Do you see it replacing your people or augmenting them? The four things AI forces you to rethink: •AI is probabilistic, not deterministic, so it needs the right context wrapped around it •AI does not stand alone, it lives inside a tech stack and your data flows •AI introduces new risk, so decide what stays with the human and what moves to the machine •AI requires orchestration across sales, finance, legal, suppliers, and customers, not just supply chain 🔑 Key Takeaways: •AI isn’t a shortcut, it’s a catalyst for a full organizational re-architecture. Leaders who treat AI as a plug‑and‑play fix will repeat the same failures we’ve already seen with lean and digitalization. The real work is root‑to‑branch: rethinking processes, decision rights, incentives, and org design so that probabilistic, AI‑driven decisions can actually stick. Without that strategic redesign from the C‑suite and board level down, AI will become just another expensive front‑end layered on top of manual, spreadsheet‑driven reality. • The real leadership challenge is marrying top-down intent with bottom-up autonomy. Durable AI is built by empowering the people closest to the work: planners, schedulers, operators, and trade compliance teams. That means leaders have to leave the conference room and get onto the floor, pressure-test "we're automated" claims, surface hidden manual work, and turn frontline judgment into system logic instead of letting it quietly override the tool. • AI only creates lasting value when leaders accept they will not get it right on day zero, or day 1,000. The shift is from project completion to the infinite game: continuously tuning data, stack, and process, recalibrating the risk line between human and machine, and iteratively earning trust in AI recommendations. • Two choices sit upstream of every technology decision: how existential AI is to your operations, and whether you treat it as replacement or augmentation. There is no off-the-shelf playbook for either. Derek's own bias is toward augmentation, but the point is that leaders have to consciously decide where they land, because those choices drive investment, org design, incentives, and risk appetite. 👤 About The Host: Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected: • https://supplychaingepodcast.com • https://april12advisors.com A project by April 12 Advisors. Produced by Speakerbox Media. Thu, 18 Jun 2026 10:00:00 -0000 full Derek Aranda AI will not fix a broken supply chain and it requires committed leadership. In this solo episode, Derek Aranda argues that autonomy demands a fundamentally different kind of leader, one who treats AI as a continuous journey rather than a project with a finish line. Using lean as both a blueprint and a warning, he lays out the two strategic choices every leader has to make, the four things AI forces you to rethink, and why many failed AI pilots are leadership failures in disguise. The takeaway: get out of the boardroom, pressure-test what your dashboards claim, and build the conditions in which AI can actually succeed. 🎧 Episode Highlights [00:00]: Why today’s geopolitical shocks expose the need for AI in supply chains. [02:45]: Two strategic AI choices every supply chain leader must make.[08:40]: Lean as the blueprint (and warning) for your AI and autonomy. journey. [15:20]: Why many AI “failures” are actually leadership and culture failure [23:55]: Getting out of the boardroom: how real leaders make AI work on the front line. 🧭 Frameworks Worth SavingThe two strategic choices that come before any technology decision: • How existential is AI and autonomy to your operations? • Do you see it replacing your people or augmenting them? The four things AI forces you to rethink: •AI is probabilistic, not deterministic, so it needs the right context wrapped around it •AI does not stand alone, it lives inside a tech stack and your data flows •AI introduces new risk, so decide what stays with the human and what moves to the machine •AI requires orchestration across sales, finance, legal, suppliers, and customers, not just supply chain 🔑 Key Takeaways: •AI isn’t a shortcut, it’s a catalyst for a full organizational re-architecture. Leaders who treat AI as a plug‑and‑play fix will repeat the same failures we’ve already seen with lean and digitalization. The real work is root‑to‑branch: rethinking processes, decision rights, incentives, and org design so that probabilistic, AI‑driven decisions can actually stick. Without that strategic redesign from the C‑suite and board level down, AI will become just another expensive front‑end layered on top of manual, spreadsheet‑driven reality. • The real leadership challenge is marrying top-down intent with bottom-up autonomy. Durable AI is built by empowering the people closest to the work: planners, schedulers, operators, and trade compliance teams. That means leaders have to leave the conference room and get onto the floor, pressure-test "we're automated" claims, surface hidden manual work, and turn frontline judgment into system logic instead of letting it quietly override the tool. • AI only creates lasting value when leaders accept they will not get it right on day zero, or day 1,000. The shift is from project completion to the infinite game: continuously tuning data, stack, and process, recalibrating the risk line between human and machine, and iteratively earning trust in AI recommendations. • Two choices sit upstream of every technology decision: how existential AI is to your operations, and whether you treat it as replacement or augmentation. There is no off-the-shelf playbook for either. Derek's own bias is toward augmentation, but the point is that leaders have to consciously decide where they land, because those choices drive investment, org design, incentives, and risk appetite. 👤 About The Host: Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected: • https://supplychaingepodcast.com • https://april12advisors.com A project by April 12 Advisors. Produced by Speakerbox Media. AI will not fix a broken supply chain and it requires committed leadership. In this solo episode, Derek Aranda argues that autonomy demands a fundamentally different kind of leader, one who treats AI as a continuous journey rather than a project with a finish line. Using lean as both a blueprint and a warning, he lays out the two strategic choices every leader has to make, the four things AI forces you to rethink, and why many failed AI pilots are leadership failures in disguise. The takeaway: get out of the boardroom, pressure-test what your dashboards claim, and build the conditions in which AI can actually succeed. 🎧 Episode Highlights [00:00]: Why today’s geopolitical shocks expose the need for AI in supply chains. [02:45]: Two strategic AI choices every supply chain leader must make.[08:40]: Lean as the blueprint (and warning) for your AI and autonomy. journey. [15:20]: Why many AI “failures” are actually leadership and culture failure

