will develop into our digital assistants, serving to us navigate the complexities of the fashionable world. They may make our lives simpler and extra environment friendly.” Inspiring and utterly unbiased assertion from somebody who already invested billions on this new expertise.
The hype is actual for AI brokers, and billions are pouring in to construct fashions that may make us extra productive and extra artistic. Arduous to disagree once I fortunately get pleasure from my morning espresso whereas Cursor is coding my unit checks. But, asking folks in my community how they use AI of their day-to-day, their solutions typically point out anecdotal use circumstances, wherever from “I take advantage of it to inform bedtime tales to my son” (I assume that might not even be a use case in case you had extra creativeness) to “I take advantage of it to optimize my schedule” (Movement AI, please cease concentrating on me for the love of god).
As a Information Scientist, my thoughts goes backwards and forwards between two conclusions. The FOMO a part of me that doesn’t need to be late to the Robotic revolution social gathering, and the cynical one which thinks that there’s nonetheless a protracted method to go earlier than synthetic intelligence truly turns into clever. To search out out which aspect of my schizophrenic persona I ought to wager on, I’m going to make use of a easy but highly effective framework: reviewing all of the tasks I’ve labored on because the starting of my profession and assessing how 2025 state-of-the-art AI fashions might have helped.
At the moment, we return to 2018. I’m a candid summer season intern at one of the crucial disruptive startups in America: Lease the Runway.
What the Challenge was about
The Lease the Runway success heart in Secaucus, NJ, was once the most important dry cleansing facility in the US.
Within the Summer time 2018, as an Operations Analyst intern, I used to be given a reasonably arduous downside to consider: on a regular basis, the success heart was receiving hundreds of items again from throughout the nation. All of the objects needed to be first inspected, then would undergo an intensive cleansing course of, earlier than being dried or receiving some particular therapies. This might be:
- Recognizing if the garment was stained in the course of the rental
- Urgent if it was too wrinkled and needed to be ironed
- Repairing if it had been broken
Most of those duties have been carried out manually by totally different departments, and required specialised employees to be obtainable as quickly as the primary batch of items have been reaching their division. With the ability to predict days forward what quantity of items must be processed (and when) was essential for the success heart planning squad, to be able to guarantee that each operations crew can be staffed appropriately.
The complexity of the circulation made it even trickier. It was not solely about predicting the inbound quantity, but in addition assessing what a part of this inbound quantity would require particular therapies, the place and when bottlenecks might seem, and understanding how the work carried out at one division would impression the opposite departments.
The 2018 Resolution
At this level chances are you’ll marvel: given the complexity and the stakes of the undertaking, why was it within the fingers of a younger inexperienced intern? To be honest, throughout my 10-week summer season internship, I solely scratched the floor and wrote an insanely sophisticated Pyomo script that was later refined by a extra senior Information Scientist, who spent two years on this undertaking alone.
However as you may think about, the answer was this enormous optimization mannequin taking as an enter the inbound quantity forecast for daily of the week, the common UPH (items per hour, i.e the variety of items that may be processed in an hour) at every division, and a few assumptions on the proportions of items that might require particular therapies. The principle constraints have been on the timing and regularity of the shifts, and the variety of full time contracts. The mannequin would then output an optimized labor planning for the week.
How AI might have helped
Let’s re-clarify issues first: you’ll not see phrases like “AI-enthusiast” or “LLM believer” in my LinkedIn bio. I’m fairly skeptical that AI will magically clear up all our issues, however I’m fascinated with seeing if with at the moment’s expertise, one other method can be attainable.
As a result of our method was, you might say, fairly old fashioned, and required months and months of refinements and testing.
The principle restrict is the static facet of the answer. If one thing sudden occurs in the course of the week (e.g a snow storm that paralyzes the logistics in some components of the nation, delaying a number of the inbound quantity), loads of assumptions of the mannequin must be modified, and its outcomes have gotten out of date.
This can be a resolution that requires information scientists to go deep into the weeds, as an alternative of counting on an out-of-the-box framework, to depend on loads of assumptions and to spend time sustaining and updating these assumptions.
Might AI provide you with a totally totally different method? No.
For this specific downside, you clearly want an optimization mannequin, and I’m but to examine an LLM having the ability to deal with a mannequin with such complexity. One might suggest a framework with an AI agent performing as a Normal Supervisor, and counting on sub-agents to deal with the planning of every division. However that framework would nonetheless require brokers to have instruments that permit them to resolve a posh optimization mannequin, and the sub-agents would wish to speak because the state of affairs of 1 division can have an effect on all of the others.
Might AI considerably improve the “human-generated” resolution? Doable.
It’s at this level fairly apparent to me that LLMs wouldn’t make the issue trivial, however they may assist enhance the answer in a number of areas:
- Initially, they may assist with reporting and determination making. The output of the optimization mannequin may need a enterprise sense, however making a call out of it may be arduous for somebody with no robust understanding of linear programming. An LLM might assist interpret the outcomes and recommend concrete enterprise selections.
- Secondly, an LLM might assist react quicker to sure sudden conditions. It might for instance summarize info on occasions that might have an effect on the Operations, resembling unhealthy climate in some components of the nation or different points with suppliers, and as such, advocate when to rerun the planning mannequin. That’s assuming it has entry to good high quality information about these exterior occasions.
- Lastly, it’s attainable AI might have additionally helped with making actual time changes to the planning. As an example, it’s sometimes predictable based mostly on the garment traits whether or not they would require particular care (e.g a cotton shirt will all the time must be ironed manually). Having a VLM scanning each garment on the receiving station might assist downstream departments perceive how a lot quantity they need to anticipate hours upfront.
Might AI allow Information Scientists to take care of and replace the mannequin? Sure!
It’s actually arduous to disclaim that with instruments like Copilot or Cursor coding and sustaining this mannequin would have been simpler. I might not have blindly requested Claude to code each constraint of the Linear Program from scratch, however with AI code editors being smarter than ever, modifying and testing particular constraints (and catching human errors!) can be simpler.
My conclusion is that an LLM in 2018 wouldn’t have trivialized the undertaking, though it might have enhanced the ultimate resolution. However it’s not unimaginable to consider that a couple of years (months?) from now, brokers with enhanced reasoning capabilities will likely be subtle sufficient to begin cracking most of these issues. Within the meantime, whereas AI might velocity up mannequin iterations and changes, the human judgment on the core stays irreplaceable. This serves as a beneficial reminder that being a Information Scientist isn’t nearly fixing mathematical or pc science issues—it’s about designing sensible options that meet evolving, typically ambiguous and never so properly outlined real-world constraints.
Article 100% human generated
