will grow to be our digital assistants, serving to us navigate the complexities of the fashionable world. They are going to make our lives simpler and extra environment friendly.” Inspiring and fully unbiased assertion from somebody who already invested billions on this new know-how.
The hype is actual for AI brokers, and billions are pouring in to construct fashions that can make us extra productive and extra inventive. Onerous to disagree after I fortunately get pleasure from my morning espresso whereas Cursor is coding my unit exams. But, asking folks in my community how they use AI of their day-to-day, their solutions usually point out anecdotal use instances, anyplace from “I exploit it to inform bedtime tales to my son” (I assume that will not even be a use case for those who had extra creativeness) to “I exploit it to optimize my schedule” (Movement AI, please cease concentrating on me for the love of god).
As a Information Scientist, my thoughts goes forwards and backwards between two conclusions. The FOMO a part of me that doesn’t wish to be late to the Robotic revolution social gathering, and the cynical one which thinks that there’s nonetheless a protracted approach to go earlier than synthetic intelligence really turns into clever. To seek out out which facet of my schizophrenic character I ought to guess on, I’m going to make use of a easy but highly effective framework: reviewing all of the initiatives I’ve labored on because the starting of my profession and assessing how 2025 state-of-the-art AI fashions may have helped.
In the present day, we return to 2018. I’m a candid summer time intern at probably the most disruptive startups in America: Lease the Runway.
What the Venture was about
The Lease the Runway achievement heart in Secaucus, NJ, was once the most important dry cleansing facility in the US.
Within the Summer season 2018, as an Operations Analyst intern, I used to be given a fairly arduous downside to consider: on a regular basis, the achievement heart was receiving hundreds of items again from throughout the nation. All of the objects needed to be first inspected, then would undergo a radical cleansing course of, earlier than being dried or receiving some particular therapies. This may very well be:
- Recognizing if the garment was stained throughout the rental
- Urgent if it was too wrinkled and needed to be ironed
- Repairing if it had been broken
Most of those duties had been finished manually by totally different departments, and required specialised staff to be out there as quickly as the primary batch of items had been reaching their division. Having the ability to predict days forward what quantity of items must be processed (and when) was essential for the achievement heart planning squad, with a view to make it possible for each operations staff can be staffed appropriately.
The complexity of the movement 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 may seem, and understanding how the work finished at one division would impression the opposite departments.
The 2018 Resolution
At this level you could surprise: given the complexity and the stakes of the venture, why was it within the palms of a younger inexperienced intern? To be honest, throughout my 10-week summer time 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 venture alone.
However as you’ll be able to think about, the answer was this large optimization mannequin taking as an enter the inbound quantity forecast for day by day of the week, the typical 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 will require particular therapies. The primary constraints had 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 may 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 remedy all our issues, however I’m concerned with seeing if with immediately’s know-how, one other method can be doable.
As a result of our method was, you can say, fairly old fashioned, and required months and months of refinements and testing.
The primary restrict is the static side of the answer. If one thing sudden occurs throughout the week (e.g a snow storm that paralyzes the logistics in some components of the nation, delaying a number of the inbound quantity), a number of assumptions of the mannequin should be modified, and its outcomes have gotten out of date.
This can be a answer that requires information scientists to go deep into the weeds, as a substitute of counting on an out-of-the-box framework, to depend on a number of assumptions and to spend time sustaining and updating these assumptions.
Might AI provide you with a totally totally different method? No.
For this explicit 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 may suggest a framework with an AI agent performing as a Basic 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 enable them to unravel a posh optimization mannequin, and the sub-agents would wish to speak because the scenario of 1 division can have an effect on all of the others.
Might AI considerably improve the “human-generated” answer? Potential.
It’s at this level fairly apparent to me that LLMs wouldn’t make the issue trivial, however they might assist enhance the answer in a number of areas:
- To begin with, they might assist with reporting and resolution making. The output of the optimization mannequin may need a enterprise sense, however making a choice out of it is perhaps arduous for somebody with no sturdy understanding of linear programming. An LLM may assist interpret the outcomes and counsel concrete enterprise selections.
- Secondly, an LLM may assist react quicker to sure sudden conditions. It may for instance summarize info on occasions that might have an effect on the Operations, akin to dangerous climate in some components of the nation or different points with suppliers, and as such, suggest when to rerun the planning mannequin. That’s assuming it has entry to good high quality information about these exterior occasions.
- Lastly, it’s doable AI may have additionally helped with making actual time changes to the planning. For example, it’s sometimes predictable primarily based on the garment traits whether or not they would require particular care (e.g a cotton shirt will all the time should be ironed manually). Having a VLM scanning each garment on the receiving station may assist downstream departments perceive how a lot quantity they need to anticipate hours upfront.
Might AI allow Information Scientists to keep up 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’d 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 venture, though it may have enhanced the ultimate answer. However it isn’t unattainable to consider that just a few years (months?) from now, brokers with enhanced reasoning capabilities shall be refined sufficient to begin cracking these kinds of issues. Within the meantime, whereas AI may velocity up mannequin iterations and changes, the human judgment on the core stays irreplaceable. This serves as a precious reminder that being a Information Scientist isn’t nearly fixing mathematical or laptop science issues—it’s about designing sensible options that meet evolving, usually ambiguous and never so nicely outlined real-world constraints.
Article 100% human generated
