Human-Machine Teams Define Supply Chain Careers for the Future 

The Estée Lauder Companies’ (ELC) Executive Chairman William P. Lauder told a great story about Duncan Hines cake mix at a Zero100 x ELC event last week. The story, which Lauder recalled from his time teaching at Wharton Business School, was about the fact that the original product flopped because it oversimplified the work of homemakers.  

“Just add water!” may have sounded great to the Mad Men-era suits in marketing, but to customers, it dumbed down the job of baking a cake a bit too far. The solution: add an egg and restore some autonomy and purpose to the task. 

For COOs and CPOs architecting human-machine teams to harness the potential efficiency breakthroughs of AI, the takeaway is clear: add meaning to supply chain careers to drive the greatest impact. 

AI Changes Everything About Work 

Zero100 research on the impact of AI on supply chain talent, career paths, and organizational designs confirms three facts: 

• AI improves productivity – Companies that lead in the use of AI and related digital technologies for supply chain jobs, from planning and sourcing to manufacturing and logistics, perform better. On average, these leaders grow revenue 34% faster, are 2.4x  more accurate in their EPS guidance, and 57% farther along on Scope 3 carbon reduction targets than their peers. 
• AI transforms work – Functional skills that rely on spreadsheets or ERP systems, for example, forecasting, inventory planning, and contracting, are being overlaid with higher-level capabilities. Areas like network strategy, predictive maintenance, and supplier collaboration that benefit from genAI tools and purpose-built large language models (LLMs) have grown three- to six-fold between 2022 and 2024. 
• AI’s thirst for data requires “translators” – AI’s insatiable need for data is known. The skill most critical to supply chain management is, therefore, knowing what data matters and how it translates to business problems AI can solve.  

If most supply chain employees are “citizens” using tech tools in their work and a handful are “wizards” who build that tech, the essential role is the “translator,” who bridges the gap between the data and the tool. Unfortunately, this vital skill is in short supply. Zero100 analysis of 784,000 supply chain job descriptions in July 2024 found only 3% featured translator skills like product management, prompt engineering, and LLM design. 

Where Is the Meaning? 

A recent Economist article explores academic research relating to the impact of robotics and AI on the “meaningfulness of work.” For some, like pharmacists enabled by prescription filling robots, the effect is positive because it gives them more time with patients. For pharmacy assistants, the opposite is true because their jobs have now been reduced to refilling machines. Meaning, it seems, depends on feeling some value in mastery for a purpose.

Supply chain organizations that get this are moving away from career ladder-style progression and toward capability-based planning. Gig-model work assignments, for instance, like those in use at Schneider Electric, allow people to bid for project assignments, enabling cross-functional career growth. ELC organizes its approach to supply chain careers as a “mosaic” of capabilities and assignments that expose people to new challenges and create small, agile teams to leverage technology faster and more cheaply than old-fashioned mega tech transformations. 

In addition, seeing data as an essential capability adds meaning and speed to the scale-up of human-machine teams. Dow Chemical, for example, created an “Integrated Data Hub” in 2022. It enables better data access and literacy as well as a data culture and community that encourages transparency and reusability. Walmart, PepsiCo, Johnson & Johnson, and Bristol Myers Squibb have also invested in programs to upskill and empower operations people with data literacy.  

Pulling it all together, as Pfizer has done for instance, is about breaking down work at the front-line level and building back to an operations control interface that empowers operators to solve problems with data. These examples form a foundation for finding and developing much needed “translators.” 

Think of AI as a Teammate 

Supply chain work is changing faster now than it has since the late nineteenth century. Careers built around capabilities that develop “translators” are the key to making the most of human-machine teams.