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“Flesh and Breath” – The Appeal of Delegating to AI and its Limits

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“flesh And Breath” – The Appeal Of Delegating To Ai And Its Limits

At the airport for an early flight, returning home after a speaking engagement, I was having a moment, feeling “tweepy” (my new word for tiredness that makes me weepy) as I stood in line to order breakfast. The trip had been a busy several days on top of a difficult year. The woman taking my order looked directly at me, smiled, and wished me a nice day. Her words were not remarkable but delivered with such utter sincerity, with light in her eyes, that they touched me just enough to lift my spirits. After eating I returned to the counter to thank her for making a difference. This may seem like an insignificant moment, but most of life is a series of small moments punctuated by a few big ones, some stupendous, others shattering. In a year heavy with crushing moments I’ve gained appreciation for the gifts of kindness, and this small exchange shone a light for me on why they matter, what I want to delegate to AI, and what remains uniquely human.

In my last commentary I argued that AI lacks what I call the 3C’s: context, collaboration and conscience. The challenges I’ve faced in the last year have reinforced the importance of two additional fundamentally human capabilities AI lacks: connection and compassion. As appetite for AI continues to grow at an astonishing pace, navigating the boundaries of what to delegate to AI and what to preserve for humans is essential.

The Appeal of Delegating to AI

AI is a tool designed by humans to do things it can do better or we cannot or do not want to do. Automation powered by AI takes over tedious grunt work involved in areas like supply chain procurement, with chatbots negotiating routine contracts with suppliers, saving money and freeing up time for professionals to handle more complex deals.

AI thrives on large volumes of data, so it can scale far beyond our cognitive capacity to crunch through reams of information quickly and synthesize the results. Googling now often places a generative AI result at the top of the page, a single answer from searching and summarizing the most relevant information. It can find and learn from patterns in big data sets to make predictions, such as when a machine is likely to fail in a factory, which external signals will most impact a demand forecast, or what actual lead times for parts will be.

AI has potential to reduce bias, since people can make inconsistent and subjective decisions based on personal opinions, so there is promise for its application in areas like hiring, lending, and medical diagnosis. But these same areas of AI promise also show risk. Since bias is primarily a problem of the underlying data reflecting existing human bias, efforts must be made to leverage AI’s strengths and mitigate its pitfalls. AI fairness is thorny, but I am hopeful that combining human and AI strengths for some of these decisions can make them more consistent and objective.

In addition to these applications of AI’s strengths, it doesn’t get tired (or weepy), irritable, bored or overwhelmed. On a different trip during another tweepy moment, exacerbated by a cascading series of flight cancellations and reroutings preventing me from getting home that night, I had to make a decision between a variety of unattractive flight and hotel options. I would have loved to simply delegate to AI finding me a room and getting me home.

The appeal of delegating to AI is borne out by data. The US National Bureau of Economic Research reported last month that Generative AI is already on track to outpace the speed of adoption of the internet and PCs. ARK Investment Management found that every four months the cost of operating AI models drops by half, beating the famous Moore’s Law on chip costs by a factor of 4-6 times. New research by Morgan Stanley finds that 50% of AI projects are delivering and 40% exceeding expected ROI. In short, delegating to AI is moving fast and making an impact.

Never Delegate Understanding – the Limits of AI

Charles and Ray Eames designed some of the most iconic furniture of the 20th century through a deep study of an object’s purpose, a process that led to their famous adage, “Never delegate understanding.” Their philosophy was grounded in foregoing assumptions about how things worked in favor of learning for themselves. We delegate understanding when we expect tools and technology to solve all our problems and surrender our own expertise. As researchers have found, we still don’t fully understand the boundaries of AI’s capabilities, a phenomenon they call the jagged technological frontier. Their experiments showed how blind trust in AI’s results was a delegation of understanding that actually led to a 19% dip in performance.

The problem is that as dazzling as generative AI can be, it doesn’t “understand,” it is a probabalistic sentence completion machine. It responds to queries based on AI models, not comprehension. Language has structure and rules, but human emotion is far less predictable. AI techniques like sentiment analysis can identify the emotions in language to provide insights for certain purposes like customer service or targeted marketing, but these methods don’t achieve true emotional intelligence, they only barely scratch the realm of human feelings, which defy rationality.

