Three Lies We Tell Ourselves In Supply Chain Management

Maybe “lies” is too strong a word. Maybe a better term would be assumptions based on our biases. But either way, we often deceive ourselves in supply chain management — we tell ourselves things we want to believe are true, but deep down inside, we know they’re not true.

Let’s start with the most current lie making the rounds:

“AI won’t take your job, someone using AI will.”

AI is creating a lot of fear and uncertainty amongst workers, with many people viewing it as a threat to their jobs. However, the message by technology companies and upper management, until recently, has been: AI is a productivity enhancer, not a ticket to the unemployment line. Sure, some jobs will go away, but it’s the jobs that nobody likes to do anyway, and a bunch of new jobs and roles will be created.

“You’re not going to lose your job to an AI, but you’re going to lose your job to someone who uses AI,” said Nvidia’s CEO Jensen Huang on May 6th at the Milken Institute’s Global Conference 2025.

But AI is replacing people so rapidly, it’s getting harder and harder to tell this lie with a straight face any more.

“Artificial intelligence is going to replace literally half of all white-collar workers in the U.S.,” Ford CEO Jim Farley said last month at the Aspen Ideas Festival, as reported by Chip Cutter and Haley Zimmerman in the Wall Street Journal. “AI will leave a lot of white-collar people behind.”

And in a note to employees last month, Amazon CEO Andy Jassy said, “​​As we roll out more Generative AI and agents, it should change the way our work is done. It’s hard to know exactly where this nets out over time, but in the next few years, we expect that this will reduce our total corporate workforce.”

Here’s more from the WSJ article (which is titled “CEOs Start Saying the Quiet Part Out Loud: AI Will Wipe Out Jobs”):

[Ford CEO’s] remarks reflect an emerging shift in how many executives explain the potential human cost from the technology. Until now, few corporate leaders have wanted to publicly acknowledge the extent to which white-collar jobs could vanish. In interviews, CEOs often hedge when asked about job losses, noting that innovation historically creates a range of new roles.

In private, though, CEOs have spent months whispering about how their businesses could likely be run with a fraction of the current staff.

Shopify Chief Executive Tobi Lütke recently told workers that the company wouldn’t make any new hires unless managers could prove artificial intelligence isn’t capable of doing the job.

Then last week Microsoft announced it was laying off 9,000 workers, which follows a 7,000 reduction in May. As reported by CNN:

The staff reduction also comes as tech companies, including Microsoft, are using artificial intelligence to make their workforce more efficient. Microsoft CEO Satya Nadella said earlier this year that 20% to 30% of the company’s code was being generated by AI, and the company is pouring billions into AI infrastructure investments.

And Saskia Koopman at CityAM reports in a June 2025 article that “the UK’s Big Four accountancy firms [Deloitte, EY, KMPG and PwC] are cutting hundreds of jobs and pulling back sharply on graduate recruitment, as artificial intelligence (AI) begins replacing the junior roles once filled by school and university graduates.”

Need more evidence? Here are some excerpts from an Axios article by Jim VandeHei and Mike Allen published in May 2025 that quotes Dario Amodei, the CEO of Anthropic:

[Amodei] has a blunt, scary warning for the U.S. government and all of us: AI could wipe out half of all entry-level white-collar jobs — and spike unemployment to 10-20% in the next one to five years, Amodei told us in an interview from his San Francisco office.

Amodei said AI companies and the government need to stop “sugar-coating” what’s coming: the possible mass elimination of jobs across technology, finance, law, consulting and other white-collar professions, especially entry-level gigs.

Anthropic research shows that right now, AI models are being used mainly for augmentation — helping people do a job. That can be good for the worker and the company, freeing them up to do high-level tasks while the AI does the rote work. The truth is that AI use in companies will tip more and more toward automation — actually doing the job. “It’s going to happen in a small amount of time — as little as a couple of years or less,” Amodei says.

We’re already seeing the impact of automation in warehouses. The title of a recent Wall Street Journal article by Sebastian Herrera says it all: “Amazon Is on the Cusp of Using More Robots Than Humans in Its Warehouses.”

It’s important to note, however, that AI is not something completely new; in reality, aspects of AI have been used in supply chain management for decades (e.g., rule-based logic, basic machine learning, pattern recognition, and deterministic optimization capabilities). These established forms of AI mostly fall under the “augmentation” category mentioned above, and they have had a relatively limited impact on jobs. It’s the newer forms of AI — that is, Generative AI, Agentic AI, and Sequential Decision-Making — that will likely result in the elimination of many jobs, including mine and yours, over the next 3-5 years.

“We collaborate with our colleagues and trading partners.”

That is another lie we tell ourselves.

In an April 2015 Harvard Business Review article titled “There’s a Difference Between Cooperation and Collaboration,” Ron Ashkenas says the following:

“Having worked with hundreds of managers over the years, I’ve seen that very few admit to being poor collaborators, mostly because they mistake their cooperativeness for being collaborative.”

This quote inspired a September 2023 survey of our Indago supply chain research community members, who are all supply chain and logistics executives from manufacturing, retail, and distribution companies. We asked them if they agreed or disagreed with Ron Ashkenas that many people mistake being cooperative with being collaborative.

Almost a third of our member respondents (32%) “Strongly Agreed” with Ashkenas and another 55% “Agreed”; only 5% “Disagreed.”

What’s the difference between collaboration and cooperation?

Per Corey Moseley, “Collaboration is when a group of people come together and work on a project in support of a shared objective, outcome, or mission [it implies shared ownership and interest in a specific outcome], while Cooperation is when a group of people work in support of another’s goals [e.g., you help me on something I’m working on and that I’m ultimately responsible for].”

In short, many companies say they collaborate with their colleagues and trading partners, but at best, they’re just cooperating with them.

“We make data-driven decisions.”

Back in May 2025, we asked our Indago members, “How effectively does your supply chain organization turn data and information into timely, informed decisions and actions?” 0% of the respondents said “Very effectively” — that is, that their decisions are consistently data-driven and coordinated. Only 29% of the respondents said they “Effectively” turn data and information into timely, informed decisions and actions; 50% said they are “Moderately” effective and 21% said they do it “Ineffectively.”

“With basic warehouse functions and the inability to export to different software, we have been manually inputting data into Excel workbooks to compute costs, inventory turns, and profitability,” said one Indago supply chain executive. “We also have departmental silos that lack transparency and have communication issues. We are exploring [implementing] new software, but it will be a couple of years before anything is operational.”

We also asked our Indago members, “What are the most significant barriers to cross-functional decision-making in your supply chain organization today?” Topping the list of barriers was “Data is available, but not trusted or easily interpreted,” selected by 46% of the respondents. (For additional insights from the research, download the report “Turning Information Into Action.”)

The truth is that most supply chain organizations have lots of data and make decisions, but it doesn’t mean they actually use the data (or use it effectively) to make decisions.

Source: Talking Logistics