[23:55]: Getting out of the boardroom: how real leaders make AI work on the front line. 🧭 Frameworks Worth SavingThe two strategic choices that come before any technology decision: • How existential is AI and autonomy to your operations? • Do you see it replacing your people or augmenting them? The four things AI forces you to rethink: •AI is probabilistic, not deterministic, so it needs the right context wrapped around it •AI does not stand alone, it lives inside a tech stack and your data flows •AI introduces new risk, so decide what stays with the human and what moves to the machine •AI requires orchestration across sales, finance, legal, suppliers, and customers, not just supply chain 🔑 Key Takeaways: •AI isn’t a shortcut, it’s a catalyst for a full organizational re-architecture. Leaders who treat AI as a plug‑and‑play fix will repeat the same failures we’ve already seen with lean and digitalization. The real work is root‑to‑branch: rethinking processes, decision rights, incentives, and org design so that probabilistic, AI‑driven decisions can actually stick. Without that strategic redesign from the C‑suite and board level down, AI will become just another expensive front‑end layered on top of manual, spreadsheet‑driven reality. • The real leadership challenge is marrying top-down intent with bottom-up autonomy. Durable AI is built by empowering the people closest to the work: planners, schedulers, operators, and trade compliance teams. That means leaders have to leave the conference room and get onto the floor, pressure-test "we're automated" claims, surface hidden manual work, and turn frontline judgment into system logic instead of letting it quietly override the tool. • AI only creates lasting value when leaders accept they will not get it right on day zero, or day 1,000. The shift is from project completion to the infinite game: continuously tuning data, stack, and process, recalibrating the risk line between human and machine, and iteratively earning trust in AI recommendations. • Two choices sit upstream of every technology decision: how existential AI is to your operations, and whether you treat it as replacement or augmentation. There is no off-the-shelf playbook for either. Derek's own bias is toward augmentation, but the point is that leaders have to consciously decide where they land, because those choices drive investment, org design, incentives, and risk appetite. 👤 About The Host: Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected: • https://supplychaingepodcast.com • https://april12advisors.com A project by April 12 Advisors. Produced by Speakerbox Media.