The Importance of “Flesh and Breath”

My father spent the last year and a half of his life in a skilled nursing facility, and while visiting him I was saddened to observe so many elder adults languishing alone, because research is clear that we are wired for connection. This exposure piqued my interest in so-called social or care robots that can mimic pet therapy, visit residents, facilitate social interactions, offer tailored suggestions for healthy behaviors like exercise, and more. While I in no way see these devices as substitutes for humans, I’m open to anything that might plug the dike of what former US Surgeon General has called “an epidemic of loneliness.”

And yet I understand the response of a friend who spent many years working with the elderly – she is adamant that stemming loneliness requires “flesh and breath,” not electronic devices. Her reaction points to the limits of AI – given enough data, it can analyze facial expressions and voices to detect emotions and even respond, but it doesn’t understand, because it doesn’t feel. And the ability to feel, in spite of the inevitable heartache, is what fuels connection and compassion.

The Power of Compassion and Connection

My circumstances over the last year forced me to discuss very personal details with strangers as well as colleagues. The kindness I’ve received in response has been astounding. People I hardly knew checked in to ask how I was doing. A colleague on maternity leave sent regular doses of baby photos. One man I know professionally but have never even met in person offered to host me at his house in New Hampshire so I could hike, knowing it is both a hobby and solace. My compassion cup has been overflowing.

When I opened to the door to my own experience, people walked in to share their own stories of heartbreaking challenges, some past, some present. I heard tales of fire, death of a parent, loss of work, sexual assault, mental illness, addiction, degenerative disease. People shared understanding of my pain and a common message that I will make it, no matter how hard it may seem now. They didn’t delegate understanding but created it by listening, greeting me with compassion, and walking alongside me in sharing their own stories, creating connection we can’t delegate to AI.

Call centers are heavily studied in part because of the abundance of data, and one area of research is how to improve customer service through analyzing emotions callers express in order to offer employees guidance in responding more effectively. I’m all for anything that can make these calls better, but the reason my small moment in the airport warmed my heart is because this woman’s customer service was exemplary, not based on a script but borne of authenticity and compassion, brimming over and creating connection. AI is impressive in its abilities to help manage crew schedules, design optimal flight paths, detect plane safety issues, predict parts needed for maintenance and reduce emissions from fuel, but in my tweepy airport moment I was grateful for compassion and connection delivered via flesh and breath.

A significant challenge with AI today is hyperinflated expectations that bring the risk of another AI winter – a phenomenon that has beset the field before, when disappointments in progress chill both interest and funding.

Polly Mitchell-Guthrie

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Walmart AI Pricing Patents Signal Shift Toward Real-Time Retail Execution

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Walmart Ai Pricing Patents Signal Shift Toward Real Time Retail Execution

Walmart’s new patents and digital shelf rollout point to a more tightly integrated model linking demand forecasting, pricing, and store-level execution.

Walmart has secured two patents related to automated pricing and demand forecasting, drawing attention to how large retailers are evolving their pricing and execution capabilities.

One patent, System and Method for Dynamically Updating Prices on an E-Commerce Platform, covers a system that can dynamically update online prices based on changing market conditions. A second, Walmart Pricing and Demand Forecasting Patent Classification, relates to demand forecasting technology designed to estimate what customers will buy and recommend pricing accordingly. At the same time, Walmart is expanding digital shelf labels across its U.S. stores, replacing paper labels with centrally managed electronic displays.

Individually, none of these elements are new. Retailers have long used forecasting models, pricing tools, and store execution processes. What is notable is the combination.

Walmart now has three capabilities aligned:

Demand forecasting tied to predictive models

Price recommendation based on that demand

Store-level infrastructure capable of rapid execution

That combination reduces the operational friction historically associated with pricing in physical retail.

Pricing Moves Closer to Execution

Traditional store pricing changes required coordination across multiple steps: analysis, approval, printing, distribution, and manual shelf updates. That process introduced delay and inconsistency.

Digital shelf labels materially change that constraint. Prices can be updated centrally and executed across stores with significantly less manual intervention.

This does not change the underlying logic of pricing decisions. Retailers have always adjusted prices based on demand, competition, and margin targets. What changes is the speed and consistency of execution.

As a result, pricing moves closer to real-time operational control.

Implications for Supply Chain Operations

Pricing is not an isolated commercial function. It directly influences demand patterns, inventory flow, replenishment timing, and markdown activity.

When pricing becomes faster and more responsive, those linkages tighten.

Three implications are clear:

1. Increased Execution Speed
Retailers can align pricing decisions more quickly with current demand conditions, reducing lag between signal and action.

2. Stronger Dependence on Forecast Accuracy
When pricing recommendations are driven by predictive models, the quality of demand sensing becomes more consequential. Forecast errors can propagate more quickly into sales and inventory outcomes.

3. Closer Coupling of Merchandising and Supply Chain
Pricing decisions influence demand. Demand impacts inventory, replenishment, and store execution. Faster pricing cycles compress the distance between these functions.

Centralization and Control

Walmart has positioned its digital shelf label rollout as an efficiency and accuracy initiative. Centralized price management improves consistency between systems and store execution while reducing labor tied to manual updates.

That positioning aligns with the operational realities of large-scale retail. At Walmart’s footprint, even small improvements in execution efficiency translate into material cost and accuracy gains.

At the same time, the shift toward algorithm-supported pricing introduces standard enterprise control requirements. Organizations need clear governance around how pricing recommendations are generated, reviewed, and executed, particularly as systems become more automated.

A Broader Technology Pattern

Walmart’s patents are best understood as part of a broader shift in supply chain and retail technology.

AI and advanced analytics are moving closer to operational decision points. Forecasting models are no longer confined to planning environments; they are increasingly connected to systems that can act.

In this case, that connection spans:

Demand sensing

Price recommendation

Store-level execution

The result is a more tightly integrated operating model in which commercial decisions and supply chain execution are linked through software.

What This Signals

The significance of Walmart’s move is not tied to public debate over surge pricing scenarios. The underlying development is structural.

Retailers now have the ability to connect demand forecasting, pricing logic, and execution infrastructure into a faster decision loop.

For supply chain leaders, that represents a clear direction:

Execution is becoming more digital, more centralized, and more tightly coupled to predictive models.

The companies that benefit will be those that can align forecasting, pricing, and operational execution within a controlled, coordinated system.

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Supply Chain and Logistics News March 16th-19th 2026

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Supply Chain And Logistics News March 16th 19th 2026

This week’s installment of Supply Chain and Logistics news includes stories about record increases in oil prices, Rivian’s autonomous taxis, and much more. Firstly, the Trump administration has issued a 60-day waiver of the Jones Act, a century-old regulation that requires goods moved between US ports to be transported by US-built vessels, etc. Additionally, this week Uber & Rivian announced a partnership for Rivian to build 50,000 autonomous robotaxis by 2031 with over a billion dollars in investment from Uber. Schneider Electric and EcoVadis announced a partnership to target emissions in the health care sector. Lastly, DHL announces 10 warehousing sites to be used for data center manufacturing capacity, and Mind Robotics raises 100 million in series A funding.

Your Biggest Stories in Supply Chain and Logistics here:

Trump Administration Issues Pause on Century-old Maritime Law to Ease Oil Prices

The Trump administration has issued a 60-day waiver of the Jones Act. This century-old regulation typically requires goods moved between US ports to be carried on vessels that are US-built, US-owned, and US-crewed. However, with oil prices surging toward $100 a barrel due to escalating conflict in the Middle East, the suspension aims to ease logistics for vital commodities like oil, natural gas, and fertilizer. While the move is intended to lower costs at the pump and support farmers during the spring planting season, it has sparked a debate between those seeking immediate economic relief and domestic maritime unions concerned about the long-term impact on American shipping and labor.