]]> 1902 Welcome to Supply ChAInge: Exploring the Autonomous, Cognitive, Agentic Supply Chain What does it actually mean for a supply chain to run itself? Season 1 opens by defining the thing everyone is racing toward before agreeing on what it is. Derek lays out a five- level pyramid, from automation to analytics to cognitive to agentic to autonomous, so leaders can locate where they really are today and see how big the next leap is. No alphabet soup of technical terms and no vendor roadmap. The goal is a shared definition you can build against. 🎧 Episode Highlights [00:30]: How AI is transforming modern supply chain careers [01:45]: Why supply chain is now a strategic AI priority [09:30]: AI supply chain pyramid: automation to autonomous operations [15:10]: Cognitive AI for tariffs: smarter supply chain decisions [20:05]: Bounded autonomy: AI, dark warehouses, and human oversight 🔑 Key Takeaways: ● Supply chain moved from cost center to strategic lever. COVID, tariffs, and global conflict exposed how fragile and decisive supply chains are, pushing them into the boardroom. AI's rise came in the same moment, bringing real opportunity and a lot of hype. ● There's a five-rung climb, and the rungs aren't evenly spaced. Automation and analytics make work faster and more visible while humans still decide. Cognitive systems understand context and trade-offs and narrow the decision space. Agentic systems act within guardrails. Autonomous removes the human from the loop. The jump from agentic to autonomous is far larger than the jumps below it, and most organizations are still near the bottom. ● You don't fix process discipline with AI. Agents need defined rules, sandboxes, and decision criteria. If your SOPs aren't solid today, that gap becomes the dividing line between firms that can deploy AI fast and firms that can't. ● Bounded autonomy is a more realistic target. Fully dark, end-to-end autonomy is distant and maybe not even desirable. The richer goal is autonomy inside defined domains, with humans on the frontier as decision makers, coaches, and trainers Autonomy means hands applied differently, not hands off. 👤 About The Host: Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected: ● https://supplychaingepodcast.com ● https://april12advisors.com Produced by Speakerbox Media Thu, 04 Jun 2026 15:00:00 -0000 full Derek Aranda What does it actually mean for a supply chain to run itself? Season 1 opens by defining the thing everyone is racing toward before agreeing on what it is. Derek lays out a five- level pyramid, from automation to analytics to cognitive to agentic to autonomous, so leaders can locate where they really are today and see how big the next leap is. No alphabet soup of technical terms and no vendor roadmap. The goal is a shared definition you can build against. 🎧 Episode Highlights [00:30]: How AI is transforming modern supply chain careers [01:45]: Why supply chain is now a strategic AI priority [09:30]: AI supply chain pyramid: automation to autonomous operations [15:10]: Cognitive AI for tariffs: smarter supply chain decisions [20:05]: Bounded autonomy: AI, dark warehouses, and human oversight 🔑 Key Takeaways: ● Supply chain moved from cost center to strategic lever. COVID, tariffs, and global conflict exposed how fragile and decisive supply chains are, pushing them into the boardroom. AI's rise came in the same moment, bringing real opportunity and a lot of hype. ● There's a five-rung climb, and the rungs aren't evenly spaced. Automation and analytics make work faster and more visible while humans still decide. Cognitive systems understand context and trade-offs and narrow the decision space. Agentic systems act within guardrails. Autonomous removes the human from the loop. The jump from agentic to autonomous is far larger than the jumps below it, and most organizations are still near the bottom. ● You don't fix process discipline with AI. Agents need defined rules, sandboxes, and decision criteria. If your SOPs aren't solid today, that gap becomes the dividing line between firms that can deploy AI fast and firms that can't. ● Bounded autonomy is a more realistic target. Fully dark, end-to-end autonomy is distant and maybe not even desirable. The richer goal is autonomy inside defined domains, with humans on the frontier as decision makers, coaches, and trainers Autonomy means hands applied differently, not hands off. 👤 About The Host: Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected: ● https://supplychaingepodcast.com ● https://april12advisors.com Produced by Speakerbox Media What does it actually mean for a supply chain to run itself? Season 1 opens by defining the thing everyone is racing toward before agreeing on what it is. Derek lays out a five- level pyramid, from automation to analytics to cognitive to agentic to autonomous, so leaders can locate where they really are today and see how big the next leap is. No alphabet soup of technical terms and no vendor roadmap. The goal is a shared definition you can build against.


🎧 Episode Highlights

[00:30]: How AI is transforming modern supply chain careers

[01:45]: Why supply chain is now a strategic AI priority

[09:30]: AI supply chain pyramid: automation to autonomous operations

[15:10]: Cognitive AI for tariffs: smarter supply chain decisions

[20:05]: Bounded autonomy: AI, dark warehouses, and human oversight


🔑 Key Takeaways:

â—Ź Supply chain moved from cost center to strategic lever. COVID, tariffs, and global conflict exposed how fragile and decisive supply chains are, pushing them into the boardroom. AI's rise came in the same moment, bringing real opportunity and a lot of hype.

â—Ź There's a five-rung climb, and the rungs aren't evenly spaced. Automation and analytics make work faster and more visible while humans still decide. Cognitive systems understand context and trade-offs and narrow the decision space. Agentic systems act within guardrails. Autonomous removes the human from the loop. The jump from agentic to autonomous is far larger than the jumps below it, and most organizations are still near the bottom.

â—Ź You don't fix process discipline with AI. Agents need defined rules, sandboxes, and decision criteria. If your SOPs aren't solid today, that gap becomes the dividing line between firms that can deploy AI fast and firms that can't.

â—Ź Bounded autonomy is a more realistic target. Fully dark, end-to-end autonomy is distant and maybe not even desirable. The richer goal is autonomy inside defined domains, with humans on the frontier as decision makers, coaches, and trainers Autonomy means hands applied differently, not hands off.


👤 About The Host:


Derek Aranda

Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms.


Stay Connected:

â—Ź https://supplychaingepodcast.com

â—Ź https://april12advisors.com


Produced by Speakerbox Media

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