Uber and Rivian Partner to Deploy up to 50,000 Fully Autonomous Robotaxis

Uber and Rivian have announced a massive strategic partnership that signals a major shift in the future of autonomous logistics and urban mobility. Under the terms of the deal, Uber is set to invest up to $1.25 billion in Rivian through 2031, a move specifically tied to the achievement of key autonomous performance milestones. The primary focus of this collaboration is the deployment of a specialized fleet of fully autonomous R2 robotaxis, with an initial order of 10,000 vehicles and an option to scale up to 50,000 units. From a supply chain perspective, this represents a significant commitment to vertical integration; Rivian is managing the end-to-end production of the vehicle, the compute stack, and the sensor suite, including its in-house RAP1 AI chips, while Uber provides the scaled platform for deployment. Commercial operations are slated to begin in San Francisco and Miami in 2028, eventually expanding to 25 cities globally by 2031.

Schneider Electric and EcoVadis Announce Partnership to Decarbonize Global Healthcare Supply Chains

Schneider Electric, a major player in the digital transformation of energy management and automation, and EcoVadis, a provider of business sustainability ratings, have announced a strategic partnership aimed at accelerating decarbonization within the healthcare industry. “Energize” is a collective initiative to engage pharmaceutical industry suppliers in climate action. The collaboration focuses on addressing Scope 3 emissions, those generated within a company’s value chain, which often represent the largest portion of a healthcare organization’s carbon footprint. By combining Schneider Electric’s expertise in energy procurement and sustainability consulting with EcoVadis’s supplier monitoring and rating platform, the partnership provides a structured pathway for pharmaceutical and medical device companies to transition their global suppliers toward renewable energy.

Mind Robotics, a Rivian spin-off, raises $500 million in Series A Funding

RJ Scaringe, CEO of Rivian, is positioning his new $2 billion spin-off, Mind Robotics, as a technological solution to the chronic shortage of manufacturing labor in the Western world. By developing a “foundation model” that acts as an industrial brain alongside specialized mechatronic bodies, the company aims to move beyond the rigid, fixed-motion plans of traditional robotics toward systems capable of human-like reasoning and adaptation. Scaringe emphasizes that while these machines must perform with human-level dexterity, they don’t necessarily need to be humanoid in form; instead, the focus is on creating a data-driven “flywheel” within Rivian’s own facilities to lower production costs and help domestic manufacturing remain globally competitive.

DHL Expands North American Logistics Infrastructure Amid Growing Global Demand for Data Center Logistics Services

DHL is significantly scaling its data center logistics (DCL) footprint in North America, announcing the addition of 10 dedicated sites totaling over seven million square feet of warehousing capacity. This expansion is a direct response to the explosive demand for AI-driven infrastructure and the specific needs of hyperscale and colocation data center operators. By offering specialized services like rack pre-configuration, white-glove handling of sensitive IT hardware, and warehouse-to-site transportation, DHL is positioning itself as an end-to-end partner in a sector where 85% of operators express a preference for a single logistics provider. This move not only addresses the logistical complexities of moving high-value components like GPUs and cooling systems across global borders but also underscores the critical role of integrated supply chains in maintaining the build speed of the digital backbone.

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How to Capitalize Quickly to Address Hyperconnected Industrial Demand

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How To Capitalize Quickly To Address Hyperconnected Industrial Demand

This first in a blog series offers a review of discussion that occurred during ARC Advisory Group’s 2026 Industry Leadership Forum. Specifically, it details a keynote conversation held with senior executives from Rolls-Royce, BTX Precision, and MxD.

The New Fabric of Demand: Modernizing Collaboration and Transparency for Real-Time Production

Industrial leaders have been talking about tearing down workflow and data silos for decades. Yet here we are again. For most, the reality is that most operations and supply chains today typically don’t indicate much progress. A few leaders have figured out how to use digital tools to scale and build pathways forward, a whopping 12.9% according to our latest data (yes, that’s sarcasm). However, even as they struggle to coordinate, orchestrate, and innovate across their operations and enterprise, much less tightly collaborate outside their four walls. In a digital world, this continued capability gap, the inability to closely link market signals to responsive production and external supply chains, is very quickly becoming a liability.

Recently, at the 30th Annual ARC Industry Leadership Forum in Orlando, I had the privilege of leading a keynote discussion entitled The New Fabric of Demand: Modernizing Collaboration and Transparency for Real-Time Production. As part of that, I moderated an excellent conversation that included Global Commodity Executive Greg Davidson of Rolls-Royce, CEO Berardino Baratta of MxD, and CRO Jamie Goettler of BTX Precision.

In this four-part series, we will explore that conversation fully, digging into how the “fabric of market demand” has fundamentally changed, and why structural modernization, both human and technological, is no longer just an option. It is an industrial imperative that will increasingly determine who wins in disrupted markets.

Why Legacy Workflow Will Actually Get Modernized

If we examine the present through the lens of the past, the fundamental laws of supply and demand haven’t really changed. What has changed is the hyperconnectivity of the world and our compressed time to both reward and volatility.

The hard truth is that legacy linear workflows simply do not work in hyperconnected, digitally-driven environments, which are non-linear by nature. As our industrial environments become more digital, they naturally open up countless new ways for how things can get done and how risk can enter the organization. As a result, disruption has shifted from a rare event to a fairly continuous and pervasive reality. In this new reality, responsiveness differentiates you from the competition, and lag time kills.

To survive and thrive in non-linear environments, tighter, integrated ecosystems are required, where silos are actively torn down or redesigned so that barriers to value can be continuously identified and quickly eliminated. At the core, this concept is unfolding around data access, contextualization, and sharing. It provides the urgency behind the need for building industrial data fabrics.

This rewiring certainly extends beyond operations and enterprise processes, enabling the entirety of the supply chain to be judged on its collective responsiveness to the market, all the way down to the individual company level. In this scenario, data can quickly point out laggards who limit value. As the orchestrators of these supply chains identify these limitations on value, they quickly break off and discard the connection and move on without these weak links.

Pillars of the New Fabric of Demand

To achieve necessary level of operational and supply chain responsiveness, the roles of every entity within an ecosystem must be rethought. In the subsequent three blogs of this series, we will take a deep dive into the three distinct pillars that make up this modern architecture, but I’ll begin by laying them out here:

The Market Signal is the catalyst of the entire ecosystem. It dictates the “what” and the “when,” defining what value, success and risk look like in real-time. In blog 2, I’ll explore how to move from reactive assumptions to proactively capturing the market signals that actually matter.
The Demand Architect is moving beyond traditional order-taking. The Demand Architect designs and orchestrates the ecosystem, aligning external partners as true extensions of the enterprise. In blog 3, I’ll discuss the structural agility required to lead this response, rather than just manage a process.
The Agile Partner is the engine of execution. The Agile Partner links supply chain dynamics directly to the shop floor, differentiating themselves through their responsiveness to the market signal. In the final blog in the series, I’ll tackle how data transparency and trust become technical requirements, not just buzzwords, without exposing mission-critical IP.

Building the Modern Industrial Enterprise

Legacy workflows cannot survive in a non-linear world. Industrial organizations must re-architect operations and ecosystems for real-time responsiveness and secure, transparent collaboration. To do so, they will need to:

Improve the measurement of responsiveness: Efficiency and margin-squeezing are important, but they aren’t game-changers. Your competitive edge now relies on how quickly you can adapt to market signals.
Embrace transparency over secrecy: Modern collaboration requires providing a contextualized “lens” into production status without compromising proprietary IP or cybersecurity. Industrial data fabrics are key.
As always, view technology as a tool, not an outcome: Industrial data fabrics are needed to break silos and AI to manage complexity and improve accuracy and speed of decisions. However, the age-old adage remains true. Just because you can apply AI to something doesn’t mean you should. It must be grounded in measurable Value on Investment (VOI), not just return.

The New Fabric of Demand Blog Series

This is the first in a series of four on The New Fabric of Demand: Modernizing Collaboration and Transparency for Real-Time Production. Over the coming days, I’ll publish a perspective from each of the three pillars of the new fabric of demand:

Pillar 1: The Market Signal
Pillar 2: The Demand Architect
Pillar 3: The Agile Partner

By Mike Guilfoyle, Vice President.

For more than two decades, Michael has assisted organizations, including numerous Fortune 500 companies, in identifying and capitalizing on growth opportunities and market disruption presented by the effects of digital economies, energy transition, and industrial sustainability on the energy, manufacturing, and technology industries.

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